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Sample records for widening imaging features

  1. The acrocallosal syndrome in first cousins: widening of the spectrum of clinical features and further support for autosomal recessive inheritance.

    Science.gov (United States)

    Schinzel, A

    1988-01-01

    First cousins, related through their mothers, showed a pattern of craniofacial, brain, and limb anomalies consistent with the acrocallosal syndrome. Both patients had a defect of the corpus callosum, macrocephaly with a protruding forehead and occiput, hypertelorism, non-horizontal palpebral fissures, a small nose, notched ear lobes, and postaxial polydactyly of the hands. The boy, in addition, had hypospadias, cryptorchidism, inguinal hernias, duplication with syndactyly of the phalanges of the big toe, and a bipartite right clavicle. The girl had an arachnoidal cyst, a calvarian defect, and digitalisation of the thumbs. Motor and mental development was retarded in both patients. This observation provides further evidence of probable autosomal recessive inheritance of the acrocallosal syndrome and widens the spectrum of clinical findings and the variability of features in this rare malformation syndrome. Images PMID:3385741

  2. Local physeal widening on MR imaging: an incidental finding suggesting prior metaphyseal insult

    International Nuclear Information System (INIS)

    Laor, T.; Hartman, A.L.; Jaramillo, D.

    1997-01-01

    To offer a descriptive review which characterizes and evaluates the significance of local physeal widening, (cartilaginous signal extending from the physis into the adjacent metaphysis), identified on magnetic resonance (MR) imaging. MR images (recollected from exams performed between 1988 and 1995) of 31 metaphyses in 22 children where we recognized local physeal widening were examined retrospectively. These areas of physeal widening were evaluated for morphology, depth, location, signal intensity, and the coexistence of epiphyseal alterations. The characteristics of the signal abnormalities were correlated with the duration and type of any identifiable insult to the adjacent metaphysis, and with the development of growth disturbance. Twenty-six metaphyses had identifiable insults (19 single event and 7 sustained or repetitive). The widened physes were of focal tongue (n = 15), broad band (n 10), or mixed (n = 6) morphology. Most (n = 27) areas of widening were isointense with the physeal cartilage on all sequences. Subsequent growth disturbance was more likely when the metaphyseal insult was a single event rather than sustained or repetitive (P = 0.023), with focal tongues (P = 0.029), and with centrally located lesions (P = 0.030). In five cases, the adjacent epiphysis showed signal abnormalities; all developed growth disturbance. Histologic examinations available in two limbs confirmed that the MR findings represented extensions of hypertrophic physeal chondrocytes into the metaphysis. Incidentally observed local physeal widening in a growing bone may represent the imprint of a previous or ongoing interference with endochondral ossification from a prior metaphyseal insult, rather than a primary metaphyseal disorder. Single event insults, physeal widening of focal tongue morphology, central distribution in the metaphysis, and concomitant epiphyseal signal abnormalities on MR imaging are significant predictors of subsequent growth disturbance. (orig.). With 9

  3. Multispectral Image Feature Points

    Directory of Open Access Journals (Sweden)

    Cristhian Aguilera

    2012-09-01

    Full Text Available This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

  4. Abdominal tuberculosis: Imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Jose M. [Department of Radiology, Hospital de S. Joao, Porto (Portugal)]. E-mail: jmpjesus@yahoo.com; Madureira, Antonio J. [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Vieira, Alberto [Department of Radiology, Hospital de S. Joao, Porto (Portugal); Ramos, Isabel [Department of Radiology, Hospital de S. Joao, Porto (Portugal)

    2005-08-01

    Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis.

  5. Abdominal tuberculosis: Imaging features

    International Nuclear Information System (INIS)

    Pereira, Jose M.; Madureira, Antonio J.; Vieira, Alberto; Ramos, Isabel

    2005-01-01

    Radiological findings of abdominal tuberculosis can mimic those of many different diseases. A high level of suspicion is required, especially in high-risk population. In this article, we will describe barium studies, ultrasound (US) and computed tomography (CT) findings of abdominal tuberculosis (TB), with emphasis in the latest. We will illustrate CT findings that can help in the diagnosis of abdominal tuberculosis and describe imaging features that differentiate it from other inflammatory and neoplastic diseases, particularly lymphoma and Crohn's disease. As tuberculosis can affect any organ in the abdomen, emphasis is placed to ileocecal involvement, lymphadenopathy, peritonitis and solid organ disease (liver, spleen and pancreas). A positive culture or hystologic analysis of biopsy is still required in many patients for definitive diagnosis. Learning objectives:1.To review the relevant pathophysiology of abdominal tuberculosis. 2.Illustrate CT findings that can help in the diagnosis

  6. Localized scleroderma: imaging features

    International Nuclear Information System (INIS)

    Liu, P.; Uziel, Y.; Chuang, S.; Silverman, E.; Krafchik, B.; Laxer, R.

    1994-01-01

    Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

  7. Localized scleroderma: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Liu, P. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Uziel, Y. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Chuang, S. (Dept. of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON (Canada)); Silverman, E. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada)); Krafchik, B. (Div. of Dermatology, Dept. of Pediatrics, Hospital for Sick Children, Toronto, ON (Canada)); Laxer, R. (Div. of Rheumatology, Hospital for Sick Children, Toronto, ON (Canada))

    1994-06-01

    Localized scleroderma is distinct from the diffuse form of scleroderma and does not show Raynaud's phenomenon and visceral involvement. The imaging features in 23 patients ranging from 2 to 17 years of age (mean 11.1 years) were reviewed. Leg length discrepancy and muscle atrophy were the most common findings (five patients), with two patients also showing modelling deformity of the fibula. One patient with lower extremity involvement showed abnormal bone marrow signals on MR. Disabling joint contracture requiring orthopedic intervention was noted in one patient. In two patients with ''en coup de sabre'' facial deformity, CT and MR scans revealed intracranial calcifications and white matter abnormality in the ipsilateral frontal lobes, with one also showing migrational abnormality. In a third patient, CT revealed white matter abnormality in the ipsilateral parietal lobe. In one patient with progressive facial hemiatrophy, CT and MR scans showed the underlying hypoplastic left maxillary antrum and cheek. Imaging studies of areas of clinical concern revealed positive findings in half our patients. (orig.)

  8. Featured Image | Galaxy of Images

    Science.gov (United States)

    2,600 images. more info The Book of the Fair The first Ferris Wheel, the creation of bridge builder George W. Ferris, was erected at the World’s Columbian Exposition in Chicago in 1893. To commemorate

  9. Imaging features of thalassemia

    Energy Technology Data Exchange (ETDEWEB)

    Tunaci, M.; Tunaci, A.; Engin, G.; Oezkorkmaz, B.; Acunas, G.; Acunas, B. [Dept. of Radiology, Istanbul Univ. (Turkey); Dincol, G. [Dept. of Internal Medicine, Istanbul Univ. (Turkey)

    1999-07-01

    Thalassemia is a kind of chronic, inherited, microcytic anemia characterized by defective hemoglobin synthesis and ineffective erythropoiesis. In all thalassemias clinical features that result from anemia, transfusional, and absorptive iron overload are similar but vary in severity. The radiographic features of {beta}-thalassemia are due in large part to marrow hyperplasia. Markedly expanded marrow space lead to various skeletal manifestations including spine, skull, facial bones, and ribs. Extramedullary hematopoiesis (ExmH), hemosiderosis, and cholelithiasis are among the non-skeletal manifestations of thalassemia. The skeletal X-ray findings show characteristics of chronic overactivity of the marrow. In this article both skeletal and non-skeletal manifestations of thalassemia are discussed with an overview of X-ray findings, including MRI and CT findings. (orig.)

  10. Imaging features of thalassemia

    International Nuclear Information System (INIS)

    Tunaci, M.; Tunaci, A.; Engin, G.; Oezkorkmaz, B.; Acunas, G.; Acunas, B.; Dincol, G.

    1999-01-01

    Thalassemia is a kind of chronic, inherited, microcytic anemia characterized by defective hemoglobin synthesis and ineffective erythropoiesis. In all thalassemias clinical features that result from anemia, transfusional, and absorptive iron overload are similar but vary in severity. The radiographic features of β-thalassemia are due in large part to marrow hyperplasia. Markedly expanded marrow space lead to various skeletal manifestations including spine, skull, facial bones, and ribs. Extramedullary hematopoiesis (ExmH), hemosiderosis, and cholelithiasis are among the non-skeletal manifestations of thalassemia. The skeletal X-ray findings show characteristics of chronic overactivity of the marrow. In this article both skeletal and non-skeletal manifestations of thalassemia are discussed with an overview of X-ray findings, including MRI and CT findings. (orig.)

  11. Pulmonary vasculitis: imaging features

    International Nuclear Information System (INIS)

    Seo, Joon Beom; Im, Jung Gi; Chung, Jin Wook; Goo, Jin Mo; Park, Jae Hyung; Yeon, Kyung Mo; Song, Jae Woo

    1999-01-01

    Vasculitis is defined as an inflammatory process involving blood vessels, and can lead to destruction of the vascular wall and ischemic damage to the organs supplied by these vessels. The lung is commonly affected. A number of attempts have been made to classify and organize pulmonary vasculitis, but because the clinical manifestations and pathologic features of the condition overlap considerably, these afforts have failed to achieve a consensus. We classified pulmonary vasculitis as belonging to either the angitiis-granulomatosis group, the diffuse pulmonary hemorrhage with capillaritis group, or 'other'. Characteristic radiographic and CT findings of the different types of pulmonary vasculitis are illustrated, with a brief discussion of the respective disease entities

  12. Imaging features of aggressive angiomyxoma

    International Nuclear Information System (INIS)

    Jeyadevan, N.N.; Sohaib, S.A.A.; Thomas, J.M.; Jeyarajah, A.; Shepherd, J.H.; Fisher, C.

    2003-01-01

    AIM: To describe the imaging features of aggressive angiomyxoma in a rare benign mesenchymal tumour most frequently arising from the perineum in young female patients. MATERIALS AND METHODS: We reviewed the computed tomography (CT) and magnetic resonance (MR) imaging features of patients with aggressive angiomyxoma who were referred to our hospital. The imaging features were correlated with clinical information and pathology in all patients. RESULTS: Four CT and five MR studies were available for five patients (all women, mean age 39, range 24-55). Three patients had recurrent tumour at follow-up. CT and MR imaging demonstrated a well-defined mass-displacing adjacent structures. The tumour was of low attenuation relative to muscle on CT. On MR, the tumour was isointense relative to muscle on T1-weighted image, hyperintense on T2-weighted image and enhanced avidly after gadolinium contrast with a characteristic 'swirled' internal pattern. MR imaging demonstrates the extent of the tumour and its relation to the pelvic floor. Recurrent tumour has a similar appearance to the primary lesion. CONCLUSION: The MR appearances of aggressive angiomyxomas are characteristic, and the diagnosis should be considered in any young woman presenting with a well-defined mass arising from the perineum. Jeyadevan, N. N. etal. (2003). Clinical Radiology58, 157--162

  13. Renal angiomyoadenomatous tumour: Imaging features

    Science.gov (United States)

    Sahni, V. Anik; Hirsch, Michelle S.; Silverman, Stuart G.

    2012-01-01

    Renal angiomyoadenomatous tumour is a rare, recently described neoplasm with a distinctive histological appearance. Although reported in the pathology literature, to our knowledge, no prior reports have described its imaging appearance. We describe the computed tomography and magnetic resonance imaging features of an incidentally detected renal angiomyoadenomatous tumour that appeared as a well-marginated, solid T2-hypointense enhancing mass, in a 50-year-old woman. It is indistinguishable from a variety of benign and malignant renal neoplasms. PMID:23093565

  14. Imaging features of kaposiform lymphangiomatosis

    International Nuclear Information System (INIS)

    Goyal, Pradeep; Alomari, Ahmad I.; Shaikh, Raja; Chaudry, Gulraiz; Kozakewich, Harry P.; Perez-Atayde, Antonio R.; Trenor, Cameron C.; Fishman, Steven J.; Greene, Arin K.

    2016-01-01

    Kaposiform lymphangiomatosis is a rare, aggressive lymphatic disorder. The imaging and presenting features of kaposiform lymphangiomatosis can overlap with those of central conducting lymphatic anomaly and generalized lymphatic anomaly. To analyze the imaging findings of kaposiform lymphangiomatosis disorder and highlight features most suggestive of this diagnosis. We retrospectively identified and characterized 20 children and young adults with histopathological diagnosis of kaposiform lymphangiomatosis and radiologic imaging referred to the vascular anomalies center between 1995 and 2015. The median age at onset was 6.5 years (range 3 months to 27 years). The most common presenting features were respiratory compromise (dyspnea, cough, chest pain; 55.5%), swelling/mass (25%), bleeding (15%) and fracture (5%). The thoracic cavity was involved in all patients; all patients had mediastinal involvement followed by lung parenchymal disease (90%) and pleural (85%) and pericardial (50%) effusions. The most common extra-thoracic sites of disease were the retroperitoneum (80%), bone (60%), abdominal viscera (55%) and muscles (45%). There was characteristic enhancing and infiltrative soft-tissue thickening in the mediastinum and retroperitoneum extending along the lymphatic distribution. Kaposiform lymphangiomatosis has overlapping imaging features with central conducting lymphatic anomaly and generalized lymphatic anomaly. Presence of mediastinal or retroperitoneal enhancing and infiltrative soft-tissue disease along the lymphatic distribution, hemorrhagic effusions and moderate thrombocytopenia (50-100,000/μl) should favor diagnosis of kaposiform lymphangiomatosis. (orig.)

  15. Imaging features of musculoskeletal tuberculosis

    International Nuclear Information System (INIS)

    Vuyst, Dimitri De; Vanhoenacker, Filip; Bernaerts, Anja; Gielen, Jan; Schepper, Arthur M. de

    2003-01-01

    The purpose of this article is to review the imaging characteristics of musculoskeletal tuberculosis. Skeletal tuberculosis represents one-third of all cases of tuberculosis occurring in extrapulmonary sites. Hematogenous spread from a distant focus elsewhere in the body is the cornerstone in the understanding of imaging features of musculoskeletal tuberculosis. The most common presentations are tuberculous spondylitis, arthritis, osteomyelitis, and soft tissue involvement. The diagnostic value of the different imaging techniques, which include conventional radiography, CT, and MR imaging, are emphasized. Whereas conventional radiography is the mainstay in the diagnosis of tuberculous arthritis and osteomyelitis, MR imaging may detect associated bone marrow and soft tissue abnormalities. MR imaging is generally accepted as the imaging modality of choice for diagnosis, demonstration of the extent of the disease of tuberculous spondylitis, and soft tissue tuberculosis. Moreover, it may be very helpful in the differential diagnosis with pyogenic spondylodiscitis, as it may easily demonstrate anterior corner destruction, the relative preservation of the intervertebral disk, multilevel involvement with or without skip lesions, and a large soft tissue abscess, as these are all arguments in favor of a tuberculous spondylitis. On the other hand, CT is still superior in the demonstration of calcifications, which are found in chronic tuberculous abscesses. (orig.)

  16. Textural features for image classification

    Science.gov (United States)

    Haralick, R. M.; Dinstein, I.; Shanmugam, K.

    1973-01-01

    Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

  17. Textural features for radar image analysis

    Science.gov (United States)

    Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

    1981-01-01

    Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

  18. Imaging features of cardiac myxoma

    International Nuclear Information System (INIS)

    Yang Youyou; Zheng Lili; Li Xiangmin; Zhou Xuhui; Kuang Jianyi; Zhang Wenzhao

    2007-01-01

    Objective: To study the imaging features of cardiac myxoma and their diagnostic values. Methods: Twenty-two patrents with cardiac myxoma were reviewed retrospectively for the clinical, pathologic, and radiologic findings. Posteroanterior and lateral chest radiographs, American Imatron C-150 XP Electron Beam CT examination, and Germany Siemens 1.5T Magnetom Vision MR scan were performed on every patient. Results: (1) Radiographs of 17 patients with left atrial myxoma showed evidence of mitral valve obstruction in 14(82.3%), radiographs of 5 patients with right atrial myxoma demonstrated right atrium enlargement in 3(60%) respectively. (2) CT scans of 22 myxomas demonstrated 18 (81.8%) lesions were hypoattenuated and 4 (19.1%) were isoattenuated relative to the myocardium. Calcification or ossification was seen in 3 patients. All myxomas apart from massive one were found attaching to the atrial septum. Movie mode could dis- play the movement of myxoma across the atrioventicular valves. (3) MRI studies of 22 myxomas showed 19 (86.3%) heterogeneous signal intensity and 3 (13.7%) homogeneous. They exhibited slight high or homogeneous signal intensity with both T 1 - and T 2 -weighted sequences, and low signal intensity with cine gradient recalled echo sequences. Point of attachment was visible in 21 (95.4%) cases. Conclusion: The typical radiograph sign of cardiac myxomas is mitral valve obstruction, CT and MR can demonstrate intracavitary lobular masses attacthing to artrial spetum. The latter two kinds of examinations not only provide accurate assessment of the size, location, and attachment point of these lesions, but also have important qualitative diagnostic advantage. (authors)

  19. Identifying Image Manipulation Software from Image Features

    Science.gov (United States)

    2015-03-26

    scales”. Educational and Psychological Measurement, 20(1):37, 1960. 7. Committee, Technical Standardization. Exchangeable image file format for digital...Digital Forensics. Springer, 2005. 23. Photography, Technical Committee. Photography and graphic technology - Ex- tended colour encodings for digital image

  20. Solving jigsaw puzzles using image features

    DEFF Research Database (Denmark)

    Nielsen, Ture R.; Drewsen, Peter; Hansen, Klaus

    2008-01-01

    In this article, we describe a method for automatic solving of the jigsaw puzzle problem based on using image features instead of the shape of the pieces. The image features are used for obtaining an accurate measure for edge similarity to be used in a new edge matching algorithm. The algorithm i...

  1. Wilson’s disease: Atypical imaging features

    Directory of Open Access Journals (Sweden)

    Venugopalan Y Vishnu

    2016-10-01

    Full Text Available Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17- year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present.

  2. Image fusion using sparse overcomplete feature dictionaries

    Science.gov (United States)

    Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

    2015-10-06

    Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

  3. Infrared image enhancement with learned features

    Science.gov (United States)

    Fan, Zunlin; Bi, Duyan; Ding, Wenshan

    2017-11-01

    Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, ;gradient reversal;, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

  4. Imaging features of iliopsoas bursitis

    Energy Technology Data Exchange (ETDEWEB)

    Wunderbaldinger, P. [Department of Radiology, University of Vienna (Austria); Center of Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA (United States); Bremer, C. [Department of Radiology, University of Muenster (Germany); Schellenberger, E. [Center of Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA (United States); Department of Radiology, Martin-Luther University of Halle-Wittenberg, Halle (Germany); Cejna, M.; Turetschek, K.; Kainberger, F. [Department of Radiology, University of Vienna (Austria)

    2002-02-01

    The aim of this study was firstly to describe the spectrum of imaging findings seen in iliopsoas bursitis, and secondly to compare cross-sectional imaging techniques in the demonstration of the extent, size and appearance of the iliopsoas bursitis as referenced by surgery. Imaging studies of 18 patients (13 women, 5 men; mean age 53 years) with surgically proven iliopsoas bursitis were reviewed. All patients received conventional radiographs of the pelvis and hip, US and MR imaging of the hip. The CT was performed in 5 of the 18 patients. Ultrasound, CT and MR all demonstrated enlarged iliopsoas bursae. The bursal wall was thin and well defined in 83% and thickened in 17% of all cases. The two cases with septations on US were not seen by CT and MRI. A communication between the bursa and the hip joint was seen, and surgically verified, in all 18 patients by MR imaging, whereas US and CT failed to demonstrate it in 44 and 40% of the cases, respectively. Hip joint effusion was seen and verified by surgery in 16 patients by MRI, whereas CT (4 of 5) and US (n=12) underestimated the number. The overall size of the bursa corresponded best between MRI and surgery, whereas CT and US tended to underestimate the size. Contrast enhancement of the bursal wall was seen in all cases. The imaging characteristics of iliopsoas bursitis are a well-defined, thin-walled cystic mass with a communication to the hip joint and peripheral contrast enhancement. The most accurate way to assess iliopsoas bursitis is with MR imaging; thus, it should be used for accurate therapy planning and follow-up studies. In order to initially prove an iliopsoas bursitis, US is the most cost-effective, easy-to-perform and fast alternative. (orig.)

  5. Imaging features of iliopsoas bursitis

    International Nuclear Information System (INIS)

    Wunderbaldinger, P.; Bremer, C.; Schellenberger, E.; Cejna, M.; Turetschek, K.; Kainberger, F.

    2002-01-01

    The aim of this study was firstly to describe the spectrum of imaging findings seen in iliopsoas bursitis, and secondly to compare cross-sectional imaging techniques in the demonstration of the extent, size and appearance of the iliopsoas bursitis as referenced by surgery. Imaging studies of 18 patients (13 women, 5 men; mean age 53 years) with surgically proven iliopsoas bursitis were reviewed. All patients received conventional radiographs of the pelvis and hip, US and MR imaging of the hip. The CT was performed in 5 of the 18 patients. Ultrasound, CT and MR all demonstrated enlarged iliopsoas bursae. The bursal wall was thin and well defined in 83% and thickened in 17% of all cases. The two cases with septations on US were not seen by CT and MRI. A communication between the bursa and the hip joint was seen, and surgically verified, in all 18 patients by MR imaging, whereas US and CT failed to demonstrate it in 44 and 40% of the cases, respectively. Hip joint effusion was seen and verified by surgery in 16 patients by MRI, whereas CT (4 of 5) and US (n=12) underestimated the number. The overall size of the bursa corresponded best between MRI and surgery, whereas CT and US tended to underestimate the size. Contrast enhancement of the bursal wall was seen in all cases. The imaging characteristics of iliopsoas bursitis are a well-defined, thin-walled cystic mass with a communication to the hip joint and peripheral contrast enhancement. The most accurate way to assess iliopsoas bursitis is with MR imaging; thus, it should be used for accurate therapy planning and follow-up studies. In order to initially prove an iliopsoas bursitis, US is the most cost-effective, easy-to-perform and fast alternative. (orig.)

  6. Feature hashing for fast image retrieval

    Science.gov (United States)

    Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

    2018-03-01

    Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

  7. Evaluating base widening methods.

    Science.gov (United States)

    2013-12-01

    The surface transportation system forms the biggest infrastructure investment in the United States of which the : roadway pavement forms an integral part. Maintaining the roadways can involve rehabilitation in the form of : widening; which require a ...

  8. Featured Image: Simulating Planetary Gaps

    Science.gov (United States)

    Kohler, Susanna

    2017-03-01

    The authors model of howthe above disk would look as we observe it in a scattered-light image. The morphology of the gap can be used to estimate the mass of the planet that caused it. [Dong Fung 2017]The above image from a computer simulation reveals the dust structure of a protoplanetary disk (with the star obscured in the center) as a newly formed planet orbits within it. A recent study by Ruobing Dong (Steward Observatory, University of Arizona) and Jeffrey Fung (University of California, Berkeley) examines how we can determine mass of such a planet based on our observations of the gap that the planet opens in the disk as it orbits. The authors models help us to better understand how our observations of gaps might change if the disk is inclined relative to our line of sight, and how we can still constrain the mass of the gap-opening planet and the viscosity of the disk from the scattered-light images we have recently begun to obtain of distant protoplanetary disks. For more information, check out the paper below!CitationRuobing Dong () and Jeffrey Fung () 2017 ApJ 835 146. doi:10.3847/1538-4357/835/2/146

  9. Quantitative imaging features: extension of the oncology medical image database

    Science.gov (United States)

    Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

    2015-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

  10. Image Processing and Features Extraction of Fingerprint Images ...

    African Journals Online (AJOL)

    To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of ...

  11. MR imaging features of hydrocephalus

    International Nuclear Information System (INIS)

    Zinn, W.; George, A.E.; Leon, M.J. de; Pinto, R.S.; Litt, A.W.; Kricheff, I.I.

    1990-01-01

    This paper compares the midsagittal dimensions of the third and lateral ventricles on MR images in cases of known hydrocephalus and atrophy. Cranial MR studies of 55 age-matched patients, 21 with known hydrocephalus and 28 with atrophy were retrospectively reviewed. Measurements of the genu-to-splenium diameter (G-S) and anterior commissure to corpus callosum distance (AC-CC) were obtained. A volumetric index (VI) was calculated as (G-S) x (AC-CC), and a ratio was calculated as (AC-CC)/(G-S). The volumetric index (VI) was 22% larger in the hydrocephalus group (Student two-tail t test, P > .001,t = -4.23). Sixty-two percent of hydrocephalus patients had either ratios greater than 56% or VIs of a least 20 cm 2

  12. Saliency image of feature building for image quality assessment

    Science.gov (United States)

    Ju, Xinuo; Sun, Jiyin; Wang, Peng

    2011-11-01

    The purpose and method of image quality assessment are quite different for automatic target recognition (ATR) and traditional application. Local invariant feature detectors, mainly including corner detectors, blob detectors and region detectors etc., are widely applied for ATR. A saliency model of feature was proposed to evaluate feasibility of ATR in this paper. The first step consisted of computing the first-order derivatives on horizontal orientation and vertical orientation, and computing DoG maps in different scales respectively. Next, saliency images of feature were built based auto-correlation matrix in different scale. Then, saliency images of feature of different scales amalgamated. Experiment were performed on a large test set, including infrared images and optical images, and the result showed that the salient regions computed by this model were consistent with real feature regions computed by mostly local invariant feature extraction algorithms.

  13. MR imaging features of craniodiaphyseal dysplasia

    Energy Technology Data Exchange (ETDEWEB)

    Marden, Franklin A. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Wippold, Franz J. [Mallinckrodt Institute of Radiology, Washington University Medical Center, 510 South Kingshighway Blvd., MO 63110, St. Louis (United States); Department of Radiology, St. Louis Children' s Hospital, Children' s Place, MO 63110, St. Louis (United States); Department of Radiology/Nuclear Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, MD 20814, Bethesda (United States)

    2004-02-01

    We report the magnetic resonance (MR) imaging findings in a 4-year-old girl with characteristic radiographic and computed tomography (CT) features of craniodiaphyseal dysplasia. MR imaging exquisitely depicted cranial nerve compression, small foramen magnum, hydrocephalus, and other intracranial complications of this syndrome. A syrinx of the cervical spinal cord was demonstrated. We suggest that MR imaging become a routine component of the evaluation of these patients. (orig.)

  14. Feature Detector and Descriptor for Medical Images

    Science.gov (United States)

    Sargent, Dusty; Chen, Chao-I.; Tsai, Chang-Ming; Wang, Yuan-Fang; Koppel, Daniel

    2009-02-01

    The ability to detect and match features across multiple views of a scene is a crucial first step in many computer vision algorithms for dynamic scene analysis. State-of-the-art methods such as SIFT and SURF perform successfully when applied to typical images taken by a digital camera or camcorder. However, these methods often fail to generate an acceptable number of features when applied to medical images, because such images usually contain large homogeneous regions with little color and intensity variation. As a result, tasks like image registration and 3D structure recovery become difficult or impossible in the medical domain. This paper presents a scale, rotation and color/illumination invariant feature detector and descriptor for medical applications. The method incorporates elements of SIFT and SURF while optimizing their performance on medical data. Based on experiments with various types of medical images, we combined, adjusted, and built on methods and parameter settings employed in both algorithms. An approximate Hessian based detector is used to locate scale invariant keypoints and a dominant orientation is assigned to each keypoint using a gradient orientation histogram, providing rotation invariance. Finally, keypoints are described with an orientation-normalized distribution of gradient responses at the assigned scale, and the feature vector is normalized for contrast invariance. Experiments show that the algorithm detects and matches far more features than SIFT and SURF on medical images, with similar error levels.

  15. Field application of feature-enhanced imaging

    International Nuclear Information System (INIS)

    Mucciardi, A.N.

    1988-01-01

    One of the more challenging ultrasonic inspection problems is bimetallic weld inspection or, in general, dissimilar metal welds. These types of welds involve complicated geometries and various mixtures of materials. Attempts to address this problem with imaging alone have fallen short of desired goals. The probable reason for this is the lack of information supplied by imaging systems, which are limited to amplitude and time displays. Having RF information available for analysis greatly enhances the information obtainable from dissimilar metal welds and, coupled with the spatial map generated by an imaging system, can significantly improve the reliability of dissimilar metal weld inspections. Ultra Image and TestPro are, respectively, an imaging system and a feature-based signal analysis system. The purpose of this project is to integrate these two systems to produce a feature-enhanced imaging system. This means that a software link is established between Ultra Image and the PC-based TestPro system so that the user of the combined system can perform all the usual imaging functions and also have available a wide variety of RF signal analysis functions. The analysis functions include waveform feature-based pattern recognition as well as artificial intelligence/expert system techniques

  16. Hemorrhage detection in MRI brain images using images features

    Science.gov (United States)

    Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

    2013-11-01

    The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

  17. Imaging features of benign adrenal cysts

    International Nuclear Information System (INIS)

    Sanal, Hatice Tuba; Kocaoglu, Murat; Yildirim, Duzgun; Bulakbasi, Nail; Guvenc, Inanc; Tayfun, Cem; Ucoz, Taner

    2006-01-01

    Benign adrenal gland cysts (BACs) are rare lesions with a variable histological spectrum and may mimic not only each other but also malignant ones. We aimed to review imaging features of BACs which can be helpful in distinguishing each entity and determining the subsequent appropriate management

  18. Remote Sensing Image Registration Using Multiple Image Features

    Directory of Open Access Journals (Sweden)

    Kun Yang

    2017-06-01

    Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

  19. Magnetic Resonance Imaging Features as Surrogate Markers of X-Linked Hypophosphatemic Rickets Activity.

    Science.gov (United States)

    Lempicki, Marta; Rothenbuhler, Anya; Merzoug, Valérie; Franchi-Abella, Stéphanie; Chaussain, Catherine; Adamsbaum, Catherine; Linglart, Agnès

    2017-01-01

    X-linked hypophosphatemic rickets (XLH) is the most common form of inheritable rickets. Rickets treatment is monitored by assessing alkaline phosphatase (ALP) levels, clinical features, and radiographs. Our objectives were to describe the magnetic resonance imaging (MRI) features of XLH and to assess correlations with disease activity. Twenty-seven XLH patients (median age 9.2 years) were included in this prospective single-center observational study. XLH activity was assessed using height, leg bowing, dental abscess history, and serum ALP levels. We looked for correlations between MRI features and markers of disease activity. On MRI, the median maximum width of the physis was 5.6 mm (range 4.8-7.8; normal 1.5 mm in all of the patients. The appearance of the zone of provisional calcification was abnormal on 21 MRI images (78%), Harris lines were present on 24 (89%), and bone marrow signal abnormalities were present on 16 (59%). ALP levels correlated with the maximum physeal widening and with the transverse extent of the widening. MRI of the knee provides precise rickets patterns that are correlated with ALP, an established biochemical marker of the disease, avoiding X-ray exposure and providing surrogate quantitative markers of disease activity. © 2017 S. Karger AG, Basel.

  20. Imaging features of posterior mediastinal chordoma in a child

    Energy Technology Data Exchange (ETDEWEB)

    Soudack, Michalle; Guralnik, Ludmilla; Engel, Ahuva [Rambam Health Care Campus, Department of Diagnostic Imaging, Haifa (Israel); Ben-Nun, Alon [Rambam Health Care Campus, Department of Thoracic Surgery, Haifa (Israel); Berkowitz, Drora [Rambam Health Care Campus, Department of Pediatrics B, Haifa (Israel); Postovsky, Sergey [Rambam Health Care Campus, Department of Pediatric Hemato-Oncology, Haifa (Israel); Vlodavsky, Eugene [Rambam Health Care Campus, Department of Pathology, Haifa (Israel)

    2007-05-15

    A 51/2-year-old boy presented with repeated episodes of stridor and cough. Chest radiography demonstrated a widened mediastinum. Evaluation by CT revealed a low-density posterior mediastinal mass initially diagnosed as benign tumor. Histopathological analysis of the resected mass disclosed a malignant chordoma. Our radiological results are described with an analysis of the imaging findings in the medical literature. We present our suggestions for preoperative evaluation of posterior mediastinal tumors. (orig.)

  1. Barrett's esophagus: clinical features, obesity, and imaging.

    LENUS (Irish Health Repository)

    Quigley, Eamonn M M

    2011-09-01

    The following includes commentaries on clinical features and imaging of Barrett\\'s esophagus (BE); the clinical factors that influence the development of BE; the influence of body fat distribution and central obesity; the role of adipocytokines and proinflammatory markers in carcinogenesis; the role of body mass index (BMI) in healing of Barrett\\'s epithelium; the role of surgery in prevention of carcinogenesis in BE; the importance of double-contrast esophagography and cross-sectional images of the esophagus; and the value of positron emission tomography\\/computed tomography.

  2. Featured Image: Diamonds in a Meteorite

    Science.gov (United States)

    Kohler, Susanna

    2018-04-01

    This unique image which measures only 60 x 80 micrometers across reveals details in the Kapoeta meteorite, an 11-kg stone that fell in South Sudan in 1942. The sparkle in the image? A cluster of nanodiamonds discovered embedded in the stone in a recent study led by Yassir Abdu (University of Sharjah, United Arab Emirates). Abdu and collaborators showed that these nanodiamonds have similar spectral features to the interiors of dense interstellar clouds and they dont show any signs of shock features. This may suggest that the nanodiamonds were formed by condensation of nebular gases early in the history of the solar system. The diamonds were trapped in the surface material of the Kapoeta meteorites parent body, thought to be the asteroid Vesta. To read more about the authors study, check out the original article below.CitationYassir A. Abdu et al 2018 ApJL 856 L9. doi:10.3847/2041-8213/aab433

  3. Special feature on imaging systems and techniques

    Science.gov (United States)

    Yang, Wuqiang; Giakos, George

    2013-07-01

    The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

  4. Feature Evaluation for Building Facade Images - AN Empirical Study

    Science.gov (United States)

    Yang, M. Y.; Förstner, W.; Chai, D.

    2012-08-01

    The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

  5. Imaging features of juxtacortical chondroma in children

    International Nuclear Information System (INIS)

    Miller, Stephen F.

    2014-01-01

    Juxtacortical chondroma is a rare benign bone lesion in children. Children usually present with a mildly painful mass, which prompts diagnostic imaging studies. The rarity of this condition often presents a diagnostic challenge. Correct diagnosis is crucial in guiding surgical management. To describe the characteristic imaging findings of juxtacortical chondroma in children. We identified all children who were diagnosed with juxtacortical chondroma between 1998 and 2012. A single experienced pediatric radiologist reviewed all diagnostic imaging studies, including plain radiographs, CT, MR and bone scans. Seven children (5 boys and 2 girls) with juxtacortical chondroma were identified, ranging in age from 6 years to 16 years (mean 12.3 years). Mild pain and a palpable mass were present in all seven children. Plain radiographs were available in 6/7, MR in 7/7, CT in 4/7 and skeletal scintigraphy in 5/7 children. Three lesions were located in the proximal humerus, with one each in the distal radius, distal femur, proximal tibia and scapula. Radiographic and CT features deemed highly suggestive of juxtacortical chondroma included cortical scalloping, underlying cortical sclerosis and overhanging margins. MRI features consistent with juxtacortical chondroma included isointensity to skeletal muscle on T1, marked hyperintensity on T2 and peripheral rim enhancement after contrast agent administration. One of seven lesions demonstrated intramedullary extension, and 2/7 showed adjacent soft-tissue edema. Juxtacortical chondroma is an uncommon benign lesion in children with characteristic features on plain radiographs, CT and MR. Recognition of these features is invaluable in guiding appropriate surgical management. (orig.)

  6. Imaging features of juxtacortical chondroma in children

    Energy Technology Data Exchange (ETDEWEB)

    Miller, Stephen F. [St. Jude Children' s Research Hospital, Department of Radiological Sciences, Memphis, TN (United States)

    2014-01-15

    Juxtacortical chondroma is a rare benign bone lesion in children. Children usually present with a mildly painful mass, which prompts diagnostic imaging studies. The rarity of this condition often presents a diagnostic challenge. Correct diagnosis is crucial in guiding surgical management. To describe the characteristic imaging findings of juxtacortical chondroma in children. We identified all children who were diagnosed with juxtacortical chondroma between 1998 and 2012. A single experienced pediatric radiologist reviewed all diagnostic imaging studies, including plain radiographs, CT, MR and bone scans. Seven children (5 boys and 2 girls) with juxtacortical chondroma were identified, ranging in age from 6 years to 16 years (mean 12.3 years). Mild pain and a palpable mass were present in all seven children. Plain radiographs were available in 6/7, MR in 7/7, CT in 4/7 and skeletal scintigraphy in 5/7 children. Three lesions were located in the proximal humerus, with one each in the distal radius, distal femur, proximal tibia and scapula. Radiographic and CT features deemed highly suggestive of juxtacortical chondroma included cortical scalloping, underlying cortical sclerosis and overhanging margins. MRI features consistent with juxtacortical chondroma included isointensity to skeletal muscle on T1, marked hyperintensity on T2 and peripheral rim enhancement after contrast agent administration. One of seven lesions demonstrated intramedullary extension, and 2/7 showed adjacent soft-tissue edema. Juxtacortical chondroma is an uncommon benign lesion in children with characteristic features on plain radiographs, CT and MR. Recognition of these features is invaluable in guiding appropriate surgical management. (orig.)

  7. Determination of the Image Complexity Feature in Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Veacheslav L. Perju

    2003-11-01

    Full Text Available The new image complexity informative feature is proposed. The experimental estimation of the image complexity is carried out. There are elaborated two optical-electronic processors for image complexity calculation. The determination of the necessary number of the image's digitization elements depending on the image complexity was carried out. The accuracy of the image complexity feature calculation was made.

  8. MR imaging features of hemispherical spondylosclerosis

    Energy Technology Data Exchange (ETDEWEB)

    Vicentini, Joao R.T.; Martinez-Salazar, Edgar L.; Chang, Connie Y.; Bredella, Miriam A.; Rosenthal, Daniel I.; Torriani, Martin [Massachusetts General Hospital and Harvard Medical School, Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Boston, MA (United States)

    2017-10-15

    Hemispherical spondylosclerosis (HS) is a rare degenerative entity characterized by dome-shaped sclerosis of a single vertebral body that may pose a diagnostic dilemma. The goal of this study was to describe the MR imaging features of HS. We identified spine radiographs and CT examinations of subjects with HS who also had MR imaging for correlation. Two musculoskeletal radiologists independently assessed sclerosis characteristics, presence of endplate erosions, marrow signal intensity, and disk degeneration (Pfirrmann scale). We identified 11 subjects (six males, five females, mean 48 ± 10 years) with radiographic/CT findings of HS. The most commonly affected vertebral body was L4 (6/11; 55%). On MR imaging, variable signal intensity was noted, being most commonly low on T1 (8/11, 73%) and high on fat-suppressed T2-weighted (8/11, 73%) images. In two subjects, diffuse post-contrast enhancement was seen in the lesion. Moderate disk degeneration and endplate bone erosions adjacent to sclerosis were present in all subjects. Erosions of the opposite endplate were present in two subjects (2/11, 18%). CT data from nine subjects showed the mean attenuation value of HS was 472 ± 96 HU. HS appearance on MR imaging is variable and may not correlate with the degree of sclerosis seen on radiographs or CT. Disk degenerative changes and asymmetric endplate erosions are consistent markers of HS. (orig.)

  9. Mass-like extramedullary hematopoiesis: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Ginzel, Andrew W. [Synergy Radiology Associates, Houston, TX (United States); Kransdorf, Mark J.; Peterson, Jeffrey J.; Garner, Hillary W. [Mayo Clinic, Department of Radiology, Jacksonville, FL (United States); Murphey, Mark D. [American Institute for Radiologic Pathology, Silver Spring, MD (United States)

    2012-08-15

    To report the imaging appearances of mass-like extramedullary hematopoiesis (EMH), to identify those features that are sufficiently characteristic to allow a confident diagnosis, and to recognize the clinical conditions associated with EMH and the relative incidence of mass-like disease. We retrospectively identified 44 patients with EMH; 12 of which (27%) had focal mass-like lesions and formed the study group. The study group consisted of 6 male and 6 female subjects with a mean age of 58 years (range 13-80 years). All 12 patients underwent CT imaging and 3 of the 12 patients had undergone additional MR imaging. The imaging characteristics of the extramedullary hematopoiesis lesions in the study group were analyzed and recorded. The patient's clinical presentation, including any condition associated with extramedullary hematopoiesis, was also recorded. Ten of the 12 (83%) patients had one or more masses located along the axial skeleton. Of the 10 patients with axial masses, 9 (90%) had multiple masses and 7 (70%) demonstrated internal fat. Eight patients (80%) had paraspinal masses and 4 patients (40%) had presacral masses. Seven patients (70%) had splenomegaly. Eleven of the 12 patients had a clinical history available for review. A predisposing condition for extramedullary hematopoiesis was present in 10 patients and included various anemias (5 cases; 45%), myelofibrosis/myelodysplastic syndrome (4 cases; 36%), and marrow proliferative disorder (1 case; 9%). One patient had no known predisposing condition. Mass-like extramedullary hematopoiesis most commonly presents as multiple, fat-containing lesions localized to the axial skeleton. When these imaging features are identified, extramedullary hematopoiesis should be strongly considered, particularly when occurring in the setting of a predisposing medical condition. (orig.)

  10. Imaging features of foot osteoid osteoma

    Energy Technology Data Exchange (ETDEWEB)

    Shukla, Satyen; Clarke, Andrew W.; Saifuddin, Asif [Royal National Orthopaedic Hospital NHS Trust, Department of Radiology, Stanmore, Middlesex (United Kingdom)

    2010-07-15

    We performed a retrospective review of the imaging of nine patients with a diagnosis of foot osteoid osteoma (OO). Radiographs, computed tomography (CT) and magnetic resonance imaging (MRI) had been performed in all patients. Radiographic features evaluated were the identification of a nidus and cortical thickening. CT features noted were nidus location (affected bone - intramedullary, intracortical, subarticular) and nidus calcification. MRI features noted were the presence of an identifiable nidus, presence and grade of bone oedema and whether a joint effusion was identified. Of the nine patients, three were female and six male, with a mean age of 21 years (range 11-39 years). Classical symptoms of OO (night pain, relief with aspirin) were identified in five of eight (62.5%) cases (in one case, the medical records could not be retrieved). In five patients the lesion was located in the hindfoot (four calcaneus, one talus), while four were in the mid- or forefoot (two metatarsal and two phalangeal). Radiographs were normal in all patients with hindfoot OO. CT identified the nidus in all cases (89%) except one terminal phalanx lesion, while MRI demonstrated a nidus in six of nine cases (67%). The nidus was of predominantly intermediate signal intensity on T1-weighted (T1W) sequences, with intermediate to high signal intensity on T2-weighted (T2W) sequences. High-grade bone marrow oedema, limited to the affected bone and adjacent soft tissue oedema was identified in all cases. In a young patient with chronic hindfoot pain and a normal radiograph, MRI features suggestive of possible OO include extensive bone marrow oedema limited to one bone, with a possible nidus demonstrated in two-thirds of cases. The presence or absence of a nidus should be confirmed with high-resolution CT. (orig.)

  11. Feature extraction & image processing for computer vision

    CERN Document Server

    Nixon, Mark

    2012-01-01

    This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, ""The main strength of the proposed book is the exemplar code of the algorithms."" Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filt

  12. Imaging features of intracranial solitary fibrous tumors

    International Nuclear Information System (INIS)

    Yu Shuilian; Man Yuping; Ma Longbai; Liu Ying; Wei Qiang; Zhu Youkai

    2012-01-01

    Objective: To summarize the imaging features of intracranial solitary fibrous tumors (ISFT). Methods: Ten patients with ISFT proven histopathologically were collected. Four cases had CT data and all cases had MR data. The imaging features and pathological results were retrospectively analyzed. Results: All cases were misdiagnosed as meningioma at pre-operation. All lesions arose from intracranial meninges including 5 lesions above the tentorium, 4 lesions beneath the tentorium and 1 lesion growing around the tentorium. The margins of all the masses were well defined, and 8 lesions presented multilobular shape. CT demonstrated hyerattenuated masses in all 4 lesions, smooth erosion of the basicranial skull in 1 lesion, and punctiform calcification of the capsule in 1 lesion. T 1 WI showed most lesions with isointense or slight hyperintense signals including homogeneous in 4 lesions and heterogeneous in 6 lesions. T 2 WI demonstrated isointense or slight hyperintense in 2 lesions, mixed hypointense and hyperintense signals in 4, cystic portion in 2, and two distinct portion of hyperintense and hypointense signal, so called 'yin-yang' pattern, in 2. Strong enhanced was found in all lesions, especially in 8 lesion with heterogeneous with the low T 2 signal. 'Dural tail' was found in 4 lesions. Conclusions: ISFI has some specific CT and MR features including heterogeneous signal intensity on T 2 WI, strong enhancement of areas with low T 2 signal intensity, slight or no 'dural tail', without skull thickening, and the typical 'yin-yang' pattern. (authors)

  13. Magnetic Resonance Imaging Features of Neuromyelitis Optica

    Energy Technology Data Exchange (ETDEWEB)

    You, Sun Kyung; Song, Chang June; Park, Woon Ju; Lee, In Ho; Son, Eun Hee [Chungnam National University College of Medicine, Chungnam National University Hospital, Daejeon (Korea, Republic of)

    2013-03-15

    To report the magnetic resonance (MR) imaging features of the spinal cord and brain in patients of neuromyelitis optica (NMO). Between January 2001 and March 2010, the MR images (spinal cord, brain, and orbit) and the clinical and serologic findings of 11 NMO patients were retrospectively reviewed. The contrast-enhancement of the spinal cord was performed (20/23). The presence and pattern of the contrast-enhancement in the spinal cord were classified into 5 types. Acute myelitis was monophasic in 8 patients (8/11, 72.7%); and optic neuritis preceded acute myelitis in most patients. Longitudinally extensive cord lesion (average, 7.3 vertebral segments) was involved. The most common type was the diffuse and subtle enhancement of the spinal cord with a multifocal nodular, linear or segmental intense enhancement (45%). Most of the brain lesions (5/11, 10 lesions) were located in the brain stem, thalamus and callososeptal interphase. Anti-Ro autoantibody was positive in 2 patients, and they showed a high relapse rate of acute myelitis. Anti-NMO IgG was positive in 4 patients (4/7, 66.7%). The imaging findings of acute myelitis in NMO may helpful in making an early diagnosis of NMO which can result in a severe damage to the spinal cord, and to make a differential diagnosis of multiple sclerosis and inflammatory diseases of the spinal cord such as toxocariasis.

  14. Magnetic Resonance Imaging Features of Neuromyelitis Optica

    International Nuclear Information System (INIS)

    You, Sun Kyung; Song, Chang June; Park, Woon Ju; Lee, In Ho; Son, Eun Hee

    2013-01-01

    To report the magnetic resonance (MR) imaging features of the spinal cord and brain in patients of neuromyelitis optica (NMO). Between January 2001 and March 2010, the MR images (spinal cord, brain, and orbit) and the clinical and serologic findings of 11 NMO patients were retrospectively reviewed. The contrast-enhancement of the spinal cord was performed (20/23). The presence and pattern of the contrast-enhancement in the spinal cord were classified into 5 types. Acute myelitis was monophasic in 8 patients (8/11, 72.7%); and optic neuritis preceded acute myelitis in most patients. Longitudinally extensive cord lesion (average, 7.3 vertebral segments) was involved. The most common type was the diffuse and subtle enhancement of the spinal cord with a multifocal nodular, linear or segmental intense enhancement (45%). Most of the brain lesions (5/11, 10 lesions) were located in the brain stem, thalamus and callososeptal interphase. Anti-Ro autoantibody was positive in 2 patients, and they showed a high relapse rate of acute myelitis. Anti-NMO IgG was positive in 4 patients (4/7, 66.7%). The imaging findings of acute myelitis in NMO may helpful in making an early diagnosis of NMO which can result in a severe damage to the spinal cord, and to make a differential diagnosis of multiple sclerosis and inflammatory diseases of the spinal cord such as toxocariasis.

  15. Unsupervised feature learning for autonomous rock image classification

    Science.gov (United States)

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

  16. Imaging features of breast echinococcus granulosus

    International Nuclear Information System (INIS)

    Zeng Li; Liu Fanming; Gong Yue; Ge Jinmei; Li Xianjun; Shi Minxin; Guo Yongzhong

    2012-01-01

    Objective: To demonstrate the X-ray and CT features of breast hydatid disease. Methods: Of 11 patients with pathologically confirmed breast Echinococcus hydatid disease were collected and the X-ray and CT image data were analyzed. Results: Of 11 patients with hydatid cysts,single cyst was found in 9 patients which one cyst was ruptured due to trauma, multiple cyst in 2 patients. Mammography showed small or large shadow in different size, with low or high density and smooth margin. Calcification was found in 5 and 2 patients with egg shell-like calcification along the wall of cyst, 3 patients with spotted calcification within cyst. One case had cavity-like change (annular solar eclipse sign). Cystic lesion with a complete capsule was demonstrated on CT scan in 1 patient. Conclusion: Molybdenum target mammography can accurately display the imaging characteristics of hydatid cyst and improve the diagnostic ability of breast hydatid cyst in combination with clinical and epidemiological data. (authors)

  17. Featured Image: Bright Dots in a Sunspot

    Science.gov (United States)

    Kohler, Susanna

    2018-03-01

    This image of a sunspot, located in in NOAA AR 12227, was captured in December 2014 by the 0.5-meter Solar Optical Telescope on board the Hinode spacecraft. This image was processed by a team of scientists led by Rahul Yadav (Udaipur Solar Observatory, Physical Research Laboratory Dewali, India) in order to examine the properties of umbral dots: transient, bright features observed in the umbral region (the central, darkest part) of a sunspot. By exploring these dots, Yadav and collaborators learned how their properties relate to the large-scale properties of the sunspots in which they form for instance, how do the number, intensities, or filling factors of dots relate to the size of a sunspots umbra? To find out more about the authors results, check out the article below.Sunspot in NOAA AR 11921. Left: umbralpenumbral boundary. Center: the isolated umbra from the sunspot. Right: The umbra with locations of umbral dots indicated by yellow plus signs. [Adapted from Yadav et al. 2018]CitationRahul Yadav et al 2018 ApJ 855 8. doi:10.3847/1538-4357/aaaeba

  18. Imaging features of colovesical fistulae on MRI.

    Science.gov (United States)

    Tang, Y Z; Booth, T C; Swallow, D; Shahabuddin, K; Thomas, M; Hanbury, D; Chang, S; King, C

    2012-10-01

    MRI is routinely used in the investigation of colovesical fistulae at our institute. Several papers have alluded to its usefulness in achieving the diagnosis; however, there is a paucity of literature on its imaging findings. Our objective was to quantify the MRI characteristics of these fistulae. We selected all cases over a 4-year period with a final clinical diagnosis of colovesical fistula which had been investigated with MRI. The MRI scans were reviewed in a consensus fashion by two consultant uroradiologists. Their MRI features were quantified. There were 40 cases of colovesical fistulae. On MRI, the fistula morphology consistently fell into three patterns. The most common pattern (71%) demonstrated an intervening abscess between the bowel wall and bladder wall. The second pattern (15%) had a visible track between the affected bowel and bladder. The third pattern (13%) was a complete loss of fat plane between the affected bladder and bowel wall. MRI correctly determined the underlying aetiology in 63% of cases. MRI is a useful imaging modality in the diagnosis of colovesical fistulae. The fistulae appear to have three characteristic morphological patterns that may aid future diagnoses of colovesical fistulae. To the authors' knowledge, this is the first publication of the MRI findings in colovesical fistulae.

  19. MR imaging features of spindle cell lipoma

    Energy Technology Data Exchange (ETDEWEB)

    Kirwadi, Anand; Abdul-Halim, Rehan; Highland, Adrian; Kotnis, Nikhil [Sheffield Teaching Hospitals NHS Trust, Radiology Department, Sheffield (United Kingdom); Fernando, Malee [Sheffield Teaching Hospitals NHS Trust, Histopathology Department, Sheffield (United Kingdom)

    2014-02-15

    To assess the MR imaging features of spindle cell lipomas (SCL) and to compare these appearances directly with the histopathological findings. A retrospective review of our soft tissue tumor database was performed. This yielded 1,327 histologically proven lipomas, of which 25 were confirmed as being SCLs. Fourteen of the 25 patients had MR examinations available for review and only these patients were included in our study. Lesions were assessed at MR examination for the degree of internal fat signal content with grade 0 representing 0 % fat signal and grade 4 100 % fat signal. The degree of fat suppression and contrast-enhancement pattern were also recorded. The excision specimens were independently reviewed by a consultant histopathologist. The histology specimens were assessed for the amount of internal fat and non-adipose tissue and graded using the same scale applied for the imaging. Where core needle biopsy (CNB) was performed, the CNB specimens were also examined for positive features of SCL. In our study, 93 % (13/14) of our patients were male and the average age was 58 years. 65 % (9/14) of the lesions presented in the upper back, shoulder, or neck. All lesions were subcutaneous. 35 % (5/14) of the SCLs demonstrated grade 3 (>75 %) or grade 4 (100 %) fat signal on MR examination. 35 % (5/14) of the lesions had grade 2 (25-75 %) fat signal and 29 % (4/14) of the lesions demonstrated grade 0 (0 %) or grade 1 (<25 %) fat signal. 43 % (6/14) of lesions demonstrated homogenous fat suppression, 28 % (4/14) showed focal areas of high internal signal, and 28 % (4/14) had diffuse internal high signal on fluid-sensitive fat-saturated sequences. 86 % (6/7) of the cases demonstrated septal/nodular enhancement. The diagnosis was evident on the CNB specimen in 100 % (9/9) cases. The histopathology fat content grade was in agreement with the imaging grade in 86 % (12/14) cases. The internal signal pattern of SCL can range broadly, with low fat content lesions seen almost

  20. Color and neighbor edge directional difference feature for image retrieval

    Institute of Scientific and Technical Information of China (English)

    Chaobing Huang; Shengsheng Yu; Jingli Zhou; Hongwei Lu

    2005-01-01

    @@ A novel image feature termed neighbor edge directional difference unit histogram is proposed, in which the neighbor edge directional difference unit is defined and computed for every pixel in the image, and is used to generate the neighbor edge directional difference unit histogram. This histogram and color histogram are used as feature indexes to retrieve color image. The feature is invariant to image scaling and translation and has more powerful descriptive for the natural color images. Experimental results show that the feature can achieve better retrieval performance than other color-spatial features.

  1. FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

    Directory of Open Access Journals (Sweden)

    M. Y. Yang

    2012-08-01

    Full Text Available The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

  2. An Effective Combined Feature For Web Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    H.M.R.B Herath

    2015-08-01

    Full Text Available Abstract Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features Colour moments Colour coherence vector and Colour Correlogram and three texture features Grey Level Co-occurrence matrix Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes images of flowers etc. Also images which have a similar colour and texture distribution were retrieved as similar even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants

  3. Image feature detectors and descriptors foundations and applications

    CERN Document Server

    Hassaballah, Mahmoud

    2016-01-01

    This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition. .

  4. Feature coding for image representation and recognition

    CERN Document Server

    Huang, Yongzhen

    2015-01-01

    This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) D

  5. Featured Image: Revealing Hidden Objects with Color

    Science.gov (United States)

    Kohler, Susanna

    2018-02-01

    Stunning color astronomical images can often be the motivation for astronomers to continue slogging through countless data files, calculations, and simulations as we seek to understand the mysteries of the universe. But sometimes the stunning images can, themselves, be the source of scientific discovery. This is the case with the below image of Lynds Dark Nebula 673, located in the Aquila constellation, that was captured with the Mayall 4-meter telescope at Kitt Peak National Observatory by a team of scientists led by Travis Rector (University of Alaska Anchorage). After creating the image with a novel color-composite imaging method that reveals faint H emission (visible in red in both images here), Rector and collaborators identified the presence of a dozen new Herbig-Haro objects small cloud patches that are caused when material is energetically flung out from newly born stars. The image adapted above shows three of the new objects, HH 118789, aligned with two previously known objects, HH 32 and 332 suggesting they are driven by the same source. For more beautiful images and insight into the authors discoveries, check out the article linked below!Full view of Lynds Dark Nebula 673. Click for the larger view this beautiful composite image deserves! [T.A. Rector (University of Alaska Anchorage) and H. Schweiker (WIYN and NOAO/AURA/NSF)]CitationT. A. Rector et al 2018 ApJ 852 13. doi:10.3847/1538-4357/aa9ce1

  6. Superpixel-Based Feature for Aerial Image Scene Recognition

    Directory of Open Access Journals (Sweden)

    Hongguang Li

    2018-01-01

    Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

  7. Histological image classification using biologically interpretable shape-based features

    International Nuclear Information System (INIS)

    Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

    2013-01-01

    Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

  8. Iris recognition based on key image feature extraction.

    Science.gov (United States)

    Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

    2008-01-01

    In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

  9. Multimodality imaging features of hereditary multiple exostoses

    OpenAIRE

    Kok, H K; Fitzgerald, L; Campbell, N; Lyburn, I D; Munk, P L; Buckley, O; Torreggiani, W C

    2013-01-01

    Hereditary multiple exostoses (HME) or diaphyseal aclasis is an inherited disorder characterised by the formation of multiple osteochondromas, which are cartilage-capped osseous outgrowths, and the development of associated osseous deformities. Individuals with HME may be asymptomatic or develop clinical symptoms, which prompt imaging studies. Different modalities ranging from plain radiographs to cross-sectional and nuclear medicine imaging studies can be helpful in the diagnosis and detecti...

  10. Feature-based Alignment of Volumetric Multi-modal Images

    Science.gov (United States)

    Toews, Matthew; Zöllei, Lilla; Wells, William M.

    2014-01-01

    This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

  11. Prototype Theory Based Feature Representation for PolSAR Images

    OpenAIRE

    Huang Xiaojing; Yang Xiangli; Huang Pingping; Yang Wen

    2016-01-01

    This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory. Then, regularized logistic regression is used to predict similarities between a test sample and each prototype set. Finally, the PolSAR image feature representation is obtained by ensemble projection. Experimental results of an unsupervised classification of PolSAR images show that our...

  12. Diffusion tensor image registration using hybrid connectivity and tensor features.

    Science.gov (United States)

    Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

    2014-07-01

    Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Copyright © 2013 Wiley Periodicals, Inc.

  13. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

    Science.gov (United States)

    Li, Baopu; Meng, Max Q-H

    2012-05-01

    Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

  14. Adapting Local Features for Face Detection in Thermal Image

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2017-11-01

    Full Text Available A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses. We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

  15. Feature Importance for Human Epithelial (HEp-2 Cell Image Classification

    Directory of Open Access Journals (Sweden)

    Vibha Gupta

    2018-02-01

    Full Text Available Indirect Immuno-Fluorescence (IIF microscopy imaging of human epithelial (HEp-2 cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods. Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

  16. Learning Hierarchical Feature Extractors for Image Recognition

    Science.gov (United States)

    2012-09-01

    feature space . . . . . . . . . . . . . . . 85 5.3.1 Preserving neighborhood relationships during coding . . . . . . 86 5.3.2 Letting only neighbors vote ...Letting only neighbors vote during pooling Pooling involves extracting an ensemble statistic from a potentially large group of in- puts. However...element. For slicing the 4D tensor S we adopt the MATLAB notation for simplicity of notation. function ConvCoD(x,D, α) Set: S = DT ∗ D Initialize: z = 0; β

  17. Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

    International Nuclear Information System (INIS)

    Quanqing, Zhu.; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

    2002-01-01

    In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images

  18. Featured Image: Identifying a Glowing Shell

    Science.gov (United States)

    Kohler, Susanna

    2018-05-01

    New nebulae are being discovered and classified every day and this false-color image reveals one of the more recent objects of interest. This nebula, IPHASX J210204.7+471015, was recently imaged by the Andalucia Faint Object Spectrograph and Camera mounted on the 2.5-m Nordic Optical Telescope in La Palma, Spain. J210204 was initially identified as a possible planetary nebula a remnant left behind at the end of a red giants lifetime. Based on the above imaging, however, a team of authors led by Martn Guerrero (Institute of Astrophysics of Andalusia, Spain) is arguing that this shell of glowing gas was instead expelled around a classical nova. In a classical nova eruption, a white dwarf and its binary companion come very close together, and mass transfers to form a thin atmosphere of hydrogen around the white dwarf. When this hydrogen suddenly ignites in runaway fusion, this outer atmosphere can be expelled, forming a short-lived nova remnant which is what Guerrero and collaborators think were seeing with J210204. If so, this nebula can reveal information about the novathat caused it. To find out more about what the authors learned from this nebula, check out the paper below.CitationMartn A. Guerrero et al 2018 ApJ 857 80. doi:10.3847/1538-4357/aab669

  19. Robust Image Hashing Using Radon Transform and Invariant Features

    Directory of Open Access Journals (Sweden)

    Y.L. Liu

    2016-09-01

    Full Text Available A robust image hashing method based on radon transform and invariant features is proposed for image authentication, image retrieval, and image detection. Specifically, an input image is firstly converted into a counterpart with a normalized size. Then the invariant centroid algorithm is applied to obtain the invariant feature point and the surrounding circular area, and the radon transform is employed to acquire the mapping coefficient matrix of the area. Finally, the hashing sequence is generated by combining the feature vectors and the invariant moments calculated from the coefficient matrix. Experimental results show that this method not only can resist against the normal image processing operations, but also some geometric distortions. Comparisons of receiver operating characteristic (ROC curve indicate that the proposed method outperforms some existing methods in classification between perceptual robustness and discrimination.

  20. Changes in Anterior Segment Morphology and Predictors of Angle Widening after Laser Iridotomy in South Indian Eyes.

    Science.gov (United States)

    Zebardast, Nazlee; Kavitha, Srinivasan; Krishnamurthy, Palaniswamy; Friedman, David S; Nongpiur, Monisha E; Aung, Tin; Quigley, Harry A; Ramulu, Pradeep Y; Venkatesh, Rengaraj

    2016-12-01

    To compare anterior segment optical coherence tomography (ASOCT) angle morphology before and after laser peripheral iridotomy (LPI) in a cohort of South Indian subjects with primary angle-closure suspect (PACS) or primary angle-closure/primary angle-closure glaucoma (PAC/PACG) and to examine baseline parameters associated with angle widening. Prospective observational study. A total of 244 subjects aged ≥30 years with PACS or PAC/PACG in at least 1 eye. The ASOCT images and angle gonioscopic grades were analyzed for all subjects at baseline and 2 weeks after LPI. Multivariable linear and logistic regression models were used to determine predictors of angle widening (change in mean angle opening distance [AOD750]) and angle opening (all 4 quadrants with trabecular meshwork [TM] visible on gonioscopy after LPI). Change in ASOCT parameters with LPI and baseline predictors of angle widening. Laser peripheral iridotomy resulted in angle widening on ASOCT with significant increases in AOD750, angle recess area, and trabecular iris surface area (P gonioscopy, although some degree of persistent iridotrabecular contact was present in approximately half of PACS eyes and approximately two thirds of PAC/PACG eyes on gonioscopy. The greatest widening by ASOCT was observed in eyes with features most consistent with greater baseline pupillary block. Copyright © 2016 American Academy of Ophthalmology. All rights reserved.

  1. A visual perceptual descriptor with depth feature for image retrieval

    Science.gov (United States)

    Wang, Tianyang; Qin, Zhengrui

    2017-07-01

    This paper proposes a visual perceptual descriptor (VPD) and a new approach to extract perceptual depth feature for 2D image retrieval. VPD mimics human visual system, which can easily distinguish regions that have different textures, whereas for regions which have similar textures, color features are needed for further differentiation. We apply VPD on the gradient direction map of an image, capture texture-similar regions to generate a VPD map. We then impose the VPD map on a quantized color map and extract color features only from the overlapped regions. To reflect the nature of perceptual distance in single 2D image, we propose and extract the perceptual depth feature by computing the nuclear norm of the sparse depth map of an image. Extracted color features and the perceptual depth feature are both incorporated to a feature vector, we utilize this vector to represent an image and measure similarity. We observe that the proposed VPD + depth method achieves a promising result, and extensive experiments prove that it outperforms other typical methods on 2D image retrieval.

  2. MR Imaging Features of Fibrocystic Change of the Breast

    Science.gov (United States)

    Chen, Jeon-Hor; Liu, Hui; Baek, Hyeon-Man; Nalcioglu, Orhan; Su, Min-Ying

    2008-01-01

    Purpose Studies specifically reporting MR imaging of fibrocystic change (FCC) of the breast are very few and its MR imaging features are not clearly known. The purpose of this study was to analyze the MR imaging features of FCC of the breast. Materials and Methods Thirty one patients of pathologically proved FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5 T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1W imaging and dynamic contrast-enhanced axial T1W imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. Results FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of non-mass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). Conclusion MRI is able to elaborate the diverse imaging features of fibrocystic change of the breast. Our result showed that FCC presenting as focal mass type lesion were usually over-diagnosed as malignancy. Understanding MR imaging of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management. PMID:18436406

  3. Features Selection for Skin Micro-Image Symptomatic Recognition

    Institute of Scientific and Technical Information of China (English)

    HUYue-li; CAOJia-lin; ZHAOQian; FENGXu

    2004-01-01

    Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92% using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

  4. Features Selection for Skin Micro-Image Symptomatic Recognition

    Institute of Scientific and Technical Information of China (English)

    HU Yue-li; CAO Jia-lin; ZHAO Qian; FENG Xu

    2004-01-01

    Automatic recognition of skin micro-image symptom is important in skin diagnosis and treatment. Feature selection is to improve the classification performance of skin micro-image symptom.This paper proposes a hybrid approach based on the support vector machine (SVM) technique and genetic algorithm (GA) to select an optimum feature subset from the feature group extracted from the skin micro-images. An adaptive GA is introduced for maintaining the convergence rate. With the proposed method, the average cross validation accuracy is increased from 88.25% using all features to 96.92 % using only selected features provided by a classifier for classification of 5 classes of skin symptoms. The experimental results are satisfactory.

  5. CT imaging features of anaplastic thyroid carcinoma

    International Nuclear Information System (INIS)

    Shi Zhenshan; You Ruixiong; Cao Dairong; Li Yueming; Zhuang Qian

    2013-01-01

    Objective: To investigate the CT characteristics of anaplastic thyroid carcinoma and evaluate the diagnostic value of CT in this disease. Methods: The CT findings of 10 patients with pathologically proved anaplastic thyroid carcinoma were retrospectively reviewed. The patients included 7 females and 3 males. Their age ranged from 25.0 to 78 years with median of 61 years. Multi-slices plain and post contrast CT scans were performed in all patients. Results: Unilateral thyroid was involved in 6 patients. Unilateral thyroid and thyroid isthmus were both involved in 2 patients due to big size. Bilateral thyroid were involved in 2 patients. The maximum diameter of anaplastic thyroid carcinoma ranged from 2.9-12.8 cm with mean of (4.5 ± 1.4) cm. All lesions demonstrated unclear margins and envelope invasion. The densities of all lesions were heterogeneous and obvious necrosis areas were noted on precontrast images. Seven lesions showed varied calcifications, and coarse granular calcifications were found in 5 lesions among them. All lesions showed remarkable heterogenous enhancement on post-contrast CT. The CT value of solid portion of the tumor increased 40 HU after contrast media administration. The ratios of CT value which comparing of the tumor with contralateral sternocleidomastoid muscle were 0.69-0.82 (0.76 ± 0.18) and 1.25-1.41 (1.33 ± 0.28) on pre and post CT, respectively. Enlarged cervical lymph nodes were found in 6 cases (60.0%). It showed obvious homogeneous enhancement or irregular ring-like enhancement on post-contrast images and dot calcifications were seen in 1 case. Conclusions: Relative larger single thyroid masses with coarse granular calcifications, necrosis,envelope invasion, remarkable heterogeneous enhancing and enlarged lymph nodes on CT are suggestive of anaplastic thyroid carcinoma. (authors)

  6. FEATURE MATCHING OF HISTORICAL IMAGES BASED ON GEOMETRY OF QUADRILATERALS

    Directory of Open Access Journals (Sweden)

    F. Maiwald

    2018-05-01

    Full Text Available This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings. It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

  7. Hand and wrist arthritis of Behcet disease: Imaging features

    International Nuclear Information System (INIS)

    Sugawara, Shunsuke; Ehara, Shigeru; Hitachi, Shin; Sugimoto, Hideharu

    2010-01-01

    Background: Reports on arthritis in Behcet disease are relatively scarce, and imaging features vary. Purpose: To document the various imaging features of articular disorders of the hand and wrist in Behcet disease. Material and Methods: Four patients, four women aged 26 to 65 years, fulfilling the diagnostic criteria of Behcet disease, with imaging findings of hand and wrist arthritis, were seen in two institutions. Radiography and magnetic resonance (MR) imaging were studied to elucidate the pattern and distribution. Results: Both non-erosive arthritis and erosive arthritis of different features were noted: one with non-erosive synovitis of the wrist, one with wrist synovitis with minimal erosion, and two with erosive arthritis of the distal interphalangeal joint. Conclusion: Imaging manifestations of arthritis of Behcet disease vary, and may be similar to other seronegative arthritides

  8. Image Mosaic Method Based on SIFT Features of Line Segment

    Directory of Open Access Journals (Sweden)

    Jun Zhu

    2014-01-01

    Full Text Available This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

  9. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  10. The analysis of image feature robustness using cometcloud

    Directory of Open Access Journals (Sweden)

    Xin Qi

    2012-01-01

    Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

  11. Feature representation of RGB-D images using joint spatial-depth feature pooling

    DEFF Research Database (Denmark)

    Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

    2016-01-01

    Recent development in depth imaging technology makes acquisition of depth information easier. With the additional depth cue, RGB-D cameras can provide effective support for many RGB-D perception tasks beyond traditional RGB information. However, current feature representation based on RGB-D image...

  12. Widening of Laths in Bainite

    Science.gov (United States)

    Yin, Jiaqing; Hillert, Mats; Borgenstam, Annika

    2017-11-01

    Units of bainite in Fe-C alloys from the upper temperature range inherit their shape from Widmanstätten plates of ferrite, which are lathlike. The thickness increases by long-range diffusion of carbon and the length by short-range diffusion of carbon from the advancing edge of the tip. Both have been studied extensively and are fairly well understood. Widening growth seems to have been much neglected, but a study of some aspects of widening is now presented. The present report is the last one in a series of four morphological studies of bainite, isothermally formed in Fe-C alloys with 0.3 or 0.7 mass pct carbon, mainly in the upper temperature range. It contains a number of morphological observations made on cross sections of packets of bainite, and it elucidated a number of interesting questions about bainite and resulted in some proposals. The ferrite plates in a packet are nucleated as a group on a grain boundary, not each one separately on the side of a prior plate. Lengthening occurs by advancement of a short edge that is formed in close contact to the grain boundary. Widening of laths does not start spontaneously. It is initiated by a modification of the structure of the long edge of the lath. When it then moves, the lattice of the new ferrite is rotated relative to the ferrite formed by lengthening and the habit plane is different. In a section through the length direction, it is difficult to recognize what part of ferrite has formed by widening growth. Furthermore, it is proposed that the individual plates in a microstructure, previously used to illustrate subunits formed by repeated nucleation, were nucleated on a hidden grain boundary.

  13. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  14. Image feature extraction based on the camouflage effectiveness evaluation

    Science.gov (United States)

    Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

    2018-04-01

    The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

  15. Featured Image: Orbiting Stars Share an Envelope

    Science.gov (United States)

    Kohler, Susanna

    2016-03-01

    This beautiful series of snapshots from a simulation (click for a better look!) shows what happens when two stars in a binary system become enclosed in the same stellar envelope. In this binary system, one of the stars has exhausted its hydrogen fuel and become a red giant, complete with an expanding stellar envelope composed of hydrogen and helium. Eventually, the envelope expands so much that the companion star falls into it, where it releases gravitational potential energy into the common envelope. A team led by Sebastian Ohlmann (Heidelberg Institute for Theoretical Studies and University of Wrzburg) recently performed hydrodynamic simulations of this process. Ohlmann and collaborators discovered that the energy release eventually triggers large-scale flow instabilities, which leads to turbulence within the envelope. This process has important consequences for how these systems next evolve (for instance, determining whether or not a supernova occurs!). You can check out the authors video of their simulated stellar inspiral below, or see their paper for more images and results from their study.CitationSebastian T. Ohlmann et al 2016 ApJ 816 L9. doi:10.3847/2041-8205/816/1/L9

  16. Featured Image: Making Dust in the Lab

    Science.gov (United States)

    Kohler, Susanna

    2017-12-01

    This remarkable photograph (which spans only 10 m across; click for a full view) reveals what happens when you form dust grains in a laboratory under conditions similar to those of interstellar space. The cosmic life cycle of dust grains is not well understood we know that in the interstellar medium (ISM), dust is destroyed at a higher rate than it is produced by stellar sources. Since the amount of dust in the ISM stays constant, however, there must be additional sources of dust production besides stars. A team of scientists led by Daniele Fulvio (Pontifical Catholic University of Rio de Janeiro and the Max Planck Institute for Astronomy at the Friedrich Schiller University Jena) have now studied formation mechanisms of dust grains in the lab by mimicking low-temperature ISM conditions and exploring how, under these conditions, carbonaceous materials condense from gas phase to form dust grains. To read more about their results and see additional images, check out the paper below.CitationDaniele Fulvio et al 2017 ApJS 233 14. doi:10.3847/1538-4365/aa9224

  17. CT and MR imaging features of hydrocephalus

    International Nuclear Information System (INIS)

    Shier, C.K.; George, A.E.; de Leon, M.J.; Stylopoulos, L.A.; Pinto, R.S.

    1989-01-01

    Sylvian fissure and sulcal enlargement is generally perceived as indicative of cortical atrophy and has been used by surgeons in cases of suspected hydrocephalus as a criterion for exclusion from ventricular shunting procedure. The authors have observed sylvian fissure collapse following ventricular shunting in several patients with communicating hydrocephalus (CH). The purpose of this study was to determine the incidence of this finding in patients with CH. The pre- and postshunt CT and MR images of 30 patients with communicating hydrocephalus were reviewed. As anticipated, after shunting a diminution in caliber of the lateral ventricle bodies, temporal horns, and third ventricle occurred in a majority of cases. However, sulcal width paradoxically decreased in 13% of cases after shunt, and sylvian fissure size decreased in seven patients after shunt (23%). In summary, large sylvian fissures and focally dilated sulci do not rule out the presence of hydrocephalus and may in fact act as cerebrospinal fluid reservoirs in cases of obstruction higher along the cerebral convexities

  18. Smart Images Search based on Visual Features Fusion

    International Nuclear Information System (INIS)

    Saad, M.H.

    2013-01-01

    Image search engines attempt to give fast and accurate access to the wide range of the huge amount images available on the Internet. There have been a number of efforts to build search engines based on the image content to enhance search results. Content-Based Image Retrieval (CBIR) systems have achieved a great interest since multimedia files, such as images and videos, have dramatically entered our lives throughout the last decade. CBIR allows automatically extracting target images according to objective visual contents of the image itself, for example its shapes, colors and textures to provide more accurate ranking of the results. The recent approaches of CBIR differ in terms of which image features are extracted to be used as image descriptors for matching process. This thesis proposes improvements of the efficiency and accuracy of CBIR systems by integrating different types of image features. This framework addresses efficient retrieval of images in large image collections. A comparative study between recent CBIR techniques is provided. According to this study; image features need to be integrated to provide more accurate description of image content and better image retrieval accuracy. In this context, this thesis presents new image retrieval approaches that provide more accurate retrieval accuracy than previous approaches. The first proposed image retrieval system uses color, texture and shape descriptors to form the global features vector. This approach integrates the yc b c r color histogram as a color descriptor, the modified Fourier descriptor as a shape descriptor and modified Edge Histogram as a texture descriptor in order to enhance the retrieval results. The second proposed approach integrates the global features vector, which is used in the first approach, with the SURF salient point technique as local feature. The nearest neighbor matching algorithm with a proposed similarity measure is applied to determine the final image rank. The second approach

  19. Disorders of the pediatric pancreas: imaging features

    International Nuclear Information System (INIS)

    Nijs, Els; Callahan, Michael J.; Taylor, George A.

    2005-01-01

    The purpose of this manuscript is to provide an overview of the normal development of the pancreas as well as pancreatic pathology in children. Diagnostic imaging plays a major role in the evaluation of the pancreas in infants and children. Familiarity with the range of normal appearance and the diseases that commonly affect this gland is important for the accurate and timely diagnosis of pancreatic disorders in the pediatric population. Normal embryology is discussed, as are the most common congenital anomalies that occur as a result of aberrant development during embryology. These include pancreas divisum, annular pancreas, agenesis of the dorsal pancreatic anlagen and ectopic pancreatic tissue. Syndromes that can manifest pancreatic pathology include: Beckwith Wiedemann syndrome, von Hippel-Lindau disease and autosomal dominant polycystic kidney disease. Children and adults with cystic fibrosis and Shwachman-Diamond syndrome frequently present with pancreatic insufficiency. Trauma is the most common cause of pancreatitis in children. In younger children, unexplained pancreatic injury must always alert the radiologist to potential child abuse. Pancreatic pseudocysts are a complication of trauma, but can also be seen in the setting of acute or chronic pancreatitis from other causes. Primary pancreatic neoplasms are rare in children and are divided into exocrine tumors such as pancreatoblastoma and adenocarcinoma and into endocrine or islet cell tumors. Islet cell tumors are classified as functioning (insulinoma, gastrinoma, VIPoma and glucagonoma) and nonfunctioning tumors. Solid-cystic papillary tumor is probably the most common pancreatic tumor in Asian children. Although quite rare, secondary tumors of the pancreas can be associated with certain primary malignancies. (orig.)

  20. Disorders of the pediatric pancreas: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Nijs, Els [University Hospital Gasthuisberg, Department of Radiology, Leuven (Belgium); Callahan, Michael J.; Taylor, George A. [Boston Children' s Hospital, Department of Radiology, Boston, MA (United States)

    2005-04-01

    The purpose of this manuscript is to provide an overview of the normal development of the pancreas as well as pancreatic pathology in children. Diagnostic imaging plays a major role in the evaluation of the pancreas in infants and children. Familiarity with the range of normal appearance and the diseases that commonly affect this gland is important for the accurate and timely diagnosis of pancreatic disorders in the pediatric population. Normal embryology is discussed, as are the most common congenital anomalies that occur as a result of aberrant development during embryology. These include pancreas divisum, annular pancreas, agenesis of the dorsal pancreatic anlagen and ectopic pancreatic tissue. Syndromes that can manifest pancreatic pathology include: Beckwith Wiedemann syndrome, von Hippel-Lindau disease and autosomal dominant polycystic kidney disease. Children and adults with cystic fibrosis and Shwachman-Diamond syndrome frequently present with pancreatic insufficiency. Trauma is the most common cause of pancreatitis in children. In younger children, unexplained pancreatic injury must always alert the radiologist to potential child abuse. Pancreatic pseudocysts are a complication of trauma, but can also be seen in the setting of acute or chronic pancreatitis from other causes. Primary pancreatic neoplasms are rare in children and are divided into exocrine tumors such as pancreatoblastoma and adenocarcinoma and into endocrine or islet cell tumors. Islet cell tumors are classified as functioning (insulinoma, gastrinoma, VIPoma and glucagonoma) and nonfunctioning tumors. Solid-cystic papillary tumor is probably the most common pancreatic tumor in Asian children. Although quite rare, secondary tumors of the pancreas can be associated with certain primary malignancies. (orig.)

  1. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

    Science.gov (United States)

    Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

    2016-01-01

    An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

  2. Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features.

    Directory of Open Access Journals (Sweden)

    Seyed Mostafa Mousavi Kahaki

    Full Text Available An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics--such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient--are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.

  3. Perinatal clinical and imaging features of CLOVES syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez-Pineda, Israel [Virgen del Rocio Children' s Hospital, Department of Pediatric Surgery, Seville (Spain); Fajardo, Manuel [Virgen del Rocio Children' s Hospital, Department of Pediatric Radiology, Seville (Spain); Chaudry, Gulraiz; Alomari, Ahmad I. [Children' s Hospital Boston and Harvard Medical School, Division of Vascular and Interventional Radiology, Boston, MA (United States)

    2010-08-15

    We report a neonate with antenatal imaging features suggestive of CLOVES syndrome. Postnatal clinical and imaging findings confirmed the diagnosis, with the constellation of truncal overgrowth, cutaneous capillary malformation, lymphatic and musculoskeletal anomalies. The clinical, radiological and histopathological findings noted in this particular phenotype help differentiate it from other overgrowth syndromes with complex vascular anomalies. (orig.)

  4. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

    Directory of Open Access Journals (Sweden)

    Yuanshen Zhao

    2016-01-01

    Full Text Available Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

  5. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

    Science.gov (United States)

    Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

    2016-01-29

    Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

  6. SAR Image Classification Based on Its Texture Features

    Institute of Scientific and Technical Information of China (English)

    LI Pingxiang; FANG Shenghui

    2003-01-01

    SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation.It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

  7. Unusual causes of spinal foraminal widening

    Energy Technology Data Exchange (ETDEWEB)

    Zibis, A.H.; Markonis, A.; Karantanas, A.H. [Dept. of CT and MRI, Larissa General Hospital (Greece)

    2000-01-01

    Spinal neural foraminal widening is usually caused by benign lesions, most commonly neurofibromas. Rare lesions can also cause spinal neural foraminal widening. Computed tomography and/or MRI are the modalities of choice for studying the spinal foraminal widening. The present pictorial review describes six rare lesions, namely a lateral thoracic meningocele, a malignant fibrous histiocytoma, a tuberculous abscess, an osteoblastoma, a chondrosarcoma and a malignant tumour of the lung which caused spinal neural foraminal widening. (orig.)

  8. A comparison of image features for registering LWIR and visual images

    CSIR Research Space (South Africa)

    Cronje, J

    2012-11-01

    Full Text Available This paper presents a comparison of several established and recent image feature-descriptors to register long wave infra-red images in the 8–14 m band to visual band images. The feature descriptors were chosen to include robust algorithms, SURF...

  9. INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION

    Directory of Open Access Journals (Sweden)

    T.-A. Teo

    2016-06-01

    Full Text Available The image quality plays an important role for Unmanned Aerial Vehicle (UAV’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS, which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear method to degree-of-motion-blur (bmotion based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM classifier and extracted features (i.e. image information, POS data, blinear and bmotion to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

  10. Feature-Based Retinal Image Registration Using D-Saddle Feature

    Directory of Open Access Journals (Sweden)

    Roziana Ramli

    2017-01-01

    Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

  11. Online Feature Selection for Classifying Emphysema in HRCT Images

    Directory of Open Access Journals (Sweden)

    M. Prasad

    2008-06-01

    Full Text Available Feature subset selection, applied as a pre- processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. In the classic formulation of the feature selection problem, it is assumed that all the features are available at the beginning. However, in many real world problems, there are scenarios where not all features are present initially and must be integrated as they become available. In such scenarios, online feature selection provides an efficient way to sort through a large space of features. It is in this context that we introduce online feature selection for the classification of emphysema, a smoking related disease that appears as low attenuation regions in High Resolution Computer Tomography (HRCT images. The technique was successfully evaluated on 61 HRCT scans and compared with different online feature selection approaches, including hill climbing, best first search, grafting, and correlation-based feature selection. The results were also compared against ldensity maskr, a standard approach used for emphysema detection in medical image analysis.

  12. Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

    Science.gov (United States)

    Kim, Deok-Hwan

    As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

  13. Imaging features of skeletal changes in children with Gaucher disease

    International Nuclear Information System (INIS)

    Zhang Ningning; Duan Xiaomin; Duan Yanlong

    2011-01-01

    Objective: To discuss the imaging features of skeletal changes in children with Gaucher disease on X-ray and MRI images. Methods: One hundred and nine children with Gaucher disease were enrolled in this study. They all received routine X-ray for spine with anterior-posterior (A-P) and lateral view and bilateral femurs with A-P view. Among them, 18 patients received X-ray for pelvic with A-P view, 14 patients received X-ray for left wrist with A-P view, and 14 patients received MRI scan for femur. The MRI scan included T 1 -weighted imaging, T 2 -weighted imaging and fat-suppressed T 2 -weighted imaging with short tau inversion recovery (STIR) sequence. The imaging features of the X-ray and MRI images were analyzed retrospectively. Results: The most common feature is osteoporosis, which presented in 91 cases (83.5%). Besides this, decreased density of metaphysis occurred in 86 cases (78.9%), erlenmeyer flask deformity of metaphysis occurred in 89 patients (81.7%), thinner cortex occurred in 69 cases (63.3%), osteolytic destruction occurred in. 31 cases (28.4%), pathological fractures occurred in 26 cases (23.9%), osteosclerosis occurred in 12 cases (11.0%). cystic degeneration of bone occurred in 16 cases (14.7%), and dislocation of the hip occurred in 4 cases. All 14 patients received MRI presented abnormal signals. Among them, 4 patients presented low signal intensity both on T 1 -weighted and T 2 -weighted images in bone marrow, the other ten presented high signal intensity mixed in low signal intensity areas on T 2 - weighted and fat-suppressed T 2 -weighted images. Conclusions: The imaging features of skeletal changes in children with Gaucher disease are of some characteristics, which could provide useful information for the clinical treatment. (authors)

  14. Multimodal Image Alignment via Linear Mapping between Feature Modalities.

    Science.gov (United States)

    Jiang, Yanyun; Zheng, Yuanjie; Hou, Sujuan; Chang, Yuchou; Gee, James

    2017-01-01

    We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

  15. Simultenious binary hash and features learning for image retrieval

    Science.gov (United States)

    Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

    2016-05-01

    Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

  16. Optimization of wavelet decomposition for image compression and feature preservation.

    Science.gov (United States)

    Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

    2003-09-01

    A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

  17. Improved image retrieval based on fuzzy colour feature vector

    Science.gov (United States)

    Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

    2013-03-01

    One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

  18. HDR IMAGING FOR FEATURE DETECTION ON DETAILED ARCHITECTURAL SCENES

    Directory of Open Access Journals (Sweden)

    G. Kontogianni

    2015-02-01

    Full Text Available 3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

  19. An image-processing methodology for extracting bloodstain pattern features.

    Science.gov (United States)

    Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

    2017-08-01

    There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

    Science.gov (United States)

    Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

    2013-04-01

    In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

  1. RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

    Directory of Open Access Journals (Sweden)

    Z. Wang

    2017-09-01

    Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  2. Imaging of Groin Pain: Magnetic Resonance and Ultrasound Imaging Features.

    Science.gov (United States)

    Lee, Susan C; Endo, Yoshimi; Potter, Hollis G

    Evaluation of groin pain in athletes may be challenging as pain is typically poorly localized and the pubic symphyseal region comprises closely approximated tendons and muscles. As such, magnetic resonance imaging (MRI) and ultrasound (US) may help determine the etiology of groin pain. A PubMed search was performed using the following search terms: ultrasound, magnetic resonance imaging, sports hernia, athletic pubalgia, and groin pain. Date restrictions were not placed on the literature search. Clinical review. Level 4. MRI is sensitive in diagnosing pathology in groin pain. Not only can MRI be used to image rectus abdominis/adductor longus aponeurosis and pubic bone pathology, but it can also evaluate other pathology within the hip and pelvis. MRI is especially helpful when groin pain is poorly localized. Real-time capability makes ultrasound useful in evaluating the pubic symphyseal region, as it can be used for evaluation and treatment. MRI and US are valuable in diagnosing pathology in athletes with groin pain, with the added utility of treatment using US-guided intervention. Strength-of Recommendation Taxonomy: C.

  3. Skull base chordoid meningioma: Imaging features and pathology

    International Nuclear Information System (INIS)

    Soo, Mark Y.S.; Gomes, Lavier; Ng, Thomas; Cruz, Malville Da; Dexter, Mark

    2004-01-01

    The clinical, imaging and pathological features of a skull base chordoid meningioma (CM) are described. The huge tumour resulted in obstructive hydrocephalus and partial erosion of the clivus such that a chordoma was suspected. The lesion's MRI findings were similar to those of a meningioma. Light microscopic, immunohistochemistry and ultrastructural features were diagnostic of CM. Chordoid meningioma is a rare subtype of meningioma and has a great tendency to recur should surgical resection be incomplete Copyright (2004) Blackwell Publishing Asia Pty Ltd

  4. Image segmentation-based robust feature extraction for color image watermarking

    Science.gov (United States)

    Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

    2018-04-01

    This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

  5. Magnetic resonance imaging features of fibrocystic change of the breast.

    Science.gov (United States)

    Chen, Jeon-Hor; Liu, Hui; Baek, Hyeon-Man; Nalcioglu, Orhan; Su, Min-Ying

    2008-11-01

    Studies specifically reporting MRI of fibrocystic change (FCC) of the breast are very few and its MRI features are not clearly known. The purpose of this study was to analyze the MRI features of FCC of the breast. Thirty-one patients with pathologically proven FCC of the breast were retrospectively reviewed. The MRI study was performed using a 1.5-T MR scanner with standard bilateral breast coil. The imaging protocol consisted of pre-contrast T1-weighed imaging and dynamic contrast-enhanced axial T1-weighed imaging. The MRI features were interpreted based on the morphologic and enhancement kinetic descriptors defined on ACR BIRADS-MRI lexicon. FCC of the breast had a wide spectrum of morphologic and kinetic features on MRI. Two types of FCC were found, including a more diffuse type of nonmass lesion (12/31, 39%) showing benign enhancement kinetic pattern with medium wash-in in early phase (9/10, 90%) and a focal mass-type lesion (11/31, 35%) with enhancement kinetic usually showing rapid up-slope mimicking a breast cancer (8/11, 73%). MRI is able to elaborate the diverse imaging features of FCC of the breast. Our result showed that FCC presenting as a focal mass-type lesion was usually overdiagnosed as malignancy. Understanding MRI of FCC is important to determine which cohort of patients should be followed up alone or receive aggressive management.

  6. Imaging features of maxillary osteoblastoma and its malignant transformation

    International Nuclear Information System (INIS)

    Ueno, Hiroshi; Ariji, Ei-ichiro; Tanaka, Takemasa; Kanda, Shigenobu; Mori, Shin-ichiro; Goto, Masaaki; Mizuno, Akio; Okabe, Haruo; Nakamura, Takashi

    1994-01-01

    We report two cases of osteoblastoma, one of them an unusual case in a 32-year-old woman in whom a maxillary tumor was confidently diagnosed as an osteoblastoma at the time of primary excision and subsequently transformed into an osteosarcoma 7 years after the onset of clinical symptoms. The other patient developed osteosarcoma arising in the maxilla, which was diagnosed 3 years after the primary excision and is very suggestive of malignant transformation in osteoblastoma. We present the radiological features, including computed tomographic and magnetic resonance imaging studies, of this unusual event of transformed tumor and compare imaging features of benign and dedifferentiated counterparts of this rare tumor complex. (orig.)

  7. Feature Recognition of Froth Images Based on Energy Distribution Characteristics

    Directory of Open Access Journals (Sweden)

    WU Yanpeng

    2014-09-01

    Full Text Available This paper proposes a determining algorithm for froth image features based on the amplitude spectrum energy statistics by applying Fast Fourier Transformation to analyze the energy distribution of various-sized froth. The proposed algorithm has been used to do a froth feature analysis of the froth images from the alumina flotation processing site, and the results show that the consistency rate reaches 98.1 % and the usability rate 94.2 %; with its good robustness and high efficiency, the algorithm is quite suitable for flotation processing state recognition.

  8. Vaginal Masses: Magnetic Resonance Imaging Features with Pathologic Correlation

    International Nuclear Information System (INIS)

    Elsayes, K.M.; Narra, V.R.; Dillman, J.R.; Velcheti, V.; Hameed, O.; Tongdee, R.; Menias, C.O.

    2007-01-01

    The detection of vaginal lesions has increased with the expanding use of cross-sectional imaging. Magnetic resonance imaging (MRI) - with its high-contrast resolution and multiplanar capabilities - is often useful for characterizing vaginal masses. Vaginal masses can be classified as congenital, inflammatory, cystic (benign), and neoplastic (benign or malignant) in etiology. Recognition of the typical MR imaging features of such lesions is important because it often determines the treatment approach and may obviate surgery. Finally, vaginal MR imaging can be used to evaluate post-treatment changes related to previous surgery and radiation therapy. In this article, we will review pertinent vaginal anatomy, vaginal and pelvic MRI technique, and the MRI features of a variety of vaginal lesions with pathological correlation

  9. Imaging features of mycobacterium in patients with acquired immunodeficiency syndrome

    International Nuclear Information System (INIS)

    Yang Jun; Sun Yue; Wei Liangui; Xu Yunliang; Li Xingwang

    2013-01-01

    Objective: To analyze the imaging features of mycobacterium in AIDS patients. Methods: Twenty-three cases of mycobacterium tuberculosis and 13 patients of non-tuberculous mycobacteria were proved etiologically and included in this study. All patients underwent X-ray and CT examinations, imaging data were analyzed and compared. Results: The imaging findings of mycobacterium tuberculosis in AIDS patients included consolidation (n = 11), pleural effusion (n = 11), mediastinal lymphadenopathy (n = 11). Pulmonary lesions were always diffuse distribution, and 14 patients of extrapulmonary tuberculosis were found. Pulmonary lesions in non-tuberculous mycobacteria tend to be circumscribed. Conclusions: Non-tuberculous mycobacterial infection in AIDS patients is more common and usually combined with other infections. Imaging features are atypical. (authors)

  10. Hyperspectral image classifier based on beach spectral feature

    International Nuclear Information System (INIS)

    Liang, Zhang; Lianru, Gao; Bing, Zhang

    2014-01-01

    The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

  11. Modality prediction of biomedical literature images using multimodal feature representation

    Directory of Open Access Journals (Sweden)

    Pelka, Obioma

    2016-08-01

    Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

  12. The fuzzy Hough Transform-feature extraction in medical images

    International Nuclear Information System (INIS)

    Philip, K.P.; Dove, E.L.; Stanford, W.; Chandran, K.B.; McPherson, D.D.; Gotteiner, N.L.

    1994-01-01

    Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final estimate of the true borders with other image processing techniques. The authors present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough Transform algorithm as part of a larger procedures to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications

  13. Deformable Image Registration with Inclusion of Autodetected Homologous Tissue Features

    Directory of Open Access Journals (Sweden)

    Qingsong Zhu

    2012-01-01

    Full Text Available A novel deformable registration algorithm is proposed in the application of radiation therapy. The algorithm starts with autodetection of a number of points with distinct tissue features. The feature points are then matched by using the scale invariance features transform (SIFT method. The associated feature point pairs are served as landmarks for the subsequent thin plate spline (TPS interpolation. Several registration experiments using both digital phantom and clinical data demonstrate the accuracy and efficiency of the method. For the 3D phantom case, markers with error less than 2 mm are over 85% of total test markers, and it takes only 2-3 minutes for 3D feature points association. The proposed method provides a clinically practical solution and should be valuable for various image-guided radiation therapy (IGRT applications.

  14. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. A comparative study of image low level feature extraction algorithms

    Directory of Open Access Journals (Sweden)

    M.M. El-gayar

    2013-07-01

    Full Text Available Feature extraction and matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods for assessing the performance of popular image matching algorithms are presented and rely on costly descriptors for detection and matching. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. This study addresses the limitations of the existing comparative tools and delivers a generalized criterion to determine beforehand the level of efficiency expected from a matching algorithm given the type of images evaluated. The algorithms and the respective images used within this work are divided into two groups: feature-based and texture-based. And from this broad classification only three of the most widely used algorithms are assessed: color histogram, FAST (Features from Accelerated Segment Test, SIFT (Scale Invariant Feature Transform, PCA-SIFT (Principal Component Analysis-SIFT, F-SIFT (fast-SIFT and SURF (speeded up robust features. The performance of the Fast-SIFT (F-SIFT feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although its slow. F-SIFT is the fastest one with good performance as the same as SURF, SIFT, PCA-SIFT show its advantages in rotation and illumination changes.

  16. MR Imaging Features of Obturator Internus Bursa of the Hip

    International Nuclear Information System (INIS)

    Hwang, Ji Young; Lee, Sun Wha; Kim, Jong Oh

    2008-01-01

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the 'boomerang'-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium

  17. MR Imaging Features of Obturator Internus Bursa of the Hip

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Ji Young; Lee, Sun Wha; Kim, Jong Oh [School of Medicine, Ewha Womans University, Seoul (Korea, Republic of)

    2008-08-15

    The authors report two cases with distension of the obturator internus bursa identified on MR images, and describe the location and characteristic features of obturator internus bursitis; the 'boomerang'-shaped fluid distension between the obturator internus tendon and the posterior grooved surface of the ischium

  18. Magnetic resonance imaging features of extremity sarcomas of uncertain differentiation

    International Nuclear Information System (INIS)

    Stacy, G.S.; Nair, L.

    2007-01-01

    The purpose of this review is to illustrate the pertinent clinical and imaging features of extremity sarcomas of uncertain differentiation, including synovial sarcoma, epithelioid sarcoma, clear-cell sarcoma, and alveolar soft part sarcoma. These tumours should be considered in the differential diagnosis when a soft-tissue mass is encountered in the extremity of an adolescent or young adult

  19. Tracking image features with PCA-SURF descriptors

    CSIR Research Space (South Africa)

    Pancham, A

    2015-05-01

    Full Text Available IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN Tracking Image Features with PCA-SURF Descriptors Ardhisha Pancham CSIR, UKZN South Africa apancham@csir.co.za Daniel Withey CSIR South Africa...

  20. Imaging features of multicentric Castleman's disease in HIV infection

    International Nuclear Information System (INIS)

    Hillier, J.C.; Shaw, P.; Miller, R.F.; Cartledge, J.D.; Nelson, M.; Bower, M.; Francis, N.; Padley, S.P.

    2004-01-01

    AIM: To describe the computed tomography (CT) features of human immunodeficiency virus (HIV)-associated Castleman's disease. MATERIALS AND METHODS: Nine HIV-positive patients with biopsy-proven Castleman's disease were studied. Clinical and demographic data, CD4 count, histological diagnosis and human herpes type 8 (HHV8) serology or immunostaining results were recorded. CT images were reviewed independently by two radiologists. RESULTS: CT findings included splenomegaly (n=7) and peripheral lymph node enlargement (axillary n=8, inguinal n=4). All nodes displayed mild to avid enhancement after intravenous administration of contrast material. Hepatomegaly was evident in seven patients. Other features included abdominal (n=6) and mediastinal (n=5) lymph node enlargement and pulmonary abnormalities (n=4). Patterns of parenchymal abnormality included bronchovascular nodularity (n=2), consolidation (n=1) and pleural effusion (n=2). On histological examination eight patients (spleen n=3, lymph node n=9, lung n=1 bone marrow n=1) had the plasma cell variant and one had mixed hyaline-vascular/plasma cell variant. The majority had either positive immunostaining for HHV8 or positive serology (n=8). CONCLUSION: Common imaging features of multicentric Castleman's disease in HIV infection are hepatosplenomegaly and peripheral lymph node enlargement. Although these imaging features may suggest the diagnosis in the appropriate clinical context, they lack specificity and so biopsy is needed for diagnosis. In distinction from multicentric Castleman's disease in other populations the plasma cell variant is most commonly encountered, splenomegaly is a universal feature and there is a strong association with Kaposi's sarcoma

  1. Widening opportunities for career guidance

    DEFF Research Database (Denmark)

    Poulsen, Bo Klindt; Skovhus, Randi Boelskifte; Thomsen, Rie

    2017-01-01

    This chapter discusses research circles as a way of organising collaboration between career guidance researchers and practitioners. Such collaboration, it is argued, helps resist neoliberal governance mechanisms and supports social justice perspectives among teachers involved in the provision...... of career education in Danish schools. Based on a research and development project on career education, case analysis is used to explore research circles as a means for collaboration between researchers and practitioners. This analysis shows that research circles provide teachers with a space to reflect...... both in and on action. Career education is the key focus of the case presented in this chapter and it is argued that, in order to increase social mobility through education, there is a need to widen opportunities through experience-based activities among pupils in Danish schools. The chapter contends...

  2. Widening opportunities for career guidance

    DEFF Research Database (Denmark)

    Poulsen, Bo Klindt; Skovhus, Randi Boelskifte; Thomsen, Rie

    2018-01-01

    This chapter discusses research circles as a way of organising collaboration between career guidance researchers and practitioners. Such collaboration, it is argued, helps resist neoliberal governance mechanisms and supports social justice perspectives among teachers involved in the provision...... of career education in Danish schools. Based on a research and development project on career education, case analysis is used to explore research circles as a means for collaboration between researchers and practitioners. This analysis shows that research circles provide teachers with a space to reflect...... both in and on action. Career education is the key focus of the case presented in this chapter and it is argued that, in order to increase social mobility through education, there is a need to widen opportunities through experience-based activities among pupils in Danish schools. The chapter contends...

  3. Adaptive Colour Feature Identification in Image for Object Tracking

    Directory of Open Access Journals (Sweden)

    Feng Su

    2012-01-01

    Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

  4. Feature extraction from mammographic images using fast marching methods

    International Nuclear Information System (INIS)

    Bottigli, U.; Golosio, B.

    2002-01-01

    Features extraction from medical images represents a fundamental step for shape recognition and diagnostic support. The present work faces the problem of the detection of large features, such as massive lesions and organ contours, from mammographic images. The regions of interest are often characterized by an average grayness intensity that is different from the surrounding. In most cases, however, the desired features cannot be extracted by simple gray level thresholding, because of image noise and non-uniform density of the surrounding tissue. In this work, edge detection is achieved through the fast marching method (Level Set Methods and Fast Marching Methods, Cambridge University Press, Cambridge, 1999), which is based on the theory of interface evolution. Starting from a seed point in the shape of interest, a front is generated which evolves according to an appropriate speed function. Such function is expressed in terms of geometric properties of the evolving interface and of image properties, and should become zero when the front reaches the desired boundary. Some examples of application of such method to mammographic images from the CALMA database (Nucl. Instr. and Meth. A 460 (2001) 107) are presented here and discussed

  5. THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

    Directory of Open Access Journals (Sweden)

    O. Akcay

    2017-05-01

    Full Text Available A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER and Speeded Up Robust Features (SURF algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

  6. Feature Tracking for High Speed AFM Imaging of Biopolymers.

    Science.gov (United States)

    Hartman, Brett; Andersson, Sean B

    2018-03-31

    The scanning speed of atomic force microscopes continues to advance with some current commercial microscopes achieving on the order of one frame per second and at least one reaching 10 frames per second. Despite the success of these instruments, even higher frame rates are needed with scan ranges larger than are currently achievable. Moreover, there is a significant installed base of slower instruments that would benefit from algorithmic approaches to increasing their frame rate without requiring significant hardware modifications. In this paper, we present an experimental demonstration of high speed scanning on an existing, non-high speed instrument, through the use of a feedback-based, feature-tracking algorithm that reduces imaging time by focusing on features of interest to reduce the total imaging area. Experiments on both circular and square gratings, as well as silicon steps and DNA strands show a reduction in imaging time by a factor of 3-12 over raster scanning, depending on the parameters chosen.

  7. Robust and efficient method for matching features in omnidirectional images

    Science.gov (United States)

    Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

    2018-04-01

    Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

  8. Estimating perception of scene layout properties from global image features.

    Science.gov (United States)

    Ross, Michael G; Oliva, Aude

    2010-01-08

    The relationship between image features and scene structure is central to the study of human visual perception and computer vision, but many of the specifics of real-world layout perception remain unknown. We do not know which image features are relevant to perceiving layout properties, or whether those features provide the same information for every type of image. Furthermore, we do not know the spatial resolutions required for perceiving different properties. This paper describes an experiment and a computational model that provides new insights on these issues. Humans perceive the global spatial layout properties such as dominant depth, openness, and perspective, from a single image. This work describes an algorithm that reliably predicts human layout judgments. This model's predictions are general, not specific to the observers it trained on. Analysis reveals that the optimal spatial resolutions for determining layout vary with the content of the space and the property being estimated. Openness is best estimated at high resolution, depth is best estimated at medium resolution, and perspective is best estimated at low resolution. Given the reliability and simplicity of estimating the global layout of real-world environments, this model could help resolve perceptual ambiguities encountered by more detailed scene reconstruction schemas.

  9. Central nervous system lymphoma: magnetic resonance imaging features at presentation

    Directory of Open Access Journals (Sweden)

    Ricardo Schwingel

    2012-02-01

    Full Text Available OBJECTIVE: This paper aimed at studying presentations of the central nervous system (CNS lymphoma using structural images obtained by magnetic resonance imaging (MRI. METHODS: The MRI features at presentation of 15 patients diagnosed with CNS lymphoma in a university hospital, between January 1999 and March 2011, were analyzed by frequency and cross tabulation. RESULTS: All patients had supratentorial lesions; and four had infra- and supratentorial lesions. The signal intensity on T1 and T2 weighted images was predominantly hypo- or isointense. In the T2 weighted images, single lesions were associated with a hypointense signal component. Six patients presented necrosis, all of them showed perilesional abnormal white matter, nine had meningeal involvement, and five had subependymal spread. Subependymal spread and meningeal involvement tended to occur in younger patients. CONCLUSION: Presentations of lymphoma are very pleomorphic, but some of them should point to this diagnostic possibility.

  10. Comparative imaging features of brucellar and tuberculous spondylitis

    International Nuclear Information System (INIS)

    Sharif, H.S.; Aldeyan, O.; Clark, D.C.; Madkour, M.M.

    1987-01-01

    Images obtained with various modalities in 17 patients with Brucella spondylitis and 12 patients with tuberculous spondylitis were analyzed in order to identify distinguishing features. All patients underwent radiography, 21 underwent bone scintigraphy, and all underwent high-resolution CT and/or MR imaging. Characteristic findings in Brucella spondylitis included a predilection for the lumbar spine, bone destruction limited to the end-plates and associated with sclerosis, and disk space collapse (16 of 19) with disk vacuum phenomenon in eight and localized soft-tissue edema. MR imaging showed diffuse increased signal in vertebrae, disks, and adjacent soft tissues on long repetition time/long echo time studies (four patients). Tuberculosis spondylitis was characterized by a midthoracic predilection, diffuse vertebral destruction with gibbus deformity, severe disk collapse, and extensive paraspinal abscesses. MR imaging findings (three patients) were similar to but more severe than findings in Brucella spondylitis

  11. SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

    Directory of Open Access Journals (Sweden)

    Yang Wei

    2015-02-01

    Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

  12. Cervical spine injury in the elderly: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Ehara, S. [Dept. of Radiology, Iwate Medical University School of Medicine, Morioka (Japan); Shimamura, Tadashi [Dept. of Orthopedic Surgery, Iwate Medical University School of Medicine, Morioka (Japan)

    2001-01-01

    An increase in the elderly population has resulted in an increased incidence of cervical spine injury in this group. No specific type of cervical spine trauma is seen in the elderly, although dens fractures are reported to be common. Hyperextension injuries due to falling and the resultant central cord syndrome in the mid and lower cervical segments due to decreased elasticity as a result of spondylosis may be also characteristic. The imaging features of cervical spine injury are often modified by associated spondylosis deformans, DISH and other systemic disorders. The value of MR imaging in such cases is emphasized. (orig.)

  13. FRACTAL IMAGE FEATURE VECTORS WITH APPLICATIONS IN FRACTOGRAPHY

    Directory of Open Access Journals (Sweden)

    Hynek Lauschmann

    2011-05-01

    Full Text Available The morphology of fatigue fracture surface (caused by constant cycle loading is strictly related to crack growth rate. This relation may be expressed, among other methods, by means of fractal analysis. Fractal dimension as a single numerical value is not sufficient. Two types of fractal feature vectors are discussed: multifractal and multiparametric. For analysis of images, the box-counting method for 3D is applied with respect to the non-homogeneity of dimensions (two in space, one in brightness. Examples of application are shown: images of several fracture surfaces are analyzed and related to crack growth rate.

  14. Primary Neuroendocrine Tumor of the Breast: Imaging Features

    International Nuclear Information System (INIS)

    Chang, Eun Deok; Kim, Min Kyun; Kim, Jeong Soo; Whang, In Yong

    2013-01-01

    Focal neuroendocrine differentiation can be found in diverse histological types of breast tumors. However, the term, neuroendocrine breast tumor, indicates the diffuse expression of neuroendocrine markers in more than 50% of the tumor cell population. The imaging features of neuroendocrine breast tumor have not been accurately described due to extreme rarity of this tumor type. We present a case of a pathologically confirmed, primary neuroendocrine breast tumor in a 42-year-old woman, with imaging findings difficult to be differentiated from that of invasive ductal carcinoma

  15. Effect of zooming on texture features of ultrasonic images

    Directory of Open Access Journals (Sweden)

    Kyriacou Efthyvoulos

    2006-01-01

    Full Text Available Abstract Background Unstable carotid plaques on subjective, visual, assessment using B-mode ultrasound scanning appear as echolucent and heterogeneous. Although previous studies on computer assisted plaque characterisation have standardised B-mode images for brightness, improving the objective assessment of echolucency, little progress has been made towards standardisation of texture analysis methods, which assess plaque heterogeneity. The aim of the present study was to investigate the influence of image zooming during ultrasound scanning on textural features and to test whether or not resolution standardisation decreases the variability introduced. Methods Eighteen still B-mode images of carotid plaques were zoomed during carotid scanning (zoom factor 1.3 and both images were transferred to a PC and normalised. Using bilinear and bicubic interpolation, the original images were interpolated in a process of simulating off-line zoom using the same interpolation factor. With the aid of the colour-coded image, carotid plaques of the original, zoomed and two resampled images for each case were outlined and histogram, first order and second order statistics were subsequently calculated. Results Most second order statistics (21/25, 84% were significantly (p Conclusion Texture analysis of ultrasonic plaques should be performed under standardised resolution settings; otherwise a resolution normalisation algorithm should be applied.

  16. Research of image retrieval technology based on color feature

    Science.gov (United States)

    Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

    2009-10-01

    Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

  17. MR imaging features of peritoneal adenomatoid mesothelioma: a case report

    International Nuclear Information System (INIS)

    Lins, Cynthia Maria Coelho; Elias Junior, Jorge; Muglia, Valdair Francisco; Monteiro, Carlos Ribeiro; Feres, Omar

    2009-01-01

    Adenomatoid mesothelioma of the peritoneum (AMP) is a rare benign tumor originating from mesothelial cells.1 Most frequently, AMP occurs between 26 and 55 years of age, at a mean age of 41 years. In contrast to diffuse malignant mesothelioma, which has been linked to asbestos exposure, the etiology of AMP has not been established. Only a minority of patients have symptoms related to the tumor. AMP may present local recurrence, but it has no potential for malignant transformation. Although there are many case reports of abdominal mesotheliomas, to date, there have been no reports of MR imaging features of AMP. In this article, we present the MR imaging features of a case of AMP with histopathological correlation. (author)

  18. MR imaging features of peritoneal adenomatoid mesothelioma: a case report

    Energy Technology Data Exchange (ETDEWEB)

    Lins, Cynthia Maria Coelho; Elias Junior, Jorge; Muglia, Valdair Francisco; Monteiro, Carlos Ribeiro [University of Sao Paulo (USP), Ribeirao Preto, SP (Brazil). School of Medicine. Dept. of Internal Medicine], e-mail: jejunior@fmrp.usp.br; Cunha, Adilson Ferreira [School of Medicine of Sao Jose do Rio Preto (FAMERP), SP (Brazil). Dept. of Gynecology and Obstetrics; Valeri, Fabio V. [Victorio Valeri Institute of Medical Diagnosis, Ribeirao Preto, SP (Brazil); Feres, Omar [University of Sao Paulo (USP), Ribeirao Preto, SP (Brazil). School of Medicine. Dept. of Surgery and Anatomy

    2009-07-01

    Adenomatoid mesothelioma of the peritoneum (AMP) is a rare benign tumor originating from mesothelial cells.1 Most frequently, AMP occurs between 26 and 55 years of age, at a mean age of 41 years. In contrast to diffuse malignant mesothelioma, which has been linked to asbestos exposure, the etiology of AMP has not been established. Only a minority of patients have symptoms related to the tumor. AMP may present local recurrence, but it has no potential for malignant transformation. Although there are many case reports of abdominal mesotheliomas, to date, there have been no reports of MR imaging features of AMP. In this article, we present the MR imaging features of a case of AMP with histopathological correlation. (author)

  19. Genetic algorithms for thyroid gland ultrasound image feature reduction

    Czech Academy of Sciences Publication Activity Database

    Tesař, Ludvík; Smutek, D.; Jiskra, J.

    2005-01-01

    Roč. 3612, č. - (2005), s. 841-844 ISSN 0302-9743. [International Conference ICNC 2005 /1./. Changsha, 27.08.2005-29.08.2005] R&D Projects: GA AV ČR 1ET101050403 Institutional research plan: CEZ:AV0Z10750506 Keywords : medical imaging * classification * Bayes classifier * Huzzolini feature * pattern recognition Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/prace/20050229.pdf

  20. Tunnel widening in anterior cruciate ligament reconstruction

    DEFF Research Database (Denmark)

    Clatworthy, M G; Annear, P; Bulow, J U

    1999-01-01

    We report a prospective series evaluating the incidence and degree of tunnel widening in a well-matched series of patients receiving a hamstring or patella tendon graft for anterior cruciate ligament (ACL) deficiency. We correlated tunnel widening with clinical factors, knee scores, KT-1000 and i...

  1. Associations between spondyloarthritis features and magnetic resonance imaging findings

    DEFF Research Database (Denmark)

    Arnbak, Bodil; Jurik, Anne Grethe; Hørslev-Petersen, Kim

    2016-01-01

    were 1) to estimate the prevalence of magnetic resonance imaging (MRI) findings and clinical features included in the ASAS criteria for SpA and 2) to explore the associations between MRI findings and clinical features. METHODS: We included patients ages 18-40 years with persistent low back pain who had...... been referred to the Spine Centre of Southern Denmark. We collected information on clinical features (including HLA-B27 and high-sensitivity C-reactive protein) and MRI findings in the spine and sacroiliac (SI) joints. RESULTS: Of 1,020 included patients, 537 (53%) had at least 1 of the clinical...... according to the ASAS definition was present in 217 patients (21%). Of those 217 patients, 91 (42%) had the minimum amount of bone marrow edema required according to the ASAS definition (a low bone marrow edema score). The presence of HLA-B27, peripheral arthritis, a good response to NSAIDs, and preceding...

  2. Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

    Science.gov (United States)

    Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

    2018-06-15

    Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

  3. 3D shape recovery from image focus using Gabor features

    Science.gov (United States)

    Mahmood, Fahad; Mahmood, Jawad; Zeb, Ayesha; Iqbal, Javaid

    2018-04-01

    Recovering an accurate and precise depth map from a set of acquired 2-D image dataset of the target object each having different focus information is an ultimate goal of 3-D shape recovery. Focus measure algorithm plays an important role in this architecture as it converts the corresponding color value information into focus information which will be then utilized for recovering depth map. This article introduces Gabor features as focus measure approach for recovering depth map from a set of 2-D images. Frequency and orientation representation of Gabor filter features is similar to human visual system and normally applied for texture representation. Due to its little computational complexity, sharp focus measure curve, robust to random noise sources and accuracy, it is considered as superior alternative to most of recently proposed 3-D shape recovery approaches. This algorithm is deeply investigated on real image sequences and synthetic image dataset. The efficiency of the proposed scheme is also compared with the state of art 3-D shape recovery approaches. Finally, by means of two global statistical measures, root mean square error and correlation, we claim that this approach, in spite of simplicity, generates accurate results.

  4. Unusual acute encephalitis involving the thalamus: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sam Soo [Kangwon National University Hospital, Chuncheon (Korea, Republic of); Chang, Kee Hyun; Kim, Kyung Won; Han Moon Hee [Seoul National University College of Medicine, Seoul (Korea, Republic of); Park, Sung Ho; Nam, Hyun Woo [Seoul City Boramae Hospital, Seoul (Korea, Republic of); Choi, Kyu Ho [Kangnam St. Mary' s Hospital, Seoul (Korea, Republic of); Cho, Woo Ho [Sanggyo Paik Hospital, Seoul (Korea, Republic of)

    2001-06-01

    To describe the brain CT and MR imaging findings of unusual acute encephalitis involving the thalamus. We retrospectively reviewed the medical records and CT and/or MR imaging findings of six patients with acute encephalitis involving the thalamus. CT (n=6) and MR imaging (n=6) were performed during the acute and/or convalescent stage of the illness. Brain CT showed brain swelling (n=2), low attenuation of both thalami (n=1) or normal findings (n=3). Initial MR imaging indicated that in all patients the thalamus was involved either bilaterally (n=5) or unilaterally (n=1). Lesions were also present in the midbrain (n=5), medial temporal lobe (n=4), pons (n=3), both hippocampi (n=3) the insular cortex (n=2), medulla (n=2), lateral temporal lobe cortex (n=1), both cingulate gyri (n=1), both basal ganglia (n=1), and the left hemispheric cortex (n=1). These CT or MR imaging findings of acute encephalitis of unknown etiology were similar to a combination of those of Japanese encephalitis and herpes simplex encephalitis. In order to document the specific causative agents which lead to the appearance of these imaging features, further investigation is required.

  5. MR imaging features of the congenital uterine anomalies

    International Nuclear Information System (INIS)

    Hamcan, S.; Akgun, V.; Battal, B.; Kocaoglu, M.

    2012-01-01

    Full text: Introduction: Congenital uterine anomalies are common and usually asymptomatic. The agenesis, malfusion or deficient resorption of the Mullerian canals during embryogenesis may lead to these anomalies. Although ultrasonography (US) is the first step imaging technique in assessment of the uterine pathologies, it can be insufficient in differentiation of them. Magnetic resonance (MR) imaging is an adequate imaging technique in depicting pelvic anatomy and different types of uterine anomalies. Objectives and tasks: In this article, we aimed to present imaging features of the uterine anomalies. Material and methods: Pelvic MR scans of the cases who were referred to our radiology department for suspicious uterine anomaly were evaluated retrospectively. Results: We determined uniconuate uterus (type II), uterus didelphys (type III), bicornuate uterus (type IV), uterine septum (type V) and arcuate uterus (type VI) anomalies according to ASRM (American Society of Reproductive Medicine) classification. Conclusion: In cases with such pathologies leading to obstruction, dysmenorrhea or palpable pelvic mass in the puberty are the main clinical presentations. In cases without obstruction, infertility or multiple abortions can be encountered in reproductive ages. The identification of the subtype of the uterine anomalies is important for the preoperative planning of the management. MR that has multiplanar imaging capability and high soft tissue resolution is a non-invasive and the most important imaging modality for the detection and classification of the uterine anomalies

  6. Image Relaxation Matching Based on Feature Points for DSM Generation

    Institute of Scientific and Technical Information of China (English)

    ZHENG Shunyi; ZHANG Zuxun; ZHANG Jianqing

    2004-01-01

    In photogrammetry and remote sensing, image matching is a basic and crucial process for automatic DEM generation. In this paper we presented a image relaxation matching method based on feature points. This method can be considered as an extention of regular grid point based matching. It avoids the shortcome of grid point based matching. For example, with this method, we can avoid low or even no texture area where errors frequently appear in cross correlaton matching. In the mean while, it makes full use of some mature techniques such as probability relaxation, image pyramid and the like which have already been successfully used in grid point matching process. Application of the technique to DEM generaton in different regions proved that it is more reasonable and reliable.

  7. Multispectral image feature fusion for detecting land mines

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Fields, D.J.; Sherwood, R.J. [Lawrence Livermore National Lab., CA (United States)] [and others

    1994-11-15

    Our system fuses information contained in registered images from multiple sensors to reduce the effect of clutter and improve the the ability to detect surface and buried land mines. The sensor suite currently consists if a camera that acquires images in sixible wavelength bands, du, dual-band infrared (5 micron and 10 micron) and ground penetrating radar. Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separate in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, holes made by animals and natural processes, etc.) and some artifacts.

  8. CFA-aware features for steganalysis of color images

    Science.gov (United States)

    Goljan, Miroslav; Fridrich, Jessica

    2015-03-01

    Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.

  9. Shape adaptive, robust iris feature extraction from noisy iris images.

    Science.gov (United States)

    Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

    2013-10-01

    In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

  10. The imaging features of neurologic complications of left atrial myxomas

    International Nuclear Information System (INIS)

    Liao, Wei-Hua; Ramkalawan, Divya; Liu, Jian-Ling; Shi, Wei; Zee, Chi-Shing; Yang, Xiao-Su; Li, Guo-Liang; Li, Jing; Wang, Xiao-Yi

    2015-01-01

    Background: Neurologic complications may be the first symptoms of atrial myxomas. Understanding the imaging features of neurologic complications of atrial myxomas can be helpful for the prompt diagnosis. Objective: To identify neuroimaging features for patients with neurologic complications attributed to atrial myxoma. Methods: We retrospectively reviewed the medical records of 103 patients with pathologically confirmed atrial myxoma at Xiangya Hospital from January 2009 to January 2014. The neuroimaging data for patients with neurologic complications were analyzed. Results: Eight patients with atrial myxomas (7.77%) presented with neurologic manifestations, which constituted the initial symptoms for seven patients (87.5%). Neuroimaging showed five cases of cerebral infarctions and three cases of aneurysms. The main patterns of the infarctions were multiplicity (100.0%) and involvement of the middle cerebral artery territory (80.0%). The aneurysms were fusiform in shape, multiple in number (100.0%) and located in the distal middle cerebral artery (100.0%). More specifically, high-density in the vicinity of the aneurysms was observed on CT for two patients (66.7%), and homogenous enhancement surrounding the aneurysms was detected in the enhanced imaging for two patients (66.7%). Conclusion: Neurologic complications secondary to atrial myxoma consist of cerebral infarctions and aneurysms, which show certain characteristic features in neuroimaging. Echocardiography should be performed in patients with multiple cerebral infarctions, and multiple aneurysms, especially when aneurysms are distal in location. More importantly, greater attention should be paid to the imaging changes surrounding the aneurysms when myxomatous aneurysms are suspected and these are going to be the relevant features in our article

  11. The imaging features of neurologic complications of left atrial myxomas

    Energy Technology Data Exchange (ETDEWEB)

    Liao, Wei-Hua; Ramkalawan, Divya; Liu, Jian-Ling; Shi, Wei [Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China); Zee, Chi-Shing [Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033 (United States); Yang, Xiao-Su; Li, Guo-Liang; Li, Jing [Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China); Wang, Xiao-Yi, E-mail: cjr.wangxiaoyi@vip.163.com [Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan (China)

    2015-05-15

    Background: Neurologic complications may be the first symptoms of atrial myxomas. Understanding the imaging features of neurologic complications of atrial myxomas can be helpful for the prompt diagnosis. Objective: To identify neuroimaging features for patients with neurologic complications attributed to atrial myxoma. Methods: We retrospectively reviewed the medical records of 103 patients with pathologically confirmed atrial myxoma at Xiangya Hospital from January 2009 to January 2014. The neuroimaging data for patients with neurologic complications were analyzed. Results: Eight patients with atrial myxomas (7.77%) presented with neurologic manifestations, which constituted the initial symptoms for seven patients (87.5%). Neuroimaging showed five cases of cerebral infarctions and three cases of aneurysms. The main patterns of the infarctions were multiplicity (100.0%) and involvement of the middle cerebral artery territory (80.0%). The aneurysms were fusiform in shape, multiple in number (100.0%) and located in the distal middle cerebral artery (100.0%). More specifically, high-density in the vicinity of the aneurysms was observed on CT for two patients (66.7%), and homogenous enhancement surrounding the aneurysms was detected in the enhanced imaging for two patients (66.7%). Conclusion: Neurologic complications secondary to atrial myxoma consist of cerebral infarctions and aneurysms, which show certain characteristic features in neuroimaging. Echocardiography should be performed in patients with multiple cerebral infarctions, and multiple aneurysms, especially when aneurysms are distal in location. More importantly, greater attention should be paid to the imaging changes surrounding the aneurysms when myxomatous aneurysms are suspected and these are going to be the relevant features in our article.

  12. Comparison on imaging features of central serous chorioretinopathy fundus

    Directory of Open Access Journals (Sweden)

    Ji-Jin Zhang

    2014-10-01

    Full Text Available AIM: To explore and analyze the image features, diagnosis and treatment of the central serous chorioretinopathy(CSCRfundus. METHODS: From May 2008 to May 2014, 97 cases of 121 eyes with central serous chorioretinopathy were treated in in our hospital. The imaging features were compared and analyzed through different methods. RESULTS: Sixty-one cases(61 eyeswere ≤45 years, including 13 case with disease in both eyes, single stove leak accounted for 48.6%, multifocal leakage(25.7%, atypical leakage accounted for 25.7%. Thirty-six cases(47 eyeswere >45 years, 11 cases with disease in both eyes, single focal leakage(8.5%, multifocal leakage(48.9%, atypical leakage accounted for 42.6%. FFA results showed acute hairstyle at the beginning of 89 eyes, chronic deferment type 32 eyes. OCT examination showed that the main features were neuroepithelial detachment, as well as the change of the retinal pigment epithelium(RPElayer, which was divided into RPE layer detachment 93 eyes, accounting for 76.9%, rough and RPE little ridges in 28 cases, accounting for 23.1%. The average thickness of macular center concave on the cortex of microns was 137.87±19.21μm, and there was no significant difference conpared with normal(137.32±4.98μmmicrons(t=0.30, P>0.05. The closer leakage area to macular fovea, the worse of eyesight.. CONCLUSION: Different imaging examination on central serous chorioretinopathy can show different features. For clinical diagnosis and treatment it had different and complementary roles, but were given significant help for diseases treatment.

  13. CT imaging and histopathological features of renal epithelioid angiomyolipomas

    International Nuclear Information System (INIS)

    Cui, L.; Zhang, J.-G.; Hu, X.-Y.; Fang, X.-M.; Lerner, A.; Yao, X.-J.; Zhu, Z.-M.

    2012-01-01

    Aim: To describe computed tomography (CT) imaging and histopathological manifestations of renal epithelioid angiomyolipomas (EAMLs) for better understanding and cognition in the diagnosis of this new category of renal tumours. Materials and methods: Clinical data and CT images from 10 cases of EAML were retrospectively analysed. All patients underwent CT with and without contrast medium administration, with multiplanar reconstruction (MPR) when needed. Results: Plain CT manifestations of EAMLs were a higher density of mass (10–25 HU) than renal parenchyma, bulging contour of the involved kidney, absence of fat, distinct edges without a lobulate appearance. Contrast-enhanced CT features were markedly heterogeneous enhancement (from rapid wash-in to slow wash-out), large tumour size without lobular appearance, complete capsule with distinct margins and frequent mild necrotic areas. Histopathological features were epithelioid cells with eosinophilic cytoplasm, large and deeply stained nuclei, and dense arrangement of tumour cells with patchy necrosis; diffuse sheets of epithelioid cells were positive for HMB-45 (melanoma-associated antigen) and negative for epithelial membrane antigen (EMA) staining. Conclusion: Multiple specific CT features correlated well with the histopathology and may play an important role in the primary diagnosis of EAMLs.

  14. Collaborative Tracking of Image Features Based on Projective Invariance

    Science.gov (United States)

    Jiang, Jinwei

    -mode sensors for improving the flexibility and robustness of the system. From the experimental results during three field tests for the LASOIS system, we observed that most of the errors in the image processing algorithm are caused by the incorrect feature tracking. This dissertation addresses the feature tracking problem in image sequences acquired from cameras. Despite many alternatives to feature tracking problem, iterative least squares solution solving the optical flow equation has been the most popular approach used by many in the field. This dissertation attempts to leverage the former efforts to enhance feature tracking methods by introducing a view geometric constraint to the tracking problem, which provides collaboration among features. In contrast to alternative geometry based methods, the proposed approach provides an online solution to optical flow estimation in a collaborative fashion by exploiting Horn and Schunck flow estimation regularized by view geometric constraints. Proposed collaborative tracker estimates the motion of a feature based on the geometry of the scene and how the other features are moving. Alternative to this approach, a new closed form solution to tracking that combines the image appearance with the view geometry is also introduced. We particularly use invariants in the projective coordinates and conjecture that the traditional appearance solution can be significantly improved using view geometry. The geometric constraint is introduced by defining a new optical flow equation which exploits the scene geometry from a set drawn from tracked features. At the end of each tracking loop the quality of the tracked features is judged using both appearance similarity and geometric consistency. Our experiments demonstrate robust tracking performance even when the features are occluded or they undergo appearance changes due to projective deformation of the template. The proposed collaborative tracking method is also tested in the visual navigation

  15. Multimodal Ultrawide-Field Imaging Features in Waardenburg Syndrome.

    Science.gov (United States)

    Choudhry, Netan; Rao, Rajesh C

    2015-06-01

    A 45-year-old woman was referred for bilateral irregular fundus pigmentation. Dilated fundus examination revealed irregular hypopigmentation posterior to the equator in both eyes, confirmed by fundus autofluorescence. A thickened choroid was seen on enhanced-depth imaging spectral-domain optical coherence tomography (EDI SD-OCT). Systemic evaluation revealed sensorineural deafness, telecanthus, and a white forelock. Further investigation revealed a first-degree relative with Waardenburg syndrome. Waardenburg syndrome is characterized by a group of features including telecanthus, a broad nasal root, synophrys of the eyebrows, piedbaldism, heterochromia irides, and deafness. Choroidal hypopigmentation is a unique feature that can be visualized with ultrawide-field fundus autofluorescence. The choroid may also be thickened and its thickness measured with EDI SD-OCT. Copyright 2015, SLACK Incorporated.

  16. Can Global Visual Features Improve Tag Recommendation for Image Annotation?

    Directory of Open Access Journals (Sweden)

    Oge Marques

    2010-08-01

    Full Text Available Recent advances in the fields of digital photography, networking and computing, have made it easier than ever for users to store and share photographs. However without sufficient metadata, e.g., in the form of tags, photos are difficult to find and organize. In this paper, we describe a system that recommends tags for image annotation. We postulate that the use of low-level global visual features can improve the quality of the tag recommendation process when compared to a baseline statistical method based on tag co-occurrence. We present results from experiments conducted using photos and metadata sourced from the Flickr photo website that suggest that the use of visual features improves the mean average precision (MAP of the system and increases the system's ability to suggest different tags, therefore justifying the associated increase in complexity.

  17. Mutual information based feature selection for medical image retrieval

    Science.gov (United States)

    Zhi, Lijia; Zhang, Shaomin; Li, Yan

    2018-04-01

    In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

  18. Clinical, imaging and histopathological features of isolated CNS lymphomatoid granulomatosis

    International Nuclear Information System (INIS)

    Patil, Anil Kumar; Alexander, Mathew; Nair, Bijesh; Chacko, Geeta; Mani, Sunithi; Sudhakar, Sniya

    2015-01-01

    Lymphomatoid granulomatosis is a rare systemic angiocentric/angiodestructive, B cell lymphoproliferative disorder. Central nervous system involvement occurs as part of systemic disease. Isolated central nervous system disease is rare with only few case reports. A 53-year-old male presented with progressive cognitive decline, extrapyramidal features, and altered sensorium with seizures over the last 4 years. His magnetic resonance imaging (MRI) of brain showed multiple small enhancing nodules in subependymal/ependymal regions and along the vessels. Brain biopsy showed atypical lymphohistiocytic infiltrate suggestive of lymphomatoid granulomatosis. There was no evidence of systemic disease; thus, isolated central nervous system lymphomatoid granulomatosis was diagnosed

  19. MR imaging features of chronically torn anterior cruciate ligament

    International Nuclear Information System (INIS)

    Niitsu, Mamoru; Kuramochi, Masashi; Ikeda, Kotaroh; Fukubayashi, Tohru; Anno, Izumi; Itai, Yuji

    1995-01-01

    Magnetic resonance (MR) images of 40 knee joints with arthroscopically proved chronic anterior cruciate ligament (ACL) tears were retrospectively evaluated. MRI demonstrated various features of chronic ACL tears: 19 knees revealed with no identifiable ligamentous structure, and 21 had residual ligamentous structures. These pseudoligaments, 14 discontinuous bands and seven continuous bands with elongation, were residual torn ligamentous fibers and/or synovial tissues. All the discontinuous bands were disrupted from the femoral attachment and were likely to traverse the lower intercondylar space. Six disrupted ligaments were attached to the lateral aspect of the posterior cruciate ligament (PCL). Coronal T2 * -weighted gradient echo images showed better delineation of the disrupted femoral attachment and adhesion to the PCL. A chronic ACL tear with minimal elongation or with PCL attachment at a higher position may occasionally be difficult to distinguish from an intact ligament. (author)

  20. [Clinical, pathological and imaging features of primary pelvic Ewing's sarcoma].

    Science.gov (United States)

    Liu, J; Chen, Y; Ling, X L; Gong, Y; Ding, J P; Zhang, Z K; Wang, Y J

    2016-07-19

    To explore the clinical, pathological and imaging features of Ewing's sarcoma in pelvis and to improve knowledge and diagnosis of the disease. A retrospective analysis of the clinical, pathological and imaging data of pathologically confirmed 13 cases of Ewing's sarcoma in pelvis was carried out between May 2008 and March 2016 in the Affiliated Hospital of Hangzhou Normal University, the Third Hospital of Hebei Medical University and the Second Hospital of Hebei Medical University. The median age 13 cases of pelvic primary Ewing's sarcoma was 17 years old.The X-ray and CT imagings showed osteolytic and mixed bone destruction, CT showed mixed type in 10 cases, 8 cases of bone tumors as a flocculent, 10 cases of bone expansion failure, 10 cases of periosteal reaction, the layered 5 cases, radial in 5 cases.Thirteen cases showed soft tissue mass, soft tissue mass was equal or slightly lower density.Four cases showed heterogeneous contrast enhancement.The lesions showed low signal in T1WI and mixed high signal in T2WI of magnetic resonance imaging(MRI). The boundary of the lesions were obscure, and 5 cases had patchy necrosis area, and 9 cases had incomplete false capsule, surrounding soft tissue was violated.Four cases showed heterogeneous contrast enhancement after MRI enhancement scan. The age of onset of Ewing's sarcoma of the pelvis is more concentrated in about 15 years.The imaging feaures are mixed bone destruction and more bone is swelling and permeability damage, soft tissue mass is larger, bone tumor is cloudy or acicular, periosteal reaction in a layered and radial, most cases show that the false envelope is not complete.Combined with clinical and imaging examination, the diagnosis of the disease can be made.

  1. Featured Image: New Detail in the Toothbrush Cluster

    Science.gov (United States)

    Kohler, Susanna

    2018-01-01

    This spectacular composite (click here for the full image) reveals the galaxy cluster 1RXS J0603.3+4214, known as the Toothbrush cluster due to the shape of its most prominent radio relic. Featured in a recent publication led by Kamlesh Rajpurohit (Thuringian State Observatory, Germany), this image contains new Very Large Array (VLA) 1.5-GHz observations (red) showing the radio emission within the cluster. This is composited with a Chandra view of the X-ray emitting gas of the cluster (blue) and an optical image of the background from Subaru data. The new deep VLA data totaling 26 hours of observations provides a detailed look at the complex structure within the Toothbrush relic, revealing enigmatic filaments and twists (see below). This new data will help us to explore the possible merger history of this cluster, which is theorized to have caused the unusual shapes we see today. For more information, check out the original article linked below.High resolution VLA 12 GHz image of the Toothbrush showing the complex, often filamentary structures. [Rajpurohit et al. 2018]CitationK. Rajpurohit et al 2018 ApJ 852 65. doi:10.3847/1538-4357/aa9f13

  2. Wavelength calibration of imaging spectrometer using atmospheric absorption features

    Science.gov (United States)

    Zhou, Jiankang; Chen, Yuheng; Chen, Xinhua; Ji, Yiqun; Shen, Weimin

    2012-11-01

    Imaging spectrometer is a promising remote sensing instrument widely used in many filed, such as hazard forecasting, environmental monitoring and so on. The reliability of the spectral data is the determination to the scientific communities. The wavelength position at the focal plane of the imaging spectrometer will change as the pressure and temperature vary, or the mechanical vibration. It is difficult for the onboard calibration instrument itself to keep the spectrum reference accuracy and it also occupies weight and the volume of the remote sensing platform. Because the spectral images suffer from the atmospheric effects, the carbon oxide, water vapor, oxygen and solar Fraunhofer line, the onboard wavelength calibration can be processed by the spectral images themselves. In this paper, wavelength calibration is based on the modeled and measured atmospheric absorption spectra. The modeled spectra constructed by the atmospheric radiative transfer code. The spectral angle is used to determine the best spectral similarity between the modeled spectra and measured spectra and estimates the wavelength position. The smile shape can be obtained when the matching process across all columns of the data. The present method is successful applied on the Hyperion data. The value of the wavelength shift is obtained by shape matching of oxygen absorption feature and the characteristics are comparable to that of the prelaunch measurements.

  3. Heterotopic Pancreas: Histopathologic Features, Imaging Findings, and Complications.

    Science.gov (United States)

    Rezvani, Maryam; Menias, Christine; Sandrasegaran, Kumaresan; Olpin, Jeffrey D; Elsayes, Khaled M; Shaaban, Akram M

    2017-01-01

    Heterotopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separate from the main gland. The most common locations of this displacement include the upper gastrointestinal tract-specifically, the stomach, duodenum, and proximal jejunum. Less common sites are the esophagus, ileum, Meckel diverticulum, biliary tree, mesentery, and spleen. Uncomplicated heterotopic pancreas is typically asymptomatic, with the lesion being discovered incidentally during an unrelated surgery, during an imaging examination, or at autopsy. The most common computed tomographic appearance of heterotopic pancreas is that of a small oval intramural mass with microlobulated margins and an endoluminal growth pattern. The attenuation and enhancement characteristics of these lesions parallel their histologic composition. Acinus-dominant lesions demonstrate avid homogeneous enhancement after intravenous contrast material administration, whereas duct-dominant lesions are hypovascular and heterogeneous. At magnetic resonance imaging, the heterotopic pancreas is isointense to the orthotopic pancreas, with characteristic T1 hyperintensity and early avid enhancement after intravenous gadolinium-based contrast material administration. Heterotopic pancreatic tissue has a rudimentary ductal system in which an orifice is sometimes visible at imaging as a central umbilication of the lesion. Complications of heterotopic pancreas include pancreatitis, pseudocyst formation, malignant degeneration, gastrointestinal bleeding, bowel obstruction, and intussusception. Certain complications may be erroneously diagnosed as malignancy. Paraduodenal pancreatitis is thought to be due to cystic degeneration of heterotopic pancreatic tissue in the medial wall of the duodenum. Recognizing the characteristic imaging features of heterotopic pancreas aids in differentiating it from cancer and thus in avoiding unnecessary surgery. © RSNA, 2017.

  4. Cloud Technology May Widen Genomic Bottleneck - TCGA

    Science.gov (United States)

    Computational biologist Dr. Ilya Shmulevich suggests that renting cloud computing power might widen the bottleneck for analyzing genomic data. Learn more about his experience with the Cloud in this TCGA in Action Case Study.

  5. CT and MR imaging features in patients with intracranial dolichoectasia

    Energy Technology Data Exchange (ETDEWEB)

    Tien, Kuang Lung; Yu, In Kyu; Yoon, Sook Ja; Yoon, Yong Kyu [Eulji College of Medicine, Eulji Hospital, Seoul (Korea, Republic of)

    2000-02-01

    To describe the CT and MR imaging features in patients with intracranial dolichoectasia. The CT (n=3D21), MR (n=3D20) and MRA (n=3D11) imaging features seen in 28 patients (M:F=3D12:16 aged between 65 and 82 (mean, 65) years) with intracranial dolichoectasia were retrospectively reviewed with regard to involved sites, arterial changes (maximum diameter, wall calcification, high signal intensity in the involved artery, as seen on T1-weighted MR images), infarction, hemorrhagic lesion, compression of brain parenchyma or cranial nerves, hydrocephalus and brain atrophy. Involved sites were classified as either type 1 (involvement of only the posterior circulation), type 2 (only the anterior circulation), or type 3 (both). In order of frequency, involved sites were type 1 (43%), type 3 (36%) and type 2 (22%). Dolichoectasia was more frequently seen in the posterior circulation (79%) than in the anterior (57%). Arterial changes as seen on T1-weighted MR images, included dolichoectasia (mean maximum diameter 7.4 mm in the distal internal carotid artery, and 6.7 mm in the basilar artery), wall calcification (100% in involved arteries) and high signal intensity in involved. Cerebral infarction in the territory of the involved artery was found in all patients, and a moderate degree of infarct was 87%. Hemorrhagic lesions were found in 19 patients (68%); these were either lobar (53%), petechial (37%), or subarachnoid (16%), and three patients showed intracranial aneurysms, including one case of dissecting aneurysm. In 19 patients (68%), lesions were compressed lesions by the dolichoectatic arteries, and were found-in order of descending frequency-in the medulla, pons, thalamus, and cerebellopontine angle cistern. Obstructive hydrocephalus was found in two patients (7%), and 23 (82%) showed a moderate degree of brain atrophy. In patients with intracranial dolichoectasia, moderate degrees of cerebral infarction and brain atrophy in the territory of involved arteries, as well as

  6. Iterative feature refinement for accurate undersampled MR image reconstruction

    Science.gov (United States)

    Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

    2016-05-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

  7. Imaging features of ovarian metastases from colonic adenocarcinoma in adolescents

    International Nuclear Information System (INIS)

    Kauffman, W.M.; Jenkins, J.J. III; Helton, K.; Rao, B.N.; Winer-Muram, H.T.; Pratt, C.B.

    1995-01-01

    This paper describes the imaging features of ovarian metastases from adenocarcinoma of the colon in adolescent females. We reviewed retrospectively abdominal and pelvic computed tomographic and pelvic ultrasound examinations, histologic slices, and clinical charts of six adolescent females with ovarian metastases secondary to adenocarcinoma of the colon. One patient had ovarian metastasis at presentation and was presumed to have a primary ovarian tumor. The ovarian metastases were either solid (n = 3), complex with both solid and cystic components (n = 2), or multilocular cysts (n = 1). The ovarian lesions were large, ranging from 6 cm to 18 cm in diameter. Colorectal carcinoma in adolescent females is frequently associated with ovarian metastases. One imaging characteristic differs in adult and adolescent ovarian metastases, although they do have features in common: in adolescents, a smaller proportion of colorectal ovarian metastases are multicystic (17%) compared with the adult series (45%). These lesions are frequently large and may be complex, multicystic, or solid. Although it is a rare disease, the differential dignosis of adnexal masses in adolescent females should include ovarian metastases from adenocarcinoma of the colon. (orig.)

  8. Iterative feature refinement for accurate undersampled MR image reconstruction

    International Nuclear Information System (INIS)

    Wang, Shanshan; Liu, Jianbo; Liu, Xin; Zheng, Hairong; Liang, Dong; Liu, Qiegen; Ying, Leslie

    2016-01-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches. (paper)

  9. Clinical feature and imaging findings of juvenile ankylosing spondylitis

    International Nuclear Information System (INIS)

    Zeng Hui; Liang Hongchang; Wang Weigang; Liu Hui; Huang Meiping; Zheng Junhui

    2003-01-01

    Objective: To analyze the clinical features and imaging findings of juvenile ankylosing spondylitis (JAS) in order to improve the diagnosis and the prognosis of JAS. Methods: Twelve cases were analyzed retrospectively and 14 cases, who were followed-up averagely for 2.3 years, were analyzed prospectively. Initially 10 were diagnosed as Still's disease and four were diagnosed as rheumatoid arthritis. Photography was performed in all cases, CT scan was done in 18 cases, and MRI in 8 cases. Lower extremity big joint disorders were observed in all cases and the small joints were reserved. The abnormalities of the sacroiliac joint were revealed in the early stage in 12 cases. The results were analyzed statistically. Results: The age of preliminary diagnosis was 9.3 years in average. There were statistical correlation between the age of the first episode and severity of the disease. And there were statistical correlation between the course of the illness and severity of the disease. The large joints of the lower extremities were most commonly involved. Conclusion: There were characteristic clinical features and imaging findings in the JAS. Early diagnosis and treatment improve the prognosis

  10. A kernel-based multi-feature image representation for histopathology image classification

    International Nuclear Information System (INIS)

    Moreno J; Caicedo J Gonzalez F

    2010-01-01

    This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of latent semantic analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, support vector machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that; the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  11. A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    J Carlos Moreno

    2010-09-01

    Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  12. Motor features in posterior cortical atrophy and their imaging correlates.

    Science.gov (United States)

    Ryan, Natalie S; Shakespeare, Timothy J; Lehmann, Manja; Keihaninejad, Shiva; Nicholas, Jennifer M; Leung, Kelvin K; Fox, Nick C; Crutch, Sebastian J

    2014-12-01

    Posterior cortical atrophy (PCA) is a neurodegenerative syndrome characterized by impaired higher visual processing skills; however, motor features more commonly associated with corticobasal syndrome may also occur. We investigated the frequency and clinical characteristics of motor features in 44 PCA patients and, with 30 controls, conducted voxel-based morphometry, cortical thickness, and subcortical volumetric analyses of their magnetic resonance imaging. Prominent limb rigidity was used to define a PCA-motor subgroup. A total of 30% (13) had PCA-motor; all demonstrating asymmetrical left upper limb rigidity. Limb apraxia was more frequent and asymmetrical in PCA-motor, as was myoclonus. Tremor and alien limb phenomena only occurred in this subgroup. The subgroups did not differ in neuropsychological test performance or apolipoprotein E4 allele frequency. Greater asymmetry of atrophy occurred in PCA-motor, particularly involving right frontoparietal and peri-rolandic cortices, putamen, and thalamus. The 9 patients (including 4 PCA-motor) with pathology or cerebrospinal fluid all showed evidence of Alzheimer's disease. Our data suggest that PCA patients with motor features have greater atrophy of contralateral sensorimotor areas but are still likely to have underlying Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  13. Motor features in posterior cortical atrophy and their imaging correlates☆

    Science.gov (United States)

    Ryan, Natalie S.; Shakespeare, Timothy J.; Lehmann, Manja; Keihaninejad, Shiva; Nicholas, Jennifer M.; Leung, Kelvin K.; Fox, Nick C.; Crutch, Sebastian J.

    2014-01-01

    Posterior cortical atrophy (PCA) is a neurodegenerative syndrome characterized by impaired higher visual processing skills; however, motor features more commonly associated with corticobasal syndrome may also occur. We investigated the frequency and clinical characteristics of motor features in 44 PCA patients and, with 30 controls, conducted voxel-based morphometry, cortical thickness, and subcortical volumetric analyses of their magnetic resonance imaging. Prominent limb rigidity was used to define a PCA-motor subgroup. A total of 30% (13) had PCA-motor; all demonstrating asymmetrical left upper limb rigidity. Limb apraxia was more frequent and asymmetrical in PCA-motor, as was myoclonus. Tremor and alien limb phenomena only occurred in this subgroup. The subgroups did not differ in neuropsychological test performance or apolipoprotein E4 allele frequency. Greater asymmetry of atrophy occurred in PCA-motor, particularly involving right frontoparietal and peri-rolandic cortices, putamen, and thalamus. The 9 patients (including 4 PCA-motor) with pathology or cerebrospinal fluid all showed evidence of Alzheimer's disease. Our data suggest that PCA patients with motor features have greater atrophy of contralateral sensorimotor areas but are still likely to have underlying Alzheimer's disease. PMID:25086839

  14. Clinical and imaging features of neonatal chlamydial pneumonia

    International Nuclear Information System (INIS)

    Cao Yongli; Peng Yun; Sun Guoqiang

    2012-01-01

    Objective: To study the clinical and imaging features of chlamydial pneumonia in newborns. Methods: Medical records,chest X-Ray and CT findings of 17 neonates with chlamydia pneumonia were reviewed. The age was ranged from 9.0 to 28.0 days with mean of (16.8 ± 5.8) days. There were 11 males and 6 females. Sixteen were full term infants and one was born post term. All babies were examined with chest X-ray film, and 13 patients also underwent chest CT scan. Serologic test using immunofluorescence method for Chlamydia IgG and IgM antibodies were performed in all patients. Results: All newborns presented with cough but without fever. Positive results of the serologic tests were demonstrated. Chest films showed bilateral hyperventilation in 10 patients, diffuse reticular nodules in 10 patients including nodules mimicking military tuberculosis in 7 patients, and accompanying consolidation in 9 patients. CT features included interstitial reticular nodules in 13 patients with size, density, and distribution varied. Subpleural nodules (11 patients) and fusion of nodules (10 patients) predominated. Bilateral hyperinflation was found in 10 patients, which combined with infiltration in 12 patients, thickening of bronchovascular bundles in 10 patients, and ground glass sign in 5 patients. No pleural effusion and lymphadenopathy was detected in any patient. Conclusions: Bilateral hyperinflation and diffuse interstitial reticular nodules were the most common imaging features of neonatal chlamydial pneumonia. The main clinical characteristic of neonatal chlamydial pneumonia is respiratory symptoms without fever, which is helpful to its diagnosis. (authors)

  15. Cystic synovial sarcomas: imaging features with clinical and histopathologic correlation

    Energy Technology Data Exchange (ETDEWEB)

    Nakanishi, Hirofumi; Araki, Nobuhito [Department of Orthopedic Surgery, Osaka Medical Center for Cancer and Cardiovascular Diseases, 1-3-3, Nakamichi, Higashinari-Ku, 537-8511, Osaka (Japan); Sawai, Yuka [Department of Radiology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka (Japan); Kudawara, Ikuo [Department of Orthopedic Surgery, Osaka National Hospital, Osaka (Japan); Mano, Masayuki; Ishiguro, Shingo [Department of Pathology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka (Japan); Ueda, Takafumi; Yoshikawa, Hideki [Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka (Japan)

    2003-12-01

    To characterize the radiological and clinicopathologic features of cystic synovial sarcoma. Seven patients with primary cystic synovial sarcoma were evaluated. Computed tomography (CT) and magnetic resonance (MR) imaging were undertaken at the first presentation. The diagnosis of synovial sarcoma was made on the basis of histological examinations followed by molecular analysis. Radiological and clinicopathologic findings were reviewed. CT showed well-defined soft tissue mass without cortical bone erosion and invasion. Calcification was seen at the periphery of the mass in three cases. T2-weighted MR images showed multilocular inhomogeneous intensity mass in all cases, five of which showed fluid-fluid levels. On gross appearance, old and/or fresh hematomas were detected in six cases. In the one remaining case, microscopic hemorrhage in the cystic lumen was proven. Four cases had poorly differentiated areas. In five cases prominent hemangiopericytomatous vasculature was observed. Histologic grade was intermediate in one tumor and high in six. One case had a history of misdiagnosis for tarsal tunnel syndrome, one for lymphadenopathy, two for sciatica and two for hematoma. All cystic synovial sarcomas demonstrated multilocularity with well-circumscribed walls and internal septae. Synovial sarcoma should be taken into consideration in patients with deeply situated multicystic mass with triple signal intensity on T2-weighted MR imaging. (orig.)

  16. Cystic synovial sarcomas: imaging features with clinical and histopathologic correlation

    International Nuclear Information System (INIS)

    Nakanishi, Hirofumi; Araki, Nobuhito; Sawai, Yuka; Kudawara, Ikuo; Mano, Masayuki; Ishiguro, Shingo; Ueda, Takafumi; Yoshikawa, Hideki

    2003-01-01

    To characterize the radiological and clinicopathologic features of cystic synovial sarcoma. Seven patients with primary cystic synovial sarcoma were evaluated. Computed tomography (CT) and magnetic resonance (MR) imaging were undertaken at the first presentation. The diagnosis of synovial sarcoma was made on the basis of histological examinations followed by molecular analysis. Radiological and clinicopathologic findings were reviewed. CT showed well-defined soft tissue mass without cortical bone erosion and invasion. Calcification was seen at the periphery of the mass in three cases. T2-weighted MR images showed multilocular inhomogeneous intensity mass in all cases, five of which showed fluid-fluid levels. On gross appearance, old and/or fresh hematomas were detected in six cases. In the one remaining case, microscopic hemorrhage in the cystic lumen was proven. Four cases had poorly differentiated areas. In five cases prominent hemangiopericytomatous vasculature was observed. Histologic grade was intermediate in one tumor and high in six. One case had a history of misdiagnosis for tarsal tunnel syndrome, one for lymphadenopathy, two for sciatica and two for hematoma. All cystic synovial sarcomas demonstrated multilocularity with well-circumscribed walls and internal septae. Synovial sarcoma should be taken into consideration in patients with deeply situated multicystic mass with triple signal intensity on T2-weighted MR imaging. (orig.)

  17. Featured Image: A Detailed Look at the Crab Nebula

    Science.gov (United States)

    Kohler, Susanna

    2017-07-01

    Planning on watching fireworks tomorrow? Heres an astronomical firework to help you start the celebrations! A new study has stunningly detailed the Crab Nebula (click for a closer look), a nebula 6,500 light-years away thought to have been formedby a supernova explosion and the subsequent ultrarelativistic wind emitted by the pulsar at its heart. Led by Gloria Dubner (University of Buenos Aires), the authors of this study obtained new observations of the Crab Nebula from five different telescopes. They compiled these observations to compare the details of the nebulas structure across different wavelengths, which allowedthem to learnabout the sources of various features within the nebula. In the images above, thetop left shows the 3 GHz data from the Very Large Array (radio). Moving clockise, the radio data (shown in red) is composited with: infrared data from Spitzer Space Telescope, optical continuum from Hubble Space Telescope, 500-nm optical datafrom Hubble, and ultraviolet data from XMM-Newton. The final two images are of the nebula center, and they are composites of the radio imagewith X-ray data from Chandra and near-infrared data from Hubble. To read more about what Dubner and collaborators learned (and to see more spectacular images!), check out the paper below.CitationG. Dubner et al 2017 ApJ 840 82. doi:10.3847/1538-4357/aa6983

  18. Biomedical imaging modality classification using combined visual features and textual terms.

    Science.gov (United States)

    Han, Xian-Hua; Chen, Yen-Wei

    2011-01-01

    We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

  19. Imaging and applied optics: introduction to the feature issue.

    Science.gov (United States)

    Zalevsky, Zeev; Arnison, Matthew R; Javidi, Bahram; Testorf, Markus

    2018-03-01

    This special issue of Applied Optics contains selected papers from OSA's Imaging Congress with particular emphasis on work from mathematics in imaging, computational optical sensing and imaging, imaging systems and applications, and 3D image acquisition and display.

  20. CT imaging features of tuberculous spondylitis in children

    International Nuclear Information System (INIS)

    Song Min; Liu Wen; Fang Weijun; Wang Fukang; Li Ziping

    2009-01-01

    Objective: To investigate CT imaging features of tuberculous spondylitis in children. Methods: The CT imagings of two groups of patients with Tuberculous Spondylitis between January 2004 and March 2008 were retrospectively reviewed. One group included 28 children from 0 to 14 years old. Another group included 159 adults. All the patients were diagnosed as tuberculous spondylitis by pathology or biopsy, or by anti-turboelectric therapy. The CT imagings of the two groups were read retrospectively, including infections of vertebras and its appendix, the proportion of the total length of paravertebral abscess to the height of relative vertebra, the information of paravertebral abscess and dura mate of spinal cord and nerve root compression. Results The ratio of kyphosis in children group was 75% (21/28), higher than that in adults'. Tuberculous spondylitis in children was most often involved thoracic vertebra (53.7%,51/95). In children, involvement was more often seen than that of cervical vertebra and lumbar. The ratio of tuberculous spondylitis of children's cervical vertebrae was 10.5% (10/95)and of lumbar was 31.6% (30/95, while in adults that of cervical vertebrae was 3.3% (16/479)and of lumbar was 44.5% (213/479). There was statistical difference between them. The percentages of central type of tuberculous vertebral osteitis in chlidren was 57.1% (16/28)and was different with that in adults'(P=0.001 0.05). The incidence of dura mate of spinal cord or nerve root compression in children was 78.6%(22/28), much higher than that in adults (49.7%(79/159), P=0.005 <0.05). Conclusion: Special features of tuberculous spondylitis in childrencan be observed on CT imaging, kyphosis is often seen. The incidence of tuberculous spondylitis of thoracic vertebra and cervical vertebrae is high, central type of tuberculous vertebral osteitis in children is more popular than that in adults, but there is higher ratio of dura mate of spinal cord or nerve root compression in children

  1. SU-E-J-237: Image Feature Based DRR and Portal Image Registration

    Energy Technology Data Exchange (ETDEWEB)

    Wang, X; Chang, J [NY Weill Cornell Medical Ctr, NY (United States)

    2014-06-01

    Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thus the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.

  2. Imaging features of lower limb malformations above the foot.

    Science.gov (United States)

    Bergère, A; Amzallag-Bellenger, E; Lefebvre, G; Dieux-Coeslier, A; Mezel, A; Herbaux, B; Boutry, N

    2015-09-01

    Lower limb malformations are generally isolated or sporadic events. However, they are sometimes associated with other anomalies of the bones and/or viscera in patients with constitutional syndromes or disorders of the skeleton. This paper reviews the main imaging features of these abnormalities, which generally exhibit a broad spectrum. This paper focuses on several different bone malformations: proximal focal femoral deficiency, congenital short femur and femoral duplication for the femur, tibial hemimelia (aplasia/hypoplasia of the tibia) and congenital bowing for the tibia, fibular hemimelia (aplasia/hypoplasia) for the fibula, and aplasia, hypoplasia and congenital dislocation for the patella. Copyright © 2015 Éditions françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

  3. Prenatal MR imaging features of isolated cerebellar haemorrhagic lesions

    International Nuclear Information System (INIS)

    Martino, Francesca; Malova, Mariya; Ramenghi, Luca A.; Cesaretti, Claudia; Parazzini, Cecilia; Doneda, Chiara; Righini, Andrea; Rossi, Andrea

    2016-01-01

    Prenatal features of isolated cerebellar haemorrhagic lesions have not been sufficiently characterised. We aimed to better define their MR imaging characteristics, documenting the location, extension, evolution stage and anatomic sequelae, and to better understand cerebellar haemorrhage pathophysiology. We screened our foetal MR imaging database (3200 cases) for reports of haemorrhagic lesions affecting only the cerebellum (without any supratentorial bleeding or other clastic lesions), defined as one of the following: T2-weighted hypointense or mixed hypo-/hyperintense signal; rim of T2-weighted hypointense signal covering the surface of volume-reduced parenchyma; T1-weighted hyperintense signal; increased DWI signal. Seventeen cases corresponded to the selection criteria. All lesions occurred before the 26th week of gestation, with prevalent origin from the peripheral-caudal portion of the hemispheres and equal frequency of unilateral/bilateral involvement. The caudal vermis appeared affected in 2/3 of cases, not in all cases confirmed postnatally. Lesions evolved towards malformed cerebellar foliation. The aetiology and pathophysiology were unknown, although in a subset of cases intra- and extracranial venous engorgement seemed to play a key role. Onset from the peripheral and caudal portion of the hemispheres seems characteristic of prenatal cerebellar haemorrhagic lesions. Elective involvement of the peripheral germinal matrix is hypothesised. (orig.)

  4. The imaging feature of multidrug-resistant tuberculosis

    International Nuclear Information System (INIS)

    Yang Jun; Zhou Xinhua; Li Xi; Fu Yuhong; Zheng Suhua; Lv Pingxin; Ma Daqing

    2004-01-01

    Objective: To evaluate the imaging features of multidrug-resistant tuberculosis by collecting multidrug-resistant tuberculosis verified by test of drug-sensitivity, which defined as resistance to three anti-tuberculosis drugs. Methods:Fifty-one cases of multidrug-resistant tuberculosis were categorized as group of observed, and 46 cases of drug sensitive tuberculosis were categorized as control. Cultures were positive for Mycobacterium tuberculosis in all cases with no other illness such as diabetes mellitus. All patients had chest radiographs available for review, while 64 cases had tomography and 30 cases had CT during the same time. All images were analyzed by three of the radiologists, disagreement among them was discussed and a consensus was reached. Results: There was no difference in the distribution of lesions between the multidrug-resistant tuberculosis group and control group. However, the radiological findings in the multidrug-resistant tuberculosis group were significantly more common than in control group, such as multiple nodules (10 cases), disseminated foci (23 cases), cavity (9 cases), and complications (10 cases). Comparing the dynamic cases, deteriorating cases were more commonly seen in observed group than in control group, while improved cases were less in observed group than in control group. Conclusion: Multidrug-resistant tuberculosis is the most serious tuberculosis, which is characterized with significant activity, more disseminated foci, cavity, and complications. The lesion deteriorated while correct anti-tuberculosis treatment is applied. (authors)

  5. Enhancement and feature extraction of RS images from seismic area and seismic disaster recognition technologies

    Science.gov (United States)

    Zhang, Jingfa; Qin, Qiming

    2003-09-01

    Many types of feature extracting of RS image are analyzed, and the work procedure of pattern recognizing in RS images of seismic disaster is proposed. The aerial RS image of Tangshan Great Earthquake is processed, and the digital features of various typical seismic disaster on the RS image is calculated.

  6. Application of eigen value expansion to feature extraction from MRI images

    International Nuclear Information System (INIS)

    Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

    1991-01-01

    The eigen value expansion technique was utilized for feature extraction of magnetic resonance (MR) images. The eigen value expansion is an orthonormal transformation method which decomposes a set of images into some statistically uncorrelated images. The technique was applied to MR images obtained with various imaging parameters at the same anatomical site. It generated one mean image and another set of images called bases for the images. Each basis corresponds to a feature in the images. A basis is, therefore, utilized for the feature extraction from MR images and a weighted sum of bases is also used for the feature enhancement. Furthermore, any MR image with specific feature can be obtained from a linear combination of the mean image and all of the bases. Images of hemorrhaged brain with a spin echo sequence and a series of cinematic cerebro spinal fluid flow images with ECG gated gradient refocused echo sequence were employed to estimate the ability of the feature extraction and the contrast enhancement. Results showed us that proposed application of an eigen value expansion technique to the feature extraction of MR images is good enough to clinical use and superior to other feature extraction methods such as producing a calculated MR image with a given TR and TE or the matched-filter method in processing speed and reproducibility of results. (author)

  7. Imagiologic features and the relative imaging factors in hepatolenticular degeneration

    International Nuclear Information System (INIS)

    Gao Wenqing; Liu Pengcheng; Huang Rong; Yan Weiqiang; Zhao Yan; Liu Yuanjian; Luo Lili; Zou Liqiu; Liu Hanqiao

    2002-01-01

    Objective: To study the CT, MR and ultrasound features of hepatolenticular degeneration (HLD), and investigate relative factors affecting the imaging manifestations. Methods: Fifty-four HLD were reported, and the 35 male and 19 female patients ranged in the age from 3 to 40 years. CT was performed in 29 patients, MR in 11, both CT and MR in 5, ultrasound in 26. Six cases were hospitalized for 3 times, and 9 for twice. Results: (1) The putamen was affected on MR in all cases (100%), the caudate nucleus in 8. The thalami in 5, the globus pallidum in 2, the red nucleus in 2, the substantia nigra in 3, the midbrain in 1, the pons in 2, the white matter of frontal lobi in 1. According to the different basal ganglia involved in brain, resembling 'woodpecker' or 'butterfly spreading the wing' in appearance were showed on the MR images respectively. (2) Positive signs were found by CT scans in 18 cases (72%), but negative in 7 cases (28%). It is important manifestation that low density in brain occurred bilaterally and symmetrically. (3) The sonographic changes of chronic liver disease were showed on US in all 26 cases. Among the number, 12 cases were regarded as cirrhosis at the same time. Conclusion: (1) T 2 signal intensity and CT density changes are often not parallel to the clinical symptoms. T 1 WI is suitable for the follow-up, but quantitative analysis is still difficult. (2) Damage of liver occurs almost in all HLD, and earlier than that of brain. On the early stage, the liver damage is reversible, the brain lesions are symmetric. Moderately, the liver damage changes static. Lately, the brain presents atrophic. (3) The investigation suggests that there are 4 factors affecting CT and MR imaging features: the systemic disease resulting from metabolic disorder and the selected affinity caused by gene defect, deposition of copper together with cellular damage, endogenous and autonomous discharge of copper and histiocyte repaired, and extro-generate expelled copper

  8. Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

    Science.gov (United States)

    Gururaj, C.; Jayadevappa, D.; Tunga, Satish

    2018-06-01

    Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

  9. Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

    Science.gov (United States)

    Gururaj, C.; Jayadevappa, D.; Tunga, Satish

    2018-02-01

    Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

  10. Feature Extraction in Sequential Multimedia Images: with Applications in Satellite Images and On-line Videos

    Science.gov (United States)

    Liang, Yu-Li

    Multimedia data is increasingly important in scientific discovery and people's daily lives. Content of massive multimedia is often diverse and noisy, and motion between frames is sometimes crucial in analyzing those data. Among all, still images and videos are commonly used formats. Images are compact in size but do not contain motion information. Videos record motion but are sometimes too big to be analyzed. Sequential images, which are a set of continuous images with low frame rate, stand out because they are smaller than videos and still maintain motion information. This thesis investigates features in different types of noisy sequential images, and the proposed solutions that intelligently combined multiple features to successfully retrieve visual information from on-line videos and cloudy satellite images. The first task is detecting supraglacial lakes above ice sheet in sequential satellite images. The dynamics of supraglacial lakes on the Greenland ice sheet deeply affect glacier movement, which is directly related to sea level rise and global environment change. Detecting lakes above ice is suffering from diverse image qualities and unexpected clouds. A new method is proposed to efficiently extract prominent lake candidates with irregular shapes, heterogeneous backgrounds, and in cloudy images. The proposed system fully automatize the procedure that track lakes with high accuracy. We further cooperated with geoscientists to examine the tracked lakes and found new scientific findings. The second one is detecting obscene content in on-line video chat services, such as Chatroulette, that randomly match pairs of users in video chat sessions. A big problem encountered in such systems is the presence of flashers and obscene content. Because of various obscene content and unstable qualities of videos capture by home web-camera, detecting misbehaving users is a highly challenging task. We propose SafeVchat, which is the first solution that achieves satisfactory

  11. Volume higher; spot price ranges widen

    International Nuclear Information System (INIS)

    Anon.

    1994-01-01

    This article is the October 1994 uranium market summary. During this reporting period, volume on the spot concentrates market doubled. Twelve deals took place: three in the spot concentrates market, one in the medium and long-term market, four in the conversion market, and four in the enrichment market. The restricted price range widened due to higher prices at the top end of the range, while the unrestricted price range widened because of lower prices at the bottom end. Spot conversion prices were higher, and enrichment prices were unchanged

  12. Clinical features and imaging of central poststroke pain

    Directory of Open Access Journals (Sweden)

    Ramesh Bhattacharyya

    2016-01-01

    Full Text Available Introduction: Central post stroke pain is a variety of neuropathic pain that occurs after stroke as a result of dysfunction of either spino-thalamic tract or thalamo-cortical sensory pathway. Hyperirritability in surviving cells along the affected pain pathways found with changes in inhibitory pathways, spinal and cortical reorganization and central sensitization. Aim: Clinical features like character of pain and other sensory features with neuroimaging findings of central post stroke pain for a part of Indian population were analyzed in this study. Materials and Method including analysis: 120 numbers of patients, who developed new onset pain symptoms after stroke, attending outpatient and inpatient department of a neurology department during a whole year were examined with history including extensive sensory symptoms analysis; sensory examinations including assessment of pain score and other neurological examinations were done and rechecked by neurologists. All were investigated by neuroimaging with either MRI or CT scan or both. Neuro imaging was interpreted by experienced neuroradiologist and corroborated by neurologists and pain physician. Results: 45% of the lesions were in Thalamus when 75% of the lesions were detected as infarction. 57.5% symptoms started within 3 months. Ataxia found with 60%, increased threshold to warm and cold were seen in 40% of patients, burning sensation was seen in 40% followed by numbness with 20%, dysesthesia found with 60%, reduced sensation to temperature changes found with 40% patients. Conclusion: CPSP patients may presents with various sensory symptoms beside pain. Distribution of sensory symptoms may be with any part of the body as well as over one half of the body. Most common trigger factor was mechanical; while thalamic lesions found in 45%, extra thalamic lesions werefound with 55% of patients.

  13. Fast hybrid fractal image compression using an image feature and neural network

    International Nuclear Information System (INIS)

    Zhou Yiming; Zhang Chao; Zhang Zengke

    2008-01-01

    Since fractal image compression could maintain high-resolution reconstructed images at very high compression ratio, it has great potential to improve the efficiency of image storage and image transmission. On the other hand, fractal image encoding is time consuming for the best matching search between range blocks and domain blocks, which limits the algorithm to practical application greatly. In order to solve this problem, two strategies are adopted to improve the fractal image encoding algorithm in this paper. Firstly, based on the definition of an image feature, a necessary condition of the best matching search and FFC algorithm are proposed, and it could reduce the search space observably and exclude most inappropriate domain blocks according to each range block before the best matching search. Secondly, on the basis of FFC algorithm, in order to reduce the mapping error during the best matching search, a special neural network is constructed to modify the mapping scheme for the subblocks, in which the pixel values fluctuate greatly (FNFC algorithm). Experimental results show that the proposed algorithms could obtain good quality of the reconstructed images and need much less time than the baseline encoding algorithm

  14. Imaging features of anterior cruciate ligament reconstruction graft insufficiency

    International Nuclear Information System (INIS)

    Shang Yao; Zhang Yue; Tian Chunyan; Zheng Zhuozhao

    2011-01-01

    Objective: To investigate the imaging features of anterior cruciate ligament (ACL) graft insufficiency. Methods: X-Ray and MR imaging examinations in 24 consecutive patients who had ACL reconstructive graft insufficiency were retrospectively evaluated for tunnel position, osteoarthrosis and its related complications. Follow-up arthroscopy showed 16 graft tears and 8 graft laxities. Fisher exact test was used to compare tunnel malpositions, the proportion of graft tear on MRI and osteoarthrosis between graft tear group and graft laxity group. Results: Two malpositions of tibial tunnel and 3 malpositions of femoral tunnel were seen in graft tear group. Three-malpositions of tibial tunnel and 4 malpositions of femoral tunnel were seen in graft laxity group. The proportion of tibial or femoral malposition showed no significant difference between the two groups (P=0.289, P=0.167). In graft tear group, 15 complete graft tears were diagnosed correctly, 1 partial tear was misdiagnosed as normal on MRI. In graft laxity group, 4 grafts were diagnosed as normal and 4 were considered as graft tear on MRI. A significant difference was seen between the two groups (P=0.028) in the proportion of graft tear diagnosed on MRI. Fourteen osteoarthrosis were seen in graft tear group and 5 in graft laxity group. No significant difference was seen between the two groups (P= 0.289) in the proportion of osteoarthrosis. Conclusion: The proportions of tunnel malposition and osteoarthrosis showed no significant difference between the graft tear group and graft Laxity group. Most graft tears can be diagnosed accurately on MRI, but some cases of graft laxity may be misdiagnosed for graft tear. (authors)

  15. W-transform method for feature-oriented multiresolution image retrieval

    Energy Technology Data Exchange (ETDEWEB)

    Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

    1995-07-01

    Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

  16. Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

    International Nuclear Information System (INIS)

    Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun

    2016-01-01

    The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process

  17. Diabetic mastopathy: Imaging features and the role of image-guided biopsy in its diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jong Hyeon; Kim, Eun Kyung; Kim, Min Jung; Moon, Hee Jung; Yoon, Jung Hyun [Dept. of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-03-15

    The goal of this study was to evaluate the imaging features of diabetic mastopathy (DMP) and the role of image-guided biopsy in its diagnosis. Two experienced radiologists retrospectively reviewed the mammographic and sonographic images of 19 pathologically confirmed DMP patients. The techniques and results of the biopsies performed in each patient were also reviewed. Mammograms showed negative findings in 78% of the patients. On ultrasonography (US), 13 lesions were seen as masses and six as non-mass lesions. The US features of the mass lesions were as follows: irregular shape (69%), oval shape (31%), indistinct margin (69%), angular margin (15%), microlobulated margin (8%), well-defined margin (8%), heterogeneous echogenicity (62%), hypoechoic echogenicity (38%), posterior shadowing (92%), parallel orientation (100%), the absence of calcifications (100%), and the absence of vascularity (100%). Based on the US findings, 17 lesions (89%) were classified as Breast Imaging Reporting and Data System category 4 and two (11%) as category 3. US-guided core biopsy was performed in 18 patients, and 10 (56%) were diagnosed with DMP on that basis. An additional vacuum-assisted biopsy was performed in seven patients and all were diagnosed with DMP. The US features of DMP were generally suspicious for malignancy, whereas the mammographic findings were often negative or showed only focal asymmetry. Core biopsy is an adequate method for initial pathological diagnosis. However, since it yields non-diagnostic results in a considerable number of cases, the evaluation of correlations between imaging and pathology plays an important role in the diagnostic process.

  18. MR imaging of the neonatal brain: Pathologic features

    International Nuclear Information System (INIS)

    McArdle, C.B.; Richardson, C.J.; Nicholas, D.A.; Hayden, C.K.; Amparo, E.G.

    1986-01-01

    Seventy-three neonates, aged 29-43 weeks since conception, were studied. US and/or CT correlations were obtained in most infants with pathology. In the first 4-5 days after hemorrhage, US and CT were superior to MR imaging, but after that time MR imaging was the single best modality for imaging blood. In early premature infants with very watery white matter, US detected infarction and brain edema that were poorly seen on both MR imaging and CT. However, in late premature and full-term infants, MR imaging was better than CT in distinguishing between normal white matter and infarction. Only MR imaging disclosed delayed myelination in 13 term infants with hydrocephalus and severe asphyxia. MR imaging with play an important role in imaging neonates once MR imaging-compatible monitors and neonatal head coils become widely available

  19. Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

    Directory of Open Access Journals (Sweden)

    Keranmu Xielifuguli

    2014-01-01

    Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

  20. Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

    Directory of Open Access Journals (Sweden)

    Xian-Hua Han

    2011-01-01

    extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

  1. A Modified Image Comparison Algorithm Using Histogram Features

    OpenAIRE

    Al-Oraiqat, Anas M.; Kostyukova, Natalya S.

    2018-01-01

    This article discuss the problem of color image content comparison. Particularly, methods of image content comparison are analyzed, restrictions of color histogram are described and a modified method of images content comparison is proposed. This method uses the color histograms and considers color locations. Testing and analyzing of based and modified algorithms are performed. The modified method shows 97% average precision for a collection containing about 700 images without loss of the adv...

  2. Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

    Directory of Open Access Journals (Sweden)

    Hui Huang

    2017-01-01

    Full Text Available According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

  3. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    Science.gov (United States)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  4. Extended local binary pattern features for improving settlement type classification of quickbird images

    CSIR Research Space (South Africa)

    Mdakane, L

    2012-11-01

    Full Text Available Despite the fact that image texture features extracted from high-resolution remotely sensed images over urban areas have demonstrated their ability to distinguish different classes, they are still far from being ideal. Multiresolution grayscale...

  5. Learning Rich Features from RGB-D Images for Object Detection and Segmentation

    OpenAIRE

    Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

    2014-01-01

    In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

  6. Neural network-based feature point descriptors for registration of optical and SAR images

    Science.gov (United States)

    Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

    2018-04-01

    Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

  7. Focal hepatic lesions with peripheral eosinophilia: imaging features of various disease

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Joon Beom; Han, Joon Koo; Kim, Tae Kyoung; Choi, Byung Ihn; Han, Man Chung [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of); Song, Chi Sung [Seoul City Boramae Hospital, Seoul (Korea, Republic of)

    1999-01-01

    Due to the recent advent of various imaging modalities such as ultrasonography, computed tomography and magnetic resonance imaging, as well as knowledge of the characteristic imaging features of hepatic lesions, radiologic examination plays a major role in the differential diagnosis of focal hepatic lesions. However, various 'nonspecific' or 'unusual' imaging features of focal hepatic lesions are occasionally encountered, and this makes correct diagnosis difficult. In such a situation, the presence of peripheral eosinophilia helps narrow the differential diagnoses. The aim of this pictorial essay is to describe the imaging features of various disease entities which cause focal hepatic lesions and peripheral eosinophilia.

  8. Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

    Science.gov (United States)

    Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

    2018-04-01

    Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

  9. Image mosaicking based on feature points using color-invariant values

    Science.gov (United States)

    Lee, Dong-Chang; Kwon, Oh-Seol; Ko, Kyung-Woo; Lee, Ho-Young; Ha, Yeong-Ho

    2008-02-01

    In the field of computer vision, image mosaicking is achieved using image features, such as textures, colors, and shapes between corresponding images, or local descriptors representing neighborhoods of feature points extracted from corresponding images. However, image mosaicking based on feature points has attracted more recent attention due to the simplicity of the geometric transformation, regardless of distortion and differences in intensity generated by camera motion in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a real digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

  10. Diffusion-weighted imaging features in spinal cord infarction

    International Nuclear Information System (INIS)

    Zhang Jingsong; Huan Yi; Sun Lijun; Chang Yingjuan; Zhao Haitao; Yang Chunmin; Zhang Guangyun

    2005-01-01

    Objective: To analyze the diffusion-weighted MR imaging findings in ischemic spinal cord lesions and discuss the value of diffusion-weighted MR imaging in differentiating diagnosis with inflammatory diseases and tumors. Methods: Six patients (2 male, 4 female) with typical sudden onset of neurological deficits caused by spinal cord ischemia were evaluated. There were no definite etiologies in all patients. DW imaging was performed within 1 to 30 days after the initial neurological symptoms using a Philips Gyroscan 1.5 TMR system. Four patients had other scans including contrast-enhanced MR imaging (CE-MRI) and/or FLAIR scans. Two of them followed up with MR images in three months. All six patients were imaged using a multi-shot, navigator-corrected, echo-planar pulse sequence, and ADC values were calculated in sagittal-oriented plane. Results: MR abnormalities were demonstrated on sagittal T 2 -weighted images with 'patch-like' or 'strip-like' hyperintensities (6/6) and cord enlargement (5/6). Axial T 2 -weighted images showed bilateral (6/6) hyperintensities. In one patient only the posterior spinal artery (PSA) territory was involved. Spinal cord was mainly affected at the cervical (2/6) and thoracolumbar (4/6) region, two of them included the conus medullaris (T10-L1). DW images showed high signals in all infarct lesions, degree of intensity depended on scanning time from ill-onset and progress of illness and whether companied with hemorrhage. In this group, except one case with closely normal ADC value due to one month course of illness, the five others ADC values of lesions calculated from ADC maps arranged from 0.23 x 10 -3 mm 2 /s to 0.47 x 10 -3 mm 2 /s [average value (0.37 ± 0.10) x 10 -3 mm 2 /s], markedly lower than normal parts [ average value (0.89 ± 0.08) x 10 -3 mm 2 /s]. There were marked difference between lesions and normal regions (t=4.71, P 2 W images. Meanwhile, lesions could be displayed much better in DW images than in T 2 W images because

  11. A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Dhanoa Jaspreet Singh

    2016-01-01

    Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

  12. Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

    Science.gov (United States)

    Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

    2014-06-01

    To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Curvature histogram features for retrieval of images of smooth 3D objects

    International Nuclear Information System (INIS)

    Zhdanov, I; Scherbakov, O; Potapov, A; Peterson, M

    2014-01-01

    We consider image features on the base of histograms of oriented gradients (HOG) with addition of contour curvature histogram (HOG-CH), and also compare it with results of known scale-invariant feature transform (SIFT) approach in application to retrieval of images of smooth 3D objects.

  14. Oriented Edge-Based Feature Descriptor for Multi-Sensor Image Alignment and Enhancement

    Directory of Open Access Journals (Sweden)

    Myung-Ho Ju

    2013-10-01

    Full Text Available In this paper, we present an efficient image alignment and enhancement method for multi-sensor images. The shape of the object captured in a multi-sensor images can be determined by comparing variability of contrast using corresponding edges across multi-sensor image. Using this cue, we construct a robust feature descriptor based on the magnitudes of the oriented edges. Our proposed method enables fast image alignment by identifying matching features in multi-sensor images. We enhance the aligned multi-sensor images through the fusion of the salient regions from each image. The results of stitching the multi-sensor images and their enhancement demonstrate that our proposed method can align and enhance multi-sensor images more efficiently than previous methods.

  15. No-reference image quality assessment based on statistics of convolution feature maps

    Science.gov (United States)

    Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

    2018-04-01

    We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

  16. Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

    Science.gov (United States)

    Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

    2018-04-01

    In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

  17. The ship edge feature detection based on high and low threshold for remote sensing image

    Science.gov (United States)

    Li, Xuan; Li, Shengyang

    2018-05-01

    In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

  18. Modeling multiple visual words assignment for bag-of-features based medical image retrieval

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-01-01

    In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

  19. Modeling multiple visual words assignment for bag-of-features based medical image retrieval

    KAUST Repository

    Wang, Jim Jing-Yan; Almasri, Islam

    2012-01-01

    In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

  20. COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

    Directory of Open Access Journals (Sweden)

    K. Seetharaman

    2015-08-01

    Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

  1. Global image feature extraction using slope pattern spectra

    CSIR Research Space (South Africa)

    Toudjeu, IT

    2008-06-01

    Full Text Available Traditionally, granulometries are obtained using a series of openings or closings with convex structuring elements of increasing size. Granulometries constitute a useful tool for texture and image analysis since they are used to characterize size...

  2. Caroli's disease: magnetic resonance imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Guy, France; Cognet, Francois; Dranssart, Marie; Cercueil, Jean-Pierre; Conciatori, Laurent; Krause, Denis [Department of Radiology and Imaging, Dijon Le Bocage University Hospital, 2 Blvd. Marechal de Lattre de Tassigny, BP 1542, 21034 Dijon Cedex (France)

    2002-11-01

    Our objective was to describe the main aspects of MR imaging in Caroli's disease. Magnetic resonance cholangiography with a dynamic contrast-enhanced study was performed in nine patients with Caroli's disease. Bile duct abnormalities, lithiasis, dot signs, hepatic enhancement, renal abnormalities, and evidence of portal hypertension were evaluated. Three MR imaging patterns of Caroli's disease were found. In all but two patients, MR imaging findings were sufficient to confirm the diagnosis. Moreover, MR imaging provided information about the severity, location, and extent of liver involvement. This information was useful in planning the best therapeutic strategy. Magnetic resonance cholangiography with a dynamic contrast-enhanced study is a good screening tool for Caroli's disease. Direct cholangiography should be reserved for confirming doubtful cases. (orig.)

  3. Classified study and clinical value of the phase imaging features

    International Nuclear Information System (INIS)

    Dang Yaping; Ma Aiqun; Zheng Xiaopu; Yang Aimin; Xiao Jiang; Gao Xinyao

    2000-01-01

    445 patients with various heart diseases were examined by the gated cardiac blood pool imaging, and the phase was classified. The relationship between the seven types with left ventricular function index, clinical heart function, different heart diseases as well as electrocardiograph was studied. The results showed that the phase image classification could match with the clinical heart function. It can visually, directly and accurately indicate clinical heart function and can be used to identify diagnosis of heart disease

  4. New feature of the neutron color image intensifier

    Science.gov (United States)

    Nittoh, Koichi; Konagai, Chikara; Noji, Takashi; Miyabe, Keisuke

    2009-06-01

    We developed prototype neutron color image intensifiers with high-sensitivity, wide dynamic range and long-life characteristics. In the prototype intensifier (Gd-Type 1), a terbium-activated Gd 2O 2S is used as the input-screen phosphor. In the upgraded model (Gd-Type 2), Gd 2O 3 and CsI:Na are vacuum deposited to form the phosphor layer, which improved the sensitivity and the spatial uniformity. A europium-activated Y 2O 2S multi-color scintillator, emitting red, green and blue photons with different intensities, is utilized as the output screen of the intensifier. By combining this image intensifier with a suitably tuned high-sensitive color CCD camera, higher sensitivity and wider dynamic range could be simultaneously attained than that of the conventional P20-phosphor-type image intensifier. The results of experiments at the JRR-3M neutron radiography irradiation port (flux: 1.5×10 8 n/cm 2/s) showed that these neutron color image intensifiers can clearly image dynamic phenomena with a 30 frame/s video picture. It is expected that the color image intensifier will be used as a new two-dimensional neutron sensor in new application fields.

  5. New feature of the neutron color image intensifier

    International Nuclear Information System (INIS)

    Nittoh, Koichi; Konagai, Chikara; Noji, Takashi; Miyabe, Keisuke

    2009-01-01

    We developed prototype neutron color image intensifiers with high-sensitivity, wide dynamic range and long-life characteristics. In the prototype intensifier (Gd-Type 1), a terbium-activated Gd 2 O 2 S is used as the input-screen phosphor. In the upgraded model (Gd-Type 2), Gd 2 O 3 and CsI:Na are vacuum deposited to form the phosphor layer, which improved the sensitivity and the spatial uniformity. A europium-activated Y 2 O 2 S multi-color scintillator, emitting red, green and blue photons with different intensities, is utilized as the output screen of the intensifier. By combining this image intensifier with a suitably tuned high-sensitive color CCD camera, higher sensitivity and wider dynamic range could be simultaneously attained than that of the conventional P20-phosphor-type image intensifier. The results of experiments at the JRR-3M neutron radiography irradiation port (flux: 1.5x10 8 n/cm 2 /s) showed that these neutron color image intensifiers can clearly image dynamic phenomena with a 30 frame/s video picture. It is expected that the color image intensifier will be used as a new two-dimensional neutron sensor in new application fields.

  6. DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    S. Eken

    2017-11-01

    Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  7. Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

    Science.gov (United States)

    Eken, S.; Aydın, E.; Sayar, A.

    2017-11-01

    In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  8. US or MR Imaging Features of Polypoid Endometriosis: A Case Report

    International Nuclear Information System (INIS)

    Park, Jae Il; Cho, Jae Ho; Kim, Geum Rae; Kim, Mi Jin

    2009-01-01

    Polypoid endometriosis is a rare variant of endometriosis that is pathologically similar to an endometrial polyp. This lesion is frequently mistaken for a solid neoplasm in clinical, radiological and pathological examinations. The clinical and pathological features of the lesion have been well described in the English literature. However, its imaging features have not been reported in the Korean literature. We describe ultrasound and magnetic resonance imaging features of pathologically-confirmed polypoid endometriosis

  9. US or MR Imaging Features of Polypoid Endometriosis: A Case Report

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jae Il; Cho, Jae Ho; Kim, Geum Rae; Kim, Mi Jin [Yeungnam University, Gyeongsan (Korea, Republic of)

    2009-12-15

    Polypoid endometriosis is a rare variant of endometriosis that is pathologically similar to an endometrial polyp. This lesion is frequently mistaken for a solid neoplasm in clinical, radiological and pathological examinations. The clinical and pathological features of the lesion have been well described in the English literature. However, its imaging features have not been reported in the Korean literature. We describe ultrasound and magnetic resonance imaging features of pathologically-confirmed polypoid endometriosis.

  10. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

    DEFF Research Database (Denmark)

    Galavis, P.E.; Hollensen, Christian; Jallow, N.

    2010-01-01

    Background. Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes...... reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results. Fifty textural features were...... classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range 30%). Conclusion. Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small...

  11. Electrical imaging of deep crustal features of Kutch, India

    Science.gov (United States)

    Sastry, R. S.; Nagarajan, Nandini; Sarma, S. V. S.

    2008-03-01

    A regional Magnetotelluric (MT) study, was carried out with 55 MT soundings, distributed along five traverses, across the Kutch Mainland Unit (KMU), on the west coast of India, a region characterized by a series of successive uplifts and intervening depressions in the form of half graben, bounded by master faults. We obtain the deeper electrical structure of the crust beneath Kutch, from 2-D modelling of MT data along the 5 traverses, in order to evaluate the geo-electrical signatures, if any, of the known primary tectonic structures in this region. The results show that the deeper electrical structure in the Kutch region presents a mosaic of high resistive crustal blocks separated by deep-rooted conductive features. Two such crustal conductive features spatially correlate with the known tectonic features, viz., the Kutch Mainland Fault (KMF), and the Katrol Hill Fault (KHF). An impressive feature of the geo-electrical sections is an additional, well-defined conductive feature, running between Jakhau and Mundra, located at the southern end of each of the five MT traverses and interpreted to be the electrical signature of yet another hidden fault at the southern margin of the KMU. This new feature is named as Jakhau-Mundra Fault (JMF). It is inferred that the presence of JMF together with the Kathiawar Fault (NKF), further south, located at the northern boundary of the Saurashtra Horst, would enhance the possibility of occurrence of a thick sedimentary column in the Gulf of Kutch. The region between the newly delineated fault (JMF) and the Kathiawar fault (NKF) could thus be significant for Hydrocarbon Exploration.

  12. Generating description with multi-feature fusion and saliency maps of image

    Science.gov (United States)

    Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

    2018-04-01

    Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

  13. Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

    OpenAIRE

    Engelke, Ulrich; Zepernick, Hans-Jürgen

    2007-01-01

    This paper addresses the evaluation of image quality in the context of wireless systems using feature-based objective metrics. The considered metrics comprise of a weighted combination of feature values that are used to quantify the extend by which the related artifacts are present in a processed image. In view of imaging applications in mobile radio and wireless communication systems, reduced-reference objective quality metrics are investigated for quantifying user-perceived quality. The exa...

  14. MR imaging features of foot involvement in patients with psoriasis

    Energy Technology Data Exchange (ETDEWEB)

    Erdem, C. Zuhal [Department of Radiology, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey)], E-mail: sunarerdem@yahoo.com; Tekin, Nilgun Solak [Department of Dermatology, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey); Sarikaya, Selda [Department of Physical Therapy and Rehabilitation, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey); Erdem, L. Oktay; Gulec, Sezen [Department of Radiology, Zonguldak Karaelmas University, School of Medicine, Zonguldak (Turkey)

    2008-09-15

    Objective: To determine alterations of the soft tissues, tendons, cartilage, joint spaces, and bones of the foot using magnetic resonance (MR) imaging in patients with psoriasis. Materials and methods: Clinical and MR examination of the foot was performed in 26 consecutive patients (52 ft) with psoriasis. As a control group, 10 healthy volunteers (20 ft) were also studied. Joint effusion/synovitis, retrocalcaneal bursitis, retroachilles bursitis, Achilles tendonitis, soft-tissue edema, para-articular enthesophytes, bone marrow edema, sinus tarsi syndrome, enthesopathy at the Achilles attachment and at the plantar fascia attachment, plantar fasciitis, tenosynovitis, subchondral cysts, and bone erosions, joint space narrowing, subchondral signal changes, osteolysis, luxation, and sub-luxation were examined. Results: Clinical signs and symptoms (pain and swelling) due to foot involvement were present in none of the patients while frequency of involvement was 92% (24/26) by MR imaging. The most common MR imaging findings were Achilles tendonitis (acute and peritendinitis) (57%), retrocalcaneal bursitis (50%), joint effusion/synovitis (46%), soft-tissue edema (46%), and para-articular enthesophytes (38%). The most commonly involved anatomical region was the hindfoot (73%). Conclusion: Our data showed that the incidence of foot involvement was very high in asymptomatic patients with psoriasis on MR imaging. Further MR studies are needed to confirm these data. We conclude that MR imaging may be of importance especially in early diagnosis and treatment of inflammatory changes in the foot.

  15. MR imaging features of foot involvement in patients with psoriasis

    International Nuclear Information System (INIS)

    Erdem, C. Zuhal; Tekin, Nilgun Solak; Sarikaya, Selda; Erdem, L. Oktay; Gulec, Sezen

    2008-01-01

    Objective: To determine alterations of the soft tissues, tendons, cartilage, joint spaces, and bones of the foot using magnetic resonance (MR) imaging in patients with psoriasis. Materials and methods: Clinical and MR examination of the foot was performed in 26 consecutive patients (52 ft) with psoriasis. As a control group, 10 healthy volunteers (20 ft) were also studied. Joint effusion/synovitis, retrocalcaneal bursitis, retroachilles bursitis, Achilles tendonitis, soft-tissue edema, para-articular enthesophytes, bone marrow edema, sinus tarsi syndrome, enthesopathy at the Achilles attachment and at the plantar fascia attachment, plantar fasciitis, tenosynovitis, subchondral cysts, and bone erosions, joint space narrowing, subchondral signal changes, osteolysis, luxation, and sub-luxation were examined. Results: Clinical signs and symptoms (pain and swelling) due to foot involvement were present in none of the patients while frequency of involvement was 92% (24/26) by MR imaging. The most common MR imaging findings were Achilles tendonitis (acute and peritendinitis) (57%), retrocalcaneal bursitis (50%), joint effusion/synovitis (46%), soft-tissue edema (46%), and para-articular enthesophytes (38%). The most commonly involved anatomical region was the hindfoot (73%). Conclusion: Our data showed that the incidence of foot involvement was very high in asymptomatic patients with psoriasis on MR imaging. Further MR studies are needed to confirm these data. We conclude that MR imaging may be of importance especially in early diagnosis and treatment of inflammatory changes in the foot

  16. Imaging features of intraductal papillary neoplasm of the bile duct

    International Nuclear Information System (INIS)

    Liu Yubao; Li Meng; Zhong Xiaomei; Liu Zaiyi; Liang Changhong

    2014-01-01

    Objective: To investigate the CT and MRI features of intraductal papillary neoplasm of the bile duct (IPNB). Methods: Thirty eight patients with IPNB finally diagnosed by puncture biopsy or surgery were enrolled in this study. All the CT or MRI data were investigated retrospectively. Twenty one patients underwent CT examinations, 17 patients underwent MRI examinations. The features of IPNB including the distribution features of the nodules or masses, CT and MRI features of cholangiectasis, mucus were analyzed. The accuracy differences of CT and MRI for the preoperatively diagnosing mucus and tumor growing along mucous were compared by nonparametric test. Results: The lesions (including 5 patients with solitary lesions and 19 patients with multiple lesions) were located in intrahepatic bile duct in 24 patients, 3 patients occurred simultaneously in intrahepatic and portal bile duct, 2 lesions occurred in portal bile duct, 8 lesions occurred in common bile duct, the lesions of 1 patient occurred simultaneously in common bile duct, cystic duct and gallbladder. Seventeen and 11 patients appeared nodules locating in dilated bile duct on CT and MRI, respectively. Four and 5 patients appeared cystic lesions with multiple nodules of the liver on CT and MRI, respectively. Higher contrast enhancement on CT and MRI in arterial phase than that in portal vein and equilibrium phase were observed in 18 and 12 patients, respectively. Excluding the patients undergoing puncture, CT was better than MRI in evaluating whether the mucus was present, with the accuracies of 30.0% (6/20) and 6.3% (1/16) for CT and MRI, respectively (Z=2.58, P<0.05). CT was worse than MRI in preoperatively evaluating the features of tumor growing along mucous, with the accuracies of 77.8% (14/18) and 92.6% (13/14) for CT and MRI, respectively (Z=4.23, P<0.01). Conclusion: IPNB had the features of growing along mucous of the bile duct, nodule or mass in dilated bile duct and other features, CT and MRI are

  17. Blind image quality assessment based on aesthetic and statistical quality-aware features

    Science.gov (United States)

    Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

    2017-07-01

    The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

  18. Image Retrieval based on Integration between Color and Geometric Moment Features

    International Nuclear Information System (INIS)

    Saad, M.H.; Saleh, H.I.; Konbor, H.; Ashour, M.

    2012-01-01

    Content based image retrieval is the retrieval of images based on visual features such as colour, texture and shape. .the Current approaches to CBIR differ in terms of which image features are extracted; recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. content-based image retrieval has many application areas such as, education, commerce, military, searching, commerce, and biomedicine and Web image classification. This paper proposes a new image retrieval system, which uses color and geometric moment feature to form the feature vectors. Bhattacharyya distance and histogram intersection are used to perform feature matching. This framework integrates the color histogram which represents the global feature and geometric moment as local descriptor to enhance the retrieval results. The proposed technique is proper for precisely retrieving images even in deformation cases such as geometric deformations and noise. It is tested on a standard the results shows that a combination of our approach as a local image descriptor with other global descriptors outperforms other approaches.

  19. Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT

    Directory of Open Access Journals (Sweden)

    Boris Jutzi

    2011-09-01

    Full Text Available The real and imaginary parts are proposed as an alternative to the usual Polar representation of complex-valued images. It is proven that the transformation from Polar to Cartesian representation contributes to decreased mutual information, and hence to greater distinctiveness. The Complex Scale-Invariant Feature Transform (ℂSIFT detects distinctive features in complex-valued images. An evaluation method for estimating the uniformity of feature distributions in complex-valued images derived from intensity-range images is proposed. In order to experimentally evaluate the proposed methodology on intensity-range images, three different kinds of active sensing systems were used: Range Imaging, Laser Scanning, and Structured Light Projection devices (PMD CamCube 2.0, Z+F IMAGER 5003, Microsoft Kinect.

  20. Iris image enhancement for feature recognition and extraction

    CSIR Research Space (South Africa)

    Mabuza, GP

    2012-10-01

    Full Text Available the employment of other algorithms and commands so as to better present and demonstrate the obtained results. Edge detection and enhancing images for use in an iris recognition system allow for efficient recognition and extraction of iris patterns. REFERENCES... Gonzalez, R.C. and Woods, R.E. 2002. Digital Image Processing 2nd Edition, Instructor?s manual .Englewood Cliffs, Prentice Hall, pp 17-36. Proen?a, H. and Alexandre, L.A. 2007. Toward Noncooperative Iris Recognition: A classification approach using...

  1. Interpretation of archaeological small-scale features in spectral images

    DEFF Research Database (Denmark)

    Grøn, Ole; Palmer, Susanna; Stylegar, Frans-Arne

    2011-01-01

    The paper's focus is the use of spectral images for the distinction of small archaeological anomalies on the basis of the authors work. Special attention is given to the ground-truthing perspective in the discussion of a number of cases from Norway. Different approaches to pattern-recognition are......The paper's focus is the use of spectral images for the distinction of small archaeological anomalies on the basis of the authors work. Special attention is given to the ground-truthing perspective in the discussion of a number of cases from Norway. Different approaches to pattern...

  2. Features Speech Signature Image Recognition on Mobile Devices

    Directory of Open Access Journals (Sweden)

    Alexander Mikhailovich Alyushin

    2015-12-01

    Full Text Available The algorithms fordynamic spectrograms images recognition, processing and soundspeech signature (SS weredeveloped. The software for mobile phones, thatcan recognize speech signatureswas prepared. The investigation of the SS recognition speed on its boundarytypes was conducted. Recommendations on the boundary types choice in the optimal ratio of recognitionspeed and required space were given.

  3. Posteromedial impingement (POMI) of the ankle: MR imaging features

    International Nuclear Information System (INIS)

    Koulouris, G.; Connell, D.

    2002-01-01

    Full text: The purpose of this study is to describe the normal MR Imaging appearance of the supporting ligaments of the first carpometacarpal joint in asymptomatic volunteers and the ligamentous injury in patients following dislocation. Three healthy volunteers underwent MR Imaging of the first carpometacarpal joint in order to describe the normal ligaments and optimise the scanning technique for assessment of this joint. Six patients underwent MR Imaging after injury to the ligament. All patients were injured after a fall in which the thumb was subluxed during hyperextension.The Anterior Oblique Ligament (AOL) was evaluated for abnormal morphology and signal intensity, including the site of the injury and the degree of tearing. Joint alignment was assessed for instability. The four ligaments felt to contribute to the stability of the first joint were reliably identified. The normal ligament is a continuous band of low signal running from one bone attachment to another. With injury, there is disruption of the ligament, hyperintensity and perforated stripping. The AOL was shown to be consistently injured after dislocation close to the metacarpal side. MR Imaging allows accurate evaluation of injuries of the AOL of the first carpometacarpal joint and identifies patients who would benefit from surgical reconstruction. Copyright (2002) Blackwell Science Pty Ltd

  4. Olfactory and imaging features in atypical Alzheimer’s disease

    Directory of Open Access Journals (Sweden)

    Huihong Zhang

    2018-02-01

    Full Text Available Cognition and speech disorders are the most common symptoms of dementia in neurodegenerative disease. Here, we present a detailed clinical evaluation of a case of logopenic variant of primary progressive aphasia (lv-PPA, an atypical form of Alzheimer disease (AD, including cognitive testing over time, brain imaging, electrophysiology, and tests of olfactory function.

  5. Clinical Features and Patterns of Imaging in Cerebral Venous Sinus ...

    African Journals Online (AJOL)

    Background: Cerebral venous sinus thrombosis (CVST) is an uncommon neurological deficit. It shows a wide range of clinical manifestations that may mimic many other neurological disorders and lead to misdiagnosis. Imaging plays a key role in the diagnosis. Objective: To evaluate the clinical characteristics and patterns ...

  6. MR imaging features of foot involvement in ankylosing spondylitis

    Energy Technology Data Exchange (ETDEWEB)

    Erdem, C. Zuhal E-mail: sunarerdem@yahoo.com; Sarikaya, Selda; Erdem, L. Oktay; Ozdolap, Senay; Gundogdu, Sadi

    2005-01-01

    Objective: To determine alterations of the soft tissue, tendon, cartilage, joint space, and bone of the foot using magnetic resonance (MR) imaging in ankylosing spondylitis (AS) patients. Materials and Method: Clinical and MR examination of the foot was performed in 23 AS patients (46 feet). Ten asymptomatic volunteers (20 feet) were studied on MR imaging, as a control group. MR imaging protocol included; T1-weighted spin-echo, T2-weighted fast-field echo (FFE) and fat-suppressed short tau inversion recovery (STIR) sequences in sagittal, sagittal oblique, and coronal planes using a head coil. Specifically, we examined: bone erosions, tendinitis (acute and chronic), para-articular enthesophyte, joint effusion, plantar fasciitis, joint space narrowing, soft tissue edema, bone marrow edema, enthesopathy in the Achilles tendon and plantar fascia attachment, subchondral signal intensity abnormalities (edema and sclerosis), tenosynovitis, retrocalcaneal bursitis, subchondral cysts, subchondral fissures, and bony ankylosis. Midfoot, hindfoot, and ankle were included in examined anatomic regions. Results: Clinical signs and symptoms (pain and swelling) due to foot involvement were present in 3 (13%) of the patients while frequency of involvement was 21 (91%) with MR imaging assessment. The MR imaging findings were bone erosions (65%), Achilles tendinitis (acute and chronic) (61%), para-articular enthesophyte (48%), joint effusion (43%), plantar fasciitis (40%), joint space narrowing (40%), subchondral sclerosis (35%), soft tissue edema (30%), bone marrow edema (30%), enthesopathy of the Achilles attachment (30%), subchondral edema (26%), enthesopathy in the plantar fascia attachment (22%), retrocalcaneal bursitis (22%), subchondral cysts (17%), subchondral fissures (17%), tendinitis and enthesopathy of the plantar ligament (13%), and bony ankylosis (9%). The most common involved anatomical region was the hindfoot (83%) following by midfoot (69% ) and ankle (22

  7. MR imaging features of foot involvement in ankylosing spondylitis

    International Nuclear Information System (INIS)

    Erdem, C. Zuhal; Sarikaya, Selda; Erdem, L. Oktay; Ozdolap, Senay; Gundogdu, Sadi

    2005-01-01

    Objective: To determine alterations of the soft tissue, tendon, cartilage, joint space, and bone of the foot using magnetic resonance (MR) imaging in ankylosing spondylitis (AS) patients. Materials and Method: Clinical and MR examination of the foot was performed in 23 AS patients (46 feet). Ten asymptomatic volunteers (20 feet) were studied on MR imaging, as a control group. MR imaging protocol included; T1-weighted spin-echo, T2-weighted fast-field echo (FFE) and fat-suppressed short tau inversion recovery (STIR) sequences in sagittal, sagittal oblique, and coronal planes using a head coil. Specifically, we examined: bone erosions, tendinitis (acute and chronic), para-articular enthesophyte, joint effusion, plantar fasciitis, joint space narrowing, soft tissue edema, bone marrow edema, enthesopathy in the Achilles tendon and plantar fascia attachment, subchondral signal intensity abnormalities (edema and sclerosis), tenosynovitis, retrocalcaneal bursitis, subchondral cysts, subchondral fissures, and bony ankylosis. Midfoot, hindfoot, and ankle were included in examined anatomic regions. Results: Clinical signs and symptoms (pain and swelling) due to foot involvement were present in 3 (13%) of the patients while frequency of involvement was 21 (91%) with MR imaging assessment. The MR imaging findings were bone erosions (65%), Achilles tendinitis (acute and chronic) (61%), para-articular enthesophyte (48%), joint effusion (43%), plantar fasciitis (40%), joint space narrowing (40%), subchondral sclerosis (35%), soft tissue edema (30%), bone marrow edema (30%), enthesopathy of the Achilles attachment (30%), subchondral edema (26%), enthesopathy in the plantar fascia attachment (22%), retrocalcaneal bursitis (22%), subchondral cysts (17%), subchondral fissures (17%), tendinitis and enthesopathy of the plantar ligament (13%), and bony ankylosis (9%). The most common involved anatomical region was the hindfoot (83%) following by midfoot (69% ) and ankle (22

  8. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

    Science.gov (United States)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized

  9. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia

    2015-07-01

    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  10. Imaging features of gossypiboma: report of two cases.

    Directory of Open Access Journals (Sweden)

    Prasad S

    1999-01-01

    Full Text Available Recognition of postoperatively retained foreign body referred euphemistically as gossypiboma is essential but is very often considerably delayed. Legal implications as well as confusing configuration patterns cause considerable dilemma in the accurate diagnosis. We present computed tomographic features of gossypiboma in two patients who presented with symptoms of fever and pain in the immediate post-operative period. A prospective radiological diagnosis is essential for further management in these patients.

  11. A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

    Directory of Open Access Journals (Sweden)

    Pattichis Marios S

    2007-11-01

    Full Text Available Abstract Background In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i the distance from the tissue (panoramic vs close up, (ii difference in viewing angles and (iii color correction. Methods We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i a variety of testing targets from a color palette with a known color distribution, (ii different viewing angles, (iv two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i Statistical Features (SF, (ii Spatial Gray Level Dependence Matrices (SGLDM, and (iii Gray Level Difference Statistics (GLDS. All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better

  12. Radiomic features analysis in computed tomography images of lung nodule classification.

    Directory of Open Access Journals (Sweden)

    Chia-Hung Chen

    Full Text Available Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.Lung nodule classification using radiomics based on Computed Tomography (CT image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.

  13. Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

    Directory of Open Access Journals (Sweden)

    Soheila Gheisari

    2018-01-01

    Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

  14. Uniform competency-based local feature extraction for remote sensing images

    Science.gov (United States)

    Sedaghat, Amin; Mohammadi, Nazila

    2018-01-01

    Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

  15. [Penile enhancement surgery: widening and lengthening lipopenisculpture].

    Science.gov (United States)

    Abecassis, M; Berreby, S; Boccara, D

    2010-04-01

    The male genitalia cosmetic surgery matches an ever growing need. The aim of this study is to describe our penile lengthening and widening procedures, the benefits and complications that could result from it. We perform these procedures since 1992 and treated close to 2000 patients. Thanks to our expertise and after succeeding in standardizing our techniques, we achieved a retrospective survey on 103 patients operated between 2004 and 2006. We describe here the two processes of our operating technique and we analyse the results obtained 1 month, 3 months and 1 year after the surgery. In most cases, combining a penis adipose tissue grafting, a suspensory ligament section and an adequate skin plasty is proven to be necessary in order to obtain both lengthening and widening increases. One year later, the increase is about 2.7 cm in length, corresponding to 28%, and 2.6 cm in circumference, corresponding to 27%. The complications (cutaneous necrosis, haematoma, lymphoedema, disharmonies), whenever they may exist, are most of the time spontaneously resolutive. Combining a penile fat tissue grafting with a suspensory ligament section allows us to answer to most of patients' expectations. However, several liposculpturing sessions might be necessary in order to get satisfactory results. Copyright 2009 Elsevier Masson SAS. All rights reserved.

  16. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

    Science.gov (United States)

    Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

    2017-06-22

    In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

  17. TRADEMARK IMAGE RETRIEVAL USING LOW LEVEL FEATURE EXTRACTION IN CBIR

    OpenAIRE

    Latika Pinjarkar*, Manisha Sharma, Smita Selot

    2016-01-01

    Trademarks work as significant responsibility in industry and commerce. Trademarks are important component of its industrial property, and violation can have severe penalty. Therefore designing an efficient trademark retrieval system and its assessment for uniqueness is thus becoming very important task now a days. Trademark image retrieval system where a new candidate trademark is compared with already registered trademarks to check that there is no possibility of resembl...

  18. Bubble feature extracting based on image processing of coal flotation froth

    Energy Technology Data Exchange (ETDEWEB)

    Wang, F.; Wang, Y.; Lu, M.; Liu, W. [China University of Mining and Technology, Beijing (China). Dept of Chemical Engineering and Environment

    2001-11-01

    Using image processing the contrast ratio between the bubble on the surface of flotation froth and the image background was enhanced, and the edges of bubble were extracted. Thus a model about the relation between the statistic feature of the bubbles in the image and the cleaned coal can be established. It is feasible to extract the bubble by processing the froth image of coal flotation on the basis of analysing the shape of the bubble. By means of processing the 51 group images sampled from laboratory column, it is thought that the use of the histogram equalization of image gradation and the medium filtering can obviously improve the dynamic contrast range and the brightness of bubbles. Finally, the method of threshold value cut and the bubble edge detecting for extracting the bubble were also discussed to describe the bubble feature, such as size and shape, in the froth image and to distinguish the froth image of coal flotation. 6 refs., 3 figs.

  19. Comparison of femoral tunnel widening between outside-in and trans-tibial double-bundle ACL reconstruction.

    Science.gov (United States)

    Lee, Yong Seuk; Lee, Beom Koo; Oh, Won Seok; Cho, Yong Kyun

    2014-09-01

    The objectives of this study were to compare (1) the degree of widening by comparing the diameter at the most widened area and the site of widening by measuring the distance from the tunnel entrance to the most widened area in two femoral tunnels (anteromedial and posterolateral), and (2) the morphologic change at the tunnel entrance between outside-in and trans-tibial double-bundle anterior cruciate ligament (ACL) reconstruction. A retrospective study that included 17 trans-tibial and 19 outside-in double-bundle ACL reconstructed patients was conducted for evaluation of serial computed tomography (CT) scan (immediate post-operation and post-operative 1 year). Digital image communication in medicine (DICOM) data was extracted from the PiViewSTAR and imported into OsiriX, which was installed on a Macbook Pro laptop computer. Diameter of the most widened area and distance from the entrance to this point were measured from each of two perpendicular (sagittal and coronal) planes that were accurately realigned parallel to the tunnel direction. Change in the morphology of the tunnel entrance between immediate post-operation and 1-year post-operation was evaluated. Widening was observed in both planes of both tunnels in the two techniques. However, no statistical significances in the diameter of most widened area and distance from the tunnel entrance to the most widened point were observed between the both techniques (n.s.). Distances from the centre point to each four sections showed an increase in all four sections of all both tunnels in both techniques. However, no statistical significance was observed between the two techniques (n.s.). Widening was observed in all tunnels using both techniques and degrees, and sites of the widening did not differ between groups. Morphologic change at the tunnel entrance was not limited to the specific direction and occurred in all directions without significant difference between groups. Retrospective comparative study, Level III.

  20. Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

    Science.gov (United States)

    Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

    2017-08-01

    Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

  1. A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

    Directory of Open Access Journals (Sweden)

    Sudeep Thepade

    2014-01-01

    Full Text Available A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

  2. Significance of the impact of motion compensation on the variability of PET image features

    Science.gov (United States)

    Carles, M.; Bach, T.; Torres-Espallardo, I.; Baltas, D.; Nestle, U.; Martí-Bonmatí, L.

    2018-03-01

    In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40% of SUVmax and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40% contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, p\\ll 0.001 ). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the

  3. Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

    Directory of Open Access Journals (Sweden)

    Xi Wenfei

    2017-07-01

    Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

  4. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

    Science.gov (United States)

    Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

    2017-07-01

    Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

  5. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning

    Directory of Open Access Journals (Sweden)

    Sun Xun

    2016-12-01

    Full Text Available In this paper, we propose a supervised classification algorithm for Polarimetric Synthetic Aperture Radar (PolSAR images using multiple-feature fusion and ensemble learning. First, we extract different polarimetric features, including extended polarimetric feature space, Hoekman, Huynen, H/alpha/A, and fourcomponent scattering features of PolSAR images. Next, we randomly select two types of features each time from all feature sets to guarantee the reliability and diversity of later ensembles and use a support vector machine as the basic classifier for predicting classification results. Finally, we concatenate all prediction probabilities of basic classifiers as the final feature representation and employ the random forest method to obtain final classification results. Experimental results at the pixel and region levels show the effectiveness of the proposed algorithm.

  6. Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

    Science.gov (United States)

    Zhang, Yu; Wu, Jianxin; Cai, Jianfei

    2016-05-01

    In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

  7. Reversible acute methotrexate leukoencephalopathy: atypical brain MR imaging features

    International Nuclear Information System (INIS)

    Ziereisen, France; Damry, Nash; Christophe, Catherine; Dan, Bernard; Azzi, Nadira; Ferster, Alina

    2006-01-01

    Unusual acute symptomatic and reversible early-delayed leukoencephalopathy has been reported to be induced by methotrexate (MTX). We aimed to identify the occurrence of such atypical MTX neurotoxicity in children and document its MR presentation. We retrospectively reviewed the clinical findings and brain MRI obtained in 90 children treated with MTX for acute lymphoblastic leukaemia or non-B malignant non-Hodgkin lymphoma. All 90 patients had normal brain imaging before treatment. In these patients, brain imaging was performed after treatment completion and/or relapse and/or occurrence of neurological symptoms. Of the 90 patients, 15 (16.7%) showed signs of MTX neurotoxicity on brain MRI, 9 (10%) were asymptomatic, and 6 (6.7%) showed signs of acute leukoencephalopathy. On the routine brain MRI performed at the end of treatment, all asymptomatic patients had classical MR findings of reversible MTX neurotoxicity, such as abnormal high-intensity areas localized in the deep periventricular white matter on T2-weighted images. In contrast, the six symptomatic patients had atypical brain MRI characterized by T2 high-intensity areas in the supratentorial cortex and subcortical white matter (n=6), cerebellar cortex and white matter (n=4), deep periventricular white matter (n=2) and thalamus (n=1). MR normalization occurred later than clinical recovery in these six patients. In addition to mostly asymptomatic classical MTX neurotoxicity, MTX may induce severe but reversible unusual leukoencephalopathy. It is important to recognize this clinicoradiological presentation in the differential diagnosis of acute neurological deterioration in children treated with MTX. (orig.)

  8. Efficient and robust model-to-image alignment using 3D scale-invariant features.

    Science.gov (United States)

    Toews, Matthew; Wells, William M

    2013-04-01

    This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

  9. Imaging features of ductal plate malformations in adults

    Energy Technology Data Exchange (ETDEWEB)

    Venkatanarasimha, N., E-mail: nandashettykv@yahoo.com [Department of Radiology, Derriford Hospital, Plymouth (United Kingdom); Thomas, R.; Armstrong, E.M.; Shirley, J.F.; Fox, B.M.; Jackson, S.A. [Department of Radiology, Derriford Hospital, Plymouth (United Kingdom)

    2011-11-15

    Ductal plate malformations, also known as fibrocystic liver diseases, are a group of congenital disorders resulting from abnormal embryogenesis of the biliary ductal system. The abnormalities include choledochal cyst, Caroli's disease and Caroli's syndrome, adult autosomal dominant polycystic liver disease, and biliary hamartoma. The hepatic lesions can be associated with renal anomalies such as autosomal recessive polycystic kidney disease (ARPKD), medullary sponge kidney, and nephronophthisis. A clear knowledge of the embryology and pathogenesis of the ductal plate is central to the understanding of the characteristic imaging appearances of these complex disorders. Accurate diagnosis of ductal plate malformations is important to direct appropriate clinical management and prevent misdiagnosis.

  10. Differential diagnostic features of the radionuclide scrotal image

    Energy Technology Data Exchange (ETDEWEB)

    Mishkin, F.S.

    1977-01-01

    Differential diagnosis of scrotal lesions is aided by correlating radionuclide images with clinical findings. Subacute torsion is associated with peripheral hyperemia and can be mistaken for an inflammatory process; however, in a review of 128 studies, torsion and orchiectomy were the only processes encountered which had a center truly devoid of activity on the tissue phase compared to the normal side. Other lesions such as acute inflammation, abscess, hematoma, and hemorrhagic tumor may superficially appear to lack central activity but invariably contain at least as much activity when compared to the normal side.

  11. Clinical and CT imaging features of abdominal fat necrosis

    International Nuclear Information System (INIS)

    Zhao Jinkun; Bai Renju

    2013-01-01

    Fat necrosis is a common pathological change at abdominal cross-sectional imaging, and it may cause abdominal pain, mimic pathological change of acute abdomen, or be asymptomatic and accompany other pathophysiologic processes. Fat necrosis is actually the result of steatosis by metabolism or mechanical injury. Common processes that are present in fat necrosis include epiploic appendagitis, infarction of the greater omentum, pancreatitis, and fat necrosis related to trauma or ischemia. As a common fat disease, fat necrosis should be known by clinicians and radiologists. Main content of this text is the clinical symptoms and CT findings of belly fat necrosis and related diseases. (authors)

  12. Differential diagnostic features of the radionuclide scrotal image

    International Nuclear Information System (INIS)

    Mishkin, F.S.

    1977-01-01

    Differential diagnosis of scrotal lesions is aided by correlating radionuclide images with clinical findings. Subacute torsion is associated with peripheral hyperemia and can be mistaken for an inflammatory process; however, in a review of 128 studies, torsion and orchiectomy were the only processes encountered which had a center truly devoid of activity on the tissue phase compared to the normal side. Other lesions such as acute inflammation, abscess, hematoma, and hemorrhagic tumor may superficially appear to lack central activity but invariably contain at least as much activity when compared to the normal side

  13. Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    International Nuclear Information System (INIS)

    Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

    2005-01-01

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

  14. Combining low level features and visual attributes for VHR remote sensing image classification

    Science.gov (United States)

    Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

    2015-12-01

    Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

  15. Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

    Science.gov (United States)

    Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

    2009-02-01

    Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

  16. Thin plate spline feature point matching for organ surfaces in minimally invasive surgery imaging

    Science.gov (United States)

    Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

    2013-03-01

    Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.

  17. Stress fractures: pathophysiology, clinical presentation, imaging features, and treatment options.

    Science.gov (United States)

    Matcuk, George R; Mahanty, Scott R; Skalski, Matthew R; Patel, Dakshesh B; White, Eric A; Gottsegen, Christopher J

    2016-08-01

    Stress fracture, in its most inclusive description, includes both fatigue and insufficiency fracture. Fatigue fractures, sometimes equated with the term "stress fractures," are most common in runners and other athletes and typically occur in the lower extremities. These fractures are the result of abnormal, cyclical loading on normal bone leading to local cortical resorption and fracture. Insufficiency fractures are common in elderly populations, secondary to osteoporosis, and are typically located in and around the pelvis. They are a result of normal or traumatic loading on abnormal bone. Subchondral insufficiency fractures of the hip or knee may cause acute pain that may present in the emergency setting. Medial tibial stress syndrome is a type of stress injury of the tibia related to activity and is a clinical syndrome encompassing a range of injuries from stress edema to frank-displaced fracture. Atypical subtrochanteric femoral fracture associated with long-term bisphosphonate therapy is also a recently discovered entity that needs early recognition to prevent progression to a complete fracture. Imaging recommendations for evaluation of stress fractures include initial plain radiographs followed, if necessary, by magnetic resonance imaging (MRI), which is preferred over computed tomography (CT) and bone scintigraphy. Radiographs are the first-line modality and may reveal linear sclerosis and periosteal reaction prior to the development of a frank fracture. MRI is highly sensitive with findings ranging from periosteal edema to bone marrow and intracortical signal abnormality. Additionally, a brief description of relevant clinical management of stress fractures is included.

  18. Featured Image: A Molecular Cloud Outside Our Galaxy

    Science.gov (United States)

    Kohler, Susanna

    2018-06-01

    What do molecular clouds look like outside of our own galaxy? See for yourself in the images above and below of N55, a molecular cloud located in the Large Magellanic Cloud (LMC). In a recent study led by Naslim Neelamkodan (Academia Sinica Institute of Astronomy and Astrophysics, Taiwan), a team of scientists explore N55 to determine how its cloud properties differ from clouds within the Milky Way. The image above reveals the distribution of infrared-emitting gas and dust observed in three bands by the Spitzer Space Telescope. Overplotted in cyan are observations from the Atacama Submillimeter Telescope Experiment tracing the clumpy, warm molecular gas. Below, new observations from the Atacama Large Millimeter/submillimeter Array (ALMA) reveal the sub-parsec-scale molecular clumps in greater detail, showing the correlation of massive clumps with Spitzer-identified young stellar objects (crosses). The study presented here indicates that this cloud in the LMC is the site of massive star formation, with properties similar to equivalent clouds in the Milky Way. To learn more about the authors findings, check out the article linked below.CitationNaslim N. et al 2018 ApJ 853 175. doi:10.3847/1538-4357/aaa5b0

  19. Featured Image: A New Dark Vortex on Neptune

    Science.gov (United States)

    Kohler, Susanna

    2018-03-01

    This remarkable series of images by the Hubble Space Telescope (click for the full view) track a dark vortex only the fifth ever observed on Neptune as it evolves in Neptunes atmosphere. These Hubble images, presented in a recent study led by Michael Wong (University of California, Berkeley), were taken in 2015 September, 2016 May, 2016 October, and 2017 October; the observations have monitored the evolution of the vortex as it has gradually weakened and drifted polewards. Confirmation of the vortex solved a puzzle that arose in 2015, when astronomers spotted an unexplained outburst of cloud activity on Neptune. This outburst was likely a group of bright companion clouds that form as air flows over high-pressure dark vortices, causing gases to freeze into methane ice crystals. To learn more about what the authors have since learned by studying this vortex, check out the paper below.CitationMichael H. Wong et al 2018 AJ 155 117. doi:10.3847/1538-3881/aaa6d6

  20. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    Science.gov (United States)

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  1. Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

    Directory of Open Access Journals (Sweden)

    Ya-Shuo Li

    2012-03-01

    Full Text Available Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.

  2. Clinicopathological and imaging features of lipoastrocytoma: Case report.

    Science.gov (United States)

    Sivaraju, Laxminadh; Aryan, Saritha; Ghosal, Nandita; Hegde, Alangar S

    2018-02-01

    Lipidized tumors of the central nervous system are very uncommon, with only a few cases described. We report a case of a 25-year-old woman with a tumor involving the left premotor area. She underwent gross total excision. Histologically, the tumor was composed of glial fibrillary acidic protein-positive glial cells with areas of lipidization. A diagnosis of lipoastrocytoma was rendered. At three-year follow-up she was doing well, supporting the presumed favorable prognosis of these uncommon tumors. Absence of xanthochromic appearance, mitotic activity, necrosis and poor reticulin activity are the differentiating features from the pleomorphic xanthoastrocytoma. We highlighted that these tumors involve the adult and pediatric population and distribute in both supratentorial and infratentorial compartments as well as in the spinal cord.

  3. Unusual magnetic resonance imaging features in Menkes disease

    International Nuclear Information System (INIS)

    Barnerias, C.; Desguerre, I.; Dulac, O.; Bahi-Buisson, N.; Boddaert, N.; Hertz-Pannier, L.; Boddaert, N.; Guiraud, P.; Hertz-Pannier, L.; Dulac, O.; Bahi-Buisson, N.; Hertz-Pannier, L.; Dulac, O.; Bahi-Buisson, N.; De Lonlay, P.

    2008-01-01

    We present a case of an inherited disorder of copper metabolism, Menkes disease in which MRI studies revealed the coexistence of T2 hyper-signal in the temporal white matter with an increase of apparent diffusion coefficient indicative of vasogenic oedema combined with T2 hyper-signal of the putamen and head of the caudate and decreased apparent diffusion coefficient indicative of cytotoxic oedema. These unusual MRI features emphasize the interest of newly developed techniques in early diagnosis in Menkes disease. The acute cerebral damage might result from the combined effects of acute metabolic stress due to infectious disease and prolonged status epilepticus, acting on a highly susceptible developing brain. Vasogenic oedema in the temporal white matter could be related to prolonged status epilepticus and vascular abnormalities. Cytotoxic oedema of the putamen and head caudate could result from energetic failure. (authors)

  4. Unusual magnetic resonance imaging features in Menkes disease

    Energy Technology Data Exchange (ETDEWEB)

    Barnerias, C; Desguerre, I; Dulac, O; Bahi-Buisson, N [Hop Necker Enfants Malades, AP-HP, Dept Paediat Neurol and Metab Dis, F-75743 Paris 15 (France); Boddaert, N; Hertz-Pannier, L [Hop Necker Enfants Malad, AP-HP, Dept Pediat Radiol, F-75743 Paris (France); Boddaert, N [CEA, Serv Hosp Frederic Joliot, INSERM, U797, F-91406 Orsay (France); Guiraud, P [Univ Grenoble, Serv Biochim, Genet Hop, Grenoble (France); Hertz-Pannier, L; Dulac, O; Bahi-Buisson, N [INSERM, U663, F-75015 Paris (France); Hertz-Pannier, L; Dulac, O; Bahi-Buisson, N [Univ Paris 05, F-75005 Paris (France); De Lonlay, P [Hop Necker Enfants Malades, AP-HP, Dept Paediat Neurol and Metab Dis, F-75743 Paris 15 (France); Hop Necker Enfants Malades, AP HP, Ctr Reference Malad Metab, F-75743 Paris 15 (France)

    2008-07-01

    We present a case of an inherited disorder of copper metabolism, Menkes disease in which MRI studies revealed the coexistence of T2 hyper-signal in the temporal white matter with an increase of apparent diffusion coefficient indicative of vasogenic oedema combined with T2 hyper-signal of the putamen and head of the caudate and decreased apparent diffusion coefficient indicative of cytotoxic oedema. These unusual MRI features emphasize the interest of newly developed techniques in early diagnosis in Menkes disease. The acute cerebral damage might result from the combined effects of acute metabolic stress due to infectious disease and prolonged status epilepticus, acting on a highly susceptible developing brain. Vasogenic oedema in the temporal white matter could be related to prolonged status epilepticus and vascular abnormalities. Cytotoxic oedema of the putamen and head caudate could result from energetic failure. (authors)

  5. An improved feature extraction algorithm based on KAZE for multi-spectral image

    Science.gov (United States)

    Yang, Jianping; Li, Jun

    2018-02-01

    Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

  6. Identification of natural images and computer-generated graphics based on statistical and textural features.

    Science.gov (United States)

    Peng, Fei; Li, Jiao-ting; Long, Min

    2015-03-01

    To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

  7. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

    NARCIS (Netherlands)

    Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin; Hariharan, A.; Vijayakumari, Anupa A.; Aarabi, Mohammad Hadi; Aballi, John; Nour, Abd Elazeim Abd Alla Mohamed; Abdelaziz, Mohammed; Abdolalizadeh, AmirHussein; Abdollahi, Mahsa; Abdul Aziz, Siti Aishah; Salam, Amritha Abdul; Abdulaziz, Nidhal; Abdulkadir, Ahmed; Abdullah, Sachal; Abdullah, Osama; Abrigo, Jill; Adachi, Noriaki; Adamson, Christopher; Adduru, Viraj; Adel, Tameem; Aderghal, Karim; Ades-Aron, Benjamin; Adeyosoye, Michael; Adlard, Paul; Srinivasa, Ag; Aganj, Iman; Agarwal, Ayush; Agarwal, Anupam; Agarwal, Anchit; Aguero, Cinthya; Aguiar, Pablo; Ahdidan, Jamila; Ahmad, Fayyaz; Ahmad, Rziwan; Ahmadi, Hessam; Ahmed, Nisar; Sid, Farid Ahmed; Ai, Edward; Ai, Qing; Aicha, Benyahia; Aitharaju, Sai; Aiyer, Aditya; Akkus, Zeynettin; Akodad, Sanae; Akramifard, Hamid; Aksman, Leon; Aktas, Said; Al-Janabi, Omar; Al-Nuaimi, Ali; AlAila, BahaaEddin; Alakwaa, Fadhl; Alam, Saruar; Alam, Fakhre; Alam Zaidi, Syed Farhan; Alan, Wiener; Alansari, Mukhtar; Alareqi, Ebrahim; Alberdi, Ane; Albsoul, Mohammad; Alderson, Thomas; Aleem, Hassan; Alex, Aishwarya; Alexander, Jacob; Alexopoulos, Panagiotis; Alfoldi, Jessica; Ali, Ayesha; Ali, Imdad; Alimoradian, Shirin; Aljabar, Paul; Aljabbouli, Hasan; Aljovic, Almir; Allen, Genevera; Alliende, Luz Maria; Almaguel, Frankis; Almgren, Hannes; Montes, Carmen Alonso; Alowaisheq, Tasneem; Alryalat, Saif Aldeen; Alsado, Majd; Alsaedi, Abdalrahman; Alshehri, Haifa; Altaf, Tooba; Altendahl, Marie; Altmann, Andre; Alvand, Ashkan; Filho, Manoel Alves; Alzubi, Raid; Amaral, Robert; Ambatipudi, Mythri; Amernath, Remya; Amlien, Inge; Amoroso, Nicola; Amri, Hakima; Anastasiou, Athanasios; Anbarasi, Jani; Anbarjafari, Gholamreza; Anderson, Wes; Anderson, Jeff; Anderson, Valerie; Anderson, Loretta; Andonov, Jovan; Andova, Vesna; Andreopoulou, Irene; Andrews, K. Abigail; Andrews, Cameron; Angeles, Michel; Anne-Laure, Aziz; Ansari, Ghulam Jillani; Ansari, Sharaf; Anstey, Kaarin; Antunes, Augusto; Aoshuang, Zhang; Aouf, Mazin; Aow Yong, Li Yew; Aporntewan, Chatchawit; Apostolova, Liana; Appiah, Frank; Apsvalka, Dace; Arab, Abazar; Araque Caballero, Miguel Ángel; Arbabyazd, Mohammad; Arbelaez, Pablo; Archer, Kellie; Ardekani, Babak; Aretouli, Eleni; Arfanakis, Konstantinos; Arisi, Ivan; Armentrout, Steven; Arnold, Matthias; Arnold, Steven; Arslan, Salim; Artacho-Perula, Emilio; Arthofer, Christoph; Aruchamy, Srinivasan; Arya, Zobair; Pizarro, Carlos Asensio; Ashford, Wes; Ashraf, Azhaar; Askland, Kathleen; Aslaksen, Per; Aslakson, Eric; Aso, Toshihiko; Astphan, Michele; Ataloglou, Dimitrios; Atay, Meltem; Athanas, Argus; Atri, Roozbeh; Au, April; Aurich, Maike; Avants, Brian; Awasthi, Niharika; Awate, Suyash; Ayaz, Aymen; Son, Yesim Aydin; Aydogan, Dogu Baran; Ayhan, Murat; Ayton, Scott; Aziz, Adel; Azmi, Mohd Hafrizal; Ba, Maowen; Bach, Kevin; Badea, Alexandra; Bag, Asim; Bagewadi, Shweta; Bai, Xiangqi; Bai, Zilong; Bai, Haoli; Baird, Geoffrey; Baiwen, Zhang; Baker, Elizabeth; Baker, John; Bakker, Arnold; Ball, Erika; Ballén Galindo, Miguel Ángel; Banaei, Amin; Bandyopadhyay, Dipankar; Bang, Ki Hun; Bangen, Katherine; Banks, Sarah; Banning, Leonie; Bao, Wan Yun; Barakat, Rita; Barbará, Eduardo; Barber, Philip; Barber, Robert; de Araujo, Flavia Roberta Barbosa; Barnes, Josephine; Barredo, Jennifer; Barret, Olivier; Barrett, Matthew; Barsamian, Barsam; Barsky, Andrey; Bartel, Fabian; Bartoszewicz, Jakub; Bartram-Shaw, David; Barwood, Caroline; Basavaraj, Suryakanth; Basavaraj, Arshitha; Basiouny, Ahmed; Baskaran, Bhuvaneshwari; Basu, Arindam; Baths, Veeky; Bathula, Deepti; Batmanghelich, Nematollah Kayhan; Bauer, Roman; Bauer, Corinna; Bawa, Vanshika; Bayley, Peter; Bayram, Ali; Bazi, Yakoub; Beach, Thomas; Beaudoin, Kristin; Beaulieu, Christian; Becker, Cassiano; Beckett, Laurel; Bedding, Alun; Beer, Simone; Beer, Joanne; Beg, Mirza Faisal; Behfar, Qumars; Behjat, Hamed; Behjat, Hamid; Behseta, Sam; Bekris, Lynn; Suresh, Mahanand Belathur; Belichenko, Nadia; Bellio, Maura; Belyaev, Mikhail; Bemiller, Shane; Ahmed, Olfa Ben; Ben Bouallègue, Fayçal; Benedikt, Michael; Benge, Jared; Benitez, Andreana; Benlloch, Jose María; Benn, Marianne; Benyoussef, El Mehdi; Bergeron, David; Bermudez, Elaine; Bessadok, Alaa; Betzel, Richard; Bezuidenhoudt, Mauritz; Bhagwat, Nikhil; Bhalerao, Shailesh; Bhandari, Anindya; Bhasin, Harsh; Bhati, Radhika; Bhatkoti, Pushkar; Bhatt, Priya; Bhattacharjee, Debotosh; Bhattacharyya, Sudeepa; Bi, Rui; Bi, Jinbo; Bi, Harvy; Biancardi, Alberto; Bidart, Rene; Bilgel, Murat; Billiet, Thibo; Binczyk, Franciszek; Bingsheng, Huang; Bird, Christopher; Bischof, Gérard; Bishnoi, Ram; Biswas, Shameek; Bjelke, David; Black, Sandra; Blackwood, Jennifer; Blaese, Elise; Blair, James; Blanchard, Gilles; Bloom, Toby; Blujus, Jenna; Blusztajn, Jan Krzysztof; Bo, Wu; Bo, Jun; Boda, Ravi; Boellaard, Ronald; Bogorodzki, Piotr; Bokde, Arun; Bolhasani, Ehsan; Bonakdarpour, Borna; Bonazzoli, Matthew; Bône, Alexandre; Borkowsky, Jennifer; Borrajo, Danielle; Bos, Isabelle; Bosco, Paolo; Bott, Nicholas; Rodrigues, Renato Botter Maio Lopes; Boughanmi, Amani; Bougias, Haralabos; Boulier, Thomas; Bourgeat, Pierrick; Bouyagoub, Samira; Bowes, Mike; Boyes, Richard; Bozoki, Andrea; Bradshaw, Tyler; Pereira, Joana Braga; Brahami, Yoann; Brambati, Simona Maria; Bras, Jose; Braskie, Meredith; Brecheisen, Ralph; Bregman, Noa; Brewer, James; Briassouli, Alexia; Brickman, Adam; Bridges, Robert; Brihmat, Nabila; Brinkmann, Benjamin; Britschgi, Markus; Broers, Thomas; Bron, Esther; Brown, Jesse; Brown, Matthew; Brown, Abel; Brown, Maria; Brunberg, James; Bu, Tao; Bubbico, Giovanna; Bubenik, Peter; Bubu, Omonigho; Buchanan, Daniel; Buchholz, Hans-Georg; Buchsbaum, Bradley; Buck, Katharina; Buckley, Rachel; Budgeon, Charley; Buhl, Derek; Sánchez, Manuel Buitrago; Bundela, Saurabh; Burciu, Irina; Burgos, Ninon; Burke, Shanna; Burn, Katherine; Burns, Jeffrey; Burns, Gully; Burzykowski, Tomasz; Bush, Sammie; Buss, Stephanie; Butcher, Bradley; Butt, Victoria; Buxbaum, Joseph; Sandeep, C. S.; Cabrera, Cristóbal; Cahyaningrum, Winda; Cai, Zhen-Nao; Cai, Siqi; Cai, Erik; Cajka, Tomas; Calamia, Matthew; Caligiuri, Maria Eugenia; Calixte, Christopher; Calon, Frederic; Cameron, Briana; Campbell, Roy; Lopez, Jose Antonio Campos; Cao, Hongliu; Cao, Jiguo; Cao, Guanqun; Cao, Bo; Capizzano, Aristides; Capon, Daniel; Carmasin, Jeremy; Carmichael, Owen; Carr, Sarah; Carrier, Jason; Carter, Greg; Carvalho, Luis; Carvalho, Janessa; Carvalho, Carolina; Casamitjana, Adrià; Casanova, Ramon; Casas, Josep R.; Cash, David; Castelluccio, Pete; Castiglioni, Isabella; Caswell, Carrie; Cattell, Liam; Cauda, Franco; Cepeda, Ileana; Çevik, Alper; Cha, Jungho; Chakrabarti, Shreya; Chakraborty, Shouvik; Chammam, Takwa; Chan, Christina; Chand, Ganesh; Chang, Catie; Chang, Yu-Ming; Chang, Rui; Chang, Hyunggi; Chang, Yu-Chuan; Chang, Ki Jung; Chang, Che-Wei; Chantrel, Steeve; Chao, Justin; Chao, Linda; Chapleau, Marianne; Charil, Arnaud; Chatterjee, Pratishtha; Chatterjee, Sambit; Chaudhry, Zainab; Chauhan, Harmanpreet; Chehade, Abdallah; Chekuri, Omkar; Cheloshkina, Kseniia; Chen, Jianhong; Chen, Gang; Chen, Geng; Chen, Ting-Huei; Chen, Yin Jie; Chen, Xi; Chen, Tzu-Chieh; Chen, Guojun; Chen, Shuzhong; Chen, Jerome; Chen, Fang; Chen, Kaifeng; Chen, Gennan; Chen, Jason; Chen, Guanhua; Chen, Ying-Hsiang; Chen, Ming-Hui; Chen, Chenbingyao; Chen, S. Y.; Chen, Hsu-Hsin; Chen, Xing; Chen, Kewei; Chen, Yuhan; Chen, Hugo; Chen, Rong; Chen, Ing-jou; Chen, Jun; Chen, Jean; Chen, Bo; Cheng, Danni; Cheng, Hewei; Cheng, Yong; Cheng, Yang; Cheng, Zhang; Cheng, Wai Ho; Chenhall, Tanya; Chepkoech, Joy-Loi; Cherukuri, Venkateswararao; Chhibber, Aparna; Chi, Haoyuan; Chi, Chih-Lin; Chiang, Gloria; Chiesa, Patrizia; Childress, Daniel Micah; Chilukuri, Yogitha; Fatt, Cherise Chin; Chincarini, Andrea; Ching, Christopher; Chiotis, Konstantinos; Cho, Soo Hyun; Cho, Yongrae; Cho, Sooyun; Choi, Jun-Sik; Choi, Hongyoon; Choi, Yeoreum; Choi, Sophia; Choi, Jaesik; Choi, Euna; Choo, I. L. Han; Chopra, Vishal; Chougrad, Hiba; Chouraki, Vincent; Christini, Amanda; Chu, Yufang; Chuang, Tzu-Chao; Chuanji, Luo; Chuanjian, Yu; Chun, Marvin; Chun, Sung; Chung, Ai; Chung, Yu-Min; Chung, Jung-Che; Chung, Ai Wern; Chung, Jaeeun; Chyzhyk, Darya; Ciarleglio, Adam; Cioli, Claudia; Cittanti, Corrado; Cives, Ana; Clark, Marissa; Clayton, David; Clement, Mark; Clifft, Daniel; Climer, Sharlee; Clouston, Sean; Clunie, David; Cohen, Phoebe; Cohen, Taco; Cole, Michael; Cole, James; Colletti, Patrick; Collingwood, Joanna; Comley, Robert; Conklin, Bryan; Conner, Lindsay; Conover, Joanne; Contardo-Berning, Ivona; Conway, Ronan; Copani, Agata; Coppola, Giovanni; Corbett, Syl; Corlier, Fabian; Correia, Rui; Cosman, Joshua; Costantino, Sebastian; Coubard, Olivier; Coulson, Elizabeth; Couser, Elizabeth; Cox, Kris; Coyle, Patrick; Cozzi, Brian; Craddock, Cameron; Crawford, Karen; Creese, Byron; Cribben, Ivor; Crisostomo-Wynne, Theodore; Crossley, Nicolas; Croteau, Etienne; Cruchaga, Carlos; Cuajungco, Math; Cui, Jing; Cui, Sue; Cullen, Nicholas; Cuneo, Daniel; Cutanda, Vicente; Cynader, Max; Binu, D.; D'Avossa, Giovanni; Dai, Tian; Dai, Peng; Dai, Hui; Davied Hong, Daivied Hong; Dakovic, Marko; Dalca, Adrian; Damiani, Stefano; Dammak, Mouna; Damoiseaux, Jessica; Dan, Zou; Dang, Xuan Hong; Dang, Shilpa; Daniel, Zinkert; Danjou, Fabrice; Darby, Eveleen; Darby, Ryan; Dardzinska, Agnieszka; Darst, Burcu; Darvesh, Sultan; Das, Kalyan; Das, Devsmita; Das, Sandhitsu; Das, Dulumani; Datta, Shounak; Dauvillier, Jérôme; Davatzikos, Christos; Davidson, Ian; de Boer, Renske; de Bruijne, Marleen; de Buhan, Maya; de Jager, Philip; de La Concha Vega, Nuño; de Lange, Siemon; de Luis Garcia, Rodrigo; de Marco, Matteo; de Sitter, Alexandra; Dean, Scott; Decarli, Charles; Decker, Summer; del Gaizo, John; Demir, Zeynep; Denby, Charles; Deng, Yanjia; Deng, Wanyu; Denisova, Kristina; Denney, William; Depue, Brendan; DeRamus, Thomas; Desikan, Rahul; Desplats, Paula; Desrosiers, Christian; Devadas, Vivek; Devanarayan, Viswanath; Devarajan, Sridharan; Devenyi, Gabriel; Dezhina, Zalina; Dhami, Devendra; Dharsee, Moyez; Dhillon, Permesh; Di, Xin; Di Mauro, Nicola; Diah, Kimberly; Diamond, Sara; Diaz-Asper, Catherine; Diciotti, Stefano; Dickerson, Bradford; Dickie, David Alexander; Dickinson, Philip; Dicks, Ellen; Diedrich, Karl; Dieumegarde, Louis; Dill, Vanderson; Dilliott, Allison; Ding, Zhaohua; Ding, Shanshan; Ding, Yanhui; Ding, Xiuhua; Ding, Xuemei; Dinov, Ivo; Dinu, Valentin; Diouf, Ibrahima; Dmitriev, Phillip; Dobromyslin, Vitaly; Dodge, Hiroko; Dolui, Sudipto; Dona, Olga; Dondelinger, Frank; Dong, Wen; Dong, Hao-Ming; Kehoe, Patricio Donnelly; Donohue, Michael; Dore, Vincent; Dougherty, Chase; Doughty, Mitchell; Dowling, N. Maritza; Doyle, Senan; Doyle, Andrew; Dragan, Matthew; Draganski, Bogdan; Draghici, Sorin; Dragomir, Andrei; Drake, Derek; Drake, Erin; Drd, Shilpa; Dronkers, Nina; Drozdowski, Madelyn; Du, Changde; Du, Yuhui; Du, Lei; Du, Guangwei; Du, Xingqi; Duan, Fang; Duan, Yuzhuo; Duan, Kuaikuai; Duchesne, Simon; Duggento, Andrea; Dukart, Juergen; Dumont, Matthieu; Dunn, Ruth; Duong, Vu; Duraisamy, Baskar; Duran, Tugce; Durrleman, Stanley; Dutta, Joyita; Dyrba, Martin; Dyvorne, Hadrien; R, Amulya E.; Eads, Jennifer; Eastman, Jennifer; Eaton, Susan; Edlund, Christopher; Edmonds, Emily; Edmondson, Mackenzie; Ehsan, Fatima; El-Gabalawy, Fady; Elander, Annie; Elango, Vidhya E.; Eldeeb, Ghaidaa; Elgamal, Fatmaelzahraa; Rodrigues, Yuri Elias; Elman, Jeremy; Elrakaiby, Nada; Emahazion, Tesfai; Emami, Behnaz; Embrechts, Jurriën; Emran Khan Emon, Mohammad Asif; Emrani, Saba; Emrani, Asieh; Emri, Miklós; Engelhardt, Barbara; Engle, Bob; Epstein, Noam; Er, Fusun; Erhardt, Erik; Eriksson, Oscar; Omay, Zeynep Erson; Escudero, Javier; Eshleman, Jason; Eskildsen, Simon; Espinosa, Luis; Essex, Ryan; Esteban, Oscar; Estrada, Karol; Ethell, Douglas; Ethridge, Kimberly; Ettehadi, Seyedrohollah; Eva, Bouguen; Evenden, Dave; Evtikheeve, Rina; Ewert, Siobhan; Fague, Scot; Fahmi, Rachid; Faizal, Sherin; Falahati, Farshad; Fan, Li; Fan, Zhen; Fan, Yong; Fan, Maohua; Fan, Yonghui; Fan, Sili; Fan, Ruzong; Fang, Chen; Fang, Xiaoling; Fanjul-Vélez, Félix; Fanti, Alessandro; Far, Bab; Farah, Martha; Farahani, Naemeh; Farahibozorg, Seyedehrezvan; Farahnak, Farhood; Farajpour, Maryam; Fardo, David; Farkhani, Sadaf; Farnsworth, Bryn; Farooq, Hamza; Farooq, Ammarah; Farouk, Yasmeen; Farrar, Danielle; Farrer, Lindsay; Fatemehh, Fatemeh; Fatemizadeh, Emad; Fatfat, Kim; Fatima, Shizza; Faux, Noel; Favan-Niven, Anne; Favary, Clélia; Fazlollahi, Amir; Fei, Gao; Feingold, Franklin; Feizi, Soheil; Félix, Eloy; Femminella, Grazia Daniela; Feng, Zijun; Feng, Ao; Feng, Brad; Feng, Xinyang; Feragen, Aasa; Fereidouni, Marzieh; Fernandes, Miguel; Fernández, Víctor; Ferrari, Ricardo; Ferraris, Sebastiano; Ferreira, Francisco; Ferreira, Luiz Kobuti; Ferreira, Hugo; Fiecas, Mark; Fieremans, Els; Fiford, Cassidy; Figurski, Michal; Filippi, Massimo; Filshtein, Teresa; Findley, Caleigh; Finger, Elizabeth; Firth, Nicholas; Fischer, Christopher; Fischer, Florian; Fitall, Simon; Fleet, Blair; Fleishman, Greg; Flokas, Lambros; Flores, Alberto; Focke, Niels; Fok, Wai Yan; Foldi, Nancy; Fôlego, Guilherme; Forero, Aura; Fornage, Myriam; Fos Guarinos, Belén; Founshtein, Gregory; Franc, Benjamin; Francois, Clement; Franke, Katja; Fraser, Mark; Frasier, Mark; Frederick, Blaise; Freitas, Fernandho; Escalin, Frency Jj; Freudenberg-Hua, Yun; Friedman, Brad; Friedmann, Theodore; Friedrich, Christoph M.; Frings, Lars; Frisoni, Giovanni; Fritzsche, Klaus; Frolov, Alexander; Frost, Robert; Fu, Ling; Fu, Zening; Fudao, Ke; Fuentes, Emmanuel; Fujishima, Motonobu; Fujiwara, Ken; Fukami, Tadanori; Funk, Cory; Furcila, Diana; Fuselier, Jessica; Nagarjuna Reddy, G.; Gaasterland, Terry; Gabelle, Audrey; Gahm, Jin; Gaiteri, Chris; Gajawelli, Niharika; Galantino, Alexis; Galarza Hernández, Javier; Galasko, Douglas; Galea, Liisa; Galisot, Gaetan; Sánchez, Antonio Javier Gallego; Gallins, Paul; Gamberger, Dragan; Gan, Hong Seng; Gan, Gavin; Ganapathi, Subha; Gancayco, Christina; Gangishetti, Umesh; Ganzetti, Marco; Gao, Fei; Gao, Jingjing; Gao, Linlin; Gao, Tianxiang; Gao, Yuanyuan; Gao, Xiaohong; Garani, Ranjini; Garbarino, Sara; Garcia, Ivan; Garcia, Xiadnai; Garcia, Jorge; Garcia, Tanya; Garcia Arias, Hernan Felipe; de La Garza, Angel Garcia; Gaig, Mireia Garcia; Novoa, Jorge Garcia; Valero, Mar Garcia; Garcia-Ojalvo, Jord; García-Polo, Pablo; Garg, Rahul; Garg, Gaurav; Garg, Divya; Garibotto, Valentina; Garvey, Matthew; Garza-Villarreal, Eduardo; Gaubert, Malo; Gauthier, Serge; Gavett, Brandon; Gavidia, Giovana; Gavtash, Barzin; Gawryluk, Jodie; Gbah, Messon; Ge, Tian; Geerts, Hugo; Geisser, Niklaus; Geng, Junxian; Gentili, Claudio; Gess, Felix; Ghaderi, Halleh; Ghahari, Shabnam; Ghanbari, Yaghoob; Ghazi-Saidi, Ladan; Ghodrati, Mojgan; Ghorbani, Behnaz; Ghoreishiamiri, Reyhaneh; Ghosal, Sayan; Ghosh, Sukanta; Ghosh, Saheb; Ghosh, Sreya; Ghoshal, Ankur; Giannicola, Galetta; Gibert, Karina; Gibson, Gary; Gieschke, Ronald; Gil Valencia, Jorge Mario; Gillen, Daniel; Giordani, Alessandro; Giraldo, Diana; Gispert, Juan D.; Gitelman, Darren; Giuffrida, Mario Valerio; Madhu, G. K.; Glass, Jesse; Glazier, Brad; Gleason, Carey; Glerean, Enrico; Glozman, Tanya; Godbey, Michael; Goettlich, Martin; Gogoi, Minakshi; Gola, Kelly; Golbabaei, Soroosh; Golden, Daniel; Goldstein, Felicia; Gomes, Carlos; de Olivera, Ramon Gomes Durães; Gomez, Isabel; Gomez Gonzalez, Juan Pablo; Gomez-Verdejo, Vanessa; Gong, Weikang; Gong, Enhao; Gong, Kuang; Gonneaud, Julie; Gonzalez, Clio; Gonzalez, Evelio; Gonzalez, Gerardo; Moreira, Eduardo Gonzalez; Goodman, James; Gopinath, Srinath; Gopu, Anusharani; Gordon, Brian; Gordon, David; Gordon, Mark; Gorriz, Juan Manuel; Gors, Dorothy; Göttler, Jens; Gounari, Xanthippi; Goyal, Devendra; Graf, John; Graff, Ariel; Graham, Leah; Graham, Jinko; Grajski, Kamil; Grami, Maziyar; Grand'Maison, Marilyn; Grant, Kiran; Grassi, Elena; Gray, Katherine; Grecchi, Elisabetta; Green, Robert; Green, Elaine; Greenberg, Jonathan; Greening, Steven; Greenwood, Bryson; Gregori, Johannes; Gregory, Michael; Greicius, Michael; Greve, Douglas; Griffin, Jason; Grill, Joshua; Grodner, Kelsey; Grolmusz, Vince; Groot, Perry; Groothuis, Irme; Gross, Alden; Grundstad, Arne; Grundy, Edward; Grzegorczyk, Tomasz; Nandith, G. S.; Gu, David; Gu, Jiena; Gu, Yun; Gu, Ginam; Guan, Sheng; Guan, Yuanfang; Guennel, Tobias; Guerin, Laurent; Guerrero, Ricardo; Guerrier, Laura; Guevara, Pamela; Guggari, Shankru; Roy, Abhijit Guha; Guidotti, Roberto; Guillon, Jérémy; Gulcher, Jeff; Gulia, Sarita; Gumedze, Freedom; Gunawardena, Nishan; Gunn, Roger; Guo, Michael; Guo, Xiao; Guo, Xingzhi; Guo, Yi; Kai, Zhang Guo; Zhao, Ma Guo; Gupta, Navin; Gupta, Anubha; Gupta, Ishaan; Guren, Onan; Gurnani, Ashita; Gurol, Mahmut Edip; Guzman, Gloria; Gyy, Gyy; Rajanna, Vanamala H.; Ha, Seongwook; Haacke, Ewart; Haaksma, Miriam; Habadi, Maryam; Habeck, Christian; Habes, Mohamad; Hackspiel Zarate, Maria Mercedes; Hadimani, Ravi; Hahn, William; Hahn, Tim; Haight, Thaddeus; Hair, Nicole; Haixing, Wang; Hajarolasvadi, Noushin; Hajjar, Ihab; Hajjo, Rima; Halchenko, Yaroslav; Hall, Anette; Hallock, Kevin; Hamdi, Shah Muhammad; Hameed, Farhan; Hamidian, Hajar; Han, Dong; Han, Yang; Han, Hio-Been; Han, Qingchang; Han, Beomsoo; Han, Duke; Han, Shizhong; Han, Xiaoxia; Han, Peipei; Han, Joo Yoon; Han, Dong-Sig; Handsaker, Robert; Hanna-Pladdy, Brenda; Hanseeuw, Bernard; Hansson, Björn; Hao, Yang; Hao, Jhon; Happ, Clara; Harischandra, Dilshan; Haritaoglu, Esin; Harris, Richard; Harris, Breanna; Hart, Brian; Hartzell, James; Harvey, Danielle; Hashimoto, Tsuyoshi; Hasooni, Hossein; Hassan, Moaied; Hassan, Mehdi; Hassanzadeh, Hamid Reza; Hassanzadeh, Oktie; Hatton, Sean; Hawchar, Jinan; Hayashi, Toshihiro; Hayashi, Norio; Hayes, Jasmeet; Hayete, Boris; Haynor, David; He, Linchen; He, Yan; He, Yao; He, Huiguang; Heegaard, Niels; Hefny, Mohamed; Heil, Julius; Heindel, William; Henderson, Samuel; Henf, Judith; Henriquez, Claudio; Herholz, Karl; Hermessi, Haithem; Hernandez, Monica; Herrera, Luis; Hibar, Derrek; Hidane, Moncef; Higuchi, Satomi; Hind, Jade; Hives, Florent; Hoang, Mimi; Hobel, Zachary; Hoffman, John; Hofmeister, Jeremy; Hohman, Timothy; Holder, Daniel; Holguin, Jess; Holmes, Robin; Hong, John; Hongliang, Zou; Hongyu, Guo; Hopkins, Paul; Hor, Soheil; Hornbeck, Russ; Horng, Andy; Horton, Wesley; Hosny, Khalid; Hosseini, Eghbal; Hosseini, Hadi; Hosseini, Zahra; Asl, Ehsan Hosseini; Hou, Beibei; Houghton, Richard; Houghton, Katherine; Householder, Erin; Howlett, James; Hsiao, John; Hsiao, Ing-Tsung; Hsu, Chih-Chin; Hu, Xixi; Hu, Lingjing; Hu, Nan; Hu, Kun; Hu, Tao; Hu, Li; Hu, Xiaolan; Hua, Fei; Huang, Marissa; Huang, Qi; Huang, Michelle; Huang, Chao; Huang, JunMing; Huang, Xingyuan; Huang, Yuhan; Huang, Sing-Hang; Huang, Shuai; Huang, Peiyu; Huang, Chun-Chao; Huang, Zhiyue; Huang, Meiyan; Huang, Zhiwen; Hubrich, Markus; Huestis, Michael; Huey, Edward; Hufton, Andrew; Huijbers, Willem; Huisman, Sjoerd; Hung, Joe; Hunsaker, Naomi; Hunt, Fostor; Huppertz, Hans-Jürgen; Huser, Vojtech; Hussain, Lal; Hutchison, R. Matthew; Hutton, Alexandre; Huyck, Els; Hwang, Jihye; Hyun, JungMoon; Iakovakis, Dimitris; Ibañez, Victoria; Ide, Kayoko; Igarashi, Takuma; Iglesias, Juan Eugenio; Muñoz, Laura Igual; Iidaka, Tetsuya; Ikeuchi, Takeshi; Ikhena, John; Ikuta, Toshikazu; Im, Hyung-Jun; Insausti, Ana; Insel, Philip; Invernizzi, Azzurra; Iosif, Ana-Maria; Ip, Nancy; Irizarry, Sierra; Irmak, Emrah; Irwin, David; Isaza, Mariano; Ishii, Makoto; Ishii, Kenji; Islam, Jyoti; Israel, Ariel; Isufi, Elvin; Ito, Kaori; Ito, Masato; Izquierdo, Walter; Alphin, J.; Akhila, J. A.; Jaberzadeh, Amir; Jackowiak, Edward; Jackson, Eric; Jackson, Chris; Jackson, Jonathan; Jacob, Samson; Jacobsen, Nina; Jacobsen, Jörn; Jacquemont, Thomas; Jacques, Nerline; Jaeger, Ralf; Jafari, Tahere; Jafari-Khouzani, Kourosh; Jagadish, Akshay Kumar; Jagtap, Priti; Jagust, William; Jahr, Joseph; Jain, Shubhankar; Jain, Shubham; Jaiswal, Ayush; Jaiswal, Akshay; Jait, Amine; Jakkoju, Chetan; Jakobsson, Andreas; James, Olga; James, Oliver; Jamlai, Maedeh; Jammeh, Emmanuel; Janardhana, Lajavanthi; Jang, Jinseong; Jang, Jae-Won; Jang, Jinhee; Jang, Hyesue; Janghel, Rekh Ram; Jawahar, Shasvat; Jean, Kharne; Jean-Baptiste, Schiratti; Jedynak, Bruno; Jefferson, Angela; Jennings, Danna; Jennings, Dominique; Jeon, Seun; Jeong, Yong; Jester, Charles; Jethwa, Ketan; Jha, Debesh; Ji, Gong-Jun; Ji, Chong; Ji, Jin; Jia, Bowen; Jiacheng, Lee; Jiajia, Guo; Jian, Weijian; Jiang, Shan; Jiang, Chunxiang; Jianhua, Gao; Jiao, Zhuqing; Jiao, Zeyu; Jiao, Du; Jimenez Alaniz, Juan Ramon; Gomez, Carolina Jimenez; Jiménez-Huete, Adolfo; Jimura, Koji; Jin, Yan; Jin, Zhu; Jogia, Jigar; Johansson, Per; John, Kimberley; Johnsen, Stian; Johnson, Leonard; Johnson, Sterling; Johnson, Kent; Johnston, Jane; Johnston, Stephen; Jomeiri, Alireza; Jonas, Katherine; Jones, Richard; Jones-Davis, Dorothy; Jönsson, Linus; Joseph, Jane; Joshi, Himanshu; Joshi, Shantanu; Joshi, Abhinay; Joyce, Katherine; Juengling, Freimut; Jung, Youngjin; Junker, Viv; Junwei, Ding; Jyothi, Singaraju; Jyotiyana, Monika; Sarthaj, K.; Kachouane, Mouloud; Kadian, Amit; Kaewaramsri, Yothin; Kaicheng, Li; Kaiser, Marcus; Kakinami, Lisa; Kalra, Sanjay; Kam, Hye Jin; Kamarudin, Nur Shazwani; Kaminker, Josh; Kandel, Benjamin; Kandiah, Nagaendran; Kaneko, Tomoki; Kang, Yun Seok; Kang, Ju Hee; Kang, Hakmook; Kang, Jian; Kansal, Anuraag; Kaouache, Mohammed; Kaplan, Adam; Kottaram, Akhil Karazhma; Karim, Faizan; Karimi-Mostowfi, Nicki; Karjoo, Mahboobe; Karlin, Daniel; Karp, Juliana; Karray, Chiheb; Kartsonis, Nick; Karu, Naama; Kasa, Jaya; Kasiri, Keyvan; Katako, Audrey; Kato, Ryo; Katsonis, Panagiotis; Katti, Hkkatti; Kaur, Prabhjot; Kauwe, John; Kawaguchi, Atsushi; Kazemi, Samaneh; Kazemi, Yosra; Rijan, K. C.; Kechin, Andrey; Kelkhoff, Douglas; Kelleher, Thomas; Kellner-Weldon, Frauke; Kennion, Oliver; Kerr, Daniel; Kesler, Shelli; Kesselman, Carl; Kessler, Daniel; Keuken, Max; Keyvanfard, Farzaneh; Khademi, April; Khajehnejad, Moein; Khan, Wasim; Khan, Tabrej; Khan, Hikmat; Khan, Anzalee; Khan, Samreen; Khanmohammadi, Sina; Khasanova, Tatiana; Khazaee, Ali; Khazan, Lenny; Kherif, Ferath; Khl, Aym; KHlif, Mohamed Salah; Khondoker, Mizanur; Khoo, Sok Kean; Khosrowabadi, Reza; Khurshid, Kiran; Kianfard, Reihaneh; Kida, Satoshi; Kiddle, Steven; Kikuchi, Masashi; Killiany, Ron; Kim, Jeongchul; Kim, Jong Hun; Kim, Hyunwoo; Kim, Jongin; Kim, Yeo Jin; Kim, Jung-Jae; Kim, Hang-Rai; Kim, Jaeyeol; Kim, Ki Hwan; Kim, Joseph; Kim, Younghoon; Kim, Mijung; Kim, Jeongsik; Kim, Bohyun; Kim, Taehyun; Kim, Heeyoung; Kim, Seonjik; Kim, Nakyoung; Kim, Byeongnam; Kim, ChanMi; Kim, Jeonghun; Kim, Seong Yoon; Kim, Sunhee; Kingery, Lisle; Kinnunen, Kirsi; Kinomes, Marie; Kirchner, Jan Hendrik; Caldwell, Jessica Kirkland; Kirwan, Brock; Kitamura, Chiemi; Kitty, Kitty; Kiviat, David; Kiyasova, Vera; Klein, Richard; Klein, Alison; Klein, Gregory; Klein, Jan; Kleinman, Aaron; Kling, Mitchel; Klinger, Joern; Klinger, Rebecca; Klink, Katharina; Kocaturk, Mustafa; Koch, Philipp Johannes; Kochova, Elena; Koenig, Loren; Koh, Natalie; Köhler, Jens Erik; Koikkalainen, Juha; Koini, Marisa; Kolachalama, Vijaya; Koncz, Rebecca; Kong, Xiang-Zhen; Kong, Vincent; Kong, Xiangzhen; Kong, Dehan; Kong, Linglong; Konukoglu, Ender; Kopeinigg, Daniel; Kopera, Krzysztof; Koppers, Simon; Korb, Matheus; Korfiatis, Panagiotis; Korolev, Igor; Korolev, Sergey; Korostyshevskiy, Valeriy; Koshiya, Heena; Kost, James; Kotari, Vikas; Koutra, Danai; Koychev, Ivan; Kruthika, K. R.; Krahnke, Tillmann; Krause, Matthew; Kraybill, Matt; Kriebel, Martin; Hari Krishna, M.; Krohn, Stephan; Kruggel, Frithjof; Kuceyeski, Amy; Kuhl, Donald; Kulshreshtha, Devang; Kumar, Santosh; Kumar, Sambath; Kumar, Kuldeep; Kumar, Anil; Kumar, Abhishek; Kumar, A.; Kumar, Saurabh; Kumar, Ashwani; Kumar, Ambar; Kumar, Dinesh; Kumar, Rishab; Kumarasinghe, Janaka; Kundu, Suprateek; Kung, Te-Han; Kuo, Li-Wei; Kuo, Phillip; Channappa, Usha Kuppe; Kuriakose, Elmy; Kurian, P.; Kwan, Kenneth; Kwasigroch, Arkadiusz; Kwon, Young Hye; Kyeong, Sunghyon; Fleur, Claire La; Wungo, Supriyadi La; Labbe, Tomas; Lacombe, Daniel; Lad, Meher; Lahoti, Geet; Lai, Ying Liang; Lai, Catherine; Lai, Dongbing; Laird, Dillon; Lakatos, Anita; Lam, Alice; Lama, Ramesh; Lambert, Christian; Landau, Susan; Landman, Bennett; Landre, Victor; Lane, Elizabeth; Lange, Catharina; Langenieux, Alexandre; Lareau, Caleb; Larson, Katelyn; Latif, Ghazanfar; Lauber, Ross; Lawliet, Z. H.; Lawrence, Emma; Lazar, Anca; Le, Ngan; Le, Thi Khuyen; Le, Matthieu; Guen, Yann Le; Scouiller, Stephanie Le; Leandrou, Stephanos; Leatherday, Christopher; Leavitt, Mackenzie; Ledbetter, Christina; Lee, Hyekyoung; Lee, Wook; Lee, Annie; Lee, Jaehong; Lee, Dongyoung; Lee, Joel; Lee, Song-Ting; Lee, Kuo-Jung; Lee, Subin; Lee, Jaeho; Lee, Catherine; Lee, Gyungtae; Lee, Suzee; Lee, Erik; Lee, Yunseong; Lee, Sang-Gil; Lee, Seonjoo; Lee, Peng Jung; Lee, Hyunna; Lee, Cheng-Hsien; Lee, Hengtong; Lee, Mi Ri; Lee, Ilgu; Lee, Qixiang; Lefterov, Iliya; Leger, Charlie; Lehallier, Benoit; Lei, B.; Lei, Shi; Lei, Hongxing; Lei, Haoyun; Leong, Tze Yun; Leong, Sharlene; Leoutsakos, Jeannie-Marie; Lepore, Natasha; Lerch, Ondrej; Leung, Yip Sang; Leung, Yuk Yee; Leung, Shuyu; Leung, Hoi-Chung; Leung, Ming-Ying; Levakov, Gidon; Levine, Abraham; Li, Chawn; Li, Miranda; Li, Huijie; Li, Junning; Li, Xiaofeng; Li, Yi; Li, Jinchao; Li, Tianhong; Li, Yongming; Li, Xiangrui; Li, Tieqiang; Li, Yan; Li, Fuhai; Li, Feijiang; Li, Shuyang; Li, Zhi; Li, Xing; Li, Rongjian; Li, Rui; Li, Y. U.; Li, Kang; Li, Zhenzhen; Li, Qingqin; Li, Wenjun; Li, Yang; Li, Jialu; Li, Guangyu; Li, Michelle; Li, Yibai; Li, Yupeng; Li, Tao; Li, Zhujun; Li, Yafen; Li, Muwei; Li, Xuan; Li, Yi-Ju; Li, Cen Sing; Li, X. W.; Li, Yingjie; Li, Lin; Li, Yihan Jessie; Li, Yaqing; Li, Xiantao; Li, Xingfeng; Li, Chenxi; Li, Chao; Li, Jicong; Li, Jiewei; Li, Tengfei; Li, Wei; Li, Xinzhong; Li, Nannan; Li, Chunfei; Li, Yeshu; Liang, Chen; Liang, Nanying; Liang, Jingjing; Liang, Shengxiang; Liang, Xiaoyun; Liang, Xia; Liang, Ying; Liberman, Sofia; Libon, David; Liébana, Sergio; Liedes, Hilkka; Lim, Wee Keong; Lim, Yen Ying; Lin, Yenching; Lin, Katherine; Lin, Ming; Lin, Ai-Ling; Lin, Ching-Heng; Lin, Bing; Lin, Lin; Lin, Jyh-Miin; Lin, W. M.; Lin, Chien-Tong; Lin, Liyan; Lin, Jing; Lindberg, Olof; Linesch, Paul; Linn, Kristin; Lippert, Christoph; Litovka, Nikita; Little, Graham; Liu, Man-Yun; Liu, Jin; Liu, Chin-Fu; Liu, Zhaowen; Liu, Eulanca; Liu, Weixiang; Liu, K. E.; Liu, Hao Chen; Liu, Jia; Liu, Richann; Liu, Dongbo; Liu, Victor; Liu, Wenjie; Liu, Tao; Liu, Xiaoli; Liu, Yong; Liu, Lin; Liu, Dan; Liu, Xiuwen; Liu, Mengmeng; Liu, Chia-Shang; Liu, Ying; Liu, Yan; Liu, Xueqing; Liu, Han; Liu, Chien-Liang; Liu, Sidong; Liu, Jundong; Liu, Yang; Liu, Tianming; Liu, Tingshan; Liu, Ning; Liu, Lan; Liuyu, Liuyu; Lizarraga, Gabriel; Llido, Jerome; Lobach, Iryna; Lockhart, Samuel; Loft, Henrik; Lohr, Kelly; Lon, Hoi Kei; Lone, Kashif Javed; Long, Ziyi; Long, Xiaojing; Longo, Frank; Alves, Isadora Lopes; Lopez, Guadalupe; Lorenzi, Marco; Lotan, Eyal; Louie, Gregory; Louis, Maxime; Loukas, Andreas; Love, Seth; Lowe, Deborah; Lu, Bin; Lu, Chia-Feng; Lu, Zixiang; Lu, Lijun; Lu, Pascal; Lu, Shen; Lu, Qing; Lu, Zheshen; Lu, Chuan; Lu, Patty; Lu, Hangquan; Lu, Bo; Luktuke, Yadnyesh; Luo, Wei; Luo, Suhuai; Luo, Sheng; Luo, Shaojun; Luo, Peggy; Luo, Shan; Luo, Weidong; Luo, Liao; Luo, Xiao; Lupton, Michelle; Lutz, Michael; Lv, Eric; Lyu, Juan; Angshul, M.; Radha, M. R.; Dinesh, M. S.; Ma, Xiangyu; Ma, Chao; Ma, Li; Ma, Yu; Ma, Qianli; MacArthur, Daniel; Macey, Paul; Mach, Eric; MacPhee, Imola; Madadi, Mahboubeh; Madan, Christopher; Madan, Bharat; Madero, Giovanny; Madhavan, Radhika; Madhyastha, Tara; Maeno, Nobuhisa; Magsood, Hamzah; Mah, Linda; Mahdavi, Shirin; Mahdavi, Asef; Mahmoud, Abeer; Mahmoud, Hentati; Mahmoud, Kariman; Mahmoudi, Ahmad; Dehkordi, Siamak Mahmoudian; Mahor, Monika; Mahseredjian, Taleen; Mai, Cha; Maia, Rui; Maiti, Taps; Maj, Carlo; Maji, Pradipta; Majidpour, Jafar; Makhlouf, Laouchedi; Makino, Satoshi; Makrievski, Stefan; Makse, Hernan; Malagi, Archana; Malakhova, Katerina; Malamon, John; Malashenkova, Irina; Malchano, Zach; Maleki-Balajoo, Somayeh; Malik, Sadia; Malik, Tamoor; Mallik, Abhirup; Malm, Tarja; Malpas, Charles; Malpica, Norberto; Malviya, Meenakshi; Mamandi, A.; Manandhar, Abinash; Mandal, Pravat; Mandali, Alekhya; Mane, Prajakta; Manning, Emily; Manoufali, Mohamed; Manser, Paul; Mantini, Dante; Mantri, Ninad; Manyakov, Nikolay; Manzak, Dİlek; Mao, Shuai; Maoyu, Tian; Maple Grødem, Jodi; Maravilla, Kenneth; Marco, Simonetti; Marcus, Daniel; Margetis, John; Margolin, Richard; Mariano, Laura; Marinescu, Razvan Valentin; Markett, Sebastian; Markiewicz, Pawel; Marnane, Michael; Maroof, Asif; Marple, Laura; Marques, Cristiane; Marrakchi, Linda; Marshall, Gad; Märtens, Kaspar; Mårtensson, Gustav; Marti, Cristian; Martin, Harold; Martinaud, Olivier; Martinez, Victor; Martinez, Oliver; Martinez, Jesus; Martinez, Carlos; Abadías, Neus Martinez; Torteya, Antonio Martinez; Martini, Jean-Baptiste; Martins, Samuel; Masciotra, Viviane; Masmoudi, Ahmed; Masny, Aliaksandr; Shah, Pir Masoom; Massaro, Tyler; Masumoto, Jun; Matan, Cristy; Mate, Karen; Mateus, Pedro; Mather, Mara; Mather, Karen; Mathew, Jesia; Mathias, Samuel; Mathiyalagan, Tamilalaghan; Matloff, Will; Matsubara, Keisuke; Matsubara, Takashi; Matsuda, Yukihisa; Matthews, Dawn; Mattis, Paul; May, Patrick; Mayburd, Anatoly; Mayo, Chantel; Mayordomo, Elvira; Mbuyi, Gaylord; McCallum, Colleen; McCann, Bryony; McCollough, Todd; McCormick, Shannon; McCurdy, Sean; McDonald, Carrie; McEligot, Archana; McEvoy, Linda; McGeown, William; McGinnis, Scott; McHugh, Thomas; McIntosh, Elissa; McIntosh, Randy; McKenzie, Andrew; McLaren, Donald; McMillan, Corey; McMillan, Alan; McPherson, Brent; McRae-McKee, Kevin; Zaini, Muhammad Hafiz Md; Meadowcroft, Mark; Mecca, Adam; Meda, Shashwath; Medikonda, Venkata Srinu; Meeker, Karin; Megherbi, Thinhinane; Mehmood, Anum; Mehrtash, Alireza; Meiberth, Dix; Meier, Dominik; Meijerman, Antoine; Mejia, Jose; Mekkayil, Lasitha; Meles, Sanne; Melie-Garcia, Lester; Melo, Hans; Melrose, Rebecca; Melzer, Corina; Mendes, Aline; Leon, Ricardo Antonio Mendoza; Gonzalez, Manuel Menendez; Meng, Dewen; Meng, Xianglai; Meng, Guilin; Mengel, David; Menon, Ramesh; Menon, Ravi; Mercado, Flavio; Messick, Viviana; Meyer, Pierre-Francois; Meyer, Carsten; Mezher, Adam; Mi, Liang; Miao, Hongyu; Michailovich, Oleg; Michels, Lars; Mickael, Guedj; Mikhail, Mark; Mikhno, Arthur; Milana, Diletta; Miller, Rachel; Miller, Brendan; Millikin, Colleen; Min, Byung Wook; Minadakis, George; Minghui, Hu; Chinh, Truong Minh; Minkova, Lora; Miranda, Michelle; Misevic, Dusan; Mishra, Amit; Mishra, Chetan; Mishra, Shiwangi; Mishra, Ashutosh; Mishra, Krishna; Misquitta, Karen; Mitchell, Brian; Mithawala, Keyur; Mitnitski, Arnold; Mitra, Sinjini; Mittal, Gaurav; Mittner, Matthias; Miyapuram, Krishna Prasad; Mlalazi, Rebaone; Mo, Daojun; Moghekar, Abhay; Moguilner, Sebastian; Moh, Heba; Mohabir, Mark; Mohajer, Bahram; Mohamed, Moataz; Mohammadi, Sadeq; Mohammadi-Nejad, Ali-Reza; Mohammady, Saed; Taqi, Arwa Mohammed; Mohan, Kishore Kumar; Mohy-Ud-Din, Hassan; Moitra, Dipanjan; Mojaradi, Mehdi; Mojtabavi, Alireza; Molina, Helena; Mollon, Jennifer; Molteni, Erika; Montajabi, Mohaddeseh; Montal, Victor; Montazami, Aram; Monté-Rubio, Gemma; Montembeault, Maxime; Montero-Odasso, Manuel; Montillo, Albert; Moon, Byung-Seung; Moon, Chan; Moon, Chooza; Moore, Archer; Morabito, Francesco C.; Moradi, Masoud; Moraes, Renato; Ballesteros, Orlando Morales; Morales-Henriquez, Daniela; Moratal, David; Moreno, Herman; Morihara, Ryuta; Mormino, Elizabeth; Morris, Jeffrey; Mortamet, Bénédicte; Morton, John; Moscato, Pablo; Rial, Alexis Moscoso; Mossa, Abdela Ahmed; Mottaghi, Setare; Mouelhi, Aymen; Moussavi, Arezou; Moustafa, Ahmed; Mowrey, Wenzhu; Mtetwa, Lungile; Muehlboeck, Sebastian; Mueller, Susanne; Mueller-Sarnowski, Felix; Mufidah, Ratna; Mukherjee, Rik; Mukherjee, Shubhabrata; Müller, Christian; Müller, Hans-Peter; Mullins, Paul; Mullins, Roger; Muncy, Nathan; Munir, Akhtar; Munirathinam, Ramesh; Munoz, David; Munro, Catherine; Muranevici, Gabriela; Rendon, Santiago Murillo; Murilo, Robson; Murphy, Sonya; Muscio, Cristina; Musso, Gabriel; Mustafa, Yasser; Myall, Daniel; Gayathri, N.; Nabavi, Shahab; Nabeel, Eman; Nagele, Robert; Naghshbandi, Hane; Naik, Shruti; Najmitabrizi, Neda; Nakawah, Mohammad Obadah; Nalls, Mike; Namboori, Krishnan; Nancy, Annie; Napolitano, Giulio; Narayan, Manjari; Narkhede, Atul; Naseri, Mahsa; Nasrallah, Ilya; Nasrallah, Fatima; Nassif, Rana; Nath, Sruthi R.; Nathoo, Farouk; Nation, Daniel; Naughton, Brian; Nault, Larry; Nautiyal, Deeksha; Nayak, Deepak Ranjan; Naz, Mufassra; Nazemian, Shayan; Nazeri, Arash; Neckoska, Emilija; Neelamegam, Malinee; Nehary, Ebrahim; Nelson, Peter; Nelson, Linda; Nematzadeh, Hosein; Nerur, Shubha; Nesteruk, Thomas; Neu, Scott; Ng, Yen-Bee; Nguyen, Tin; Nguyen, Thanh; Nguyen, Harrison; Nguyen, Nghi; Trung, Hieu Nguyen; Ni, Lucy; Nian, Yongjian; Nichols, Thomas; Nicodemus, Kristin; Nie, Yunlong; Nielsen, Casper; Nikolov, Robert; Nila, Jessica; Nishioka, Christopher; Njeh, Ines; Njie, Emalick; Nobakht, Samaneh; Noble, Andrew; Noda, Art; Noroozi, Ali; Norton, Derek; Nosarti, Chiara; Nosheny, Rachel; Notsu, Akifumi; Novak, Gerald; Nozadi, Seyed Hossein; Nu, Fen; Nudelman, Kelly; Nunes, Adonay; Nunes, Ana; Núñez, Christian; Nuno, Michelle; Nuriel, Tal; Nygaard, Haakon; Nyquist, Paul; O'Bott, Jacob; O'Charoen, Sirimon; O'Neill, William; O'Rawe, Jonathan; Obrzut, Grzegorz; Och, Ganzorig; Odaibo, David; Odry, Benjamin; Oehmichen, Axel; Ofori, Edward; Ogunsanmi, Abdulfatai; Oguz, Kaya; Oh, Jungsu; Oh, Minyoung; Oh, Hwamee; Ohigashi, Hironori; Oishi, Kenichi; Oishi, Naoya; Okhravi, Hamid; Okonkwo, Ozioma; Okyay, Savaş; Oliveira, Cyrill; Oliveira, João; Oliveira, Francisco; Oliver, Ruth; Olmos, Salvador; Olszowy, Wiktor; Oltra-Cucarella, Javier; Önen, Zehra; Ong, Rowena; Onoda, Keiichi; Onyike, Chiadi; Operto, Grégory; Oppedal, Ketil; Orejuela, Juan; Orhon, Atila; Orozco, Max; Ortuño, Juan; Osadebey, Michael; Osborn, Joseph; Osoba, Osonde; Ostadrahimi, Hamid; Ostovari, Parisa; Otis, Sarah; Overgaard, Shauna; Owen, Catrin Elin; Oxtoby, Neil; Öziç, Muhammet Üsame; Ozkaya, Gorkem; Okur, Ozlem Ozmen; Ozsolak, Fatih; Ozyildirim, Melis; Pa, Judy; Pacheco, Joe; Pack, Gary; Padilla, Daniel; Cerezo, Berizohar Padilla; Padovese, Bruno; Pae, Chongwon; Pagano, Gennaro; Pahuja, Gunjan; Pai, Shraddha; Pajavand, Shahryar; Pajula, Juha; Pak, Kyoungjune; Pakzad, Ashkan; Palaniappan, Mathiyalagan; Palanisamy, Sindhu; Palmqvist, Sebastian; Palsson, Frosti; Pan, Dan; Pan, Tiffany; Pan, Yuqing; Pan, Wei; Pan, Sun; Pan, Hongliang; Pan, Xiaoxi; Pandey, Lokesh; Pang, Qiaoyu; Pangilinan, Erin; Pannetier, Nicolas; Panpan, Xu; Panyavaraporn, Jantana; Pardini, Matteo; Paredes, José; Parikh, Jignesh; Park, Seongbeom; Park, Young Ho; Park, Min Tae; Park, Hyunjin; Park, Sejin; Park, JongSeong; Park, DooHyun; Park, Ji Eun; Park, Yuhyun; Park, Jiyong; Parker, Jason; Parker, Richard; Parodi, Alice; Bautista, Yohn Jairo Parra; Parrish, Marcus; Parthiban, Preethy; Pascariello, Guido; Pascual, Belen; Paskov, Hristo; Pasquini, Lorenzo; Tantaleán, Julio Sergio Eduardo Pastor; Pastur, Lucas; Patel, Raihaan; Patel, Sejal; Paterson, Ross; Paton, Bryan; Patriarche, Julia; Patriat, Rémi; Pattichis, Constantinos; Paul, Debashis; Pawar, Kuldeep; Pawlak, Mikolaj; Paz, Rotem; Pedroto, Maria; Pelekanos, Matthew; Péléraux, Annick; Peng, Dan; Peng, Jing; Pengfei, Tian; Perani, Daniela; Peraza, Luis; Pereira, Fabricio; Pereira, Francisco; Perkins, Diana; Perneczky, Robert; Persad, Umesh; Peter, Jessica; Peters, Mette; Peters, Ruth; Pether, Mark; Petrella, Jeffrey; Petrenko, Roman; Petrone, Paula; Petrov, Dmitry; Pezzatini, Daniele; Pfenning, Andreas; Pham, Chi-Tuan; Philipson, Pete; Phillips, Jeffrey; Phillips, Nicole; Phophalia, Ashish; Phuah, Chia-Ling; Pichai, Shanthi; Pichardo, Cesar; Binette, Alexa Pichet; Pietras, Olga; Pietrzyk, Mariusz; Pike, Kerryn; Pillai, Jagan; Piludu, Francesca; Pineda, Joanna; Ping, He; Pirraglia, Elizabeth; Pither, Richard; Piyush, Ranjan; Pizzi, Nick; Gonzalez, Luis Fernando Planella; Plassard, Andrew; Platero, Carlos; Plocharski, Maciej; Podhorski, Adam; Poggiali, Davide; Poghosyan, Mher; Pohl, Kilian; Poirier, Judes; Polakow, Jean Jacques; Politis, Marios; Poljak, Anne; Poloni, Katia Maria; Poole, Victoria; Poppenk, Jordan; Porsteinsson, Anton; Portelius, Erik; Posta, Filippo; Posthuma, Danielle; 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    2017-01-01

    Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions,

  8. Introduction to the feature section on functional imaging of the pelvic floor.

    Science.gov (United States)

    Maccioni, Francesca

    2013-10-01

    This is the introduction to the feature section of functional imaging of the pelvic floor, which includes 6 articles, two focused on clinical issues, while four on radiological aspects, mostly on dynamic pelvic floor MRI.

  9. Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image

    International Nuclear Information System (INIS)

    Wang Huan; Guo Xiuhua; Jia Zhongwei; Li Hongkai; Liang Zhigang; Li Kuncheng; He Qian

    2010-01-01

    Purpose: To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image. Materials and methods: Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights. Results: Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P < 0.05) between benign and malignant small solitary pulmonary nodules. Conclusion: Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.

  10. Skeletal deformities of acardius anceps: the gross and imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Chen Chihping [Dept. of Medical Research, Mackay Memorial Hospital, Taipei (Taiwan, Province of China); Shih Shinlin [Dept. of Radiology, Mackay Memorial Hospital, Taipei (Taiwan, Province of China); Liu Fenfen [Dept. of Medical Research, Mackay Memorial Hospital, Taipei (Taiwan, Province of China); Jan Sheauwen [Dept. of Medical Research, Mackay Memorial Hospital, Taipei (Taiwan, Province of China); Lin Yunnan [Dept. of Pathology, Mackay Memorial Hospital, Taipei (Taiwan, Province of China); Lan Chungchi [Dept. of Obstetrics and Gynecology, Mackay Memorial Hospital, Taipei (Taiwan, Province of China)

    1997-03-01

    A morphology based imaging review is presented of the characteristic skeletal deformities associated with acardius anceps in three acardiac twins. These fetuses demonstrated poorly developed skulls, limb reduction defects, and phocomelia of the upper limbs, as well as narrow thoracic cages with or without the complete development of ribs, clavicles, scapulae, and cervical, thoracic, or lumbar vertebrae. However, their lower limbs and pelvic girdles were almost normal. The authors conclude that skeletal development is likely to be jeopardized in the area adjacent to the heart and in the cephalic portion of the body in fetuses with acardius anceps, and suggest vascular deficiency and hypoperfusion as pathogenetic mechanisms in this type of skeletal deformity. (orig.)

  11. Skeletal deformities of acardius anceps: the gross and imaging features

    International Nuclear Information System (INIS)

    Chen Chihping; Shih Shinlin; Liu Fenfen; Jan Sheauwen; Lin Yunnan; Lan Chungchi

    1997-01-01

    A morphology based imaging review is presented of the characteristic skeletal deformities associated with acardius anceps in three acardiac twins. These fetuses demonstrated poorly developed skulls, limb reduction defects, and phocomelia of the upper limbs, as well as narrow thoracic cages with or without the complete development of ribs, clavicles, scapulae, and cervical, thoracic, or lumbar vertebrae. However, their lower limbs and pelvic girdles were almost normal. The authors conclude that skeletal development is likely to be jeopardized in the area adjacent to the heart and in the cephalic portion of the body in fetuses with acardius anceps, and suggest vascular deficiency and hypoperfusion as pathogenetic mechanisms in this type of skeletal deformity. (orig.)

  12. Imaging Features of Pediatric Pentastomiasis Infection: a Case Report

    Energy Technology Data Exchange (ETDEWEB)

    Lai, Can; Wang, Xi Qun; Lin, Long; Gao, De Chun; Zhang, Hong Xi; Zhang, Yi Ying; Zhou, Yin Bao [Zhejiang University School of Medicine, Zhejiang (China)

    2010-08-15

    We report here a case of pentastomiasis infection in a 3-year-old girl who had high fever, abdominal pain, abdominal tension and anemia. Ultrasound scanning of the abdomen revealed disseminated hyperechoic nodules in the liver and a small amount of ascites. Abdominal MRI showed marked hepatomegaly with disseminated miliary nodules of high signal intensity throughout the hepatic parenchyma on T2-weighted images; retroperitoneal lymphadenopathy and disseminated miliary nodules on the peritoneum were also noted. Chest CT showed scattered small hyperdense nodules on both sides of the lungs. The laparoscopy demonstrated diffuse white nodules on the liver surface and the peritoneum. After the small intestinal wall and peritoneal biopsy, histological examination revealed parenchymal tubercles containing several larvae of pentastomids and a large amount of inflammatory cell infiltration around them. The pathological diagnosis was parasitic granuloma from pentastomiasis infection

  13. Imaging Features of Pediatric Pentastomiasis Infection: a Case Report

    International Nuclear Information System (INIS)

    Lai, Can; Wang, Xi Qun; Lin, Long; Gao, De Chun; Zhang, Hong Xi; Zhang, Yi Ying; Zhou, Yin Bao

    2010-01-01

    We report here a case of pentastomiasis infection in a 3-year-old girl who had high fever, abdominal pain, abdominal tension and anemia. Ultrasound scanning of the abdomen revealed disseminated hyperechoic nodules in the liver and a small amount of ascites. Abdominal MRI showed marked hepatomegaly with disseminated miliary nodules of high signal intensity throughout the hepatic parenchyma on T2-weighted images; retroperitoneal lymphadenopathy and disseminated miliary nodules on the peritoneum were also noted. Chest CT showed scattered small hyperdense nodules on both sides of the lungs. The laparoscopy demonstrated diffuse white nodules on the liver surface and the peritoneum. After the small intestinal wall and peritoneal biopsy, histological examination revealed parenchymal tubercles containing several larvae of pentastomids and a large amount of inflammatory cell infiltration around them. The pathological diagnosis was parasitic granuloma from pentastomiasis infection

  14. Image features of herniation pit of the femoral neck

    International Nuclear Information System (INIS)

    Zhang Xuezhe; Li Guangming; Wang Cunli; Wang Guimin

    2008-01-01

    Objective: To evaluate imaging appearances of herniation pit of the femoral neck. Methods: We retrospectively analyzed the X-ray, CT and MRI findings of 9 patients with herniation pit of the femoral neck. All nine patients were male with the age ranging from 21 to 73 years. They had pain in the hip from two months to two years duration. Results: The bilateral hips were affected in six patients, the right hips in the other 3 patients. Of the nine patients, X-ray plain films (2 cases), CT scanning(6 cases), and MR scanning (5 cases ) were performed. The size of the lesions ranged from 0.5 cm x 0.6 cm to 1.0 cm x 1.5 cm, located in the anterosuperior portion of the femoral neck (n=7) or anteroinferior portion (n=2). X-ray plain films showed an osteolytic lesion surrounded by a sclerotic rim. CT scanning showed the lesion just below the cortex of the femoral neck surrounded by a rim of sclerosis or associated with a small cortical break in two patients. MR scanning showed low signal intensity in five patients on T 1 WI and high signal intensity surrounded by a rim of low signal intensity (n=3) or low signal intensity (n=2) on T 2 WI, and high signal intensity on fat suppression MR image. A small joint effusion was observed in two cases on T 2 WI. Conclusion: The CT and MRI findings of herniation pit of the femoral neck are characteristic, it is useful in defining the diagnosis of the herniation pit of the femoral neck. (authors)

  15. Global Journalism Ethics: Widening the Conceptual Base

    Directory of Open Access Journals (Sweden)

    Stephen J. A. Ward

    2008-01-01

    Full Text Available For most of its history, journalism ethics has been highly practical in aim, in theorizing, and in application. Inquiry analyzed what was occurring inside newsrooms and its scope was parochial. Starting from the premise that a parochial approach no longer serves journalism, the study of journalism, or the public of journalism, in this paper it is argued that a major task of journalism ethics is to construct a more non-parochial ethics—a global journalism ethics informed by critical work from various disciplines and cultures. The discussion presented charts the trajectory of journalism ethics over several centuries to explain the role of parochialism and the limits of theorizing in journalism ethics. This historical perspective also serves as a foundation for outlining what a future journalism ethics might look like, if we widen the conceptual base by incorporating new knowledge of media from outside journalism ethics, and by redefining journalism ethics as a global enterprise.

  16. Classification of radiolarian images with hand-crafted and deep features

    Science.gov (United States)

    Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

    2017-12-01

    Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

  17. Graduate entry to medicine: widening psychological diversity

    Directory of Open Access Journals (Sweden)

    Munro Don

    2009-11-01

    Full Text Available Abstract Background At Nottingham University more than 95% of entrants to the traditional 5-year medical course are school leavers. Since 2003 we have admitted graduate entrants (GEM to a shortened (4-year course to 'widen access to students from more disadvantaged backgrounds'. We have recently shown that the GEM course widens academic and socio-demographic diversity of the medical student population. This study explored whether GEM students also bring psychological diversity and whether this could be beneficial. Methods We studied: a 217 and 96 applicants to the Nottingham 5- and 4-year courses respectively, applying in the 2002-3 UCAS cycle, and, b 246 school leavers starting the 5-year course and 39 graduate entrants to the 4-year course in October 2003. The psychological profiles of the two groups of applicants and two groups of entrants were compared using their performance in the Goldberg 'Big 5' Personality test, the Personal Qualities Assessment (PQA; measuring interpersonal traits and interpersonal values, and the Lovibond and Lovibond measure of depression, anxiety and stress. For the comparison of the Entrants we excluded the 33 school leavers and seven graduates who took the tests as Applicants. Statistical analyses were undertaken using SPSS software (version 16.0. Results Graduate applicants compared to school leaver applicants were significantly more conscientious, more confident, more self controlled, more communitarian in moral orientation and less anxious. Only one of these differences was preserved in the entrants with graduates being less anxious. However, the graduate entrants were significantly less empathetic and conscientious than the school leavers. Conclusion This study has shown that school leaver and graduate entrants to medical school differ in some psychological characteristics. However, if confirmed in other studies and if they were manifest in the extreme, not all the traits brought by graduates would be

  18. A Feature Subtraction Method for Image Based Kinship Verification under Uncontrolled Environments

    DEFF Research Database (Denmark)

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    The most fundamental problem of local feature based kinship verification methods is that a local feature can capture the variations of environmental conditions and the differences between two persons having a kin relation, which can significantly decrease the performance. To address this problem...... the feature distance between face image pairs with kinship and maximize the distance between non-kinship pairs. Based on the subtracted feature, the verification is realized through a simple Gaussian based distance comparison method. Experiments on two public databases show that the feature subtraction method...

  19. Edge enhancement and noise suppression for infrared image based on feature analysis

    Science.gov (United States)

    Jiang, Meng

    2018-06-01

    Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

  20. Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

    Directory of Open Access Journals (Sweden)

    Shibin Wu

    2013-01-01

    Full Text Available A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR, and contrast improvement index (CII.

  1. Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

    Directory of Open Access Journals (Sweden)

    Mei Yu

    2012-01-01

    Full Text Available The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT scans. The algorithm includes mainly two processes: (1 distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2 representation using bag of visual words (BoW based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

  2. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

    International Nuclear Information System (INIS)

    Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.

    2015-01-01

    Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

  3. UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

    Directory of Open Access Journals (Sweden)

    A. Kianisarkaleh

    2015-12-01

    Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

  4. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

    Science.gov (United States)

    Galavis, Paulina E; Hollensen, Christian; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert

    2010-10-01

    Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor

  5. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

    International Nuclear Information System (INIS)

    Galavis, Paulina E.; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert; Hollensen, Christian

    2010-01-01

    Background. Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [ 18 F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range = 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% = range = 25%) were entropy-GLCM, sum entropy, high gray level run emphasis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion. Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be

  6. Improved medical image modality classification using a combination of visual and textual features.

    Science.gov (United States)

    Dimitrovski, Ivica; Kocev, Dragi; Kitanovski, Ivan; Loskovska, Suzana; Džeroski, Sašo

    2015-01-01

    In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariant feature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results from the extensive experimental evaluation identify the SIFT and opponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Extended feature-fusion guidelines to improve image-based multi-modal biometrics

    CSIR Research Space (South Africa)

    Brown, Dane

    2016-09-01

    Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

  8. Profiles of US and CT imaging features with a high probability of appendicitis

    NARCIS (Netherlands)

    van Randen, A.; Laméris, W.; van Es, H.W.; ten Hove, W.; Bouma, W.H.; van Leeuwen, M.S.; van Keulen, E.M.; van der Hulst, V.P.M.; Henneman, O.D.; Bossuyt, P.M.; Boermeester, M.A.; Stoker, J.

    2010-01-01

    To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with

  9. Schnitzler's syndrome: an unusual cause of bone pain with suggestive nuclear imaging features

    International Nuclear Information System (INIS)

    Raedt, N. de; Mortelmans, L.

    2009-01-01

    Schnitzler's syndrome is a rare inflammatory disorder characterised by chronic urticarial rash and monoclonal IgM gammopathy accompanied by at least two of the following features: fever, arthralgia or arthritis, bone pain, lymphadenopathy, hepato- or splenomegaly, leucocytosis and elevated sedimentation. The association of these clinical and biological features with radiographic and bone scan findings are suggestive of the disease. The case of a 37-year-old man presenting with Schnitzler's syndrome, emphasizing nuclear imaging features is reported here. (N.C.)

  10. The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2014-09-01

    Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

  11. Learning effective color features for content based image retrieval in dermatology

    NARCIS (Netherlands)

    Bunte, Kerstin; Biehl, Michael; Jonkman, Marcel F.; Petkov, Nicolai

    We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn

  12. Comparison of clustering methods for tracking features in RGB-D images

    CSIR Research Space (South Africa)

    Pancham, Ardhisha

    2016-10-01

    Full Text Available difficult to track individually over an image sequence. Clustering techniques have been recommended and used to cluster image features to improve tracking results. New and affordable RGB-D cameras, provide both color and depth information. This paper...

  13. Multi-example feature-constrained back-projection method for image super-resolution

    Institute of Scientific and Technical Information of China (English)

    Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

    2017-01-01

    Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

  14. Features and limitations of mobile tablet devices for viewing radiological images.

    Science.gov (United States)

    Grunert, J H

    2015-03-01

    Mobile radiological image display systems are becoming increasingly common, necessitating a comparison of the features of these systems, specifically the operating system employed, connection to stationary PACS, data security and rang of image display and image analysis functions. In the fall of 2013, a total of 17 PACS suppliers were surveyed regarding the technical features of 18 mobile radiological image display systems using a standardized questionnaire. The study also examined to what extent the technical specifications of the mobile image display systems satisfy the provisions of the Germany Medical Devices Act as well as the provisions of the German X-ray ordinance (RöV). There are clear differences in terms of how the mobile systems connected to the stationary PACS. Web-based solutions allow the mobile image display systems to function independently of their operating systems. The examined systems differed very little in terms of image display and image analysis functions. Mobile image display systems complement stationary PACS and can be used to view images. The impacts of the new quality assurance guidelines (QS-RL) as well as the upcoming new standard DIN 6868 - 157 on the acceptance testing of mobile image display units for the purpose of image evaluation are discussed. © Georg Thieme Verlag KG Stuttgart · New York.

  15. A blur-invariant local feature for motion blurred image matching

    Science.gov (United States)

    Tong, Qiang; Aoki, Terumasa

    2017-07-01

    Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

  16. Malignant fibrous histiocytoma of bone: conventional X-ray and MR imaging features

    International Nuclear Information System (INIS)

    Link, T.M.; Haeussler, M.D.; Poppek, S.; Woertler, K.; Rummeny, E.J.; Blasius, S.; Lindner, N.

    1998-01-01

    Objective. To evaluate the conventional X-ray and MR imaging features of malignant fibrous histiocytoma (MFH) of bone. Design. MRI examinations and conventional radiographs were reviewed in 39 patients with biopsy-proven MFH. Imaging characteristics were analyzed and the differential diagnoses assessed in a masked fashion by two experienced radiologists. Results. Typical X-ray features included aggressive, destructive tumor growth centrally located in the metaphysis of long bones. Periosteal reactions and expansive growth were rarely seen. On MR images extraosseous tumor spread was frequently noted. On T2-weighted images and contrast-enhanced T1-weighted images most of the tumors displayed an inhomogeneous, nodular signal pattern with peripheral Gd-DTPA enhancement. Conclusions. Although several MR imaging criteria were typical for MFH none of them was specific. X-ray diagnosis of MFH may also prove difficult, with the main differential diagnosis being metastasis in the older and osteosarcoma in the younger population. (orig.)

  17. Extending the MEDAS Feature Dictionary to Support Access to Radiological Images

    OpenAIRE

    Kaufman, Bryan L.; Naeymi-Rad, Frank; Charletta, Dale A.; Kepic, Anna; Trace, David A.; Naeymirad, Shon; Carmony, Lowell; Spigos, Dimitrios; Evens, Martha

    1989-01-01

    This paper discusses a method of adding a library of radiological images to MEDAS (the Medical Emergency Decision Assistance System). This library is interfaced with the MEDAS Feature Dictionary [1, 2], a dictionary containing terminology for MEDAS knowledge bases. The connections between the radiological images and the terms in the dictionary are used in two ways: 1) To retrieve the images with free text queries. 2) To help in the evaluation of radiological findings during the diagnostic cyc...

  18. Unexpected diagnosis of superficial neurofibroma in a lesion with imaging features of a vascular malformation

    Energy Technology Data Exchange (ETDEWEB)

    O' Keefe, Patrick; Reid, Janet; Morrison, Stuart [Cleveland Clinic Foundation, Department of Radiology, Cleveland, OH (United States); Vidimos, Allison [Cleveland Clinic Foundation, Department of Dermatology, Cleveland, OH (United States); DiFiore, John [Cleveland Clinic Foundation, Department of Pediatric Surgery, Cleveland, OH (United States)

    2005-12-01

    Plexiform neurofibroma is a pathognomonic, often disabling feature of neurofibromatosis type I. Although the target-like appearance of deep plexiform neurofibroma on T2-weighted MRI has been well-described, a second superficial form of plexiform neurofibroma has differing imaging features. We report a 15-year-old boy who presented with multiple cutaneous lesions exhibiting clinical and imaging characteristics of a venolymphatic malformation. These lesions were histologically proved to represent superficial plexiform neurofibromas. We wish to emphasize the unique MR findings of superficial plexiform neurofibromas; these findings are different from the imaging characteristics of the deep form and can be confused with a low-flow vascular malformation. (orig.)

  19. Imaging features of nontumorous conditions involving the trachea and main-stem bronchi

    International Nuclear Information System (INIS)

    Jeon, Kyung Nyeo; Kang, Duk Sik; Bae, Kyung Soo

    2002-01-01

    A number of nontumorous diseases may affect the trachea and main-stem bronchi, and their nonspecific symptoms may include coughing, dyspnea, wheezing and stridor. The clinical course is often long-term and a misdiagnosis of bronchial asthma is common. The imaging findings of these nontumorous conditions are, however, relatively characteristic, and diagnosis either without or in conjunction with clinical information is often possible. For specific diagnosis, recognition of their imaging features is therefore of prime importance. In this pictorial essay, we illustrate the imaging features of various nontumorous conditions involving the trachea and main-stem bronchi

  20. Profiles of US and CT imaging features with a high probability of appendicitis

    International Nuclear Information System (INIS)

    Randen, A. van; Lameris, W.; Es, H.W. van; Hove, W. ten; Bouma, W.H.; Leeuwen, M.S. van; Keulen, E.M. van; Hulst, V.P.M. van der; Henneman, O.D.; Bossuyt, P.M.; Boermeester, M.A.; Stoker, J.

    2010-01-01

    To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

  1. Profiles of US and CT imaging features with a high probability of appendicitis

    Energy Technology Data Exchange (ETDEWEB)

    Randen, A. van; Lameris, W. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands); University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Es, H.W. van [St Antonius Hospital, Department of Radiology, Nieuwegein (Netherlands); Hove, W. ten; Bouma, W.H. [Gelre Hospitals, Department of Surgery, Apeldoorn (Netherlands); Leeuwen, M.S. van [University Medical Centre, Department of Radiology, Utrecht (Netherlands); Keulen, E.M. van [Tergooi Hospitals, Department of Radiology, Hilversum (Netherlands); Hulst, V.P.M. van der [Onze Lieve Vrouwe Gasthuis, Department of Radiology, Amsterdam (Netherlands); Henneman, O.D. [Bronovo Hospital, Department of Radiology, The Hague (Netherlands); Bossuyt, P.M. [University of Amsterdam, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, Amsterdam (Netherlands); Boermeester, M.A. [University of Amsterdam, Department of Surgery, Academic Medical Center, Amsterdam (Netherlands); Stoker, J. [University of Amsterdam, Department of Radiology, Academic Medical Center, Amsterdam (Netherlands)

    2010-07-15

    To identify and evaluate profiles of US and CT features associated with acute appendicitis. Consecutive patients presenting with acute abdominal pain at the emergency department were invited to participate in this study. All patients underwent US and CT. Imaging features known to be associated with appendicitis, and an imaging diagnosis were prospectively recorded by two independent radiologists. A final diagnosis was assigned after 6 months. Associations between appendiceal imaging features and a final diagnosis of appendicitis were evaluated with logistic regression analysis. Appendicitis was assigned to 284 of 942 evaluated patients (30%). All evaluated features were associated with appendicitis. Imaging profiles were created after multivariable logistic regression analysis. Of 147 patients with a thickened appendix, local transducer tenderness and peri-appendiceal fat infiltration on US, 139 (95%) had appendicitis. On CT, 119 patients in whom the appendix was completely visualised, thickened, with peri-appendiceal fat infiltration and appendiceal enhancement, 114 had a final diagnosis of appendicitis (96%). When at least two of these essential features were present on US or CT, sensitivity was 92% (95% CI 89-96%) and 96% (95% CI 93-98%), respectively. Most patients with appendicitis can be categorised within a few imaging profiles on US and CT. When two of the essential features are present the diagnosis of appendicitis can be made accurately. (orig.)

  2. Automatic detection of solar features in HSOS full-disk solar images using guided filter

    Science.gov (United States)

    Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

    2018-02-01

    A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

  3. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes.

    Science.gov (United States)

    Zhong, Zichun; Guo, Xiaohu; Cai, Yiqi; Yang, Yin; Wang, Jing; Jia, Xun; Mao, Weihua

    2016-01-01

    By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

  4. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

    Directory of Open Access Journals (Sweden)

    Zichun Zhong

    2016-01-01

    Full Text Available By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

  5. Segmentation of Clinical Endoscopic Images Based on the Classification of Topological Vector Features

    Directory of Open Access Journals (Sweden)

    O. A. Dunaeva

    2013-01-01

    Full Text Available In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.

  6. A unified framework of image latent feature learning on Sina microblog

    Science.gov (United States)

    Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui

    2015-10-01

    Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.

  7. Computed Tomography and Magnetic Resonance Imaging Features of the Temporomandibular Joint in Two Normal Camels

    Directory of Open Access Journals (Sweden)

    Alberto Arencibia

    2012-01-01

    Full Text Available Computed tomography (CT and magnetic resonance (MR image features of the temporomandibular joint (TMJ and associated structures in two mature dromedary camels were obtained with a third-generation equipment CT and a superconducting magnet RM at 1.5 Tesla. Images were acquired in sagittal and transverse planes. Medical imaging processing with imaging software was applied to obtain postprocessing CT and MR images. Relevant anatomic structures were identified and labelled. The resulting images provided excellent anatomic detail of the TMJ and associated structures. Annotated CT and MR images from this study are intended as an anatomical reference useful in the interpretation for clinical CT and MR imaging studies of the TMJ of the dromedary camels.

  8. Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

    Directory of Open Access Journals (Sweden)

    Seymour Rowan

    2008-01-01

    Full Text Available Abstract We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

  9. Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

    Directory of Open Access Journals (Sweden)

    Ji Ming

    2008-03-01

    Full Text Available We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

  10. Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

    Directory of Open Access Journals (Sweden)

    R. Youmaran

    2012-01-01

    Full Text Available This paper develops an approach to measure the information content in a biometric feature representation of iris images. In this context, the biometric feature information is calculated using the relative entropy between the intraclass and interclass feature distributions. The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates processed using Principal-Component Analysis- (PCA- and Independent-Component Analysis- (ICA- based feature decomposition schemes. From this, the biometric feature information is calculated to be approximately 278 bits for PCA and 288 bits for ICA iris features using Masek's iris recognition scheme. This value approximately matches previous estimates of iris information content.

  11. The algorithm of fast image stitching based on multi-feature extraction

    Science.gov (United States)

    Yang, Chunde; Wu, Ge; Shi, Jing

    2018-05-01

    This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

  12. High resolution satellite image indexing and retrieval using SURF features and bag of visual words

    Science.gov (United States)

    Bouteldja, Samia; Kourgli, Assia

    2017-03-01

    In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

  13. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    International Nuclear Information System (INIS)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

    2015-01-01

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy

  14. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Hollingsworth, Alan B. [Mercy Women’s Center, Mercy Health Center, Oklahoma City, Oklahoma 73120 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 (United States)

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  15. Automatic plankton image classification combining multiple view features via multiple kernel learning.

    Science.gov (United States)

    Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

    2017-12-28

    Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

  16. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

    Science.gov (United States)

    Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

    2017-10-06

    The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

  17. Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

    Science.gov (United States)

    Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

    2017-03-01

    Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

  18. Axial vessel widening in arborescent monocots.

    Science.gov (United States)

    Petit, Giai; DeClerck, Fabrice A J; Carrer, Marco; Anfodillo, Tommaso

    2014-02-01

    Dicotyledons have evolved a strategy to compensate for the increase in hydraulic resistance to water transport with height growth by widening xylem conduits downwards. In monocots, the accumulation of hydraulic resistance with height should be similar, but the absence of secondary growth represents a strong limitation for the maintenance of xylem hydraulic efficiency during ontogeny. The hydraulic architecture of monocots has been studied but it is unclear how monocots arrange their axial vascular structure during ontogeny to compensate for increases in height. We measured the vessel lumina and estimated the hydraulic diameter (Dh) at different heights along the stem of two arborescent monocots, Bactris gasipaes (Kunth) and Guadua angustifolia (Kunth). For the former, we also estimated the variation in Dh along the leaf rachis. Hydraulic diameter increased basally from the stem apex to the base with a scaling exponent (b) in the range of those reported for dicot trees (b = 0.22 in B. gasipaes; b = 0.31 and 0.23 in G. angustifolia). In B. gasipaes, vessels decrease in Dh from the stem's centre towards the periphery, an opposite pattern compared with dicot trees. Along the leaf rachis, a pattern of increasing Dh basally was also found (b = 0.13). The hydraulic design of the monocots studied revealed an axial pattern of xylem conduits similar to those evolved by dicots to compensate and minimize the negative effect of root-to-leaf length on hydrodynamic resistance to water flow.

  19. Learning representative features for facial images based on a modified principal component analysis

    Science.gov (United States)

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  20. A change detection method for remote sensing image based on LBP and SURF feature

    Science.gov (United States)

    Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

    2018-04-01

    Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

  1. An age estimation method using brain local features for T1-weighted images.

    Science.gov (United States)

    Kondo, Chihiro; Ito, Koichi; Kai Wu; Sato, Kazunori; Taki, Yasuyuki; Fukuda, Hiroshi; Aoki, Takafumi

    2015-08-01

    Previous statistical analysis studies using large-scale brain magnetic resonance (MR) image databases have examined that brain tissues have age-related morphological changes. This fact indicates that one can estimate the age of a subject from his/her brain MR image by evaluating morphological changes with healthy aging. This paper proposes an age estimation method using local features extracted from T1-weighted MR images. The brain local features are defined by volumes of brain tissues parcellated into local regions defined by the automated anatomical labeling atlas. The proposed method selects optimal local regions to improve the performance of age estimation. We evaluate performance of the proposed method using 1,146 T1-weighted images from a Japanese MR image database. We also discuss the medical implication of selected optimal local regions.

  2. Side-Scan Sonar Image Mosaic Using Couple Feature Points with Constraint of Track Line Positions

    Directory of Open Access Journals (Sweden)

    Jianhu Zhao

    2018-06-01

    Full Text Available To obtain large-scale seabed surface image, this paper proposes a side-scan sonar (SSS image mosaic method using couple feature points (CFPs with constraint of track line positions. The SSS geocoded images are firstly used to form a coarsely mosaicked one and the overlapping areas between adjacent strip images can be determined based on geographic information. Inside the overlapping areas, the feature point (FP detection and registration operation are adopted for both strips. According to the detected CFPs and track line positions, an adjustment model is established to accommodate complex local distortions as well as ensure the global stability. This proposed method effectively solves the problem of target ghosting or dislocation and no accumulated errors arise in the mosaicking process. Experimental results show that the finally mosaicked image correctly reflects the object distribution, which is meaningful for understanding and interpreting seabed topography.

  3. Image features dependant correlation-weighting function for efficient PRNU based source camera identification.

    Science.gov (United States)

    Tiwari, Mayank; Gupta, Bhupendra

    2018-04-01

    For source camera identification (SCI), photo response non-uniformity (PRNU) has been widely used as the fingerprint of the camera. The PRNU is extracted from the image by applying a de-noising filter then taking the difference between the original image and the de-noised image. However, it is observed that intensity-based features and high-frequency details (edges and texture) of the image, effect quality of the extracted PRNU. This effects correlation calculation and creates problems in SCI. For solving this problem, we propose a weighting function based on image features. We have experimentally identified image features (intensity and high-frequency contents) effect on the estimated PRNU, and then develop a weighting function which gives higher weights to image regions which give reliable PRNU and at the same point it gives comparatively less weights to the image regions which do not give reliable PRNU. Experimental results show that the proposed weighting function is able to improve the accuracy of SCI up to a great extent. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

    Directory of Open Access Journals (Sweden)

    Pradipta Maji

    Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

  5. An efficient fractal image coding algorithm using unified feature and DCT

    International Nuclear Information System (INIS)

    Zhou Yiming; Zhang Chao; Zhang Zengke

    2009-01-01

    Fractal image compression is a promising technique to improve the efficiency of image storage and image transmission with high compression ratio, however, the huge time consumption for the fractal image coding is a great obstacle to the practical applications. In order to improve the fractal image coding, efficient fractal image coding algorithms using a special unified feature and a DCT coder are proposed in this paper. Firstly, based on a necessary condition to the best matching search rule during fractal image coding, the fast algorithm using a special unified feature (UFC) is addressed, and it can reduce the search space obviously and exclude most inappropriate matching subblocks before the best matching search. Secondly, on the basis of UFC algorithm, in order to improve the quality of the reconstructed image, a DCT coder is combined to construct a hybrid fractal image algorithm (DUFC). Experimental results show that the proposed algorithms can obtain good quality of the reconstructed images and need much less time than the baseline fractal coding algorithm.

  6. Feature selection from a facial image for distinction of sasang constitution.

    Science.gov (United States)

    Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

    2009-09-01

    Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

  7. Feature Selection from a Facial Image for Distinction of Sasang Constitution

    Directory of Open Access Journals (Sweden)

    Imhoi Koo

    2009-01-01

    Full Text Available Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

  8. Feature Selection from a Facial Image for Distinction of Sasang Constitution

    Science.gov (United States)

    Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

    2009-01-01

    Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

  9. Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

    Science.gov (United States)

    Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

    Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

  10. Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

    Directory of Open Access Journals (Sweden)

    Fang Yang

    2017-01-01

    Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

  11. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

    Science.gov (United States)

    Ma, Ling; Liu, Xiabi; Fei, Baowei

    2017-01-01

    Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

  12. Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

    Directory of Open Access Journals (Sweden)

    Jie Zhao

    2013-01-01

    Full Text Available Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

  13. Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

    Science.gov (United States)

    Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

    2018-03-01

    Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

  14. Image cytometric nuclear texture features in inoperable head and neck cancer: a pilot study

    International Nuclear Information System (INIS)

    Strojan-Flezar, Margareta; Lavrencak, Jaka; Zganec, Mario; Strojan, Primoz

    2011-01-01

    Image cytometry can measure numerous nuclear features which could be considered a surrogate end-point marker of molecular genetic changes in a nucleus. The aim of the study was to analyze image cytometric nuclear features in paired samples of primary tumor and neck metastasis in patients with inoperable carcinoma of the head and neck. Image cytometric analysis of cell suspensions prepared from primary tumor tissue and fine needle aspiration biopsy cell samples of neck metastases from 21 patients treated with concomitant radiochemotherapy was performed. Nuclear features were correlated with clinical characteristics and response to therapy. Manifestation of distant metastases and new primaries was associated (p<0.05) with several chromatin characteristics from primary tumor cells, whereas the origin of index cancer and disease response in the neck was related to those in the cells from metastases. Many nuclear features of primary tumors and metastases correlated with the TNM stage. A specific pattern of correlation between well-established prognostic indicators and nuclear features of samples from primary tumors and those from neck metastases was observed. Image cytometric nuclear features represent a promising candidate marker for recognition of biologically different tumor subgroups

  15. Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

    Science.gov (United States)

    Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

    2018-04-01

    This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

  16. New method for identifying features of an image on a digital video display

    Science.gov (United States)

    Doyle, Michael D.

    1991-04-01

    The MetaMap process extends the concept of direct manipulation human-computer interfaces to new limits. Its specific capabilities include the correlation of discrete image elements to relevant text information and the correlation of these image features to other images as well as to program control mechanisms. The correlation is accomplished through reprogramming of both the color map and the image so that discrete image elements comprise unique sets of color indices. This process allows the correlation to be accomplished with very efficient data storage and program execution times. Image databases adapted to this process become object-oriented as a result. Very sophisticated interrelationships can be set up between images text and program control mechanisms using this process. An application of this interfacing process to the design of an interactive atlas of medical histology as well as other possible applications are described. The MetaMap process is protected by U. S. patent #4

  17. Using image quality measures and features to choose good images for classification of ISAR imagery

    CSIR Research Space (South Africa)

    Steyn, JM

    2014-10-01

    Full Text Available the quality measures and to determine the minimum dwell-time for ISAR image formation. Keywords—ISAR (inverse synthetic aperture radar), Dwell-time, Quality Measure, Image Contrast, Image Entropy, SNR (signal-to-noise ratio), Maritime Vessels ...

  18. Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

    Directory of Open Access Journals (Sweden)

    Minh-Tan Pham

    2017-10-01

    Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

  19. A unified framework for image retrieval using keyword and visual features.

    Science.gov (United States)

    Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

    2005-07-01

    In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

  20. Rosai-Dorfman Disease with Epidural and Spinal Bone Marrow Involvement: Magnetic Resonance Imaging and Diffusion-Weighted Imaging Features

    International Nuclear Information System (INIS)

    Oner, A.Y.; Akpek, S.; Tali, T.

    2007-01-01

    Sinus histiocytosis with massive lymphadenopathy (SHML), or Rosai-Dorfman disease, is a rare histiocytic disorder that typically presents with chronic, self-limiting cervical lymphadenopathy. Although this disease mainly affects histiocytes, there are a few reports of bone marrow infiltration. Diffusion-weighted imaging (DWI) is a promising technology in differentiating between various bone marrow pathologies. We here present conventional magnetic resonance imaging and DWI features of a patient with SHML and bone marrow involvement

  1. Rosai-Dorfman Disease with Epidural and Spinal Bone Marrow Involvement: Magnetic Resonance Imaging and Diffusion-Weighted Imaging Features

    Energy Technology Data Exchange (ETDEWEB)

    Oner, A.Y.; Akpek, S.; Tali, T. [Dept. of Radiology, Gazi Univ. School of Medicine. Besevler-Ankara (Turkey)

    2007-04-15

    Sinus histiocytosis with massive lymphadenopathy (SHML), or Rosai-Dorfman disease, is a rare histiocytic disorder that typically presents with chronic, self-limiting cervical lymphadenopathy. Although this disease mainly affects histiocytes, there are a few reports of bone marrow infiltration. Diffusion-weighted imaging (DWI) is a promising technology in differentiating between various bone marrow pathologies. We here present conventional magnetic resonance imaging and DWI features of a patient with SHML and bone marrow involvement.

  2. Imaging features of intracerebral hemorrhage with cerebral amyloid angiopathy: Systematic review and meta-analysis.

    Directory of Open Access Journals (Sweden)

    Neshika Samarasekera

    Full Text Available We sought to summarize Computed Tomography (CT/Magnetic Resonance Imaging (MRI features of intracerebral hemorrhage (ICH associated with cerebral amyloid angiopathy (CAA in published observational radio-pathological studies.In November 2016, two authors searched OVID Medline (1946-, Embase (1974- and relevant bibliographies for studies of imaging features of lobar or cerebellar ICH with pathologically proven CAA ("CAA-associated ICH". Two authors assessed studies' diagnostic test accuracy methodology and independently extracted data.We identified 22 studies (21 cases series and one cross-sectional study with controls of CT features in 297 adults, two cross-sectional studies of MRI features in 81 adults and one study which reported both CT and MRI features in 22 adults. Methods of CAA assessment varied, and rating of imaging features was not masked to pathology. The most frequently reported CT features of CAA-associated ICH in 21 case series were: subarachnoid extension (pooled proportion 82%, 95% CI 69-93%, I2 = 51%, 12 studies and an irregular ICH border (64%, 95% CI 32-91%, I2 = 85%, five studies. CAA-associated ICH was more likely to be multiple on CT than non-CAA ICH in one cross-sectional study (CAA-associated ICH 7/41 vs. non-CAA ICH 0/42; χ2 = 7.8, p = 0.005. Superficial siderosis on MRI was present in 52% of CAA-associated ICH (95% CI 39-65%, I2 = 35%, 3 studies.Subarachnoid extension and an irregular ICH border are common imaging features of CAA-associated ICH, but methodologically rigorous diagnostic test accuracy studies are required to determine the sensitivity and specificity of these features.

  3. Illumination invariant feature point matching for high-resolution planetary remote sensing images

    Science.gov (United States)

    Wu, Bo; Zeng, Hai; Hu, Han

    2018-03-01

    Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

  4. Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

    Science.gov (United States)

    Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

    We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

  5. DEVELOPING AN IMAGE PROCESSING APPLICATION THAT SUPPORTS NEW FEATURES OF JPEG2000 STANDARD

    Directory of Open Access Journals (Sweden)

    Evgin GÖÇERİ

    2007-03-01

    Full Text Available In recent years, developing technologies in multimedia brought the importance of image processing and compression. Images that are reduced in size using lossless and lossy compression techniques without degrading the quality of the image to an unacceptable level take up much less space in memory. This enables them to be sent and received over the Internet or mobile devices in much shorter time. The wavelet-based image compression standard JPEG2000 has been created by the Joint Photographic Experts Group (JPEG committee to superseding the former JPEG standard. Works on various additions to this standard are still under development. In this study, an Application has been developed in Visual C# 2005 which implies important image processing techniques such as edge detection and noise reduction. The important feature of this Application is to support JPEG2000 standard as well as supporting other image types, and the implementation does not only apply to two-dimensional images, but also to multi-dimensional images. Modern software development platforms that support image processing have also been compared and several features of the developed software have been identified.

  6. Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

    Science.gov (United States)

    Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

    2014-03-01

    Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

  7. Color feature extraction of HER2 Score 2+ overexpression on breast cancer using Image Processing

    Directory of Open Access Journals (Sweden)

    Muhimmah Izzati

    2018-01-01

    Full Text Available One of the major challenges in the development of early diagnosis to assess HER2 status is recognized in the form of Gold Standard. The accuracy, validity and refraction of the Gold Standard HER2 methods are widely used in laboratory (Perez, et al., 2014. Method determining the status of HER2 (human epidermal growth factor receptor 2 is affected by reproductive problems and not reliable in predicting the benefit from anti-HER2 therapy (Nuciforo, et al., 2016. We extracted color features by methods adopting Statistics-based segmentation using a continuous-scale naïve Bayes approach. In this study, there were three parts of the main groups, namely image acquisition, image segmentation, and image testing. The stages of image acquisition consisted of image data collection and color deconvolution. The stages of image segmentation consisted of color features, classifier training, classifier prediction, and skeletonization. The stages of image testing were image testing, expert validation, and expert validation results. Area segmentation of the membrane is false positive and false negative. False positive and false negative from area are called the area of system failure. The failure of the system can be validated by experts that the results of segmentation region is not membrane HER2 (noise and the segmentation of the cytoplasm region. The average from 40 data of HER2 score 2+ membrane images show that 75.13% of the area is successfully recognized by the system.

  8. [Relationship of motor deficits and imaging features in metastatic epidural spinal cord compression].

    Science.gov (United States)

    Liu, Shu-Bin; Liu, Yao-Sheng; Li, Ding-Feng; Fan, Hai-Tao; Huai, Jian-Ye; Guo, Jun; Wang, Lei; Liu, Cheng; Zhang, Ping; Cui, Qiu; Jiang, Wei-Hao; Cao, Yun-Cen; Jiang, Ning; Sui, Jia-Hong; Zhang, Bin; Zhou, Jiu

    2010-06-15

    To explore the relationship of motor deficits of the lower extremities with the imaging features of malignant spinal cord compression (MESCCs). From July 2006 through December 2008, 56 successive MESCC patients were treated at our department. All were evaluated by magnetic resonance imaging and computed tomography and were scored according to motor deficits Frankel grading on admission. Imaging assessment factors of main involved vertebrae were level of vertebral metastatic location, epidural space involvement, vertebral body involvement, lamina involvement, posterior protrusion of posterior wall, pedicle involvement, continuity of main involved vertebrae, fracture of anterior column, fracture of posterior wall, location in upper thoracic spine and/or cervicothoracic junction. Occurrence was the same between paralytic state of MESCCs and epidural space involvement of imaging features. Multiple regression equation showed that paralytic state had a linear regression relationship with imaging factors of lamina involvement (X1), posterior protrusion of posterior wall (X2), location in upper thoracic spine and/or cervicothoracic junction (X7) of main involved vertebrae. The optimal regression equation of paralytic state (Y) and imaging feature (X) was Y = -0.009 +0.639X, + 0.149X, +0.282X. Lamina involvement of main involved vertebrae has a greatest influence upon paralytic state of MESCC patients. Imaging factors of lamina involvement, posterior protrusion of posterior wall, location in upper thoracic spine and/or cervicothoracic junction of main involved vertebrae can predict the paralytic state of MESCC patients. MESCC with lamina involvement is more easily encroached on epidural space.

  9. High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

    Science.gov (United States)

    Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

    2018-04-01

    Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

  10. A comparative analysis of image features between weave embroidered Thangka and piles embroidered Thangka

    Science.gov (United States)

    Li, Zhenjiang; Wang, Weilan

    2018-04-01

    Thangka is a treasure of Tibetan culture. In its digital protection, most of the current research focuses on the content of Thangka images, not the fabrication process. For silk embroidered Thangka of "Guo Tang", there are two craft methods, namely, weave embroidered and piles embroidered. The local texture of weave embroidered Thangka is rough, and that of piles embroidered Thangka is more smooth. In order to distinguish these two kinds of fabrication processes from images, a effectively segmentation algorithm of color blocks is designed firstly, and the obtained color blocks contain the local texture patterns of Thangka image; Secondly, the local texture features of the color block are extracted and screened; Finally, the selected features are analyzed experimentally. The experimental analysis shows that the proposed features can well reflect the difference between methods of weave embroidered and piles embroidered.

  11. Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

    Science.gov (United States)

    Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

    2018-04-01

    A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

  12. Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming

    Science.gov (United States)

    Chiu, Stephanie J.; Toth, Cynthia A.; Bowes Rickman, Catherine; Izatt, Joseph A.; Farsiu, Sina

    2012-01-01

    This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique. PMID:22567602

  13. Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain

    Directory of Open Access Journals (Sweden)

    Xiangzeng Liu

    2011-12-01

    Full Text Available This paper proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT features in Steerable-Domain, which can deal with the large variations of scale, rotation and illumination between images. First, a new robust SIFT descriptor is presented, which is invariant under affine transformation. Then, an adaptive similarity measure is developed according to the robust SIFT descriptor and the adaptive normalized cross correlation of feature point’s neighborhood. Finally, the corresponding feature points can be determined by the adaptive similarity measure in Steerable-Domain of the two input images, and the final refined transformation parameters determined by using gradual optimization are adopted to achieve the registration results. Quantitative comparisons of our algorithm with the related methods show a significant improvement in the presence of large scale, rotation changes, and illumination contrast. The effectiveness of the proposed method is demonstrated by the experimental results.

  14. Prolonged QRS Widening After Aripiprazole Overdose.

    Science.gov (United States)

    Mazer-Amirshahi, Maryann; Porter, Robert; Dewey, Kayla

    2018-05-05

    Aripiprazole is an atypical antipsychotic with a long half-life. Overdose can result in protracted somnolence and cardiac disturbances, particularly QT interval prolongation. This is a single case report of a 14-year-old boy who took an overdose of aripiprazole and developed QRS widening. A 14-year-old boy intentionally ingested 20 tablets of aripiprazole (5 mg). He was brought to the emergency department when his ingestion was discovered. The patient's vital signs were as follows: temperature, 37.7°C; heart rate, 108 beats/min; blood pressure, 138/98 mm Hg; and respirations, 16 breaths/min. Activated charcoal was administered within 90 minutes of ingestion. Initial electrocardiogram (EKG) showed sinus tachycardia, with a QRS of 138 ms and QT interval of 444 ms. QRS duration was 90 ms on an EKG performed 3 months earlier. A bolus of sodium bicarbonate was administered, and the patient was transferred to the pediatric intensive care unit. Repeat EKG demonstrated a QRS of 156 ms, and a sodium bicarbonate infusion was initiated. The patient continued to have QRS prolongation for the next 8 days, reaching a peak of 172 ms 3 days postingestion. Despite aggressive treatment with sodium bicarbonate, there was persistent QRS prolongation; however, the patient did not have any dysrhythmias and remained hemodynamically stable. The patient was discharged 9 days postingestion when the QRS duration normalized to 82 ms. Genetic testing revealed that the patient was a CYP2D6 poor metabolizer. This case suggests that aripiprazole toxicity may possibly be associated with QRS prolongation without associated dysrhythmias or cardiovascular compromise. In addition, toxicity may be prolonged in patients who are CYP2D6 poor metabolizers.

  15. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

    2015-06-15

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

  16. Bag-of-features based medical image retrieval via multiple assignment and visual words weighting

    KAUST Repository

    Wang, Jingyan; Li, Yongping; Zhang, Ying; Wang, Chao; Xie, Honglan; Chen, Guoling; Gao, Xin

    2011-01-01

    Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. © 2011 IEEE.

  17. Bag-of-features based medical image retrieval via multiple assignment and visual words weighting

    KAUST Repository

    Wang, Jingyan

    2011-11-01

    Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. © 2011 IEEE.

  18. Derivative-based scale invariant image feature detector with error resilience.

    Science.gov (United States)

    Mainali, Pradip; Lafruit, Gauthier; Tack, Klaas; Van Gool, Luc; Lauwereins, Rudy

    2014-05-01

    We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.

  19. Which patellofemoral joint imaging features are associated with patellofemoral pain? Systematic review and meta-analysis.

    Science.gov (United States)

    Drew, B T; Redmond, A C; Smith, T O; Penny, F; Conaghan, P G

    2016-02-01

    To review the association between patellofemoral joint (PFJ) imaging features and patellofemoral pain (PFP). A systematic review of the literature from AMED, CiNAHL, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PEDro, EMBASE and SPORTDiscus was undertaken from their inception to September 2014. Studies were eligible if they used magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US) or X-ray (XR) to compare PFJ features between a PFP group and an asymptomatic control group in people patellofemoral contact area. Limited evidence was found to support the association of other imaging features with PFP. A sensitivity analysis showed an increase in the SMD for patella bisect offset at 0° knee flexion (1.91; 95% CI: 1.31, 2.52) and patella tilt at 0° knee flexion (0.99; 95% CI: 0.47, 1.52) under full weight bearing. Certain PFJ imaging features were associated with PFP. Future interventional strategies may be targeted at these features. CRD 42014009503. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. Cryptogenic organizing pneumonia: typical and atypical imaging features on computed tomography

    International Nuclear Information System (INIS)

    Hamer, O.W.; Silva, C.I.; Mueller, N.L.

    2008-01-01

    Organizing pneumonia (OP) occurs without any identifiable cause (''cryptogenic organizing pneumonia'') as well as secondary to a multitude of disorders of various origins (''secondary organizing pneumonia''). Possible triggers are infections, drugs, collagen vascular disease, inflammatory bowel disease, transplantations, and radiation directed to the chest. The present manuscript provides an overview of the histopathological, clinical and CT imaging features of OP. Classic CT morphologies (peripheral and peribronchovascular consolidations and ground glass opacities) and atypical imaging features (nodules, crazy paving, lines and bands, perilobular consolidations and the reversed halo sign) are discussed. (orig.)

  1. An effective image classification method with the fusion of invariant feature and a new color descriptor

    Science.gov (United States)

    Mansourian, Leila; Taufik Abdullah, Muhamad; Nurliyana Abdullah, Lili; Azman, Azreen; Mustaffa, Mas Rina

    2017-02-01

    Pyramid Histogram of Words (PHOW), combined Bag of Visual Words (BoVW) with the spatial pyramid matching (SPM) in order to add location information to extracted features. However, different PHOW extracted from various color spaces, and they did not extract color information individually, that means they discard color information, which is an important characteristic of any image that is motivated by human vision. This article, concatenated PHOW Multi-Scale Dense Scale Invariant Feature Transform (MSDSIFT) histogram and a proposed Color histogram to improve the performance of existing image classification algorithms. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods.

  2. Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing

    Directory of Open Access Journals (Sweden)

    B. Kuhlenkötter

    2006-01-01

    Full Text Available In production processes the use of image processing systems is widespread. Hardware solutions and cameras respectively are available for nearly every application. One important challenge of image processing systems is the development and selection of appropriate algorithms and software solutions in order to realise ambitious quality control for production processes. This article characterises the development of innovative software by combining features for an automatic defect classification on product surfaces. The artificial intelligent method Support Vector Machine (SVM is used to execute the classification task according to the combined features. This software is one crucial element for the automation of a manually operated production process. 

  3. Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    J. Del Rio Vera

    2009-01-01

    Full Text Available This paper presents a new supervised classification approach for automated target recognition (ATR in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

  4. Color Image Segmentation Based on Statistics of Location and Feature Similarity

    Science.gov (United States)

    Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

    The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

  5. An alternative to scale-space representation for extracting local features in image recognition

    DEFF Research Database (Denmark)

    Andersen, Hans Jørgen; Nguyen, Phuong Giang

    2012-01-01

    In image recognition, the common approach for extracting local features using a scale-space representation has usually three main steps; first interest points are extracted at different scales, next from a patch around each interest point the rotation is calculated with corresponding orientation...... and compensation, and finally a descriptor is computed for the derived patch (i.e. feature of the patch). To avoid the memory and computational intensive process of constructing the scale-space, we use a method where no scale-space is required This is done by dividing the given image into a number of triangles...... with sizes dependent on the content of the image, at the location of each triangle. In this paper, we will demonstrate that by rotation of the interest regions at the triangles it is possible in grey scale images to achieve a recognition precision comparable with that of MOPS. The test of the proposed method...

  6. Feature Matching for SAR and Optical Images Based on Gaussian-Gamma-shaped Edge Strength Map

    Directory of Open Access Journals (Sweden)

    CHEN Min

    2016-03-01

    Full Text Available A matching method for SAR and optical images, robust to pixel noise and nonlinear grayscale differences, is presented. Firstly, a rough correction to eliminate rotation and scale change between images is performed. Secondly, features robust to speckle noise of SAR image are detected by improving the original phase congruency based method. Then, feature descriptors are constructed on the Gaussian-Gamma-shaped edge strength map according to the histogram of oriented gradient pattern. Finally, descriptor similarity and geometrical relationship are combined to constrain the matching processing.The experimental results demonstrate that the proposed method provides significant improvement in correct matches number and image registration accuracy compared with other traditional methods.

  7. Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

    International Nuclear Information System (INIS)

    Li, Bangyu; Zhang, Hui; Xu, Fanjiang

    2014-01-01

    This paper addresses the problem of water extraction from high resolution remote sensing images (including R, G, B, and NIR channels), which draws considerable attention in recent years. Previous work on water extraction mainly faced two difficulties. 1) It is difficult to obtain accurate position of water boundary because of using low resolution images. 2) Like all other image based object classification problems, the phenomena of ''different objects same image'' or ''different images same object'' affects the water extraction. Shadow of elevated objects (e.g. buildings, bridges, towers and trees) scattered in the remote sensing image is a typical noise objects for water extraction. In many cases, it is difficult to discriminate between water and shadow in a remote sensing image, especially in the urban region. We propose a water extraction method with two hierarchies: the statistical feature of spectral characteristic based on image segmentation and the shape feature based on shadow removing. In the first hierarchy, the Statistical Region Merging (SRM) algorithm is adopted for image segmentation. The SRM includes two key steps: one is sorting adjacent regions according to a pre-ascertained sort function, and the other one is merging adjacent regions based on a pre-ascertained merging predicate. The sort step is done one time during the whole processing without considering changes caused by merging which may cause imprecise results. Therefore, we modify the SRM with dynamic sort processing, which conducts sorting step repetitively when there is large adjacent region changes after doing merging. To achieve robust segmentation, we apply the merging region with six features (four remote sensing image bands, Normalized Difference Water Index (NDWI), and Normalized Saturation-value Difference Index (NSVDI)). All these features contribute to segment image into region of object. NDWI and NSVDI are discriminate between water and

  8. Feature Detection of Curve Traffic Sign Image on The Bandung - Jakarta Highway

    Science.gov (United States)

    Naseer, M.; Supriadi, I.; Supangkat, S. H.

    2018-03-01

    Unsealed roadside and problems with the road surface are common causes of road crashes, particularly when those are combined with curves. Curve traffic sign is an important component for giving early warning to driver on traffic, especially on high-speed traffic like on the highway. Traffic sign detection has became a very interesting research now, and in this paper will be discussed about the detection of curve traffic sign. There are two types of curve signs are discussed, namely the curve turn to the left and the curve turn to the right and the all data sample used are the curves taken / recorded from some signs on the Bandung - Jakarta Highway. Feature detection of the curve signs use Speed Up Robust Feature (SURF) method, where the detected scene image is 800x450. From 45 curve turn to the right images, the system can detect the feature well to 35 images, where the success rate is 77,78%, while from the 45 curve turn to the left images, the system can detect the feature well to 34 images and the success rate is 75,56%, so the average accuracy in the detection process is 76,67%. While the average time for the detection process is 0.411 seconds.

  9. Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

    Directory of Open Access Journals (Sweden)

    Margarita Gamarra

    2017-08-01

    Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

  10. Research on Remote Sensing Image Classification Based on Feature Level Fusion

    Science.gov (United States)

    Yuan, L.; Zhu, G.

    2018-04-01

    Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

  11. Mycobacterial infections: Still a millennium bug - the imaging features of mycobacterial infections

    International Nuclear Information System (INIS)

    Koh, D.M.; Bell, J.R.G.; Burkill, G.J.C.; Padley, S.P.G.; Healy, J.C.

    2001-01-01

    Mycobacterial infection is re-emerging as a major health care concern. Although Mycobacterium tuberculosis (MTB) is still the most important pathogen, atypical mycobacterium (AMB) infections are becoming increasingly common. We present a pictorial review of the imaging features of these infections in the chest, abdomen, brain and musculoskeletal system. Imaging similarities and differences between the normal and the immunocompromised host will be highlighted. Koh, D. M. et al. Clinical Radiology (2001)

  12. Segmenting texts from outdoor images taken by mobile phones using color features

    Science.gov (United States)

    Liu, Zongyi; Zhou, Hanning

    2011-01-01

    Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

  13. Dengue encephalitis with predominant cerebellar involvement: Report of eight cases with MR and CT imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Hegde, Vinay; Bhat, Maya; Prasad, Chandrajit; Gupta, A.K.; Saini, Jitender [National Institute of Mental Health and Neurosciences, Department of Neuroimaging and Interventional Radiology, Bangalore, Karnataka (India); Aziz, Zarina [Sri Sathya Sai Institute of Medical Science, Department of Radiology, Bangalore (India); Kumar, Sharath [Apollo Hospital, Department of Neuroradiology, Bangalore (India); Netravathi, M. [National Institute of Mental Health and Neurosciences, Department of Neurology, Bangalore (India)

    2014-11-01

    CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis. (orig.)

  14. Face detection on distorted images using perceptual quality-aware features

    Science.gov (United States)

    Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

    2014-02-01

    We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

  15. Dengue encephalitis with predominant cerebellar involvement: Report of eight cases with MR and CT imaging features

    International Nuclear Information System (INIS)

    Hegde, Vinay; Bhat, Maya; Prasad, Chandrajit; Gupta, A.K.; Saini, Jitender; Aziz, Zarina; Kumar, Sharath; Netravathi, M.

    2015-01-01

    CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis. (orig.)

  16. An image processing approach to analyze morphological features of microscopic images of muscle fibers.

    Science.gov (United States)

    Comin, Cesar Henrique; Xu, Xiaoyin; Wang, Yaming; Costa, Luciano da Fontoura; Yang, Zhong

    2014-12-01

    We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

    Directory of Open Access Journals (Sweden)

    Dane Brown

    2017-07-01

    Full Text Available The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

  18. Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

    Science.gov (United States)

    Fesharaki, Nooshin Jafari; Pourghassem, Hossein

    2013-07-01

    Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

  19. MR imaging of pregnancy luteoma: a case report and correlation with the clinical features

    Energy Technology Data Exchange (ETDEWEB)

    Kao, Hung Wen; Wu, Ching Jiunn; Chung, Kuo Teng; Wang, Sheng Ru; Chen, Cheng Yu [Tri-Service General Hospital, National Defense Medical Center, Taipei (Taiwan)

    2005-03-15

    We report here on a 26-year-old pregnant female who developed hirsutism and virilization during her third trimester along with a significantly elevated serum testosterone level. Abdominal US and MR imaging studies were performed, and they showed unique imaging features that may suggest the diagnosis of pregnancy luteoma in the clinical context. After the delivery, the serum testosterone level continued to decrease, and it returned to normal three weeks postpartum. The follow-up imaging findings were closely correlated with the clinical presentation.

  20. Aircraft Detection from VHR Images Based on Circle-Frequency Filter and Multilevel Features

    Directory of Open Access Journals (Sweden)

    Feng Gao

    2013-01-01

    Full Text Available Aircraft automatic detection from very high-resolution (VHR images plays an important role in a wide variety of applications. This paper proposes a novel detector for aircraft detection from very high-resolution (VHR remote sensing images. To accurately distinguish aircrafts from background, a circle-frequency filter (CF-filter is used to extract the candidate locations of aircrafts from a large size image. A multi-level feature model is then employed to represent both local appearance and spatial layout of aircrafts by means of Robust Hue Descriptor and Histogram of Oriented Gradients. The experimental results demonstrate the superior performance of the proposed method.

  1. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme

    Science.gov (United States)

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-08-01

    The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were given to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC = 0.793  ±  0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.

  2. Image features for misalignment correction in medical flat-detector CT

    International Nuclear Information System (INIS)

    Wicklein, Julia; Kunze, Holger; Kalender, Willi A.; Kyriakou, Yiannis

    2012-01-01

    Purpose: Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment. Methods: Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry. Results: The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window). Conclusions: Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

  3. IMAGING DIAGNOSIS-MAGNETIC RESONANCE IMAGING FEATURES OF CRANIOMANDIBULAR OSTEOPATHY IN AN AIREDALE TERRIER.

    Science.gov (United States)

    Matiasovic, Matej; Caine, Abby; Scarpante, Elena; Cherubini, Giunio Bruto

    2016-05-01

    An Airedale Terrier was presented for evaluation of depression and reluctance to be touched on the head. Magnetic resonance (MR) imaging of the head was performed. The images revealed bone lesions affecting the calvarium at the level of the coronal suture and left mandibular ramus, with focal cortical destruction, expansion, and reactive new bone formation. Skull lesions were hypointense on T1-weighted sequences, hyperintense on T2-weighted sequences, and showed an intense and homogeneous enhancement after gadolinium administration. Reactive new bone formation and periosteal proliferation were confirmed histopathologically. The clinical signs, imaging findings, and histopathological examination were consistent with craniomandibular osteopathy. © 2015 American College of Veterinary Radiology.

  4. Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

    Science.gov (United States)

    Rezaeian, A.; Homayouni, S.; Safari, A.

    2015-12-01

    Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  5. SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    A. Rezaeian

    2015-12-01

    Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR sensors can collect useful observations from earth’s surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT. Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  6. Comparative study on the performance of textural image features for active contour segmentation.

    Science.gov (United States)

    Moraru, Luminita; Moldovanu, Simona

    2012-07-01

    We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

  7. MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

    Directory of Open Access Journals (Sweden)

    Y. Di

    2017-05-01

    Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

  8. Spinal focal lesion detection in multiple myeloma using multimodal image features

    Science.gov (United States)

    Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

    2015-03-01

    Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

  9. Photoacoustic imaging of blood vessels with a double-ring sensor featuring a narrow angular aperture

    NARCIS (Netherlands)

    Kolkman, R.G.M.; Hondebrink, Erwin; Steenbergen, Wiendelt; van Leeuwen, Ton; de Mul, F.F.M.

    2004-01-01

    A photoacoustic double-ring sensor, featuring a narrow angular aperture, is developed for laser-induced photoacoustic imaging of blood vessels. An integrated optical fiber enables reflection-mode detection of ultrasonic waves. By using the cross-correlation between the signals detected by the two

  10. STUDY ON SHADOW EFFECTS OF VARIOUS FEATURES ON CLOSE RANGE THERMAL IMAGES

    Directory of Open Access Journals (Sweden)

    C. L. Liao

    2012-07-01

    Full Text Available Thermal infrared data become more popular in remote sensing investigation, for it could be acquired both in day and night. The change of temperature has special characteristic in natural environment, so the thermal infrared images could be used in monitoring volcanic landform, the urban development, and disaster prevention. Heat shadow is formed by reflecting radiating capacity which followed the objects. Because of poor spatial resolution of thermal infrared images in satellite sensor, shadow effects were usually ignored. This research focus on discussing the shadow effects of various features, which include metals and nonmetallic materials. An area-based thermal sensor, FLIR-T360 was selected to acquire thermal images. Various features with different emissivity were chosen as reflective surface to obtain thermal shadow in normal atmospheric temperature. Experiments found that the shadow effects depend on the distance between sensors and features, depression angle, object temperature and emissivity of reflective surface. The causes of shadow effects have been altered in the experiment for analyzing the variance in thermal infrared images. The result shows that there were quite different impacts by shadow effects between metals and nonmetallic materials. The further research would be produced a math model to describe the shadow effects of different features in the future work.

  11. Joint Applied Optics and Chinese Optics Letters feature introduction: digital holography and three-dimensional imaging

    OpenAIRE

    Poon, Ting-Chung

    2011-01-01

    This feature issue serves as a pilot issue promoting the joint issue of Applied Optics and Chinese Optics Letters. It focuses upon topics of current relevance to the community working in the area of digital holography and 3-D imaging. (C) 2011 Optical Society of America

  12. Joint Applied Optics and Chinese Optics Letters feature introduction: digital holography and three-dimensional imaging.

    Science.gov (United States)

    Poon, Ting-Chung

    2011-12-01

    This feature issue serves as a pilot issue promoting the joint issue of Applied Optics and Chinese Optics Letters. It focuses upon topics of current relevance to the community working in the area of digital holography and 3-D imaging. © 2011 Optical Society of America

  13. a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

    Science.gov (United States)

    Li, L.; Yang, H.; Chen, Q.; Liu, X.

    2018-04-01

    Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

  14. Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

    Science.gov (United States)

    Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

    2014-01-01

    We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

  15. Research on improving image recognition robustness by combining multiple features with associative memory

    Science.gov (United States)

    Guo, Dongwei; Wang, Zhe

    2018-05-01

    Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

  16. Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

    Science.gov (United States)

    Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

    2001-01-01

    Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

  17. Development of estimation system of knee extension strength using image features in ultrasound images of rectus femoris

    Science.gov (United States)

    Murakami, Hiroki; Watanabe, Tsuneo; Fukuoka, Daisuke; Terabayashi, Nobuo; Hara, Takeshi; Muramatsu, Chisako; Fujita, Hiroshi

    2016-04-01

    The word "Locomotive syndrome" has been proposed to describe the state of requiring care by musculoskeletal disorders and its high-risk condition. Reduction of the knee extension strength is cited as one of the risk factors, and the accurate measurement of the strength is needed for the evaluation. The measurement of knee extension strength using a dynamometer is one of the most direct and quantitative methods. This study aims to develop a system for measuring the knee extension strength using the ultrasound images of the rectus femoris muscles obtained with non-invasive ultrasonic diagnostic equipment. First, we extract the muscle area from the ultrasound images and determine the image features, such as the thickness of the muscle. We combine these features and physical features, such as the patient's height, and build a regression model of the knee extension strength from training data. We have developed a system for estimating the knee extension strength by applying the regression model to the features obtained from test data. Using the test data of 168 cases, correlation coefficient value between the measured values and estimated values was 0.82. This result suggests that this system can estimate knee extension strength with high accuracy.

  18. Real-time UAV trajectory generation using feature points matching between video image sequences

    Science.gov (United States)

    Byun, Younggi; Song, Jeongheon; Han, Dongyeob

    2017-09-01

    Unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance mission. In this paper, we present a systematic approach for the generation of UAV trajectory using a video image matching system based on SURF (Speeded up Robust Feature) and Preemptive RANSAC (Random Sample Consensus). Video image matching to find matching points is one of the most important steps for the accurate generation of UAV trajectory (sequence of poses in 3D space). We used the SURF algorithm to find the matching points between video image sequences, and removed mismatching by using the Preemptive RANSAC which divides all matching points to outliers and inliers. The inliers are only used to determine the epipolar geometry for estimating the relative pose (rotation and translation) between image sequences. Experimental results from simulated video image sequences showed that our approach has a good potential to be applied to the automatic geo-localization of the UAVs system

  19. Hybrid image representation learning model with invariant features for basal cell carcinoma detection

    Science.gov (United States)

    Arevalo, John; Cruz-Roa, Angel; González, Fabio A.

    2013-11-01

    This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classi cation. In BOF, patches are usually represented using descriptors such as SIFT and DCT. We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

  20. The linear attenuation coefficients as features of multiple energy CT image classification

    International Nuclear Information System (INIS)

    Homem, M.R.P.; Mascarenhas, N.D.A.; Cruvinel, P.E.

    2000-01-01

    We present in this paper an analysis of the linear attenuation coefficients as useful features of single and multiple energy CT images with the use of statistical pattern classification tools. We analyzed four CT images through two pointwise classifiers (the first classifier is based on the maximum-likelihood criterion and the second classifier is based on the k-means clustering algorithm) and one contextual Bayesian classifier (ICM algorithm - Iterated Conditional Modes) using an a priori Potts-Strauss model. A feature extraction procedure using the Jeffries-Matusita (J-M) distance and the Karhunen-Loeve transformation was also performed. Both the classification and the feature selection procedures were found to be in agreement with the predicted discrimination given by the separation of the linear attenuation coefficient curves for different materials

  1. An adaptive clustering algorithm for image matching based on corner feature

    Science.gov (United States)

    Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

    2018-04-01

    The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

  2. Imaging Features of Helical Computed Tomography Suggesting Advanced Urothelial Carcinoma Arising from the Pelvocalyceal System

    International Nuclear Information System (INIS)

    Kwak, Kyung Won; Park, Byung Kwan; Kim, Chan Kyo; Lee, Hyun Moo; Choi, Han Y ong

    2008-01-01

    Background: Urothelial carcinoma is the most common malignant tumor arising from the pelvocalyceal system. Helical computed tomography (CT) is probably the best preoperative-stage modality for the determination of treatment plan and prognosis. Purpose: To obtain helical CT imaging features suggesting advanced pelvocalyceal urothelial carcinoma. Material and Methods: Preoperative CT images in 44 patients with pelvocalyceal urothelial carcinoma were retrospectively reviewed and correlated with the pathological examination to determine imaging features suggesting stage III or IV of the disease. Results: Pathological stages revealed stage I in 16, stage II in three, stage III in 17, and stage IV in eight patients. Seven patients had metastatic lymph nodes. CT imaging showed that renal parenchymal invasion, sinus fat invasion, and lymph node metastasis were highly suggestive of advanced urothelial cell carcinoma (P<0.05). Helical CT sensitivity, specificity, and accuracy for advanced pelvocalyceal urothelial carcinoma were 76% (19/25), 84% (16/19), and 80% (35/44), respectively. Conclusion: Preoperative helical CT may suggest imaging features of advanced urothelial carcinoma, influencing treatment plan and patient prognosis, even though its accuracy is not so high

  3. Computer Aided Quantification of Pathological Features for Flexor Tendon Pulleys on Microscopic Images

    Directory of Open Access Journals (Sweden)

    Yung-Chun Liu

    2013-01-01

    Full Text Available Quantifying the pathological features of flexor tendon pulleys is essential for grading the trigger finger since it provides clinicians with objective evidence derived from microscopic images. Although manual grading is time consuming and dependent on the observer experience, there is a lack of image processing methods for automatically extracting pulley pathological features. In this paper, we design and develop a color-based image segmentation system to extract the color and shape features from pulley microscopic images. Two parameters which are the size ratio of abnormal tissue regions and the number ratio of abnormal nuclei are estimated as the pathological progression indices. The automatic quantification results show clear discrimination among different levels of diseased pulley specimens which are prone to misjudgments for human visual inspection. The proposed system provides a reliable and automatic way to obtain pathological parameters instead of manual evaluation which is with intra- and interoperator variability. Experiments with 290 microscopic images from 29 pulley specimens show good correspondence with pathologist expectations. Hence, the proposed system has great potential for assisting clinical experts in routine histopathological examinations.

  4. Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit

    Science.gov (United States)

    Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan

    2017-05-01

    Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here, we train a generative adversarial network (GAN) on a sample of 4550 images of nearby galaxies at 0.01 < z < 0.02 from the Sloan Digital Sky Survey and conduct 10× cross-validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance that far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low signal to noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.

  5. A Method of Road Extraction from High-resolution Remote Sensing Images Based on Shape Features

    Directory of Open Access Journals (Sweden)

    LEI Xiaoqi

    2016-02-01

    Full Text Available Road extraction from high-resolution remote sensing image is an important and difficult task.Since remote sensing images include complicated information,the methods that extract roads by spectral,texture and linear features have certain limitations.Also,many methods need human-intervention to get the road seeds(semi-automatic extraction,which have the great human-dependence and low efficiency.The road-extraction method,which uses the image segmentation based on principle of local gray consistency and integration shape features,is proposed in this paper.Firstly,the image is segmented,and then the linear and curve roads are obtained by using several object shape features,so the method that just only extract linear roads are rectified.Secondly,the step of road extraction is carried out based on the region growth,the road seeds are automatic selected and the road network is extracted.Finally,the extracted roads are regulated by combining the edge information.In experiments,the images that including the better gray uniform of road and the worse illuminated of road surface were chosen,and the results prove that the method of this study is promising.

  6. Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

    Science.gov (United States)

    Sinha, S K; Karray, F

    2002-01-01

    Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

  7. MRI of Creutzfeldt-Jakob disease: Imaging features and recommended MRI protocol

    Energy Technology Data Exchange (ETDEWEB)

    Collie, D.A.; Sellar, R.J.; Zeidler, M.; Colchester, A.C.F.; Knight, R.; Will, R.G

    2001-09-01

    Creutzfeldt-Jakob Disease (CJD) is a rare, progressive and invariably fatal neurodegenerative disease characterized by specific histopathological features. Of the four subtypes of CJD described, the commonest is sporadic CJD (sCJD). More recently, a new clinically distinct form of the disease affecting younger patients, known as variant CJD (vCJD), has been identified, and this has been causally linked to the bovine spongiform encephalopathy (BSE) agent in cattle. Characteristic appearances on magnetic resonance imaging (MRI) have been identified in several forms of CJD; sCJD may be associated with high signal changes in the putamen and caudate head and vCJD is usually associated with hyperintensity of the pulvinar (posterior nuclei) of the thalamus. These appearances and other imaging features are described in this article. Using appropriate clinical and radiological criteria and tailored imaging protocols, MRI plays an important part in the in vivodiagnosis of this disease. Collie, D.A. et al. (2001)

  8. MRI of Creutzfeldt-Jakob disease: Imaging features and recommended MRI protocol

    International Nuclear Information System (INIS)

    Collie, D.A.; Sellar, R.J.; Zeidler, M.; Colchester, A.C.F.; Knight, R.; Will, R.G.

    2001-01-01

    Creutzfeldt-Jakob Disease (CJD) is a rare, progressive and invariably fatal neurodegenerative disease characterized by specific histopathological features. Of the four subtypes of CJD described, the commonest is sporadic CJD (sCJD). More recently, a new clinically distinct form of the disease affecting younger patients, known as variant CJD (vCJD), has been identified, and this has been causally linked to the bovine spongiform encephalopathy (BSE) agent in cattle. Characteristic appearances on magnetic resonance imaging (MRI) have been identified in several forms of CJD; sCJD may be associated with high signal changes in the putamen and caudate head and vCJD is usually associated with hyperintensity of the pulvinar (posterior nuclei) of the thalamus. These appearances and other imaging features are described in this article. Using appropriate clinical and radiological criteria and tailored imaging protocols, MRI plays an important part in the in vivodiagnosis of this disease. Collie, D.A. et al. (2001)

  9. The MR imaging and DSA features and embolization therapy of spinal dural arteriovenous fistulae

    International Nuclear Information System (INIS)

    Zhang Hua; Hu Jinqing; Lin Dong; Wu Daming; Wang Dengbin; Yang Yanmin; Cheng Kemin

    2005-01-01

    Objective: To investigate the MR imaging and DSA features together with endovascular embolization of spinal dural arteriovenous fistulae (SDAVF). Methods: Twelve patients with SDAVF underwent both MR imaging and angiography of spinal cord, 4 of them received endovascular embolization. The imaging findings of MRI and angiography in all patients were analyzed. Results: Among 12 cases with SDAVF, 11 cases showed diffuse long T 2 signal, 2 cases demonstrated inhomogeneous maculate enhancement in the spinal cord and 6 cases revealed abnormal vessels in the dorsal spaces of spinal cord on MRI. Angiography of spinal cord showed orifices of all fistulae, draining veins, the extent of lesions, amount of feeding vessels and the angiographic features in all the 12 cases. 4 cases with embolization treatment showed improvement clinically. Conclusions: Angiography of spinal cord is the main method and MRI provides important assistance for diagnosing SDAVF while endovascular embolization is an effective method for the treatment. (authors)

  10. Constructing New Biorthogonal Wavelet Type which Matched for Extracting the Iris Image Features

    International Nuclear Information System (INIS)

    Isnanto, R Rizal; Suhardjo; Susanto, Adhi

    2013-01-01

    Some former research have been made for obtaining a new type of wavelet. In case of iris recognition using orthogonal or biorthogonal wavelets, it had been obtained that Haar filter is most suitable to recognize the iris image. However, designing the new wavelet should be done to find a most matched wavelet to extract the iris image features, for which we can easily apply it for identification, recognition, or authentication purposes. In this research, a new biorthogonal wavelet was designed based on Haar filter properties and Haar's orthogonality conditions. As result, it can be obtained a new biorthogonal 5/7 filter type wavelet which has a better than other types of wavelets, including Haar, to extract the iris image features based on its mean-squared error (MSE) and Euclidean distance parameters.

  11. Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies

    Directory of Open Access Journals (Sweden)

    Bo Liu

    2014-01-01

    Full Text Available Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.

  12. Fusion of shallow and deep features for classification of high-resolution remote sensing images

    Science.gov (United States)

    Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

    2018-02-01

    Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

  13. The widening gap of gender inequality in Nigerian politics ...

    African Journals Online (AJOL)

    The widening gap of gender inequality in Nigerian politics: Advocating a quota system ... Advocates on gender equality in Nigeria have noted that a viable means of ... This research paper therefore argues that for Nigeria to breach the gender ...

  14. Analysis and classification of commercial ham slice images using directional fractal dimension features.

    Science.gov (United States)

    Mendoza, Fernando; Valous, Nektarios A; Allen, Paul; Kenny, Tony A; Ward, Paddy; Sun, Da-Wen

    2009-02-01

    This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

  15. Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study

    Science.gov (United States)

    Kontos, Despina; Berger, Rachelle; Bakic, Predrag R.; Maidment, Andrew D. A.

    2009-02-01

    Mammographic breast density is a known breast cancer risk factor. Studies have shown the potential to automate breast density estimation by using computerized texture-based segmentation of the dense tissue in mammograms. Digital breast tomosynthesis (DBT) is a tomographic x-ray breast imaging modality that could allow volumetric breast density estimation. We evaluated the feasibility of distinguishing between dense and fatty breast regions in DBT using computer-extracted texture features. Our long-term hypothesis is that DBT texture analysis can be used to develop 3D dense tissue segmentation algorithms for estimating volumetric breast density. DBT images from 40 women were analyzed. The dense tissue area was delineated within each central source projection (CSP) image using a thresholding technique (Cumulus, Univ. Toronto). Two (2.5cm)2 ROIs were manually selected: one within the dense tissue region and another within the fatty region. Corresponding (2.5cm)3 ROIs were placed within the reconstructed DBT images. Texture features, previously used for mammographic dense tissue segmentation, were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance. Different texture features appeared to perform best in the 3D reconstructed DBT compared to the 2D CSP images. Fractal dimension was superior in DBT (AUC=0.90), while contrast was best in CSP images (AUC=0.92). We attribute these differences to the effects of tissue superimposition in CSP and the volumetric visualization of the breast tissue in DBT. Our results suggest that novel approaches, different than those conventionally used in projection mammography, need to be investigated in order to develop DBT dense tissue segmentation algorithms for estimating volumetric breast density.

  16. A methodology for texture feature-based quality assessment in nucleus segmentation of histopathology image

    Directory of Open Access Journals (Sweden)

    Si Wen

    2017-01-01

    Full Text Available Context: Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis. It is very labor intensive to do quality assessment manually with large image datasets because a whole-slide tissue image may have hundreds of thousands of nuclei. Semi-automatic mechanisms are needed to assist researchers and application developers to detect image regions with bad segmentations efficiently. Aims: Our goal is to develop and evaluate a machine-learning-based semi-automated workflow to assess quality of nucleus segmentation results in a large set of whole-slide tissue images. Methods: We propose a quality control methodology, in which machine-learning algorithms are trained with image intensity and texture features to produce a classification model. This model is applied to image patches in a whole-slide tissue image to predict the quality of nucleus segmentation in each patch. The training step of our methodology involves the selection and labeling of regions by a pathologist in a set of images to create the training dataset. The image regions are partitioned into patches. A set of intensity and texture features is computed for each patch. A classifier is trained with the features and the labels assigned by the pathologist. At the end of this process, a classification model is generated. The classification step applies the classification model to unlabeled test images. Each test image is partitioned into patches. The classification model is applied to each patch to predict the patch

  17. A comparison of image features for registering LWIR and visual images

    CSIR Research Space (South Africa)

    Cronje, J

    2012-11-01

    Full Text Available and SIFT — and fast algorithms, BRISK and BFROST. To evaluate the feature-descriptors a ground truth was created by determining the intrinsic and extrinsic camera calibration parameters for the cameras and using this to photogrammetrically relate pixel...

  18. Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

    Science.gov (United States)

    Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

    2015-06-01

    Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for

  19. Ventriculus Terminalis in Adults: Unusual Magnetic Resonance Imaging Features and Review of the Literature

    Energy Technology Data Exchange (ETDEWEB)

    Suh, Sang Hyun; Chung, Tae Sub; Cho, Yong Eun; Kim, Keun Su [Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Lee, Seong Koo [Dept. of Radiology, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2012-09-15

    The ventriculus terminalis (VT) in adults is a rare pathology. We report various MR imaging features of the adult VT. Ten patients were included in this retrospective review.. All patients had undergone magnetic resonance (MR imaging with a surface coil that used two different 1.5T MR systems. All patients had undergone initial and follow-up MR imaging with contrast enhancement using gadopentate dimeglumine. Three patients underwent additional MR imaging using the echocardiogram-gated spatial modulation of magnetization (SPAMM) technique. If a shift in tagging band during the systolic phase was less than half of the band space, it was defined as a 'non-pulsatile fluid'. Two neuroradiologists independently reviewed these images, while clinical symptoms and outcomes were statistically analyzed between the treated and non-treated group. All cases presented an intramedullary cystic lesion in the conus medullaris and showed the same signal intensity as CSF. Three VTs had intracystic septation and cord edema, which were pathologically confirmed after surgery; two of these were associated with kyphotic deformity and spinal arteriovenous malformation. SPAMM-MRI of 3 patients demonstrated non-pulsatile fluid motion within the VT. In the treated group, clinical symptoms improved better than the non-treated group. The adult VT shows some unusual imaging features, including septation, cord edema, and coexistence of a spinal AVM, as well as the typical findings. Surgical maneuvers may be considered as a treatment option in adult VT with progressive neurological symptoms.

  20. Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

    Directory of Open Access Journals (Sweden)

    B. Sirmacek

    2013-10-01

    Full Text Available Fusion of 3D airborne laser (LIDAR data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

  1. Gd-EOB-DTPA-enhanced magnetic resonance imaging features of hepatic hemangioma compared with enhanced computed tomography

    OpenAIRE

    Tateyama, Akihiro; Fukukura, Yoshihiko; Takumi, Koji; Shindo, Toshikazu; Kumagae, Yuichi; Kamimura, Kiyohisa; Nakajo, Masayuki

    2012-01-01

    AIM: To clarify features of hepatic hemangiomas on gadolinium-ethoxybenzyl-diethylenetriaminpentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) compared with enhanced computed tomography (CT).

  2. Sodium channel blockade with QRS widening after an escitalopram overdose.

    Science.gov (United States)

    Schreffler, Susan M; Marraffa, Jeanna M; Stork, Christine M; Mackey, Jennifer

    2013-09-01

    Escitalopram is rarely associated with prolongation of the QTc interval; however, there are no reported cases of QRS complex widening associated with escitalopram overdose. We report a case of a patient who presented with both QRS complex widening and QTc interval prolongation after an escitalopram overdose. A 16-year-old girl presented to the emergency department after ingestion of escitalopram, tramadol/acetaminophen, and hydrocodone/acetaminophen. Laboratory results were significant for 4-hour acetaminophen 21.1 μg/mL. Serum electrolytes including potassium, magnesium, and calcium were all normal. Initial electrocardiogram (ECG) revealed a widened QRS with an incomplete right bundle branch pattern. After administration of 100-mEq sodium bicarbonate, a repeat ECG revealed narrowing of the QRS complex and a prolonged QTc interval. Magnesium sulfate 2 g intravenous and sodium bicarbonate drip were initiated. A repeat ECG, 1 hour after the second, revealed normalization of the QRS complex and QTc interval. Prolongation of the QTc interval is an expected effect of escitalopram. Both escitalopram and citalopram are metabolized to the cardiotoxic metabolite S-didesmethylcitalopram and didesmethylcitalopram, respectively, which have been implicated in numerous cardiac abnormalities including widening of the QRS com