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Sample records for infection imaging features

  1. Imaging features of central nervous system fungal infections

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    Jain Krishan

    2007-01-01

    Full Text Available Fungal infections of the central nervous system (CNS are rare in the general population and are invariably secondary to primary focus elsewhere, usually in the lung or intestine. Except for people with longstanding diabetes, they are most frequently encountered in immunocompromised patients such as those with acquired immunodeficiency syndrome or after organ transplantation. Due to the lack of inflammatory response, neuroradiological findings are often nonspecific and are frequently mistaken for tuberculous meningitis, pyogenic abscess or brain tumor. Intracranial fungal infections are being identified more frequently due to the increased incidence of AIDS patients, better radiological investigations, more sensitive microbiological techniques and better critical care of moribund patients. Although almost any fungus may cause encephalitis, cryptococcal meningoencephalitis is most frequently seen, followed by aspergillosis and candidiasis. The biology, epidemiology and imaging features of the common fungal infections of the CNS will be reviewed. The radiographic appearance alone is often not specific, but the combination of the appropriate clinical setting along with computed tomography or magnetic resonance may help to suggest the correct diagnosis.

  2. Analysis on the imaging features of AIDS with pulmonary fungal infection

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    GAO Jian-bo; ZHANG Yong-gao; YUE Song-wei; LI Hong-jun; NING Pei-gang; GUO Hua; XIAO Hui-juan

    2010-01-01

    Background Pulmonary fungal infection is one type of the common opportunistic infections in AIDS patients. The disease is hard to diagnose because of its complicated imaging features. The objective of this study was to investigate the imaging performance characteristics of pulmonary fungal infection in AIDS patients.Methods Fifty-one patients with AIDS complicated with pulmonary fungal infection and 56 patients of non-AIDS with pulmonary fungal infection were examined by CT scans and high-resolution CT scans. The contrast enhanced scans were performed in patients with the mass or suspected enlarged mediastinal lymph nodes. Results were compared between the two groups.Results The most common fungal infection in the two groups of patients was Candida albicans. The infection rates were 54.8% (28 cases) in the group (AIDS patients with pulmonary fungal infection) and 58.3% (32 cases) in another group (non-AIDS patients with pulmonary fungal infection). In the two groups, the difference in diffuse distribution and the difference in incidence of affected upper and lower lobes in the bilateral lung fields were statistically significant. The differences in patchy or large consolidation shadow, cavitas, enlarged lymph nodes in mediastinum and pleural effusion were also significant when comparing the two groups.Conclusions The lesion in most of AIDS patients with pulmonary fungal infection tends to exhibit diffuse distribution,patchy or large consolidation shadow covering a more extensive region. The differences between AIDS with pulmonary fungal infection and non-AIDS with pulmonary fungal infection are statistically significant in lesion location and complicated imaging features. The most common fungal infection in AIDS patients is Candida albicans.

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

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    Hillier, J.C. E-mail: chris_julia.hillier@talk21.com; Shaw, P.; Miller, R.F.; Cartledge, J.D.; Nelson, M.; Bower, M.; Francis, N.; Padley, S.P

    2004-07-01

    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.

  4. Pulmonary Complication of Novel Influenza A (H1N1) Infection: Imaging Features in Two Patients

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    Lee, Choong Wook; Seo, Joon Beom; Song, Jae Woo; Lee, Hyun Joo; Lee, Jin Seong; Kim, Mi Young; Chae, Eun Jin; Song, Jin Woo; Kim, Won Young [Asan Medical Center, University of Ulsan College of Medicine, Seoul (Korea, Republic of)

    2009-12-15

    Novel influenza A (H1N1) virus is the pathogen of recent global outbreaks of febrile respiratory infection. We herein report the imaging findings of pulmonary complication in two patients with novel influenza A (H1N1) infection. The first patient without secondary infection showed the ill-defined ground-glass opacity nodules and patch areas of ground-glass opacities. The second patient with secondary pneumococcal pneumonia showed areas of lobar consolidation in the right middle lobe and left lower lobe and ground-glass opacities.

  5. Early detection and classification of powdery mildew-infected rose leaves using ANFIS based on extracted features of thermal images

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    Jafari, Mehrnoosh; Minaei, Saeid; Safaie, Naser; Torkamani-Azar, Farah

    2016-05-01

    Spatial and temporal changes in surface temperature of infected and non-infected rose plant (Rosa hybrida cv. 'Angelina') leaves were visualized using digital infrared thermography. Infected areas exhibited a presymptomatic decrease in leaf temperature up to 2.3 °C. In this study, two experiments were conducted: one in the greenhouse (semi-controlled ambient conditions) and the other, in a growth chamber (controlled ambient conditions). Effect of drought stress and darkness on the thermal images were also studied in this research. It was found that thermal histograms of the infected leaves closely follow a standard normal distribution. They have a skewness near zero, kurtosis under 3, standard deviation larger than 0.6, and a Maximum Temperature Difference (MTD) more than 4. For each thermal histogram, central tendency, variability, and parameters of the best fitted Standard Normal and Laplace distributions were estimated. To classify healthy and infected leaves, feature selection was conducted and the best extracted thermal features with the largest linguistic hedge values were chosen. Among those features independent of absolute temperature measurement, MTD, SD, skewness, R2l, kurtosis and bn were selected. Then, a neuro-fuzzy classifier was trained to recognize the healthy leaves from the infected ones. The k-means clustering method was utilized to obtain the initial parameters and the fuzzy "if-then" rules. Best estimation rates of 92.55% and 92.3% were achieved in training and testing the classifier with 8 clusters. Results showed that drought stress had an adverse effect on the classification of healthy leaves. More healthy leaves under drought stress condition were classified as infected causing PPV and Specificity index values to decrease, accordingly. Image acquisition in the dark had no significant effect on the classification performance.

  6. Helicobacter Pylori infection detection from gastric X-ray images based on feature fusion and decision fusion.

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    Ishihara, Kenta; Ogawa, Takahiro; Haseyama, Miki

    2017-05-01

    In this paper, a fully automatic method for detection of Helicobacter pylori (H. pylori) infection is presented with the aim of constructing a computer-aided diagnosis (CAD) system. In order to realize a CAD system with good performance for detection of H. pylori infection, we focus on the following characteristic of stomach X-ray examination. The accuracy of X-ray examination differs depending on the symptom of H. pylori infection that is focused on and the position from which X-ray images are taken. Therefore, doctors have to comprehensively assess the symptoms and positions. In order to introduce the idea of doctors' assessment into the CAD system, we newly propose a method for detection of H. pylori infection based on the combined use of feature fusion and decision fusion. As a feature fusion scheme, we adopt Multiple Kernel Learning (MKL). Since MKL can combine several features with determination of their weights, it can represent the differences in symptoms. By constructing an MKL classifier for each position, we can obtain several detection results. Furthermore, we introduce confidence-based decision fusion, which can consider the relationship between the classifier's performance and the detection results. Consequently, accurate detection of H. pylori infection becomes possible by the proposed method. Experimental results obtained by applying the proposed method to real X-ray images show that our method has good performance, close to the results of detection by specialists, and indicate that the realization of a CAD system for determining the risk of H. pylori infection is possible. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Imaging of hepatic infections

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    Doyle, D.J. [Department of Medical Imaging, University Health Network and Mount Sinai Hospital, University of Toronto, Toronto, Ont. (Canada)]. E-mail: doyledj@hotmail.com; Hanbidge, A.E. [Department of Medical Imaging, University Health Network and Mount Sinai Hospital, University of Toronto, Toronto, Ont. (Canada); O' Malley, M.E. [Department of Medical Imaging, University Health Network and Mount Sinai Hospital, University of Toronto, Toronto, Ont. (Canada)

    2006-09-15

    Imaging plays a significant role in the detection, characterization and treatment of hepatic infections. Infectious diseases of the liver include pyogenic and amoebic abscesses and parasitic, fungal, viral and granulomatous infections. With increases in worldwide travel, immunosuppression and changing population demographics, identification of cases of hepatic infection is becoming more common in daily practice. Knowledge of the imaging features seen with hepatic infections can assist in early diagnosis and timely initiation of appropriate therapy. This review presents the imaging appearances of hepatic infections, emphasizing specific features that may contribute to the diagnosis. Examples of the imaging findings seen with pyogenic and amoebic abscesses, infection with Echinococcus granulosus (Hydatid), schistosomiasis, candidiasis and tuberculosis (TB) are presented.

  8. Multispectral Image Feature Points

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    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.

  9. Abdominal tuberculosis: Imaging features

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    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.

  10. Featured Image: Interacting Galaxies

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    Kohler, Susanna

    2017-06-01

    This beautiful image shows two galaxies, IC 2163 and NGC 2207, as they undergo a grazing collision 114 million light-years away. The image is composite, constructed from Hubble (blue), Spitzer (green), and ALMA (red) data. In a recent study, Debra Elmegreen (Vassar College) and collaborators used this ALMA data to trace the individual molecular clouds in the two interacting galaxies, identifying a total of over 200 clouds that each contain a mass of over a million solar masses. These clouds represent roughly half the molecular gas in the two galaxies total. Elmegreen and collaborators track the properties of these clouds and their relation to star-forming regions observed with Hubble. For more information about their observations, check out the paper linked below.A closer look at the ALMA observations for these galaxies, with the different emission regions labeled. Most of the molecular gas emission comes from the eyelids of IC 2163, and the nuclear ring and Feature i in NGC 2207. [Elmegreen et al. 2017]CitationDebra Meloy Elmegreen et al 2017 ApJ 841 43. doi:10.3847/1538-4357/aa6ba5

  11. Localized scleroderma: imaging features

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    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.)

  12. Identifying Image Manipulation Software from Image Features

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    2015-03-26

    an overview of the DCT based encoding process [5]. When an image is processed by lossless compression, a file’s size is reduced while still...IDENTIFYING IMAGE MANIPULATION SOFTWARE FROM IMAGE FEATURES THESIS Devlin T. Boyter, CPT, USA AFIT-ENG-MS-15-M-051 DEPARTMENT OF THE AIR FORCE AIR...to copyright protection in the United States. AFIT-ENG-MS-15-M-051 IDENTIFYING IMAGE MANIPULATION SOFTWARE FROM IMAGE FEATURES THESIS Presented to

  13. Imaging features of thalassemia

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    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.)

  14. Imaging spinal infection

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    Jay Acharya

    2016-06-01

    Full Text Available Infection involving the vertebral column, including the bone, intervertebral disk, and paravertebral soft tissues is critical and early diagnosis and directed treatment is paramount. Different infectious organisms present with variable imaging characteristics, which when examined in conjunction with the clinical history, can facilitate early diagnosis and treatment and ultimately prevent patient morbidity and mortality. This article discusses the pathophysiology of infection of the vertebral column, as well as the imaging findings of bacterial, tuberculous, and fungal spondylitis/spondylodiskitis. We review the imaging findings utilizing plain radiography, computed tomography, and magnetic resonance imaging, as well as a discussion regarding advanced imaging techniques.

  15. Imaging features of aggressive angiomyxoma

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

    2003-02-01

    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.

  16. Imaging of Periprosthetic Infection.

    LENUS (Irish Health Repository)

    Carty, Fiona

    2013-05-22

    Periprosthetic infection is one of the most challenging and difficult complications in orthopaedics. It can result in significant patient distress and disability, with repeated surgeries, increased cost and utilization of medical resources, and in rare cases even mortality. The biggest challenge to date is the correct diagnosis of periprosthetic infection and implementation of effective treatment regimens capable of eradicating the organism. This article reviews the various modalities used in the imaging of periprosthetic and post-arthroplasty infection.

  17. Imaging of Orbital Infections

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    Seyed Hassan Mostafavi

    2010-05-01

    Full Text Available Preseptal and orbital cellulitis occur more commonly in children than adults. The history and physical examination are crucial in distinguishing between preseptal and orbital cellulitis. The orbital septum delineates the anterior eyelid soft tissues from the orbital soft tissue. Infections anterior to the orbital septum are classified as preseptal cellulitis and those posterior to the orbital septum are termed orbital cellulitis. "nRecognition of orbital involvement is important not only because of the threatened vision loss associated with orbital cellulitis but also because of the potential for central nervous system complications including cavernous sinus thrombosis, meningitis, and death. "nOrbital imaging should be obtained in all patients suspected of having orbital cellulitis. CT is preferred to MR imaging, as the orbital tissues have high con-trast and the bone can be well visualized. Orbital CT scanning allows localization of the disease process to the preseptal area, the extraconal or intraconal fat, or the subperiosteal space. Axial CT views allow evaluation of the medial orbit and ethmoid sinuses, whereas coronal scans image the orbital roof and floor and the frontal and maxillary sinuses. If direct coronal imaging is not possible, reconstruction of thin axial cuts may help the assessment of the orbital roof and floor. Potential sources of orbital cellulitis such as sinusitis, dental infection, and facial cellulitis are often detectable on CT imaging. "nIn this presentation, the imaging considerations of the orbital infections; including imaging differentiation criteria of all types of orbital infections are reviewed.

  18. Progressing features of atypical mycobacterial infection in the lung on conventional and high resolution CT (HRCT) images

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    Tanaka, Daizo; Niwatsukino, Hiroshi; Nakajo, Masayuki [Kagoshima Univ. (Japan). Faculty of Medicine; Oyama, Takao

    2001-10-01

    The aim of this study was to clarify the localization of abnormalities within secondary pulmonary lobules and the changes in follow-up studies of pulmonary atypical mycobacterial infection (AMI) by conventional and high-resolution computed tomography (HRCT). Forty-six patients (16 men and 30 women; 43-84 years) with pulmonary AMI (M. intracellulare 36; M. avium 10) in the lung were examined by conventional and HRCT. In peripheral zones, all patients had the nodule located in the terminal or lobular bronchiole, and most of the patients also had nodules accompanied with a wedge-shaped or linear shadow connected with the pleura. In the follow-up scans, new centrilobular nodules appeared in other segments, and consolidation or ground-glass pattern appeared newly and was preceded by nodules. Bronchiectasis became more severe in five of 38 follow-up patients. The common HRCT findings of AMI were centrilobular, peribronchovascular nodules, bronchiectasis, consolidation, and pleural thickening/adhesion. The nodules frequently connected with the pleura. The initial and follow-up studies suggest that the disease may begin in the terminal bronchiole or as preexisting bronchiectasis and spread transbronchially along the draining bronchus or towards the pleura to produce lesions such as new nodules, cavities, consolidation, pleuritis, and bronchiectasis, or more severe bronchiectasis. (author)

  19. Featured Image: A Comet's Coma

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    Kohler, Susanna

    2016-11-01

    This series of images (click for the full view!) features the nucleus of comet 67P/Churymov-Gerasimenko. The images were taken with the Wide Angle Camera of RosettasOSIRIS instrument asRosetta orbited comet 67P. Each column represents a different narrow-band filter that allows us to examine the emission of a specific fragment species, and the images progress in time from January 2015 (top) to June 2015 (bottom). In a recent study, Dennis Bodewits (University of Maryland) and collaborators used these images to analyze the comets inner coma, the cloud of gas and dust produced around the nucleus as ices sublime. OSIRISs images allowed the team to explore how the 67Ps inner coma changed over time as the comet approached the Sun marking the first time weve been able to study such an environment at this level of detail. To read more about what Bodewits and collaborators learned, you can check out their paper below!CitationD. Bodewits et al 2016 AJ 152 130. doi:10.3847/0004-6256/152/5/130

  20. Image feature localization by multiple hypothesis testing of Gabor features.

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    Ilonen, Jarmo; Kamarainen, Joni-Kristian; Paalanen, Pekka; Hamouz, Miroslav; Kittler, Josef; Kälviäinen, Heikki

    2008-03-01

    Several novel and particularly successful object and object category detection and recognition methods based on image features, local descriptions of object appearance, have recently been proposed. The methods are based on a localization of image features and a spatial constellation search over the localized features. The accuracy and reliability of the methods depend on the success of both tasks: image feature localization and spatial constellation model search. In this paper, we present an improved algorithm for image feature localization. The method is based on complex-valued multi resolution Gabor features and their ranking using multiple hypothesis testing. The algorithm provides very accurate local image features over arbitrary scale and rotation. We discuss in detail issues such as selection of filter parameters, confidence measure, and the magnitude versus complex representation, and show on a large test sample how these influence the performance. The versatility and accuracy of the method is demonstrated on two profoundly different challenging problems (faces and license plates).

  1. Infrared image mosaic using point feature operators

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    Huang, Zhen; Sun, Shaoyuan; Shen, Zhenyi; Hou, Junjie; Zhao, Haitao

    2016-10-01

    In this paper, we study infrared image mosaic around a single point of rotation, aiming at expanding the narrow view range of infrared images. We propose an infrared image mosaic method using point feature operators including image registration and image synthesis. Traditional mosaic algorithms usually use global image registration methods to extract the feature points in the global image, which cost too much time as well as considerable matching errors. To address this issue, we first roughly calculate the image shift amount using phase correlation and determine the overlap region between images, and then extract image features in overlap region, which shortens the registration time and increases the quality of feature points. We improve the traditional algorithm through increasing constraints of point matching based on prior knowledge of image shift amount based on which the weighted map is computed using fade in-out method. The experimental results verify that the proposed method has better real time performance and robustness.

  2. Medical Image Feature, Extraction, Selection And Classification

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    M.VASANTHA,

    2010-06-01

    Full Text Available Breast cancer is the most common type of cancer found in women. It is the most frequent form of cancer and one in 22 women in India is likely to suffer from breast cancer. This paper proposes a image classifier to classify the mammogram images. Mammogram image is classified into normal image, benign image and malignant image. Totally 26 features including histogram intensity features and GLCM features are extracted from mammogram image. A hybrid approach of feature selection is proposed in this paper which reduces 75% of the features. Decision tree algorithms are applied to mammography lassification by using these reduced features. Experimental results have been obtained for a data set of 113 images taken from MIAS of different types. This technique of classification has not been attempted before and it reveals the potential of Data mining in medical treatment.

  3. Tongue Image Feature Extraction in TCM

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    LI Dong; DU Lian-xiang; LU Fu-ping; DU Jun-ping

    2004-01-01

    In this paper, digital image processing and computer vision techniques are applied to study tongue images for feature extraction with VC++ and Matlab. Extraction and analysis of the tongue surface features are based on shape, color, edge, and texture. The developed software has various functions and good user interface and is easy to use. Feature data for tongue image pattern recognition is provided, which form a sound basis for the future tongue image recognition.

  4. Imaging features in Hirayama disease

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    Sonwalkar Hemant

    2008-01-01

    Full Text Available Purpose: To evaluate the MR findings in clinically suspected cases of Hirayama disease. Materials and Methods: The pre and post contrast neutral and flexion position cervical MR images of eight patients of clinically suspected Hirayama disease were evaluated for the following findings: localized lower cervical cord atrophy, asymmetric cord flattening, abnormal cervical curvature, loss of attachment between the posterior dural sac and subjacent lamina, anterior shifting of the posterior wall of the cervical dural canal and enhancing epidural component with flow voids. The distribution of the above features in our patient population was noted and correlated with their clinical presentation and electromyography findings. Observations: Although lower cervical cord atrophy was noted in all eight cases of suspected Hirayama disease, the rest of the findings were variably distributed with asymmetric cord flattening, abnormal cervical curvature, anterior shifting of the posterior wall of the cervical dural canal and enhancing epidural component seen in six out of eight (75% cases. An additional finding of thoracic extension of the enhancing epidural component was also noted in five out of eight cases. Conclusion: Dynamic post contrast MRI evaluation of cervicothoracic spine is an accurate method for the diagnosis of Hirayama disease.

  5. Feature-based Image Sequence Compression Coding

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A novel compressing method for video teleconference applications is presented. Semantic-based coding based on human image feature is realized, where human features are adopted as parameters. Model-based coding and the concept of vector coding are combined with the work on image feature extraction to obtain the result.

  6. Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

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    Salam Shuleenda Devi

    2016-12-01

    Full Text Available Microscopic image analysis of blood smear plays a very important role in characterization of erythrocytes in screening of malaria parasites. The characteristics feature of erythrocyte changes due to malaria parasite infection. The microscopic features of the erythrocyte include morphology, intensity and texture. In this paper, the different features used to differentiate the non- infected and malaria infected erythrocyte have been reviewed.

  7. Hepatic CT image query using Gabor features

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    Chenguang Zhao(赵晨光); Hongyan Cheng(程红岩); Tiange Zhuang(庄天戈)

    2004-01-01

    A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented.For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

  8. Comparative features of retroviral infections of livestock.

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    Evermann, J F

    1990-01-01

    Retroviral infections of livestock have become of increasing importance due to their usefulness as comparative models for human retroviral infections and their effects upon animal health and marketability of animals and animal products nationally and internationally. This paper presents a perspective on the retroviruses of economic concern in veterinary medicine with emphasis on the importance of understanding the modes of virus transmission and the species specificity of the viruses. The retroviruses reviewed include the oncovirus, bovine leukosis virus, and the lentiviruses, equine infectious anemia virus; maedi/visna virus, caprine arthritis-encephalitis virus and bovine visna-like virus. The comparative features amongst these animal retroviruses and those of humans must be recognized by the veterinary and medical professions since the similarities in virus replication and spread by blood transfer can provide important clues in controlling and perhaps preventing human retroviruses infections, such as the human immunodeficiency virus.

  9. Wilson’s disease: Atypical imaging features

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    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.

  10. Image segmentation using association rule features.

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    Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

    2002-01-01

    A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

  11. Image fusion using sparse overcomplete feature dictionaries

    Energy Technology Data Exchange (ETDEWEB)

    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.

  12. 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.)

  13. Content Based Image Retrieval by Multi Features using Image Blocks

    Directory of Open Access Journals (Sweden)

    Arpita Mathur

    2013-12-01

    Full Text Available Content based image retrieval (CBIR is an effective method of retrieving images from large image resources. CBIR is a technique in which images are indexed by extracting their low level features like, color, texture, shape, and spatial location, etc. Effective and efficient feature extraction mechanisms are required to improve existing CBIR performance. This paper presents a novel approach of CBIR system in which higher retrieval efficiency is achieved by combining the information of image features color, shape and texture. The color feature is extracted using color histogram for image blocks, for shape feature Canny edge detection algorithm is used and the HSB extraction in blocks is used for texture feature extraction. The feature set of the query image are compared with the feature set of each image in the database. The experiments show that the fusion of multiple features retrieval gives better retrieval results than another approach used by Rao et al. This paper presents comparative study of performance of the two different approaches of CBIR system in which the image features color, shape and texture are used.

  14. Finding curvilinear features in speckled images

    Science.gov (United States)

    Samadani, Ramin; Vesecky, John F.

    1990-01-01

    A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.

  15. Multi Feature Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Rajshree S. Dubey,

    2010-09-01

    Full Text Available There are numbers of methods prevailing for Image Mining Techniques. This Paper includes the features of four techniques I,e Color Histogram, Color moment, Texture, and Edge Histogram Descriptor. The nature of the Image is basically based on the Human Perception of the Image. The Machine interpretation of the Image is based on the Contours and surfaces of the Images. The study of the Image Mining is a very challenging task because it involves the Pattern Recognition which is a very important tool for the Machine Vision system. A combination of four feature extraction methods namely color istogram, Color Moment, texture, and Edge Histogram Descriptor. There is a provision to add new features in future for better retrievalefficiency. In this paper the combination of the four techniques are used and the Euclidian distances are calculated of the every features are added and the averages are made .The user interface is provided by the Mat lab. The image properties analyzed in this work are by using computer vision and image processing algorithms. For colorthe histogram of images are computed, for texture co occurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD that is found. For retrieval of images, the averages of the four techniques are made and the resultant Image is retrieved.

  16. Featured Image: Identifying Weird Galaxies

    Science.gov (United States)

    Kohler, Susanna

    2017-08-01

    Hoags Object, an example of a ring galaxy. [NASA/Hubble Heritage Team/Ray A. Lucas (STScI/AURA)]The above image (click for the full view) shows PanSTARRSobservationsof some of the 185 galaxies identified in a recent study as ring galaxies bizarre and rare irregular galaxies that exhibit stars and gas in a ring around a central nucleus. Ring galaxies could be formed in a number of ways; one theory is that some might form in a galaxy collision when a smaller galaxy punches through the center of a larger one, triggering star formation around the center. In a recent study, Ian Timmis and Lior Shamir of Lawrence Technological University in Michigan explore ways that we may be able to identify ring galaxies in the overwhelming number of images expected from large upcoming surveys. They develop a computer analysis method that automatically finds ring galaxy candidates based on their visual appearance, and they test their approach on the 3 million galaxy images from the first PanSTARRS data release. To see more of the remarkable galaxies the authors found and to learn more about their identification method, check out the paper below.CitationIan Timmis and Lior Shamir 2017 ApJS 231 2. doi:10.3847/1538-4365/aa78a3

  17. 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.

  18. Extraction of Facial Features from Color Images

    Directory of Open Access Journals (Sweden)

    J. Pavlovicova

    2008-09-01

    Full Text Available In this paper, a method for localization and extraction of faces and characteristic facial features such as eyes, mouth and face boundaries from color image data is proposed. This approach exploits color properties of human skin to localize image regions – face candidates. The facial features extraction is performed only on preselected face-candidate regions. Likewise, for eyes and mouth localization color information and local contrast around eyes are used. The ellipse of face boundary is determined using gradient image and Hough transform. Algorithm was tested on image database Feret.

  19. An Image Retrieval Method Using DCT Features

    Institute of Scientific and Technical Information of China (English)

    樊昀; 王润生

    2002-01-01

    In this paper a new image representation for compressed domain image re-trieval and an image retrieval system are presented. To represent images compactly and hi-erarchically, multiple features such as color and texture features directly extracted from DCTcoefficients are structurally organized using vector quantization. To train the codebook, a newMinimum Description Length vector quantization algorithm is used and it automatically decidesthe number of code words. To compare two images using the proposed representation, a newefficient similarity measure is designed. The new method is applied to an image database with1,005 pictures. The results demonstrate that the method is better than two typical histogrammethods and two DCT-based image retrieval methods.

  20. Featured Image: Modeling Supernova Remnants

    Science.gov (United States)

    Kohler, Susanna

    2016-05-01

    This image shows a computer simulation of the hydrodynamics within a supernova remnant. The mixing between the outer layers (where color represents the log of density) is caused by turbulence from the Rayleigh-Taylor instability, an effect that arises when the expanding core gas of the supernova is accelerated into denser shell gas. The past standard for supernova-evolution simulations was to perform them in one dimension and then, in post-processing, manually smooth out regions that undergo Rayleigh-Taylor turbulence (an intrinsically multidimensional effect). But in a recent study, Paul Duffell (University of California, Berkeley) has explored how a 1D model could be used to reproduce the multidimensional dynamics that occur in turbulence from this instability. For more information, check out the paper below!CitationPaul C. Duffell 2016 ApJ 821 76. doi:10.3847/0004-637X/821/2/76

  1. Imaging features of ciliated hepatic foregut cyst

    Institute of Scientific and Technical Information of China (English)

    Song-Hua Fang; Dan-Jun Dong; Shi-Zheng Zhang

    2005-01-01

    Ciliated hepatic foregut cyst (CHFC) is a very rare cystic lesion of the liver that is histologically similar to bronchogenic cyst. We report one case of CHFC that was hard to distinguish from solid-cystic neoplasm in imaging features. Magnetic resonance imaging was helpful in differentiating these cysts from other lesions.

  2. 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.)

  3. Solving jigsaw puzzles using image features

    DEFF Research Database (Denmark)

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

    2008-01-01

    algorithm which exploits the divide and conquer paradigm to reduce the combinatorially complex problem by classifying the puzzle pieces and comparing pieces drawn from the same group. The paper includes a brief preliminary investigation of some image features used in the classification.......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...... is used in a general puzzle solving method which is based on a greedy algorithm previously proved successful. We have been able to solve computer generated puzzles of 320 pieces as well as a real puzzle of 54 pieces by exclusively using image information. Additionally, we investigate a new scalable...

  4. Exact feature probabilities in images with occlusion

    CERN Document Server

    Pitkow, Xaq

    2010-01-01

    To understand the computations of our visual system, it is important to understand also the natural environment it evolved to interpret. Unfortunately, existing models of the visual environment are either unrealistic or too complex for mathematical description. Here we describe a naturalistic image model and present a mathematical solution for the statistical relationships between the image features and model variables. The world described by this model is composed of independent, opaque, textured objects which occlude each other. This simple structure allows us to calculate the joint probability distribution of image values sampled at multiple arbitrarily located points, without approximation. This result can be converted into probabilistic relationships between observable image features as well as between the unobservable properties that caused these features, including object boundaries and relative depth. Using these results we explain the causes of a wide range of natural scene properties, including high...

  5. Automatic extraction of planetary image features

    Science.gov (United States)

    LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

    2013-01-01

    A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

  6. 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.

  7. Clinical features and pathobiology of Ebolavirus infection.

    Science.gov (United States)

    Ansari, Aftab A

    2014-12-01

    There has clearly been a deluge of international press coverage of the recent outbreak of Ebolavirus in Africa and is partly related to the "fear factor" that comes across when one is confronted with the fact that once infected, not only is the speed of death in a majority of cases rapid but also the images of the cause of death such as bleeding from various orifices gruesome and frightening. The fact that it leads to infection and death of health care providers (10% during the current epidemic) and the visualization of protective gear worn by these individuals to contain such infection adds to this "fear factor". Finally, there is a clear perceived notion that such an agent can be utilized as a bioterrorism agent that adds to the apprehension. Thus, in efforts to gain an objective view of the growing threat Ebolavirus poses to the general public, it is important to provide some basic understanding for the lethality of Ebolavirus infection that is highlighted in Fig. 1. This virus infection first appears to disable the immune system (the very system needed to fight the infection) and subsequently disables the vascular system that leads to blood leakage (hemorrhage), hypotension, drop in blood pressure, followed by shock and death. The virus appears to sequentially infect dendritic cells disabling the interferon system (one of the major host anti-viral immune systems) then macrophages (that trigger the formation of blood clots, release of inflammatory proteins and nitric oxide damaging the lining of blood vessels leading to blood leakage) and finally endothelial cells that contribute to blood leakage. The virus also affects organs such as the liver (that dysregulates the formation of coagulation proteins), the adrenal gland (that destroys the ability of the patient to synthesize steroids and leads to circulation failure and disabling of regulators of blood pressure) and the gastro-intestinal tract (leading to diarrhea). The ability of the virus to disable such major

  8. Imaging of Orbital Infections

    OpenAIRE

    Seyed Hassan Mostafavi

    2010-01-01

    Preseptal and orbital cellulitis occur more commonly in children than adults. The history and physical examination are crucial in distinguishing between preseptal and orbital cellulitis. The orbital septum delineates the anterior eyelid soft tissues from the orbital soft tissue. Infections anterior to the orbital septum are classified as preseptal cellulitis and those posterior to the orbital septum are termed orbital cellulitis. "nRecognition of orbital involvement is important not only...

  9. Wood recognition using image texture features.

    Directory of Open Access Journals (Sweden)

    Hang-jun Wang

    Full Text Available Inspired by theories of higher local order autocorrelation (HLAC, this paper presents a simple, novel, yet very powerful approach for wood recognition. The method is suitable for wood database applications, which are of great importance in wood related industries and administrations. At the feature extraction stage, a set of features is extracted from Mask Matching Image (MMI. The MMI features preserve the mask matching information gathered from the HLAC methods. The texture information in the image can then be accurately extracted from the statistical and geometrical features. In particular, richer information and enhanced discriminative power is achieved through the length histogram, a new histogram that embodies the width and height histograms. The performance of the proposed approach is compared to the state-of-the-art HLAC approaches using the wood stereogram dataset ZAFU WS 24. By conducting extensive experiments on ZAFU WS 24, we show that our approach significantly improves the classification accuracy.

  10. Onboard Image Registration from Invariant Features

    Science.gov (United States)

    Wang, Yi; Ng, Justin; Garay, Michael J.; Burl, Michael C

    2008-01-01

    This paper describes a feature-based image registration technique that is potentially well-suited for onboard deployment. The overall goal is to provide a fast, robust method for dynamically combining observations from multiple platforms into sensors webs that respond quickly to short-lived events and provide rich observations of objects that evolve in space and time. The approach, which has enjoyed considerable success in mainstream computer vision applications, uses invariant SIFT descriptors extracted at image interest points together with the RANSAC algorithm to robustly estimate transformation parameters that relate one image to another. Experimental results for two satellite image registration tasks are presented: (1) automatic registration of images from the MODIS instrument on Terra to the MODIS instrument on Aqua and (2) automatic stabilization of a multi-day sequence of GOES-West images collected during the October 2007 Southern California wildfires.

  11. Imaging features of alveolar soft part sarcoma

    Institute of Scientific and Technical Information of China (English)

    Teng Jin; Ping Zhang Co-first author; Xiaoming Li

    2015-01-01

    Objective The aim of this study was to analyze the imaging features of alveolar soft part sarcoma (ASPS). Methods The imaging features of 11 cases with ASPS were retrospectively analyzed. Results ASPS mainly exhibited an isointense or slightly high signal intensity on T1-weighted imaging (T1WI), and a mixed high signal on T2-weighted imaging (T2WI). ASPS was partial, with rich tortuous flow voids, or “line-like” low signal septa. The essence of the mass was heterogeneous enhancement. The 1H-MRS showed a slight choline peak at 3.2 ppm. Conclusion The wel-circumscribed mass and blood voids, combined with “line-like” low signals play a significant role in diagnosis. The choline peak and the other signs may be auxiliary diagnoses.

  12. Straight line feature based image distortion correction

    Institute of Scientific and Technical Information of China (English)

    Zhang Haofeng; Zhao Chunxia; Lu Jianfeng; Tang Zhenmin; Yang Jingyu

    2008-01-01

    An image distortion correction method is proposed, which uses the straight line features. Many parallel lines of different direction from different images were extracted, and then were used to optimize the distortion parameters by nonlinear least square. The thought of step by step was added when the optimization method working. 3D world coordi-nation is not need to know, and the method is easy to implement. The experiment result shows its high accuracy.

  13. Image Mining Using Texture and Shape Feature

    Directory of Open Access Journals (Sweden)

    Prof.Rupali Sawant

    2010-12-01

    Full Text Available Discovering knowledge from data stored in typical alphanumeric databases, such as relational databases, has been the focal point of most of the work in database mining. However, with advances in secondary and tertiary storage capacity, coupled with a relatively low storage cost, more and more non standard data (in the form of images is being accumulated. This vast collection of image data can also be mined to discover new and valuable knowledge. During theprocess of image mining, the concepts in different hierarchiesand their relationships are extracted from different hierarchies and granularities, and association rule mining and concept clustering are consequently implemented. The generalization and specialization of concepts are realized in different hierarchies, lower layer concepts can be upgraded to upper layer concepts, and upper layer concepts guide the extraction of lower layer concepts. It is a process from image data to image information, from image information to imageknowledge, from lower layer concepts to upper layer concept lattice and cloud model theory is proposed. The methods of image mining from image texture and shape features are introduced here, which include the following basic steps: firstly pre-process images secondly use cloud model to extract concepts, lastly use concept lattice to extracta series of image knowledge.

  14. Imaging features of benign adrenal cysts

    Energy Technology Data Exchange (ETDEWEB)

    Sanal, Hatice Tuba [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey)]. E-mail: tubasanal@yahoo.com; Kocaoglu, Murat [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey); Yildirim, Duzgun [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey); Bulakbasi, Nail [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey); Guvenc, Inanc [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey); Tayfun, Cem [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey); Ucoz, Taner [Department of Radiology, Gulhane Military Medical Academy, Ankara (Turkey)

    2006-12-15

    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.

  15. Disorders of cortical formation: MR imaging features.

    Science.gov (United States)

    Abdel Razek, A A K; Kandell, A Y; Elsorogy, L G; Elmongy, A; Basett, A A

    2009-01-01

    The purpose of this article was to review the embryologic stages of the cerebral cortex, illustrate the classification of disorders of cortical formation, and finally describe the main MR imaging features of these disorders. Disorders of cortical formation are classified according to the embryologic stage of the cerebral cortex at which the abnormality occurred. MR imaging shows diminished cortical thickness and sulcation in microcephaly, enlarged dysplastic cortex in hemimegalencephaly, and ipsilateral focal cortical thickening with radial hyperintense bands in focal cortical dysplasia. MR imaging detects smooth brain in classic lissencephaly, the nodular cortex with cobblestone cortex with congenital muscular dystrophy, and the ectopic position of the gray matter with heterotopias. MR imaging can detect polymicrogyria and related syndromes as well as the types of schizencephaly. We concluded that MR imaging is essential to demonstrate the morphology, distribution, and extent of different disorders of cortical formation as well as the associated anomalies and related syndromes.

  16. Clinical and imaging features of fludarabine neurotoxicity.

    Science.gov (United States)

    Lee, Michael S; McKinney, Alexander M; Brace, Jeffrey R; Santacruz, Karen

    2010-03-01

    Neurotoxicity from intravenous fludarabine is a rare but recognized clinical entity. Its brain imaging features have not been extensively described. Three patients received 38.5 mg or 40 mg/m per day fludarabine in a 5-day intravenous infusion before bone marrow transplantation in treatment of hematopoietic malignancies. Several weeks later, each patient developed progressive neurologic decline, including retrogeniculate blindness, leading to coma and death. Brain MRI showed progressively enlarging but mild T2/FLAIR hyperintensities in the periventricular white matter. The lesions demonstrated restricted diffusion but did not enhance. Because the neurotoxicity of fludarabine appears long after exposure, neurologic decline in this setting is likely to be attributed to opportunistic disease. However, the imaging features are distinctive in their latency and in being mild relative to the profound clinical features. The safe dose of fludarabine in this context remains controversial.

  17. Ophthalmic imaging features of posterior scleritis

    Directory of Open Access Journals (Sweden)

    Zhi Li

    2014-07-01

    Full Text Available AIM: To analyze, summarize and describe ophthalmic imaging features of posterior scleritis. METHODS: Clinical data of 16 patients(21 eyeswith posterior scleritis diagnosed in our hospital from October 2008 to June 2013 were retrospectively analyzed. The results of type-B ultrasonic, fundus chromophotograph, fundus fluorescein angiography, CT were recorded for comprehensive evaluation and analysis of ophthalmic imaging features of posterior scleritis. RESULTS: All patients underwent type-B ultrasonic examination and manifested as diffuse and nodular types. The diffuse type showed diffusely thickened sclera and a dark hypoechoic area that connected with the optic nerve to form a typical “T”-shaped sign. The nodular type showed scleral echogenic nodules and relatively regular internal structure. FFA showed that relatively weak mottled fluorescences were visible in the arterial early phase and strong multiple needle-like fluorescences were visible in the arteriovenous phase, which were then progressively larger and fused; fluorescein was leaked to the subretinal tissue in the late phase; varying degrees of strong fluorescences with less clear or unclear boundaries were visible in the optic disk. CT results showed thickened eyeball wall. CONCLUSION: Posterior scleritis is common in young female patients, whose ophthalmic imaging features are varied and more specific in type-B ultrasonic. Selection of rational ophthalmic imaging examination method, combined with clinical manifestations, can accurately diagnose posterior scleritis and avoid the incidence of missed and delayed diagnosis.

  18. Multispectral image fusion based on fractal features

    Science.gov (United States)

    Tian, Jie; Chen, Jie; Zhang, Chunhua

    2004-01-01

    Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the

  19. Radionuclide imaging of spinal infections

    Energy Technology Data Exchange (ETDEWEB)

    Gemmel, Filip [Ghent Maria-Middelares, General Hospital, Division of Nuclear Medicine, Ghent (Belgium); Medical Center Leeuwarden (MCL), Division of Nuclear Medicine, Henri Dunantweg 2, Postbus 888, Leeuwarden (Netherlands); Dumarey, Nicolas [Universite Libre de Bruxelles, Hopital Erasme, Division of Nuclear Medicine, Brussels (Belgium); Palestro, Christopher J. [Long Island Jewish Medical Center, Division of Nuclear Medicine, Long Island, NY (United States)

    2006-10-15

    The diagnosis of spinal infection, with or without implants, has been a challenge for physicians for many years. Spinal infections are now being recognised more frequently, owing to aging of the population and the increasing use of spinal-fusion surgery. The diagnosis in many cases is delayed, and this may result in permanent neurological damage or even death. Laboratory evidence of infection is variable. Conventional radiography and radionuclide bone imaging lack both sensitivity and specificity. Neither in vitro labelled leucocyte scintigraphy nor {sup 99m}Tc-anti-granulocyte antibody scintigraphy is especially useful, because of the frequency with which spinal infection presents as a non-specific photopenic area on these tests. Sequential bone/gallium imaging and {sup 67}Ga-SPECT are currently the radionuclide procedures of choice for spinal osteomyelitis, but these tests lack specificity, suffer from poor spatial resolution and require several days to complete. [{sup 18}F]Fluoro-2-deoxy-D-glucose (FDG) PET is a promising technique for diagnosing spinal infection, and has several potential advantages over conventional radionuclide tests. The study is sensitive and is completed in a single session, and image quality is superior to that obtained with single-photon emitting tracers. The specificity of FDG-PET may also be superior to that of conventional tracers because degenerative bone disease and fractures usually do not produce intense FDG uptake; moreover, spinal implants do not affect FDG imaging. However, FDG-PET images have to be read with caution in patients with instrumented spinal-fusion surgery since non-specific accumulation of FDG around the fusion material is not uncommon. In the future, PET-CT will likely provide more precise localisation of abnormalities. FDG-PET may prove to be useful for monitoring response to treatment in patients with spinal osteomyelitis. Other tracers for diagnosing spinal osteomyelitis are also under investigation, including

  20. 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.

  1. 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

  2. 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.)

  3. Radionuclide imaging of musculoskeletal infection

    Directory of Open Access Journals (Sweden)

    Christopher J. Palestro

    2007-09-01

    Full Text Available Radionuclide imaging studies are routinely used to evaluate patients suspected of having musculoskeletal infection. Three-phase bone imaging is readily available, relatively inexpensive, and very accurate in the setting of otherwise normal bone. Labeled leukocyte imaging should be used in cases of "complicating osteomyelitis" such as prosthetic joint infection. This test also is useful in clinically unsuspected diabetic pedal osteomyelitis as well as in the neuropathic joint. It is often necessary, however, to perform complementary bone marrow imaging, to maximize the accuracy of labeled leukocyte imaging. In contrast to other regions in the skeleton, labeled leukocyte imaging is not useful for diagnosing spinal osteomyelitis. At the moment, gallium is the preferred radionuclide procedure for this condition and is a useful adjunct to magnetic resonance imaging. FDG-PET likely will play an important role in the evaluation of musculoskeletal infection, especially spinal osteomyelitis, and may replace gallium imaging for this purpose.Estudos através de imagens com o uso de radionuclídeos são rotineiramente usadas para avaliar pacientes suspeitos de terem infecção músculo-esquelética. A imagem óssea em tridimensional é facilmente avaliável, relativamente de baixo custo, e muito precisa na localização de alterações ósseas. Imagem com leucócito marcado poderia ser usada nos casos de "osteomielite com complicações" tais como infecção prostética articular. Esse teste também é útil na não suspeita clinica de osteomielite associada ao pé diabético tanto quanto nas junções neuropáticas. É sempre necessário, por outro lado, realizar imagem complementar da medula óssea para aumentar a precisão da imagem com leucócito marcado. Em contraste com outras regiões no esqueleto, imagem com leucócito marcado não é útil para diagnosticar osteomielite da coluna vertebral. Até agora, o gálio é o radionuclídeo preferido para

  4. 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.)

  5. Imaging internal features of whole, unfixed bacteria.

    Science.gov (United States)

    Thomson, Nicholas M; Channon, Kevin; Mokhtar, Noor Azlin; Staniewicz, Lech; Rai, Ranjana; Roy, Ipsita; Sato, Shun; Tsuge, Takeharu; Donald, Athene M; Summers, David; Sivaniah, Easan

    2011-01-01

    Wet scanning-transmission electron microscopy (STEM) is a technique that allows high-resolution transmission imaging of biological samples in a hydrated state, with minimal sample preparation. However, it has barely been used for the study of bacterial cells. In this study, we present an analysis of the advantages and disadvantages of wet STEM compared with standard transmission electron microscopy (TEM). To investigate the potential applications of wet STEM, we studied the growth of polyhydroxyalkanoate and triacylglycerol carbon storage inclusions. These were easily visible inside cells, even in the early stages of accumulation. Although TEM produces higher resolution images, wet STEM is useful when preservation of the sample is important or when studying the relative sizes of different features, since samples do not need to be sectioned. Furthermore, under carefully selected conditions, it may be possible to maintain cell viability, enabling new types of experiments to be carried out. To our knowledge, internal features of bacterial cells have not been imaged previously by this technique.

  6. 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.)

  7. 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

  8. Discussion of CT Image Features of Specific Lung Infection after Renal Transplantation%肾移植患者术后特异性肺部感染的 CT 影像特点

    Institute of Scientific and Technical Information of China (English)

    赵鹤亮; 陈昕; 洪波; 张惠英

    2014-01-01

    OBJECTIVE To observe the CT image features of specific lung infection after renal transplantation so as to provide references for clinical treatments .METHODS Totally 70 patients with specific lung infections after renal transplantation were chosen from Jan .2009 to Dec .2013 and their lungs were scanned by Light Speed 64 VCT scanner .In addition ,all patients were given blood cultures to find infectious pathogens .RESULTS Bacterial pneu‐monia accounted for 42 .86% ,cytomegalovirus pneumonia accounted for 12 .86% ,pneumocystis carinii pneumonia accounted for 5 .71% , pulmonary tuberculosis accounted for 17 .14% , and mycotic pneumonia accounted for 21 .43% .Among them ,there were 17 cases with CT manifestation as diffuse patch and grinding glassy spots or net‐shaped shadow including 18 .57% of cytomegalovirus pneumonia and 5 .71% of pneumocystis carinii pneumoni‐a .CONCLUSION Specific lung infections after renal transplantation were mainly bacterial pneumonias ,and differ‐ent infection types have distinctive CT features .Bacterial and mycotic pneumonias mainly feature with lung paren‐chyma seepage ,cytomegalovirus and pneumocystis carinii pneumonia mainly feature with pulmonary interstitial thickening ,and pulmonary tuberculosis mainly features with uneven millets .%目的:观察肾移植术后特异性肺部感染的C T影像特点,为临床治疗提供参考依据。方法临床纳入2009年1月-2013年12月70例肾移植术后发生特异性肺部感染的患者,采用飞利浦256层螺旋C T 机进行肺部扫描;此外,所有患者进行血液培养,查找感染病原体。结果细菌感染性肺炎、巨细胞病毒性肺炎、卡氏肺囊虫肺炎、肺结核、真菌性肺炎分别占42.86%、12.86%、5.71%、21.43%;其中,C T表现为弥漫性斑片与磨玻璃状斑点状或网状阴影患者17例,巨细胞病毒性肺炎18.57%、卡氏肺囊虫肺炎5.71%。结论肾移植术后特异性肺感染以

  9. Feature selection with the image grand tour

    Science.gov (United States)

    Marchette, David J.; Solka, Jeffrey L.

    2000-08-01

    The grand tour is a method for visualizing high dimensional data by presenting the user with a set of projections and the projected data. This idea was extended to multispectral images by viewing each pixel as a multidimensional value, and viewing the projections of the grand tour as an image. The user then looks for projections which provide a useful interpretation of the image, for example, separating targets from clutter. We discuss a modification of this which allows the user to select convolution kernels which provide useful discriminant ability, both in an unsupervised manner as in the image grand tour, or in a supervised manner using training data. This approach is extended to other window-based features. For example, one can define a generalization of the median filter as a linear combination of the order statistics within a window. Thus the median filter is that projection containing zeros everywhere except for the middle value, which contains a one. Using the convolution grand tour one can select projections on these order statistics to obtain new nonlinear filters.

  10. [Clinical features of Pseudomonas aeruginosa infections].

    Science.gov (United States)

    Sarlangue, J; Brissaud, O; Labrèze, C

    2006-10-01

    Pseudomonas aeruginosa is a ubiquitous environmental organism usually considered as opportunistic pathogen in immunocompromised subjects. However it can produce disease in healthy children, mainly on moist body sites. Familial, community and nosocomial outbreaks of cutaneous infections have been reported. Ecthyma gangrenosum is possible without bacteremia. P. aeruginosa is also the most common cause of otitis externa in swimmers and osteomyelitis after puncture wound of the foot.

  11. FEATURES OF PNEUMONIA IN HIV-INFECTED PATIENTS

    Directory of Open Access Journals (Sweden)

    M. T. Vatutin

    2016-01-01

    Full Text Available The article presents the clinical, diagnostic and treatment features of pneumocystis pneumonia in HIV-infected patients. The clinical case of diagnosis verification in a patient 58 years old with severe respiratory failure is described.

  12. 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.

  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. [Imaging in urinary tract infections in adults].

    Science.gov (United States)

    Puech, P; Lagard, D; Leroy, C; Dracon, M; Biserte, J; Lemaître, L

    2004-02-01

    Uncomplicated infection of the urinary tract is frequent and usually resolves rapidly with treatment and imaging is unnecessary. Progression to complex infection often occurs in patients with predisposing factors. Imaging assists in evaluating the extent of disease, plays a role in directing therapy and guides interventional procedures if necessary. This pictorial essay reviews the role of imaging and intervention in infections of the urinary tract.

  15. Toward Automated Feature Detection in UAVSAR Images

    Science.gov (United States)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  16. 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.

  17. Polymicrobial Infective Endocarditis: Clinical Features and Prognosis

    Science.gov (United States)

    García-Granja, Pablo Elpidio; López, Javier; Vilacosta, Isidre; Ortiz-Bautista, Carlos; Sevilla, Teresa; Olmos, Carmen; Sarriá, Cristina; Ferrera, Carlos; Gómez, Itziar; Román, José Alberto San

    2015-01-01

    Abstract To describe the profile of left-sided polymicrobial endocarditis (PE) and to compare it with monomicrobial endocarditis (ME). Among 1011 episodes of left-sided endocarditis consecutively diagnosed in 3 tertiary centers, between January 1, 1996 and December 31, 2014, 60 were polymicrobial (5.9%), 821 monomicrobial (81.7%), and in 123 no microorganism was detected (12.2%). Seven patients (0.7%) were excluded from the analysis because contamination of biologic tissue could not be discarded. The authors described the clinical, microbiologic, echocardiographic, and outcome of patients with PE and compared it with ME. Mean age was 64 years SD 16 years, 67% were men and 30% nosocomial. Diabetes mellitus (35%) were the most frequent comorbidities, fever (67%) and heart failure (43%) the most common symptoms at admission. Prosthetic valves (50%) were the most frequent infection location and coagulase-negative Staphylococci (48%) and enterococci (37%) the leading etiologies. The most repeated combination was coagulase-negative Staphylococci with enterococci (n = 9). Polymicrobial endocarditis appeared more frequently in patients with underlying disease (70% versus 56%, P = 0.036), mostly diabetics (35% versus 24%, P = 0.044) with previous cardiac surgery (15% versus 8% P = 0.049) and prosthetic valves (50% versus 37%, P = 0.038). Coagulase-negative Staphylococci, enterococci, Gram-negative bacilli, anaerobes, and fungi were more frequent in PE. No differences on age, sex, symptoms, need of surgery, and in-hospital mortality were detected. Polymicrobial endocarditis represents 5.9% of episodes of left-sided endocarditis in our series. Despite relevant demographic and microbiologic differences between PE and ME, short-term outcome is similar. PMID:26656328

  18. Fast Fractal Image Encoding Based on Special Image Features

    Institute of Scientific and Technical Information of China (English)

    ZHANG Chao; ZHOU Yiming; ZHANG Zengke

    2007-01-01

    The fractal image encoding method has received much attention for its many advantages over other methods,such as high decoding quality at high compression ratios. However, because every range block must be compared to all domain blocks in the codebook to find the best-matched one during the coding procedure, baseline fractal coding (BFC) is quite time consuming. To speed up fractal coding, a new fast fractal encoding algorithm is proposed. This algorithm aims at reducing the size of the search window during the domain-range matching process to minimize the computational cost. A new theorem presented in this paper shows that a special feature of the image can be used to do this work. Based on this theorem, the most inappropriate domain blocks, whose features are not similar to that of the given range block, are excluded before matching. Thus, the best-matched block can be captured much more quickly than in the BFC approachThe experimental results show that the runtime of the proposed method is reduced greatly compared to the BFC method. At the same time,the new algorithm also achieves high reconstructed image quality. In addition,the method can be incorporated with other fast algorithms to achieve better performance.Therefore, the proposed algorithm has a much better application potential than BFC.

  19. Accurate Image Retrieval Algorithm Based on Color and Texture Feature

    Directory of Open Access Journals (Sweden)

    Chunlai Yan

    2013-06-01

    Full Text Available Content-Based Image Retrieval (CBIR is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content (feature of the image, CBIR aims to find images that contain specified content (feature in the image database. In this paper, several key technologies of CBIR, e. g. the extraction of the color and texture features of the image, as well as the similarity measures are investigated. On the basis of the theoretical research, an image retrieval system based on color and texture features is designed. In this system, the Weighted Color Feature based on HSV space is adopted as a color feature vector, four features of the Co-occurrence Matrix, saying Energy, Entropy, Inertia Quadrature and Correlation, are used to construct texture vectors, and the Euclidean distance for similarity measure is employed as well. Experimental results show that this CBIR system is efficient in image retrieval.

  20. Imaging of musculoskeletal soft tissue infections

    Energy Technology Data Exchange (ETDEWEB)

    Turecki, Marcin B.; Taljanovic, Mihra S.; Holden, Dean A.; Hunter, Tim B.; Rogers, Lee F. [University of Arizona HSC, Department of Radiology, Tucson, AZ (United States); Stubbs, Alana Y. [Southern Arizona VA Health Care System, Department of Radiology, Tucson, AZ (United States); Graham, Anna R. [University of Arizona HSC, Department of Pathology, Tucson, AZ (United States)

    2010-10-15

    Prompt and appropriate imaging work-up of the various musculoskeletal soft tissue infections aids early diagnosis and treatment and decreases the risk of complications resulting from misdiagnosis or delayed diagnosis. The signs and symptoms of musculoskeletal soft tissue infections can be nonspecific, making it clinically difficult to distinguish between disease processes and the extent of disease. Magnetic resonance imaging (MRI) is the imaging modality of choice in the evaluation of soft tissue infections. Computed tomography (CT), ultrasound, radiography and nuclear medicine studies are considered ancillary. This manuscript illustrates representative images of superficial and deep soft tissue infections such as infectious cellulitis, superficial and deep fasciitis, including the necrotizing fasciitis, pyomyositis/soft tissue abscess, septic bursitis and tenosynovitis on different imaging modalities, with emphasis on MRI. Typical histopathologic findings of soft tissue infections are also presented. The imaging approach described in the manuscript is based on relevant literature and authors' personal experience and everyday practice. (orig.)

  1. Image retrieval using both color and texture features

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.

  2. Imaging systems and applications: introduction to the feature.

    Science.gov (United States)

    Imai, Francisco H; Linne von Berg, Dale C; Skauli, Torbjørn; Tominaga, Shoji; Zalevsky, Zeev

    2014-05-01

    Imaging systems have numerous applications in industrial, military, consumer, and medical settings. Assembling a complete imaging system requires the integration of optics, sensing, image processing, and display rendering. This issue features original research ranging from design of stimuli for human perception, optics applications, and image enhancement to novel imaging modalities in both color and infrared spectral imaging, gigapixel imaging as well as a systems perspective to imaging.

  3. Receptor Binding Ligands to Image Infection

    NARCIS (Netherlands)

    Chianelli, M.; Boerman, O. C.; Malviya, G.; Galli, F.; Oyen, W. J. G.; Signore, A.

    2008-01-01

    The current gold standard for imaging infection is radiolabeled white blood cells. For reasons of safety, simplicity and cost, it would be desirable to have a receptor-specific ligand that could be used for imaging infection and that would allow a differential diagnosis between sterile and septic in

  4. 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. .

  5. Comparative Study of Triangulation based and Feature based Image Morphing

    Directory of Open Access Journals (Sweden)

    Ms. Bhumika G. Bhatt

    2012-01-01

    Full Text Available Image Morphing is one of the most powerful Digital Image processing technique, which is used to enhancemany multimedia projects, presentations, education and computer based training. It is also used inmedical imaging field to recover features not visible in images by establishing correspondence of featuresamong successive pair of scanned images. This paper discuss what morphing is and implementation ofTriangulation based morphing Technique and Feature based Image Morphing. IT analyze both morphingtechniques in terms of different attributes such as computational complexity, Visual quality of morphobtained and complexity involved in selection of features.

  6. Neurological manifestation as presenting feature of dengue infection

    Directory of Open Access Journals (Sweden)

    Anju Aggarwal

    2015-01-01

    Full Text Available Neurological manifestation as the presenting feature of dengue infection is rare. This is a brief description of five children 5 months to 11 years with presenting features as seizures or altered sensorium. Bleeding manifestations were seen in two. Cerebrospinal fluid examination was normal in all. All were diagnosed as per WHO definition of dengue hemorrhagic fever and managed as per standard protocol. Serology (IgM dengue or nonstructural protein 1 antigen was positive in all.

  7. 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

  8. Tracking image features with PCA-SURF descriptors

    CSIR Research Space (South Africa)

    Pancham, A

    2015-05-01

    Full Text Available The tracking of moving points in image sequences requires unique features that can be easily distinguished. However, traditional feature descriptors are of high dimension, leading to larger storage requirement and slower computation. In this paper...

  9. Evaluation of textural features for multispectral images

    Science.gov (United States)

    Bayram, Ulya; Can, Gulcan; Duzgun, Sebnem; Yalabik, Nese

    2011-11-01

    Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.

  10. New synthesizing feature parameter of wear particles image

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper outlines the application of wavelet analysis method to computering wear par-ticles image processing and introduces the concept of grain parameter for wear particle imagebased on statistical feature parameters. The feature of wear particles image can be obtained fromthe wavelet decomposition and the statistics analysis. Test results showed that grain parametercan be used as a synthesizing feature parameter for wear particle image.

  11. A NOVEL REGION FEATURE USED IN MULTISENSOR IMAGE FUSION

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A new region feature which emphasized the salience of target region and its neighbors is proposed.In region segmentation-based multisensor image fusion scheme, the presented feature can be extracted from each segmented region to determine the fusion weight. Experimental results demonstrate that the proposed feature has extensive application scope and it provides much more information for each region. It can not only be used in image fusion but also be used in other image processing applications.

  12. 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

    utilizes depth information only to extract local features, without considering it to improve robustness and discriminability of the feature representation by merging depth cues into feature pooling. Spatial pyramid model (SPM) has become the standard protocol to split a 2D image plane into sub......-regions for feature pooling of RGB-D images. We argue that SPM may not be the optimal pooling scheme for RGB-D images, as it only pools features spatially and completely discards their depth topological structures. Instead, we propose a novel joint spatial-depth pooling (JSDP) scheme which further partitions SPM...

  13. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.

    Science.gov (United States)

    Gevaert, Olivier; Mitchell, Lex A; Achrol, Achal S; Xu, Jiajing; Echegaray, Sebastian; Steinberg, Gary K; Cheshier, Samuel H; Napel, Sandy; Zaharchuk, Greg; Plevritis, Sylvia K

    2014-10-01

    To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data. Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways. Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was

  14. Intrinsic feature-based pose measurement for imaging motion compensation

    Science.gov (United States)

    Baba, Justin S.; Goddard, Jr., James Samuel

    2014-08-19

    Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

  15. Retinal image analysis: preprocessing and feature extraction

    Energy Technology Data Exchange (ETDEWEB)

    Marrugo, Andres G; Millan, Maria S, E-mail: andres.marrugo@upc.edu [Grup d' Optica Aplicada i Processament d' Imatge, Departament d' Optica i Optometria Univesitat Politecnica de Catalunya (Spain)

    2011-01-01

    Image processing, analysis and computer vision techniques are found today in all fields of medical science. These techniques are especially relevant to modern ophthalmology, a field heavily dependent on visual data. Retinal images are widely used for diagnostic purposes by ophthalmologists. However, these images often need visual enhancement prior to apply a digital analysis for pathological risk or damage detection. In this work we propose the use of an image enhancement technique for the compensation of non-uniform contrast and luminosity distribution in retinal images. We also explore optic nerve head segmentation by means of color mathematical morphology and the use of active contours.

  16. Image mosaic method based on SIFT features of line segment.

    Science.gov (United States)

    Zhu, Jun; Ren, Mingwu

    2014-01-01

    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.

  17. Introduction: feature issue on In Vivo Microcirculation Imaging.

    Science.gov (United States)

    Dunn, Andrew K; Leitgeb, Rainer; Wang, Ruikang K; Zhang, Hao F

    2011-07-01

    The editors introduce the Biomedical Optics Express feature issue, "In Vivo Microcirculation Imaging," which includes 14 contributions from the biomedical optics community, covering such imaging techniques as optical coherence tomography, photoacoustic microscopy, laser Doppler /speckle imaging, and near infrared spectroscopy and fluorescence imaging.

  18. [Multiple transmission electron microscopic image stitching based on sift features].

    Science.gov (United States)

    Li, Mu; Lu, Yanmeng; Han, Shuaihu; Wu, Zhuobin; Chen, Jiajing; Liu, Zhexing; Cao, Lei

    2015-08-01

    We proposed a new stitching method based on sift features to obtain an enlarged view of transmission electron microscopic (TEM) images with a high resolution. The sift features were extracted from the images, which were then combined with fitted polynomial correction field to correct the images, followed by image alignment based on the sift features. The image seams at the junction were finally removed by Poisson image editing to achieve seamless stitching, which was validated on 60 local glomerular TEM images with an image alignment error of 62.5 to 187.5 nm. Compared with 3 other stitching methods, the proposed method could effectively reduce image deformation and avoid artifacts to facilitate renal biopsy pathological diagnosis.

  19. Remote Sensing Image Feature Extracting Based Multiple Ant Colonies Cooperation

    Directory of Open Access Journals (Sweden)

    Zhang Zhi-long

    2014-02-01

    Full Text Available This paper presents a novel feature extraction method for remote sensing imagery based on the cooperation of multiple ant colonies. First, multiresolution expression of the input remote sensing imagery is created, and two different ant colonies are spread on different resolution images. The ant colony in the low-resolution image uses phase congruency as the inspiration information, whereas that in the high-resolution image uses gradient magnitude. The two ant colonies cooperate to detect features in the image by sharing the same pheromone matrix. Finally, the image features are extracted on the basis of the pheromone matrix threshold. Because a substantial amount of information in the input image is used as inspiration information of the ant colonies, the proposed method shows higher intelligence and acquires more complete and meaningful image features than those of other simple edge detectors.

  20. Modern imaging: introduction to the feature issue.

    Science.gov (United States)

    Catrysse, Peter B; Irsch, Kristina; Javidi, Bahram; Preza, Chrysanthe; Testorf, Markus; Zalevsky, Zeev

    2017-03-20

    This special issue of Applied Optics contains selected papers reflecting the various disciplines that are needed for the design, implementation and advancement of imaging technology and systems, and it highlights the state-of-the-art research developments in the areas of modern imaging use.

  1. Infection imaging in nuclear medicine

    African Journals Online (AJOL)

    Although there are many imaging agents, only a few are ... suspected osteomyelitis in the spine, chronic ... magnetic resonance imaging (MRI) is the ... A 76-year-old female patient with a painful right knee prosthesis inserted 18 months before.

  2. Imaging fungal infections in children

    NARCIS (Netherlands)

    Ankrah, Alfred O.; Sathekge, Mike M; Dierckx, Rudi A.J.O.; Glaudemans, Andor W.J.M.

    2016-01-01

    Fungal infections in children rarely occur, but continue to have a high morbidity and mortality despite the development of newer antifungal agents. It is essential for these infections to be diagnosed at the earliest possible stage so appropriate treatment can be initiated promptly. The addition of

  3. Registration of multitemporal aerial optical images using line features

    Science.gov (United States)

    Zhao, Chenyang; Goshtasby, A. Ardeshir

    2016-07-01

    Registration of multitemporal images is generally considered difficult because scene changes can occur between the times the images are obtained. Since the changes are mostly radiometric in nature, features are needed that are insensitive to radiometric differences between the images. Lines are geometric features that represent straight edges of rigid man-made structures. Because such structures rarely change over time, lines represent stable geometric features that can be used to register multitemporal remote sensing images. An algorithm to establish correspondence between lines in two images of a planar scene is introduced and formulas to relate the parameters of a homography transformation to the parameters of corresponding lines in images are derived. Results of the proposed image registration on various multitemporal images are presented and discussed.

  4. Remote sensing image classification based on block feature point density analysis and multiple-feature fusion

    Science.gov (United States)

    Li, Shijin; Jiang, Yaping; Zhang, Yang; Feng, Jun

    2015-10-01

    With the development of remote sensing (RS) and the related technologies, the resolution of RS images is enhancing. Compared with moderate or low resolution images, high-resolution ones can provide more detailed ground information. However, a variety of terrain has complex spatial distribution. The different objectives of high-resolution images have a variety of features. The effectiveness of these features is not the same, but some of them are complementary. Considering the above information and characteristics, a new method is proposed to classify RS images based on hierarchical fusion of multi-features. Firstly, RS images are pre-classified into two categories in terms of whether feature points are uniformly or non-uniformly distributed. Then, the color histogram and Gabor texture feature are extracted from the uniformly-distributed categories, and the linear spatial pyramid matching using sparse coding (ScSPM) feature is obtained from the non-uniformly-distributed categories. Finally, the classification is performed by two support vector machine classifiers. The experimental results on a large RS image database with 2100 images show that the overall classification accuracy is boosted by 10.1% in comparison with the highest accuracy of single feature classification method. Compared with other multiple-feature fusion methods, the proposed method has achieved the highest classification accuracy on this dataset which has reached 90.1%, and the time complexity of the algorithm is also greatly reduced.

  5. Mining Mid-level Features for Image Classification

    OpenAIRE

    Fernando, Basura; Fromont, Elisa; Tuytelaars, Tinne

    2014-01-01

    International audience; Mid-level or semi-local features learnt using class-level information are potentially more distinctive than the traditional low-level local features constructed in a purely bottom-up fashion. At the same time they preserve some of the robustness properties with respect to occlusions and image clutter. In this paper we propose a new and effective scheme for extracting mid-level features for image classification, based on relevant pattern mining. In par- ticular, we mine...

  6. Featured Image: A New Look at Fomalhaut

    Science.gov (United States)

    Kohler, Susanna

    2017-06-01

    ALMA continuum image overlaid as contours on the Hubble STIS image of Fomalhaut. [MacGregor et al. 2017]This stunning image of the Fomalhaut star system was taken by the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile. This image maps the 1.3-mm continuum emission from the dust around the central star, revealing a ring that marks the outer edge of the planet-forming debris disk surrounding the star. In a new study, a team of scientists led by Meredith MacGregor (Harvard-Smithsonian Center for Astrophysics) examines these ALMA observations of Fomalhaut, which beautifully complement former Hubble images of the system. ALMAs images provide the first robust detection of apocenter glow the brightening of the ring at the point farthest away from the central star, a side effect of the rings large eccentricity. The authors use ALMAsobservations to measure properties of the disk, such as its span (roughly 136 x 14 AU), eccentricity (e 0.12), and inclination angle ( 66). They then explore the implications for Fomalhaut b, the planet located near the outer disk. To read more about the teams observations, check out the paper below.CitationMeredith A. MacGregor et al 2017 ApJ 842 8. doi:10.3847/1538-4357/aa71ae

  7. 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

  8. Feature extraction for an image retrieving scheme

    OpenAIRE

    Fuertes García, José Manuel; Lucena López, Manuel José; Pérez de la Blanca Capilla, Nicolás; Fernández Valdivia, Joaquín

    1999-01-01

    In this paper we present two basic modules whithin the designed scheme for retrieving images of a database from the object colour and shape in the scenes. On the one hand, we desing a new method to detect edges in colour images. We offer a new approach to the perceptual space (H,S,I) (an Uniform Chromatic Scale space) About wich we describe its properties as well as the metric to work in it. On the other hand, we develop an information simplifying process to form a graphic structure en wich t...

  9. Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features

    Directory of Open Access Journals (Sweden)

    Jian Cheng

    2015-05-01

    Full Text Available A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM for segmentation and support vector machine (SVM for classification. In the segmentation step, we propose an improved local binary pattern (LBP operator named the regional homogeneity local binary pattern (RHLBP to guarantee the regional homogeneity in PolSAR images. In the classification step, the color features extracted from false color images are applied to improve the classification accuracy. The RHLBP operator and color features can provide discriminative information to separate those pixels and regions with similar polarimetric features, which are from different classes. Extensive experimental comparison results with conventional methods on L-band PolSAR data demonstrate the effectiveness of our proposed method for PolSAR image classification.

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

    Science.gov (United States)

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

    2002-07-01

    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.

  11. Registration of Standardized Histological Images in Feature Space

    CERN Document Server

    Bagci, Ulas; 10.1117/12.770219

    2009-01-01

    In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the similar intensities correspond to similar tissues meaning. Second, 2D histological images are mapped into a feature space where continuous variables are used as high confidence image features for accurate registration. Third, we propose an automatic best reference slice selection algorithm that improves reconstruction quality based on both image entropy and mean square error of the registration process. We demonstrate that the choice of reference slice has a significant impact on registration error, standardization, feature space and entropy information. After 2D histological slices are registered through an affine transformation with respect to an automatically chosen reference, the 3D volume is reconstruct...

  12. 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.

  13. Featured Image: Active Cryovolcanism on Europa?

    Science.gov (United States)

    Kohler, Susanna

    2017-05-01

    Nighttime thermal image from the Galileo Photopolarimeter-Radiometer, revealing a thermal anomaly around the region where the plumes were observed. [Sparks et al. 2017]This image shows a 1320 900 km, high-resolution Galileo/Voyager USGS map of the surface of Europa, one of Jupiters moons. In March 2014, observations of Europa revealed a plume on its icy surface coming from somewhere within the green ellipse. In February 2016, another plume was observed, this time originating from somewhere within the cyan ellipse. In addition, a nighttime thermal image from the Galileo Photopolarimeter-Radiometer has revealed a thermal anomaly a region of unusually high temperature near the same location. In a recent study led by William Sparks (Space Telescope Science Institute), a team of scientists presents these observations and argues that they provide mounting evidence of active water-vapor venting from ongoing cryovolcanism beneath Europas icy surface. If this is true, then Europas surface is active and provides access to the liquid water at depth boosting the case for Europas potential habitability and certainly making for an interesting target point for future spacecraft exploration of this moon. For more information, check out the paper below!CitationW. B. Sparks et al 2017 ApJL 839 L18. doi:10.3847/2041-8213/aa67f8

  14. Simple Low Level Features for Image Analysis

    Science.gov (United States)

    Falcoz, Paolo

    As human beings, we perceive the world around us mainly through our eyes, and give what we see the status of “reality”; as such we historically tried to create ways of recording this reality so we could augment or extend our memory. From early attempts in photography like the image produced in 1826 by the French inventor Nicéphore Niépce (Figure 2.1) to the latest high definition camcorders, the number of recorded pieces of reality increased exponentially, posing the problem of managing all that information. Most of the raw video material produced today has lost its memory augmentation function, as it will hardly ever be viewed by any human; pervasive CCTVs are an example. They generate an enormous amount of data each day, but there is not enough “human processing power” to view them. Therefore the need for effective automatic image analysis tools is great, and a lot effort has been put in it, both from the academia and the industry. In this chapter, a review of some of the most important image analysis tools are presented.

  15. Featured Image: A Filament Forms and Erupts

    Science.gov (United States)

    Kohler, Susanna

    2017-06-01

    This dynamic image of active region NOAA 12241 was captured by the Solar Dynamics Observatorys Atmospheric Imaging Assembly in December 2014. Observations of this region from a number of observatories and instruments recently presented by Jincheng Wang (University of Chinese Academy of Sciences) and collaborators reveal details about the formation and eruption of a long solar filament. Wang and collaborators show that the right part of the filament formed by magnetic reconnection between two bundles of magnetic field lines, while the left part formed as a result of shearing motion. When these two parts interacted, the filament erupted. You can read more about the teams results in the article linked below. Also, check out this awesome video of the filament formation and eruption, again by SDO/AIA:http://cdn.iopscience.com/images/0004-637X/839/2/128/Full/apjaa6bf3f1_video.mp4CitationJincheng Wang et al 2017 ApJ 839 128. doi:10.3847/1538-4357/aa6bf3

  16. SOCIAL AND PSYCHOLOGICAL FEATURES OF HIV-INFECTED INDIVIDUALS

    Directory of Open Access Journals (Sweden)

    Liliya Anatolyevna Kudrich

    2016-02-01

    Full Text Available By 2020 the prevalence of HIV in the Russian Federation may increase by 250%, unless we provide appropriate treatment to as many HIV-infected people as possible (V.I. Skvortsova, 2015. Previous research in this field shows that the psychotraumatic character of the disease lowers the psychological resource of HIV-infected individuals. In most cases, they are not psychologically prepared for the negative life events, unable to find an optimal behavioral pattern when their life stereotypes are being destroyed. In fact, being HIV-infected is an example of an acute event (V.V. Pokrovsky, 1993. The ability to overcome the life crisis and effectiveness of using adaptation and compensatory mechanisms to fight the disease depend on the level of adaptation to the fact of being infected and resistance to stress. The aim of the current study was to determine social and psychological features of HIV-infected individuals and assess their influence on the stress resistance and adaptation abilities of HIV+ patients. We observed men and women aged 21-30 who had been HIV+ for 1-5 years. Investigation methods included the following diagnostic tools: The Cattel Sixteen Personality Factor Questionnaire (Form C, The State-Trait Anxiety Inventory (conducted by Spielberger, adapted for use in Russia by Hanin, The Social Readjustment Rating Scale (The Holmes-Rahe Stress Inventory, The Social and Psychological Adaptation Questionnaire (by C. Rogers and R. Diamond, methods of mathematical statistics. As a result of the study, we have developed comparative factor profiles of individual psychological features of HIV-infected individuals that show their dependence on the social environment and form certain behavioral patterns. We have revealed significant difference in state and trait anxiety between HIV-infected and non-HIV-infected individuals. Self-blame, inadequate self-esteem and level of aspiration indicate low cognitive assessment of the condition by the patients

  17. Osteosarcoma of pelvic bones: imaging features.

    Science.gov (United States)

    Park, Se Kyoung; Lee, In Sook; Cho, Kil Ho; Lee, Young Hwan; Yi, Jae Hyuck; Choi, Kyung Un

    The metaphyseal locations of tubular bones with osteoid mineralization in young patients are important diagnostic radiologic features of osteosarcoma. The pelvic bones are an unusual location of osteosarcoma. Although osteosarcoma occurring in pelvic bones is not common, the osteoid matrix may be a critical finding for differentiating osteosarcoma from other common pelvic bone tumors. Therefore, the possibility of osteosarcoma in pelvic bones may be considered in the presence of osteoid matrix even in the old age group. Copyright © 2016. Published by Elsevier Inc.

  18. Performance Analysis of Texture Image Classification Using Wavelet Feature

    Directory of Open Access Journals (Sweden)

    Dolly Choudhary

    2013-01-01

    Full Text Available This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.

  19. THE IDENTIFICATION OF PILL USING FEATURE EXTRACTION IN IMAGE MINING

    Directory of Open Access Journals (Sweden)

    A. Hema

    2015-02-01

    Full Text Available With the help of image mining techniques, an automatic pill identification system was investigated in this study for matching the images of the pills based on its several features like imprint, color, size and shape. Image mining is an inter-disciplinary task requiring expertise from various fields such as computer vision, image retrieval, image matching and pattern recognition. Image mining is the method in which the unusual patterns are detected so that both hidden and useful data images can only be stored in large database. It involves two different approaches for image matching. This research presents a drug identification, registration, detection and matching, Text, color and shape extraction of the image with image mining concept to identify the legal and illegal pills with more accuracy. Initially, the preprocessing process is carried out using novel interpolation algorithm. The main aim of this interpolation algorithm is to reduce the artifacts, blurring and jagged edges introduced during up-sampling. Then the registration process is proposed with two modules they are, feature extraction and corner detection. In feature extraction the noisy high frequency edges are discarded and relevant high frequency edges are selected. The corner detection approach detects the high frequency pixels in the intersection points. Through the overall performance gets improved. There is a need of segregate the dataset into groups based on the query image’s size, shape, color, text, etc. That process of segregating required information is called as feature extraction. The feature extraction is done using Geometrical Gradient feature transformation. Finally, color and shape feature extraction were performed using color histogram and geometrical gradient vector. Simulation results shows that the proposed techniques provide accurate retrieval results both in terms of time and accuracy when compared to conventional approaches.

  20. Immunopathological Features of Canine Myocarditis Associated with Leishmania infantum Infection

    Science.gov (United States)

    Piegari, Giuseppe; Otrocka-Domagala, Iwona; Ciccarelli, Davide; Iovane, Valentina; Oliva, Gaetano; Russo, Valeria; Rinaldi, Laura; Papparella, Serenella; Paciello, Orlando

    2016-01-01

    Myocarditis associated with infectious diseases may occur in dogs, including those caused by the protozoa Neospora caninum, Trypanosoma cruzi, Babesia canis, and Hepatozoon canis. However, although cardiac disease due to Leishmania infection has also been documented, the immunopathological features of myocarditis have not been reported so far. The aim of this study was to examine the types of cellular infiltrates and expression of MHC classes I and II in myocardial samples obtained at necropsy from 15 dogs with an established intravitam diagnosis of visceral leishmaniasis. Pathological features of myocardium were characterized by hyaline degeneration of cardiomyocytes, necrosis, and infiltration of mononuclear inflammatory cells consisting of lymphocytes and macrophages, sometimes with perivascular pattern; fibrosis was also present in various degrees. Immunophenotyping of inflammatory cells was performed by immunohistochemistry on cryostat sections obtained from the heart of the infected dogs. The predominant leukocyte population was CD8+ with a fewer number of CD4+ cells. Many cardiomyocytes expressed MHC classes I and II on the sarcolemma. Leishmania amastigote forms were not detected within macrophages or any other cell of the examined samples. Our study provided evidence that myocarditis in canine visceral leishmaniasis might be related to immunological alterations associated with Leishmania infection. PMID:27413751

  1. Immunopathological Features of Canine Myocarditis Associated with Leishmania infantum Infection

    Directory of Open Access Journals (Sweden)

    Alessandro Costagliola

    2016-01-01

    Full Text Available Myocarditis associated with infectious diseases may occur in dogs, including those caused by the protozoa Neospora caninum, Trypanosoma cruzi, Babesia canis, and Hepatozoon canis. However, although cardiac disease due to Leishmania infection has also been documented, the immunopathological features of myocarditis have not been reported so far. The aim of this study was to examine the types of cellular infiltrates and expression of MHC classes I and II in myocardial samples obtained at necropsy from 15 dogs with an established intravitam diagnosis of visceral leishmaniasis. Pathological features of myocardium were characterized by hyaline degeneration of cardiomyocytes, necrosis, and infiltration of mononuclear inflammatory cells consisting of lymphocytes and macrophages, sometimes with perivascular pattern; fibrosis was also present in various degrees. Immunophenotyping of inflammatory cells was performed by immunohistochemistry on cryostat sections obtained from the heart of the infected dogs. The predominant leukocyte population was CD8+ with a fewer number of CD4+ cells. Many cardiomyocytes expressed MHC classes I and II on the sarcolemma. Leishmania amastigote forms were not detected within macrophages or any other cell of the examined samples. Our study provided evidence that myocarditis in canine visceral leishmaniasis might be related to immunological alterations associated with Leishmania infection.

  2. Immunopathological Features of Canine Myocarditis Associated with Leishmania infantum Infection.

    Science.gov (United States)

    Costagliola, Alessandro; Piegari, Giuseppe; Otrocka-Domagala, Iwona; Ciccarelli, Davide; Iovane, Valentina; Oliva, Gaetano; Russo, Valeria; Rinaldi, Laura; Papparella, Serenella; Paciello, Orlando

    2016-01-01

    Myocarditis associated with infectious diseases may occur in dogs, including those caused by the protozoa Neospora caninum, Trypanosoma cruzi, Babesia canis, and Hepatozoon canis. However, although cardiac disease due to Leishmania infection has also been documented, the immunopathological features of myocarditis have not been reported so far. The aim of this study was to examine the types of cellular infiltrates and expression of MHC classes I and II in myocardial samples obtained at necropsy from 15 dogs with an established intravitam diagnosis of visceral leishmaniasis. Pathological features of myocardium were characterized by hyaline degeneration of cardiomyocytes, necrosis, and infiltration of mononuclear inflammatory cells consisting of lymphocytes and macrophages, sometimes with perivascular pattern; fibrosis was also present in various degrees. Immunophenotyping of inflammatory cells was performed by immunohistochemistry on cryostat sections obtained from the heart of the infected dogs. The predominant leukocyte population was CD8+ with a fewer number of CD4+ cells. Many cardiomyocytes expressed MHC classes I and II on the sarcolemma. Leishmania amastigote forms were not detected within macrophages or any other cell of the examined samples. Our study provided evidence that myocarditis in canine visceral leishmaniasis might be related to immunological alterations associated with Leishmania infection.

  3. Feature Selection for Image Retrieval based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Preeti Kushwaha

    2016-12-01

    Full Text Available This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.

  4. Intracranial Infections: Clinical and Imaging Characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Foerster, B.R.; Thurnher, M.M.; Malani, P.N.; Petrou, M.; Carets-Zumelzu, F.; Sundgren, P.C. [Dept. of Radiology, and Divisions of Infectious Diseases and G eriatric Medicine, Dept. of Internal Medicine, Univ. of Michigan Medical Center, Ann Arbor, MI (United States)

    2007-10-15

    The radiologist plays a crucial role in identifying and narrowing the differential diagnosis of intracranial infections. A thorough understanding of the intracranial compartment anatomy and characteristic imaging findings of specific pathogens, as well incorporation of the clinical information, is essential to establish correct diagnosis. Specific types of infections have certain propensities for different anatomical regions within the brain. In addition, the imaging findings must be placed in the context of the clinical setting, particularly in immunocompromised and human immunodeficiency virus (HIV)-positive patients. This paper describes and depicts infections within the different compartments of the brain. Pathology-proven infectious cases are presented in both immunocompetent and immunocompromised patients, with a discussion of the characteristic findings of each pathogen. Magnetic resonance spectroscopy (MRS) characteristics for several infections are also discussed.

  5. Trichophyton tonsurans infection in Japan: epidemiology, clinical features, diagnosis and infection control.

    Science.gov (United States)

    Hiruma, Junichiro; Ogawa, Yumi; Hiruma, Masataro

    2015-03-01

    In this review, we summarize the status of Trichophyton tonsurans infection in Japan in terms of epidemiology, clinical features, diagnosis and infection control. Since approximately 2000, outbreaks of T. tonsurans infections among combat sports club members have been reported frequently, with the infection then spreading to their friends and family members. The most common clinical features of T. tonsurans infection are tinea corporis, which is difficult to differentiate from eczema, and tinea capitis. Tinea capitis is classified as the seborrheic form, kerion celsi form or "black dot" form, although 90% or more of patients are asymptomatic carriers. The diagnosis of symptomatic T. tonsurans infection is established by potassium hydroxide examination and fungal culture. However, because there are many asymptomatic carriers of T. tonsurans infection, tests using the hairbrush culture method are necessary. An increase in asymptomatic carriers of T. tonsurans makes assessment of the current prevalence of the infection challenging and underscores the importance of educational efforts and public awareness campaigns to prevent T. tonsurans epidemics.

  6. Ship Targets Discrimination Algorithm in SAR Images Based on Hu Moment Feature and Texture Feature

    Directory of Open Access Journals (Sweden)

    Liu Lei

    2016-01-01

    Full Text Available To discriminate the ship targets in SAR images, this paper proposed the method based on combination of Hu moment feature and texture feature. Firstly,7 Hu moment features should be extracted, while gray level co-occurrence matrix is then used to extract the features of mean, variance, uniformity, energy, entropy, inertia moment, correlation and differences. Finally the k-neighbour classifier was used to analysis the 15 dimensional feature vectors. The experimental results show that the method of this paper has a good effect.

  7. 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.

  8. 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.

  9. An adaptive multi-feature segmentation model for infrared image

    Science.gov (United States)

    Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

    2016-04-01

    Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

  10. Segmentation of MR images using multiple-feature vectors

    Science.gov (United States)

    Cole, Orlean I. B.; Daemi, Mohammad F.

    1996-04-01

    Segmentation is an important step in the analysis of MR images (MRI). Considerable progress has been made in this area, and numerous reports on 3D segmentation, volume measurement and visualization have been published in recent years. The main purpose of our study is to investigate the power and use of fractal techniques in extraction of features from MR images of the human brain. These features which are supplemented by other features are used for segmentation, and ultimately for the extraction of a known pathology, in our case multiple- sclerosis (MS) lesions. We are particularly interested in the progress of the lesions and occurrence of new lesions which in a typical case are scattered within the image and are sometimes difficult to identify visually. We propose a technique for multi-channel segmentation of MR images using multiple feature vectors. The channels are proton density, T1-weighted and T2-weighted images containing multiple-sclerosis (MS) lesions at various stages of development. We first represent each image as a set of feature vectors which are estimated using fractal techniques, and supplemented by micro-texture features and features from the gray-level co-occurrence matrix (GLCM). These feature vectors are then used in a feature selection algorithm to reduce the dimension of the feature space. The next stage is segmentation and clustering. The selected feature vectors now form the input to the segmentation and clustering routines and are used as the initial clustering parameters. For this purpose, we have used the classical K-means as the initial clustering method. The clustered image is then passed into a probabilistic classifier to further classify and validate each region, taking into account the spatial properties of the image. Initially, segmentation results were obtained using the fractal dimension features alone. Subsequently, a combination of the fractal dimension features and the supplementary features mentioned above were also obtained

  11. Image Retrieval Using a Combination of Keywords and Image Features

    OpenAIRE

    Reddy, Vishwanath Reddy Keshi; Bandikolla, Praveen

    2008-01-01

    Information retrieval systems are playing an important role in our day to day life for getting the required information. Many text retrieval systems are available and are working successfully. Even though internet is full of other media like images, audio and video, retrieval systems for these media are rare and have not achieved success as that of text retrieval systems. Image retrieval systems are useful in many applications; there is a high demand for effective and efficient tool for image...

  12. Featured Image: The Birth of Spiral Arms

    Science.gov (United States)

    Kohler, Susanna

    2017-01-01

    In this figure, the top panels show three spiral galaxies in the Virgo cluster, imaged with the Sloan Digital Sky Survey. The bottom panels provide a comparison with three morphologically similar galaxies generated insimulations. The simulations run by Marcin Semczuk, Ewa okas, and Andrs del Pino (Nicolaus Copernicus Astronomical Center, Poland) were designed to examine how the spiral arms of galaxies like the Milky Way may have formed. In particular, the group exploredthe possibility that so-called grand-design spiral arms are caused by tidal effects as a Milky-Way-like galaxy orbits a cluster of galaxies. The authors show that the gravitational potential of the cluster can trigger the formation of two spiral arms each time the galaxy passes through the pericenter of its orbit around the cluster. Check out the original paper below for more information!CitationMarcin Semczuk et al 2017 ApJ 834 7. doi:10.3847/1538-4357/834/1/7

  13. Image processing tool for automatic feature recognition and quantification

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xing; Stoddard, Ryan J.

    2017-05-02

    A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

  14. 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.

  15. 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).

  16. 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.

  17. Featured Image: Mapping Jupiter with Hubble

    Science.gov (United States)

    Kohler, Susanna

    2016-07-01

    Zonal wind profile for Jupiter, describing the speed and direction of its winds at each latitude. [Simon et al. 2015]This global map of Jupiters surface (click for the full view!) was generated by the Hubble Outer Planet Atmospheres Legacy (OPAL) program, which aims to createnew yearly global maps for each of the outer planets. Presented in a study led by Amy Simon (NASA Goddard Space Flight Center), the map above is the first generated for Jupiter in the first year of the OPAL campaign. It provides a detailed look at Jupiters atmospheric structure including the Great Red Spot and allowed the authors to measure the speed and direction of the wind across Jupiters latitudes, constructing an updated zonal wind profile for Jupiter.In contrast to this study, the Juno mission (which will be captured into Jupiters orbit today after a 5-year journey to Jupiter!) will be focusing more on the features below Jupiters surface, studying its deep atmosphere and winds. Some of Junos primary goals are to learn about Jupiters composition, gravitational field, magnetic field, and polar magnetosphere. You can follow along with the NASATV livestream as Juno arrives at Jupiter tonight; orbit insertion coverage starts at 10:30 EDT.CitationAmy A. Simon et al 2015 ApJ 812 55. doi:10.1088/0004-637X/812/1/55

  18. Automated blood vessel extraction using local features on retinal images

    Science.gov (United States)

    Hatanaka, Yuji; Samo, Kazuki; Tajima, Mikiya; Ogohara, Kazunori; Muramatsu, Chisako; Okumura, Susumu; Fujita, Hiroshi

    2016-03-01

    An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels.

  19. 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.

  20. Featured Image: A Looping Stellar Stream

    Science.gov (United States)

    Kohler, Susanna

    2016-11-01

    This negative image of NGC 5907 (originally published inMartinez-Delgadoet al. 2008; click for the full view!) reveals the faint stellar stream that encircles the galaxy, forming loops around it a fossil of a recent merger. Mergers between galaxies come in several different flavors: major mergers, in which the merging galaxies are within a 1:5 ratio in stellar mass; satellite cannibalism, in which a large galaxy destroys a small satellite less than a 50th of its size; and the in-between case of minor mergers, in which the merging galaxieshave stellar mass ratios between 1:5 and 1:50. These minor mergers are thought to be relatively common, and they can have a significant effect on the dynamics and structure of the primary galaxy. A team of scientists led by Seppo Laine (Spitzer Science Center Caltech) has recently analyzed the metallicity and age of the stellar population in the stream around NGC 5907. By fitting these observations with a stellar population synthesis model, they conclude that this stream is an example of a massive minor merger, with a stellar mass ratio of at least 1:8. For more information, check out the paper below!CitationSeppo Laine et al 2016 AJ 152 72. doi:10.3847/0004-6256/152/3/72

  1. Featured Image: Fireball After a Temporary Capture?

    Science.gov (United States)

    Kohler, Susanna

    2016-06-01

    This image of a fireball was captured in the Czech Republic by cameras at a digital autonomous observatory in the village of Kunak. This observatory is part of a network of stations known as the European Fireball Network, and this particular meteoroid detection, labeled EN130114, is notable because it has the lowest initial velocity of any natural object ever observed by the network. Led by David Clark (University of Western Ontario), the authors of a recent study speculate that before this meteoroid impacted Earth, it may have been a Temporarily Captured Orbiter (TCO). TCOs are near-Earth objects that make a few orbits of Earth before returning to heliocentric orbits. Only one has ever been observed to date, and though they are thought to make up 0.1% of all meteoroids, EN130114 is the first event ever detected that exhibits conclusive behavior of a TCO. For more information on EN130114 and why TCOs are important to study, check out the paper below!CitationDavid L. Clark et al 2016 AJ 151 135. doi:10.3847/0004-6256/151/6/135

  2. Featured Image: The Cosmic Velocity Web

    Science.gov (United States)

    Kohler, Susanna

    2017-09-01

    You may have heard of the cosmic web, a network of filaments, clusters and voids that describes the three-dimensional distribution of matter in our universe. But have you ever considered the idea of a cosmic velocity web? In a new study led by Daniel Pomarde (IRFU CEA-Saclay, France), a team of scientists has built a detailed 3D view of the flows in our universe, showing in particular motions along filaments and in collapsing knots. In the image above (click for the full view), surfaces of knots (red) are embedded within surfaces of filaments (grey). The rainbow lines show the flow motion, revealing acceleration (redder tones) toward knots and retardation (bluer tones) beyond them. You can learn more about Pomarde and collaborators work and see their unusual and intriguing visualizationsin the video they produced, below. Check out the original paper for more information.CitationDaniel Pomarde et al 2017 ApJ 845 55. doi:10.3847/1538-4357/aa7f78

  3. Featured Image: Experimental Simulation of Melting Meteoroids

    Science.gov (United States)

    Kohler, Susanna

    2017-03-01

    Ever wonder what experimental astronomy looks like? Some days, it looks like this piece of rock in a wind tunnel (click for a betterlook!). In this photo, a piece of agrillite (a terrestrial rock) is exposed to conditions in a plasma wind tunnel as a team of scientists led by Stefan Loehle (Stuttgart University) simulate what happens to a meteoroid as it hurtles through Earths atmosphere. With these experiments, the scientists hope to better understand meteoroid ablation the process by which meteoroids are heated, melt, and evaporateas they pass through our atmosphere so that we can learn more from the meteorite fragments that make it to the ground. In the scientists experiment, the rock samples were exposed to plasma flow until they disintegrated, and this process was simultaneously studied via photography, video, high-speed imaging, thermography, and Echelle emission spectroscopy. To find out what the team learned from these experiments, you can check out the original article below.CitationStefan Loehle et al 2017 ApJ 837 112. doi:10.3847/1538-4357/aa5cb5

  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. 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.)

  6. Multiresolution image fusion scheme based on fuzzy region feature

    Institute of Scientific and Technical Information of China (English)

    LIU Gang; JING Zhong-liang; SUN Shao-yuan

    2006-01-01

    This paper proposes a novel region based image fusion scheme based on multiresolution analysis. The low frequency band of the image multiresolution representation is segmented into important regions, sub-important regions and background regions. Each feature of the regions is used to determine the region's degree of membership in the multiresolution representation,and then to achieve multiresolution representation of the fusion result. The final image fusion result can be obtained by using the inverse multiresolution transform. Experiments showed that the proposed image fusion method can have better performance than existing image fusion methods.

  7. 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.)

  8. Image Processing Techniques and Feature Recognition in Solar Physics

    Science.gov (United States)

    Aschwanden, Markus J.

    2010-04-01

    This review presents a comprehensive and systematic overview of image-processing techniques that are used in automated feature-detection algorithms applied to solar data: i) image pre-processing procedures, ii) automated detection of spatial features, iii) automated detection and tracking of temporal features (events), and iv) post-processing tasks, such as visualization of solar imagery, cataloguing, statistics, theoretical modeling, prediction, and forecasting. For each aspect the most recent developments and science results are highlighted. We conclude with an outlook on future trends.

  9. Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

    Science.gov (United States)

    Teo, Tee-Ann; Zhan, Kai-Zhi

    2016-06-01

    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. Face recognition with multi-resolution spectral feature images.

    Directory of Open Access Journals (Sweden)

    Zhan-Li Sun

    Full Text Available The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.

  11. Face recognition with multi-resolution spectral feature images.

    Science.gov (United States)

    Sun, Zhan-Li; Lam, Kin-Man; Dong, Zhao-Yang; Wang, Han; Gao, Qing-Wei; Zheng, Chun-Hou

    2013-01-01

    The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.

  12. Histopathological features of Capillaria hepatica infection in laboratory rabbits.

    Science.gov (United States)

    Mowat, Vasanthi; Turton, John; Stewart, Jacqui; Lui, Kai Chiu; Pilling, Andrew M

    2009-08-01

    Capillaria hepatica is a nematode parasite of wild rodents and other mammals. Adult worms inhabit the liver. Recently, during the necropsy examination of a group of 160 rabbits from a commercial supplier, firm pale or cystic areas (1-5 mm) were noted on the liver in thirteen animals. On further investigation, these animals were found to be infected with C. hepatica. The histopathological features of the infection in the rabbit are described for the first time and diagnostic features recorded. Lesions were identified predominantly in portal tracts consisting of dilated bile ducts with luminal debris, peribiliary inflammatory cell infiltrates, and fibrosis. Large granulomas (macrogranulomas) were evident in portal areas and involved the bile ducts. Macrogranulomas contained collections of characteristic C. hepatica eggs, macrophages, eosinophils, and lymphocytes. Small granulomas (microgranulomas), characterized by epithelioid macrophages surrounded by lymphocytes and eosinophils, were also identified. C. hepatica eggs were also observed in the lumina of the bile ducts and gall bladder. No adult C. hepatica worms were identified. Oocysts of Eimeria stiedae were also evident in the biliary epithelium in some animals. The unique characteristics of the C. hepatica life cycle are described, and the differential diagnosis of hepatic capillariasis is discussed.

  13. An image segmentation based method for iris feature extraction

    Institute of Scientific and Technical Information of China (English)

    XU Guang-zhu; ZHANG Zai-feng; MA Yi-de

    2008-01-01

    In this article, the local anomalistic blocks such ascrypts, furrows, and so on in the iris are initially used directly asiris features. A novel image segmentation method based onintersecting cortical model (ICM) neural network was introducedto segment these anomalistic blocks. First, the normalized irisimage was put into ICM neural network after enhancement.Second, the iris features were segmented out perfectly and wereoutput in binary image type by the ICM neural network. Finally,the fourth output pulse image produced by ICM neural networkwas chosen as the iris code for the convenience of real timeprocessing. To estimate the performance of the presentedmethod, an iris recognition platform was produced and theHamming Distance between two iris codes was computed tomeasure the dissimilarity between them. The experimentalresults in CASIA vl.0 and Bath iris image databases show thatthe proposed iris feature extraction algorithm has promisingpotential in iris recognition.

  14. Feature preserving compression of high resolution SAR images

    Science.gov (United States)

    Yang, Zhigao; Hu, Fuxiang; Sun, Tao; Qin, Qianqing

    2006-10-01

    Compression techniques are required to transmit the large amounts of high-resolution synthetic aperture radar (SAR) image data over the available channels. Common Image compression methods may lose detail and weak information in original images, especially at smoothness areas and edges with low contrast. This is known as "smoothing effect". It becomes difficult to extract and recognize some useful image features such as points and lines. We propose a new SAR image compression algorithm that can reduce the "smoothing effect" based on adaptive wavelet packet transform and feature-preserving rate allocation. For the reason that images should be modeled as non-stationary information resources, a SAR image is partitioned to overlapped blocks. Each overlapped block is then transformed by adaptive wavelet packet according to statistical features of different blocks. In quantifying and entropy coding of wavelet coefficients, we integrate feature-preserving technique. Experiments show that quality of our algorithm up to 16:1 compression ratio is improved significantly, and more weak information is reserved.

  15. Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning.

    Science.gov (United States)

    Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, U K Arvind

    2016-03-01

    In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.

  16. SPECIFIC CONSTITUTIONAL FEATURES OF CHILDREN INFECTED WITH TUBERCULOSIS

    Directory of Open Access Journals (Sweden)

    Yu. A. Yarovaya

    2017-01-01

    Full Text Available In order to define specific constitutional features of the children infected with tuberculosis 222 children in the age from 1 to 14 years old have been examined: 106 children with active tuberculosis; 54 children with remaining post-tuberculosis changes; 62 children infected with tuberculous mycobacteria. The following types of diatheses were identified: lymphohypoplastic, allergic, neuroarthritic, exudative-catarrhal. It has been found out that among those with active tuberculosis the children suffering from lymphohypoplastic and neuroarthritic diatheses prevail (17.0 ± 3.7%, and allergic diathesis is less common (10.4 ± 3.0% cases. Children with lymphohypoplastic diathesis have a complicated course of tuberculosis (27.8 ± 10.6% and more intensive intoxication syndrome (55.6 ± 11.7%. The frequency of allergic diathesis is higher in the children with remaining post-tuberculosis changes (29.6 ± 6.2% and those infected with tuberculosis (33.8 ± 6.1% compared to children with active tuberculosis (10.4 ± 3.0%.

  17. Perceptual image hashing via feature points: performance evaluation and tradeoffs.

    Science.gov (United States)

    Monga, Vishal; Evans, Brian L

    2006-11-01

    We propose an image hashing paradigm using visually significant feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to introduce randomness, which, in turn, reduces vulnerability to adversarial attacks. The proposed hash algorithm withstands standard benchmark (e.g., Stirmark) attacks, including compression, geometric distortions of scaling and small-angle rotation, and common signal-processing operations. Content changing (malicious) manipulations of image data are also accurately detected. Detailed statistical analysis in the form of receiver operating characteristic (ROC) curves is presented and reveals the success of the proposed scheme in achieving perceptual robustness while avoiding misclassification.

  18. 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.

  19. Hybrid edge and feature-based single-image superresolution

    Science.gov (United States)

    Islam, Mohammad Moinul; Islam, Mohammed Nazrul; Asari, Vijayan K.; Karim, Mohammad A.

    2016-07-01

    A neighborhood-dependent component feature learning method for regression analysis in single-image superresolution is presented. Given a low-resolution input, the method uses a directional Fourier phase feature component to adaptively learn the regression kernel based on local covariance to estimate the high-resolution image. The unique feature of the proposed method is that it uses image features to learn about the local covariance from geometric similarity between the low-resolution image and its high-resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. This gives us edge information and Fourier phase gives features, which are combined to interpolate using kernel regression. In order to compare quantitatively with other state-of-the-art techniques, root-mean-square error and measure mean-square similarity are computed for the example images, and experimental results show that the proposed algorithm outperforms similar techniques available in the literature, especially at higher resolution scales.

  20. MR imaging evaluation of perianal fistulas: spectrum of imaging features.

    Science.gov (United States)

    de Miguel Criado, Jaime; del Salto, Laura García; Rivas, Patricia Fraga; del Hoyo, Luis Felipe Aguilera; Velasco, Leticia Gutiérrez; de las Vacas, M Isabel Díez Pérez; Marco Sanz, Ana G; Paradela, Marcos Manzano; Moreno, Eduardo Fraile

    2012-01-01

    Perianal fistulization is an inflammatory condition that affects the region around the anal canal, causing significant morbidity and often requiring repeated surgical treatments due to its high tendency to recur. To adopt the best surgical strategy and avoid recurrences, it is necessary to obtain precise radiologic information about the location of the fistulous track and the affected pelvic structures. Until recently, imaging techniques played a limited role in evaluation of perianal fistulas. However, magnetic resonance (MR) imaging now provides more precise information on the anatomy of the anal canal, the anal sphincter complex, and the relationships of the fistula to the pelvic floor structures and the plane of the levator ani muscle. MR imaging allows precise definition of the fistulous track and identification of secondary fistulas or abscesses. It provides accurate information for appropriate surgical treatment, decreasing the incidence of recurrence and allowing side effects such as fecal incontinence to be avoided. Radiologists should be familiar with the anatomic and pathologic findings of perianal fistulas and classify them using the St James's University Hospital MR imaging-based grading system.

  1. Nonprogressing HIV-infected children share fundamental immunological features of nonpathogenic SIV infection

    DEFF Research Database (Denmark)

    Muenchhoff, Maximilian; Adland, Emily; Karimanzira, Owen

    2016-01-01

    nonprogressors. These children therefore express two cardinal immunological features of nonpathogenic SIV infection in sooty mangabeys-low immune activation despite high viremia and low CCR5 expression on long-lived central memory CD4 T cells-suggesting closer similarities with nonpathogenetic mechanisms evolved......Disease-free infection in HIV-infected adults is associated with human leukocyte antigen-mediated suppression of viremia, whereas in the sooty mangabey and other healthy natural hosts of simian immunodeficiency virus (SIV), viral replication continues unabated. To better understand factors...... preventing HIV disease, we investigated pediatric infection, where AIDS typically develops more rapidly than in adults. Among 170 nonprogressing antiretroviral therapy-naïve children aged >5 years maintaining normal-for-Age CD4 T cell counts, immune activation levels were low despite high viremia (median, 26...

  2. 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.

  3. Modeling neuron selectivity over simple midlevel features for image classification.

    Science.gov (United States)

    Shu Kong; Zhuolin Jiang; Qiang Yang

    2015-08-01

    We now know that good mid-level features can greatly enhance the performance of image classification, but how to efficiently learn the image features is still an open question. In this paper, we present an efficient unsupervised midlevel feature learning approach (MidFea), which only involves simple operations, such as k-means clustering, convolution, pooling, vector quantization, and random projection. We show this simple feature can also achieve good performance in traditional classification task. To further boost the performance, we model the neuron selectivity (NS) principle by building an additional layer over the midlevel features prior to the classifier. The NS-layer learns category-specific neurons in a supervised manner with both bottom-up inference and top-down analysis, and thus supports fast inference for a query image. Through extensive experiments, we demonstrate that this higher level NS-layer notably improves the classification accuracy with our simple MidFea, achieving comparable performances for face recognition, gender classification, age estimation, and object categorization. In particular, our approach runs faster in inference by an order of magnitude than sparse coding-based feature learning methods. As a conclusion, we argue that not only do carefully learned features (MidFea) bring improved performance, but also a sophisticated mechanism (NS-layer) at higher level boosts the performance further.

  4. Optimized Image Steganalysis through Feature Selection using MBEGA

    CERN Document Server

    Geetha, S

    2010-01-01

    Feature based steganalysis, an emerging branch in information forensics, aims at identifying the presence of a covert communication by employing the statistical features of the cover and stego image as clues/evidences. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviours, optimizing the performance of steganalysers becomes an important open problem. This paper is focussed at fine tuning the performance of six promising steganalysers in this field, through feature selection. We propose to employ Markov Blanket-Embedded Genetic Algorithm (MBEGA) for stego sensitive feature selection process. In particular, the embedded Markov blanket based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive pow...

  5. MRI and PET image fusion using fuzzy logic and image local features.

    Science.gov (United States)

    Javed, Umer; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Ali, Syed Sohaib; Cheema, Tanveer Ahmed

    2014-01-01

    An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.

  6. 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.

  7. Hdr Imaging for Feature Detection on Detailed Architectural Scenes

    Science.gov (United States)

    Kontogianni, G.; Stathopoulou, E. K.; Georgopoulos, A.; Doulamis, A.

    2015-02-01

    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.

  8. 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.

  9. Content Based Image Recognition by Information Fusion with Multiview Features

    Directory of Open Access Journals (Sweden)

    Rik Das

    2015-09-01

    Full Text Available Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

  10. Automated Classification of Glaucoma Images by Wavelet Energy Features

    Directory of Open Access Journals (Sweden)

    N.Annu

    2013-04-01

    Full Text Available Glaucoma is the second leading cause of blindness worldwide. As glaucoma progresses, more optic nerve tissue is lost and the optic cup grows which leads to vision loss. This paper compiles a systemthat could be used by non-experts to filtrate cases of patients not affected by the disease. This work proposes glaucomatous image classification using texture features within images and efficient glaucoma classification based on Probabilistic Neural Network (PNN. Energy distribution over wavelet sub bands is applied to compute these texture features. Wavelet features were obtained from the daubechies (db3, symlets (sym3, and biorthogonal (bio3.3, bio3.5, and bio3.7 wavelet filters. It uses a technique to extract energy signatures obtained using 2-D discrete wavelet transform and the energy obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images. We observedan accuracy of around 95%, this demonstrates the effectiveness of these methods.

  11. Feature element theory for image recognition and retrieval

    Science.gov (United States)

    Xu, Yin; Zhang, Yujin

    2001-12-01

    Traditional systems of image retrieval work as black boxes. The concrete process and result data or coefficients are not the real care point. Thus it brings the problem: these systems cannot fulfill general semantic application. To overcome the problem we turn to psychology and neuroscience to study the cognition mechanism of human brain. Based on the analysis of experiments and evidences, a new hypothesis - element presence theory is proposed to explain the truth of the whole visual cognition. As its basic level that deals with low-level feature data from camera or retina, feature element theory is illustrated in details. Besides, the evaluation on feature elements is discussed and the illustration on feature element theory based image retrieval system is also given.

  12. Feature extraction with LIDAR data and aerial images

    Science.gov (United States)

    Mao, Jianhua; Liu, Yanjing; Cheng, Penggen; Li, Xianhua; Zeng, Qihong; Xia, Jing

    2006-10-01

    Raw LIDAR data is a irregular spacing 3D point cloud including reflections from bare ground, buildings, vegetation and vehicles etc., and the first task of the data analyses of point cloud is feature extraction. However, the interpretability of LIDAR point cloud is often limited due to the fact that no object information is provided, and the complex earth topography and object morphology make it impossible for a single operator to classify all the point cloud precisely 100%. In this paper, a hierarchy method for feature extraction with LIDAR data and aerial images is discussed. The aerial images provide us information of objects figuration and spatial distribution, and hierarchic classification of features makes it easy to apply automatic filters progressively. And the experiment results show that, using this method, it was possible to detect more object information and get a better result of feature extraction than using automatic filters alone.

  13. Imaging strategies in pediatric urinary tract infection

    Energy Technology Data Exchange (ETDEWEB)

    Dacher, Jean-Nicolas [University of Rouen, Quant-IF Laboratory, School of Medicine and Pharmacy, Rouen (France); Rouen University Hospital Charles Nicolle, Department of Radiology, Rouen (France); UFR Medecine Pharmacie de Rouen, Laboratoire Quant-If, Rouen (France); Hitzel, Anne; Vera, Pierre [University of Rouen, Quant-IF Laboratory, School of Medicine and Pharmacy, Rouen (France); CRLCC Henri Becquerel, Department of Nuclear Medicine, Rouen (France); Avni, Fred E. [Free University of Brussels, Department of Radiology, Erasmus Hospital, Brussels (Belgium)

    2005-07-01

    This article is focused on the controversial topic of imaging strategies in pediatric urinary tract infection. A review of the recent literature illustrates the complementary roles of ultrasound, diagnostic radiology and nuclear medicine. The authors stress the key role of ultrasound which has recently been debated. The commonly associated vesicoureteric reflux has to be classified as congenital or secondary due to voiding dysfunction. A series of frequently asked questions are addressed in a second section. The proposed answers are not the product of a consensus but should rather be considered as proposals to enrich the ongoing debate concerning the evaluation of urinary tract infection in children. (orig.)

  14. Radionuclide imaging of non osseous infection

    Energy Technology Data Exchange (ETDEWEB)

    Palestro, C.J. (Long Island Jewish Medical Center, New York, NY, (United States). Dept. Nuclear Medicine New York, Yeshiva Univ., NY (United States). Albert Einstein College of Medicine); Torres, M.A. (Long Island Jewish Medical Center, New York, NY, (United States). Dept. Nuclear Medicine)

    1999-03-01

    Nuclear medicine is an important tool in the diagnostic evaluation of patients with a variety of non osseous infections. In the immunocompetent population labeled leukocyte imaging is the radionuclide procedure of choice, with Gallium imaging reserved for those situations in which the leukocyte study is non diagnostic or cannot be performed. Fever of unknown origin is caused by infection in less than one-third of cases, and therefore the number of positive leukocyte studies will be relatively low. The negative leukocyte study is also useful as it has been demonstrated that a negative study excludes, with a high degree of certainty, focal infection as the cause of an FUO. In the cardiovascular system, labeled leukocyte scintigraphy is very useful for diagnosing mycotic aneurysms and infected prosthetic vascular grafts. The specificity of the study is somewhat more variable. In the central nervous system, labeled leukocyte imaging can provide important information about the etiology of contrast enhancing brain lesions identified on computed tomography. In the immunocompromised population, typified by the AIDS patient, Gallium scintigraphy is the radionuclide procedure of choice for diagnosing opportunistic diseases. In the thorax, a normal Gallium scan, in the setting of a negative chest X-ray, virtually excludes pulmonary disease. In the abdomen, Gallium is also useful for detecting nodal disease, but is not reliable for detecting large bowel disease. Labeled leukocyte imaging should be performed when colitis is a concern. Both [sup 18]FDG PET and [sup 201]T1 SPECT imaging of the brain are useful for distinguishing between central nervous system lymphoma and toxoplasmosis in the HIV (+) patient. On both studies, lymphoma manifests as a focus of increased tracer uptake, whereas toxoplasmosis shows little or no uptake of either tracer.

  15. INVESTIGATION OF ECOLOGICAL FEATURES OF ACUTE DIARRHEAL INFECTION PATHOGENS

    Directory of Open Access Journals (Sweden)

    Malysh N.G.

    2015-12-01

    Full Text Available Introduction. Microbiocenosis of human body also differs in extreme multicomponents and diverse content of microflora representatives forming its part. According to the biotype of bacterial contamination certain inter-bacterial relations are formed, which is reflected in the qualitative and quantitative characteristics of appropriate microbial landscape. Analysis of numerous microbial association manifestations allows evaluating changes in the pathogen properties influenced by associative microbiota. Work objective - based on the study ecological features of microorganisms isolated from intestine of patients with acute intestinal infections and apparently healthy people, identify potential risk factors for diarrheal infections. Materials & methods. A retrospective epidemiological analysis of acute diarrheal infections incidence was conducted during 2004-2013, using the statistics of the Main Department of the State Sanitary and Epidemiological Service of Ukraine in Sumy region. The intestinal microflora of 93 patients with acute diarrheal infections and 60 persons of the control group (apparently healthy people. As the result 130 bacterial cultures were allocated. Permanence rate was used to estimate biocenosis. Relationships between microbiocenosis members were investigated by determining degree of bond conjunction in associations, using Jaccard coefficient (g. Results & discussion. In 2005-2014 acute diarrheal infection incidence rates of Sumy region population were within 163.7 - 193.6 per 100 people without tendency to decrease. Acute intestinal infections and food toxicoinfections caused by opportunistic pathogens and viruses (p<0.05 dominated in nosological structure. In 35.5 % of cases diarrheal infections were of polyetiological nature. Noroviruses in associations with Candida bacteriaand fungi most often occurred (p<0.05 in the intestinal biotypes. Permanence rate of K. pneumonia, noroviruses, S. aureus, C. albicans was the highest and

  16. Morphologic Features of Extrahepatic Manifestations of Hepatitis C Virus Infection

    Directory of Open Access Journals (Sweden)

    Huaibin M. Ko

    2012-01-01

    Full Text Available Cirrhosis and hepatocellular carcinoma are the prototypic complications of chronic hepatitis C virus infection in the liver. However, hepatitis C virus also affects a variety of other organs that may lead to significant morbidity and mortality. Extrahepatic manifestations of hepatitis C infection include a multitude of disease processes affecting the small vessels, skin, kidneys, salivary gland, eyes, thyroid, and immunologic system. The majority of these conditions are thought to be immune mediated. The most documented of these entities is mixed cryoglobulinemia. Morphologically, immune complex depositions can be identified in small vessels and glomerular capillary walls, leading to leukoclastic vasculitis in the skin and membranoproliferative glomerulonephritis in the kidney. Other HCV-associated entities include porphyria cutanea tarda, lichen planus, necrolytic acral erythema, membranous glomerulonephritis, diabetic nephropathy, B-cell non-Hodgkin lymphomas, insulin resistance, sialadenitis, sicca syndrome, and autoimmune thyroiditis. This paper highlights the histomorphologic features of these processes, which are typically characterized by chronic inflammation, immune complex deposition, and immunoproliferative disease in the affected organ.

  17. Clinical Features of Right-sided Infective Endocarditis

    Institute of Scientific and Technical Information of China (English)

    杨莉; 伍卫; 王景峰; 张燕; 张小玲

    2002-01-01

    Objective To discuss thepathogenesis, etiology, clinical manifestations, diagnosis, treatment and prognosis of right-sided infective endocarditis (RIE) . Methods To investigate retrospectively the clinical data of patients with RIE admitted in our hospital from Jan 1985 to Dec 2000.Results There were 17 cases of RIE (12 male, 5female, mean age 22 years), among which 7 with congenital heart disease, 1 with pacemaker implantation and 9 with a history of intravenous drug abuse but without underlying heart disease. Fever and multiple pulmonary emboli were the major clinical manifestations. Blood cultures were positive in 8 cases with Staphylococcus aureus as the predominant microorganism. Echocardiography detected right heart vegetations in all cases, with tricuspid valve as the structure most frequently affected. Most patients were successfully treated with antimicrobials. The outcome was favourable, with a mortality of 11.8 % . Conclusions The clinical features of RIE are different from that of left-sided infective endocarditis (LIE) . Echocardiography plays an important role in the diagnosis of RIE.

  18. Morphologic features of extrahepatic manifestations of hepatitis C virus infection.

    Science.gov (United States)

    Ko, Huaibin M; Hernandez-Prera, Juan C; Zhu, Hongfa; Dikman, Steven H; Sidhu, Harleen K; Ward, Stephen C; Thung, Swan N

    2012-01-01

    Cirrhosis and hepatocellular carcinoma are the prototypic complications of chronic hepatitis C virus infection in the liver. However, hepatitis C virus also affects a variety of other organs that may lead to significant morbidity and mortality. Extrahepatic manifestations of hepatitis C infection include a multitude of disease processes affecting the small vessels, skin, kidneys, salivary gland, eyes, thyroid, and immunologic system. The majority of these conditions are thought to be immune mediated. The most documented of these entities is mixed cryoglobulinemia. Morphologically, immune complex depositions can be identified in small vessels and glomerular capillary walls, leading to leukoclastic vasculitis in the skin and membranoproliferative glomerulonephritis in the kidney. Other HCV-associated entities include porphyria cutanea tarda, lichen planus, necrolytic acral erythema, membranous glomerulonephritis, diabetic nephropathy, B-cell non-Hodgkin lymphomas, insulin resistance, sialadenitis, sicca syndrome, and autoimmune thyroiditis. This paper highlights the histomorphologic features of these processes, which are typically characterized by chronic inflammation, immune complex deposition, and immunoproliferative disease in the affected organ.

  19. Automatic wound infection interpretation for postoperative wound image

    Science.gov (United States)

    Hsu, Jui-Tse; Ho, Te-Wei; Shih, Hsueh-Fu; Chang, Chun-Che; Lai, Feipei; Wu, Jin-Ming

    2017-02-01

    With the growing demand for more efficient wound care after surgery, there is a necessity to develop a machine learning based image analysis approach to reduce the burden for health care professionals. The aim of this study was to propose a novel approach to recognize wound infection on the postsurgical site. Firstly, we proposed an optimal clustering method based on unimodal-rosin threshold algorithm to extract the feature points from a potential wound area into clusters for regions of interest (ROI). Each ROI was regarded as a suture site of the wound area. The automatic infection interpretation based on the support vector machine is available to assist physicians doing decision-making in clinical practice. According to clinical physicians' judgment criteria and the international guidelines for wound infection interpretation, we defined infection detector modules as the following: (1) Swelling Detector, (2) Blood Region Detector, (3) Infected Detector, and (4) Tissue Necrosis Detector. To validate the capability of the proposed system, a retrospective study using the confirmation wound pictures that were used for diagnosis by surgical physicians as the gold standard was conducted to verify the classification models. Currently, through cross validation of 42 wound images, our classifiers achieved 95.23% accuracy, 93.33% sensitivity, 100% specificity, and 100% positive predictive value. We believe this ability could help medical practitioners in decision making in clinical practice.

  20. Imaging features of complex sclerosing lesions of the breast

    Energy Technology Data Exchange (ETDEWEB)

    Myong, Joo Hwa; Choi, Byung Gil; Kim, Sung Hun; Kang, Bong Joo; Lee, Ah Won; Song, Byung Joo [Seoul St. Mary' s Hospital, The Catholic University of Korea College of Medicine, Seoul (Korea, Republic of)

    2014-03-15

    The purpose of this study was to evaluate the imaging features of complex sclerosing lesions of the breast and to assess the rate of upgrade to breast cancer. From March 2008 to May 2012, seven lesions were confirmed as complex sclerosing lesions by ultrasonography-guided core needle biopsy. Final results by either surgical excision or follow-up imaging studies were reviewed to assess the rate of upgrade to breast cancer. Two radiologists retrospectively analyzed the imaging findings according to the Breast Imaging Reporting and Data System classification. Five lesions underwent subsequent surgical excision and two of them revealed ductal carcinoma in situ (n=1) and invasive ductal carcinoma (n=1). Our study showed a breast cancer upgrade rate of 28.6% (2 of 7 lesions). Two lesions were stable on imaging follow-up beyond 1 year. The mammographic features included masses (n=4, 57.1%), architectural distortion (n=2, 28.6%), and focal asymmetry (n=1, 14.3%). Common B-mode ultrasonographic features were irregular shape (n=6, 85.7%), spiculated margin (n=5, 71.4 %), and hypoechogenicity (n=7, 100%). The final assessment categories were category 4 (n=6, 85.7%) and category 5 (n=1, 14.3%). The complex sclerosing lesions were commonly mass-like on mammography and showed the suspicious ultrasonographic features of category 4. Due to a high underestimation rate, all complex sclerosing lesions by core needle biopsy should be excised.

  1. Dermoscopy analysis of RGB-images based on comparative features

    Science.gov (United States)

    Myakinin, Oleg O.; Zakharov, Valery P.; Bratchenko, Ivan A.; Artemyev, Dmitry N.; Neretin, Evgeny Y.; Kozlov, Sergey V.

    2015-09-01

    In this paper, we propose an algorithm for color and texture analysis for dermoscopic images of human skin based on Haar wavelets, Local Binary Patterns (LBP) and Histogram Analysis. This approach is a modification of «7-point checklist» clinical method. Thus, that is an "absolute" diagnostic method because one is using only features extracted from tumor's ROI (Region of Interest), which can be selected manually and/or using a special algorithm. We propose additional features extracted from the same image for comparative analysis of tumor and healthy skin. We used Euclidean distance, Cosine similarity, and Tanimoto coefficient as comparison metrics between color and texture features extracted from tumor's and healthy skin's ROI separately. A classifier for separating melanoma images from other tumors has been built by SVM (Support Vector Machine) algorithm. Classification's errors with and without comparative features between skin and tumor have been analyzed. Significant increase of recognition quality with comparative features has been demonstrated. Moreover, we analyzed two modes (manual and automatic) for ROI selecting on tumor and healthy skin areas. We have reached 91% of sensitivity using comparative features in contrast with 77% of sensitivity using the only "absolute" method. The specificity was the invariable (94%) in both cases.

  2. 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.

  3. Benign Conditions That Mimic Prostate Carcinoma: MR Imaging Features with Histopathologic Correlation1

    Science.gov (United States)

    Kitzing, Yu Xuan; Prando, Adilson; Varol, Celi; Karczmar, Gregory S.; Maclean, Fiona; Oto, Aytekin

    2017-01-01

    Multiparametric magnetic resonance (MR) imaging combines anatomic and functional imaging techniques for evaluating the prostate and is increasingly being used in diagnosis and management of prostate cancer. A wide spectrum of anatomic and pathologic processes in the prostate may masquerade as prostate cancer, complicating the imaging interpretation. The histopathologic and imaging findings of these potential mimics are reviewed. These entities include the anterior fibromuscular stroma, surgical capsule, central zone, periprostatic vein, periprostatic lymph nodes, benign prostatic hyperplasia (BPH), atrophy, necrosis, calcification, hemorrhage, and prostatitis. An understanding of the prostate zonal anatomy is helpful in distinguishing the anatomic entities from prostate cancer. The anterior fibromuscular stroma, surgical capsule, and central zone are characteristic anatomic features of the prostate with associated low T2 signal intensity due to dense fibromuscular tissue or complex crowded glandular tissue. BPH, atrophy, necrosis, calcification, and hemorrhage all have characteristic features with one or more individual multiparametric MR imaging modalities. Prostatitis constitutes a heterogeneous group of infective and inflammatory conditions including acute and chronic bacterial prostatitis, infective and noninfective granulomatous prostatitis, and malacoplakia. These entities are associated with variable clinical manifestations and are characterized by the histologic hallmark of marked inflammatory cellular infiltration. In some cases, these entities are indistinguishable from prostate cancer at multiparametric MR imaging and may even exhibit extraprostatic extension and lymphadenopathy, mimicking locally advanced prostate cancer. It is important for the radiologists interpreting prostate MR images to be aware of these pitfalls for accurate interpretation. PMID:26587887

  4. Benign Conditions That Mimic Prostate Carcinoma: MR Imaging Features with Histopathologic Correlation.

    Science.gov (United States)

    Kitzing, Yu Xuan; Prando, Adilson; Varol, Celi; Karczmar, Gregory S; Maclean, Fiona; Oto, Aytekin

    2016-01-01

    Multiparametric magnetic resonance (MR) imaging combines anatomic and functional imaging techniques for evaluating the prostate and is increasingly being used in diagnosis and management of prostate cancer. A wide spectrum of anatomic and pathologic processes in the prostate may masquerade as prostate cancer, complicating the imaging interpretation. The histopathologic and imaging findings of these potential mimics are reviewed. These entities include the anterior fibromuscular stroma, surgical capsule, central zone, periprostatic vein, periprostatic lymph nodes, benign prostatic hyperplasia (BPH), atrophy, necrosis, calcification, hemorrhage, and prostatitis. An understanding of the prostate zonal anatomy is helpful in distinguishing the anatomic entities from prostate cancer. The anterior fibromuscular stroma, surgical capsule, and central zone are characteristic anatomic features of the prostate with associated low T2 signal intensity due to dense fibromuscular tissue or complex crowded glandular tissue. BPH, atrophy, necrosis, calcification, and hemorrhage all have characteristic features with one or more individual multiparametric MR imaging modalities. Prostatitis constitutes a heterogeneous group of infective and inflammatory conditions including acute and chronic bacterial prostatitis, infective and noninfective granulomatous prostatitis, and malacoplakia. These entities are associated with variable clinical manifestations and are characterized by the histologic hallmark of marked inflammatory cellular infiltration. In some cases, these entities are indistinguishable from prostate cancer at multiparametric MR imaging and may even exhibit extraprostatic extension and lymphadenopathy, mimicking locally advanced prostate cancer. It is important for the radiologists interpreting prostate MR images to be aware of these pitfalls for accurate interpretation. Online supplemental material is available for this article.

  5. Image Retrieval via Relevance Vector Machine with Multiple Features

    Directory of Open Access Journals (Sweden)

    Zemin Liu

    2014-05-01

    Full Text Available With the fast development of computer network technique, there is large amount of image information every day. Researchers have paid more and more attention to the problem of how users quickly retrieving and identifying the images that they may interest. Meanwhile, with the rapid development of artificial intelligence and pattern recognition techniques, it provides people with new thought on the study on complex image retrieval while it’s very difficult for traditional machine learning method to get ideal retrieval results. For this reason, we in this paper propose a new approach for image retrieval based on multiple types of image features and relevance vector machine (RVM. The proposed method, termed as MF-RVM, integrates the informative cures of features and the discrimination ability of RVM. The retrieval experiment is conducted on COREL image library which is collected from internet. The experimental results show that the proposed method can significantly improve the performance for image retrieval, so MF-RVM presented in this paper has very high practicability in image retrieval.

  6. Geometrically invariant color image watermarking scheme using feature points

    Institute of Scientific and Technical Information of China (English)

    WANG XiangYang; MENG Lan; YANG HongYing

    2009-01-01

    Geometric distortion is known as one of the most difficult attacks to resist.Geometric distortion desynchronizes the location of the watermark and hence causes incorrect watermark detection.In this paper,we propose a geometrically invariant digital watermarking method for color images.In order to synchronize the location for watermark insertion and detection,we use a multi-scale Harris-Laplace detector,by which feature points of a color image can be extracted that are invariant to geometric distortions.Then,the self-adaptive local image region (LIR) detection based on the feature scale theory was considered for watermarking.At each local image region,the watermark is embedded after image normalization.By binding digital watermark with invariant image regions,resilience against geometric distortion can be readily obtained.Our method belongs to the category of blind watermarking techniques,because we do not need the original image during detection.Experimental results show that the proposed color image watermarking is not only invisible and robust against common signal processing such as sharpening,noise adding,and JPEG compression,but also robust against the geometric distortions such as rotation,translation,scaling,row or column removal,shearing,and local random bend.

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

    Energy Technology Data Exchange (ETDEWEB)

    Philip, K.P.; Dove, E.L.; Stanford, W.; Chandran, K.B. (Univ. of Iowa, Iowa City, IA (United States)); McPherson, D.D.; Gotteiner, N.L. (Northwestern Univ., Chicago, IL (United States). Dept. of Internal Medicine)

    1994-06-01

    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.

  8. Feature analysis for detecting people from remotely sensed images

    Science.gov (United States)

    Sirmacek, Beril; Reinartz, Peter

    2013-01-01

    We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probability density function (PDF) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding PDF. First, we use estimated PDF to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in noncrowd regions automatically for each image in the sequence. To test our crowd and people detection algorithm, we use airborne images taken over Munich during the Oktoberfest event, two different open-air concerts, and an outdoor festival. In addition, we apply tests on GeoEye-1 satellite images. Our experimental results indicate possible use of the algorithm in real-life mass events.

  9. Feature statistic analysis of ultrasound images of liver cancer

    Science.gov (United States)

    Huang, Shuqin; Ding, Mingyue; Zhang, Songgeng

    2007-12-01

    In this paper, a specific feature analysis of liver ultrasound images including normal liver, liver cancer especially hepatocellular carcinoma (HCC) and other hepatopathy is discussed. According to the classification of hepatocellular carcinoma (HCC), primary carcinoma is divided into four types. 15 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM) are extracted. Experiments for the discrimination of each type of HCC, normal liver, fatty liver, angioma and hepatic abscess have been conducted. Corresponding features to potentially discriminate them are found.

  10. Scene classification of infrared images based on texture feature

    Science.gov (United States)

    Zhang, Xiao; Bai, Tingzhu; Shang, Fei

    2008-12-01

    Scene Classification refers to as assigning a physical scene into one of a set of predefined categories. Utilizing the method texture feature is good for providing the approach to classify scenes. Texture can be considered to be repeating patterns of local variation of pixel intensities. And texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity. Texture describes the structural information of images, so it provides another data to classify comparing to the spectrum. Now, infrared thermal imagers are used in different kinds of fields. Since infrared images of the objects reflect their own thermal radiation, there are some shortcomings of infrared images: the poor contrast between the objectives and background, the effects of blurs edges, much noise and so on. Because of these shortcomings, it is difficult to extract to the texture feature of infrared images. In this paper we have developed an infrared image texture feature-based algorithm to classify scenes of infrared images. This paper researches texture extraction using Gabor wavelet transform. The transformation of Gabor has excellent capability in analysis the frequency and direction of the partial district. Gabor wavelets is chosen for its biological relevance and technical properties In the first place, after introducing the Gabor wavelet transform and the texture analysis methods, the infrared images are extracted texture feature by Gabor wavelet transform. It is utilized the multi-scale property of Gabor filter. In the second place, we take multi-dimensional means and standard deviation with different scales and directions as texture parameters. The last stage is classification of scene texture parameters with least squares support vector machine (LS-SVM) algorithm. SVM is based on the principle of structural risk minimization (SRM). Compared with SVM, LS-SVM has overcome the shortcoming of

  11. Nonprogressing HIV-infected children share fundamental immunological features of nonpathogenic SIV infection.

    Science.gov (United States)

    Muenchhoff, Maximilian; Adland, Emily; Karimanzira, Owen; Crowther, Carol; Pace, Matthew; Csala, Anna; Leitman, Ellen; Moonsamy, Angeline; McGregor, Callum; Hurst, Jacob; Groll, Andreas; Mori, Masahiko; Sinmyee, Smruti; Thobakgale, Christina; Tudor-Williams, Gareth; Prendergast, Andrew J; Kloverpris, Henrik; Roider, Julia; Leslie, Alasdair; Shingadia, Delane; Brits, Thea; Daniels, Samantha; Frater, John; Willberg, Christian B; Walker, Bruce D; Ndung'u, Thumbi; Jooste, Pieter; Moore, Penny L; Morris, Lynn; Goulder, Philip

    2016-09-28

    Disease-free infection in HIV-infected adults is associated with human leukocyte antigen-mediated suppression of viremia, whereas in the sooty mangabey and other healthy natural hosts of simian immunodeficiency virus (SIV), viral replication continues unabated. To better understand factors preventing HIV disease, we investigated pediatric infection, where AIDS typically develops more rapidly than in adults. Among 170 nonprogressing antiretroviral therapy-naïve children aged >5 years maintaining normal-for-age CD4 T cell counts, immune activation levels were low despite high viremia (median, 26,000 copies/ml). Potent, broadly neutralizing antibody responses in most of the subjects and strong virus-specific T cell activity were present but did not drive pediatric nonprogression. However, reduced CCR5 expression and low HIV infection in long-lived central memory CD4 T cells were observed in pediatric nonprogressors. These children therefore express two cardinal immunological features of nonpathogenic SIV infection in sooty mangabeys-low immune activation despite high viremia and low CCR5 expression on long-lived central memory CD4 T cells-suggesting closer similarities with nonpathogenetic mechanisms evolved over thousands of years in natural SIV hosts than those operating in HIV-infected adults.

  12. 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.

  13. Two-level hierarchical feature learning for image classification

    Institute of Scientific and Technical Information of China (English)

    Guang-hui SONG; Xiao-gang JIN; Gen-lang CHEN; Yan NIE

    2016-01-01

    In some image classifi cation tasks, similarities among different categories are different and the samples are usually misclassifi ed as highly similar categories. To distinguish highly similar categories, more specifi c features are required so that the classifi er can improve the classifi cation performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fi ne-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specifi c feature extracted from highly similar categories are fused into a feature vector. Then the fi nal feature representation is fed into a linear classifi er. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classifi cation accuracy in comparison with fl at multiple classifi cation methods.

  14. Computational optical sensing and imaging: introduction to feature issue.

    Science.gov (United States)

    Gerwe, David R; Harvey, Andrew; Gehm, Michael E

    2013-04-01

    The 2012 Computational Optical Sensing and Imaging (COSI) conference of the Optical Society of America was one of six colocated meetings composing the Imaging and Applied Optics Congress held in Monterey, California, 24-28 June. COSI, together with the Imaging Systems and Applications, Optical Sensors, Applied Industrial Optics, and Optical Remote Sensing of the Environment conferences, brought together a diverse group of scientists and engineers sharing a common interest in measuring and processing of information carried by optical fields. This special feature includes several papers based on presentations given at the 2012 COSI conference as well as independent contributions, which together highlight several important trends.

  15. Deep optical images of Malin 1 reveal new features

    CERN Document Server

    Galaz, Gaspar; Suc, Vincent; Busta, Luis; Lizana, Guadalupe; Infante, Leopoldo; Royo, Santiago

    2015-01-01

    We present Megacam deep optical images (g and r) of Malin 1 obtained with the 6.5m Magellan/Clay telescope, detecting structures down to ~ 28 B mag arcsec-2. In order to enhance galaxy features buried in the noise, we use a noise reduction filter based on the total generalized variation regularizator. This method allows us to detect and resolve very faint morphological features, including spiral arms, with a high visual contrast. For the first time, we can appreciate an optical image of Malin 1 and its morphology in full view. The images provide unprecedented detail, compared to those obtained in the past with photographic plates and CCD, including HST imaging. We detect two peculiar features in the disk/spiral arms. The analysis suggests that the first one is possibly a background galaxy, and the second is an apparent stream without a clear nature, but could be related to the claimed past interaction between Malin 1 and the galaxy SDSSJ123708.91 + 142253.2. Malin 1 exhibits features suggesting the presence o...

  16. Identification and Quantification Soil Redoximorphic Features by Digital Image Processing

    Science.gov (United States)

    Soil redoximorphic features (SRFs) have provided scientists and land managers with insight into relative soil moisture for approximately 60 years. The overall objective of this study was to develop a new method of SRF identification and quantification from soil cores using a digital camera and imag...

  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. Multi-modal image registration using structural features.

    Science.gov (United States)

    Kasiri, Keyvan; Clausi, David A; Fieguth, Paul

    2014-01-01

    Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing the complex phase and gradient-based information. By employing structural relationships between different modalities instead of complex similarity measures, the multi-modal registration problem is converted into a mono-modal one. Therefore, conventional mono-modal similarity measures can be utilized to evaluate the registration results. This new registration paradigm has been tested on magnetic resonance (MR) brain images of different modes. The method has been evaluated based on target registration error (TRE) to determine alignment accuracy. Quantitative results demonstrate that the proposed method is capable of achieving comparable registration accuracy compared to the conventional mutual information.

  19. Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

    Directory of Open Access Journals (Sweden)

    A F M Saifuddin Saif

    Full Text Available Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA. Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

  20. No-reference face image assessment based on deep features

    Science.gov (United States)

    Liu, Guirong; Xu, Yi; Lan, Jinpeng

    2016-09-01

    Face quality assessment is important to improve the performance of face recognition system. For instance, it is required to select images of good quality to improve recognition rate for the person of interest. Current methods mostly depend on traditional image assessment, which use prior knowledge of human vision system. As a result, the quality score of face images shows consistency with human vision perception but deviates from the processing procedure of a real face recognition system. It is the fact that the state-of-art face recognition systems are all built on deep neural networks. Naturally, it is expected to propose an efficient quality scoring method of face images, which should show high consistency with the recognition rate of face images from current face recognition systems. This paper proposes a non-reference face image assessment algorithm based on the deep features, which is capable of predicting the recognition rate of face images. The proposed face image assessment algorithm provides a promising tool to filter out the good input images for the real face recognition system to achieve high recognition rate.

  1. New learning subspace method for image feature extraction

    Institute of Scientific and Technical Information of China (English)

    CAO Jian-hai; LI Long; LU Chang-hou

    2006-01-01

    A new method of Windows Minimum/Maximum Module Learning Subspace Algorithm(WMMLSA) for image feature extraction is presented. The WMMLSM is insensitive to the order of the training samples and can regulate effectively the radical vectors of an image feature subspace through selecting the study samples for subspace iterative learning algorithm,so it can improve the robustness and generalization capacity of a pattern subspace and enhance the recognition rate of a classifier. At the same time,a pattern subspace is built by the PCA method. The classifier based on WMMLSM is successfully applied to recognize the pressed characters on the gray-scale images. The results indicate that the correct recognition rate on WMMLSM is higher than that on Average Learning Subspace Method,and that the training speed and the classification speed are both improved. The new method is more applicable and efficient.

  2. Characterisation of Feature Points in Eye Fundus Images

    Science.gov (United States)

    Calvo, D.; Ortega, M.; Penedo, M. G.; Rouco, J.

    The retinal vessel tree adds decisive knowledge in the diagnosis of numerous opthalmologic pathologies such as hypertension or diabetes. One of the problems in the analysis of the retinal vessel tree is the lack of information in terms of vessels depth as the image acquisition usually leads to a 2D image. This situation provokes a scenario where two different vessels coinciding in a point could be interpreted as a vessel forking into a bifurcation. That is why, for traking and labelling the retinal vascular tree, bifurcations and crossovers of vessels are considered feature points. In this work a novel method for these retinal vessel tree feature points detection and classification is introduced. The method applies image techniques such as filters or thinning to obtain the adequate structure to detect the points and sets a classification of these points studying its environment. The methodology is tested using a standard database and the results show high classification capabilities.

  3. TOPOGRAPHIC FEATURE EXTRACTION FOR BENGALI AND HINDI CHARACTER IMAGES

    Directory of Open Access Journals (Sweden)

    Soumen Bag

    2011-06-01

    Full Text Available Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West. We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.

  4. Topographic Feature Extraction for Bengali and Hindi Character Images

    Directory of Open Access Journals (Sweden)

    Soumen Bag

    2011-09-01

    Full Text Available Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West. We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.

  5. Pelvic musculoskeletal infection in infants -- diagnostic difficulties and radiological features.

    Science.gov (United States)

    Kearney, S E; Carty, H

    1997-10-01

    Musculoskeletal infection involving the pelvis has rarely been reported in infants. When such infections involve the pelvic muscles they are generally believed to result from secondary spread from adjacent structures. We report five cases of primary pelvic musculoskeletal infection affecting infants pelvic musculoskeletal infection in infants and the role of the various radiological investigations in its diagnosis is discussed.

  6. Image Recognition and Feature Detection in Solar Physics

    Science.gov (United States)

    Martens, Petrus C.

    2012-05-01

    The Solar Dynamics Observatory (SDO) data repository will dwarf the archives of all previous solar physics missions put together. NASA recognized early on that the traditional methods of analyzing the data -- solar scientists and grad students in particular analyzing the images by hand -- would simply not work and tasked our Feature Finding Team (FFT) with developing automated feature recognition modules for solar events and phenomena likely to be observed by SDO. Having these metadata available on-line will enable solar scientist to conduct statistical studies involving large sets of events that would be impossible now with traditional means. We have followed a two-track approach in our project: we have been developing some existing task-specific solar feature finding modules to be "pipe-line" ready for the stream of SDO data, plus we are designing a few new modules. Secondly, we took it upon us to develop an entirely new "trainable" module that would be capable of identifying different types of solar phenomena starting from a limited number of user-provided examples. Both approaches are now reaching fruition, and I will show examples and movies with results from several of our feature finding modules. In the second part of my presentation I will focus on our “trainable” module, which is the most innovative in character. First, there is the strong similarity between solar and medical X-ray images with regard to their texture, which has allowed us to apply some advances made in medical image recognition. Second, we have found that there is a strong similarity between the way our trainable module works and the way our brain recognizes images. The brain can quickly recognize similar images from key characteristics, just as our code does. We conclude from that that our approach represents the beginning of a more human-like procedure for computer image recognition.

  7. Single Image Superresolution via Directional Group Sparsity and Directional Features.

    Science.gov (United States)

    Li, Xiaoyan; He, Hongjie; Wang, Ruxin; Tao, Dacheng

    2015-09-01

    Single image superresolution (SR) aims to construct a high-resolution version from a single low-resolution (LR) image. The SR reconstruction is challenging because of the missing details in the given LR image. Thus, it is critical to explore and exploit effective prior knowledge for boosting the reconstruction performance. In this paper, we propose a novel SR method by exploiting both the directional group sparsity of the image gradients and the directional features in similarity weight estimation. The proposed SR approach is based on two observations: 1) most of the sharp edges are oriented in a limited number of directions and 2) an image pixel can be estimated by the weighted averaging of its neighbors. In consideration of these observations, we apply the curvelet transform to extract directional features which are then used for region selection and weight estimation. A combined total variation regularizer is presented which assumes that the gradients in natural images have a straightforward group sparsity structure. In addition, a directional nonlocal means regularization term takes pixel values and directional information into account to suppress unwanted artifacts. By assembling the designed regularization terms, we solve the SR problem of an energy function with minimal reconstruction error by applying a framework of templates for first-order conic solvers. The thorough quantitative and qualitative results in terms of peak signal-to-noise ratio, structural similarity, information fidelity criterion, and preference matrix demonstrate that the proposed approach achieves higher quality SR reconstruction than the state-of-the-art algorithms.

  8. Document image retrieval based on multi-density features

    Institute of Scientific and Technical Information of China (English)

    HU Zhilan; LIN Xinggang; YAN Hong

    2007-01-01

    The development of document image databases is becoming a challenge for document image retrieval techniques.Traditional layout-reconstructed-based methods rely on high quality document images as well as an optical character recognition (OCR) precision,and can only deal with several widely used languages.The complexity of document layouts greatly hinders layout analysis-based approaches.This paper describes a multi-density feature based algorithm for binary document images,which is independent of OCR or layout analyses.The text area was extracted after preprocessing such as skew correction and marginal noise removal.Then the aspect ratio and multi-density features were extracted from the text area to select the best candidates from the document image database.Experimental results show that this approach is simple with loss rates less than 3% and can efficiently analyze images with different resolutions and different input systems.The system is also robust to noise due to its notes and complex layouts,etc.

  9. GPU Accelerated Automated Feature Extraction From Satellite Images

    Directory of Open Access Journals (Sweden)

    K. Phani Tejaswi

    2013-04-01

    Full Text Available The availability of large volumes of remote sensing data insists on higher degree of automation in featureextraction, making it a need of thehour. Fusingdata from multiple sources, such as panchromatic,hyperspectraland LiDAR sensors, enhances the probability of identifying and extracting features such asbuildings, vegetation or bodies of water by using a combination of spectral and elevation characteristics.Utilizing theaforementioned featuresin remote sensing is impracticable in the absence ofautomation.Whileefforts are underway to reduce human intervention in data processing, this attempt alone may notsuffice. Thehuge quantum of data that needs to be processed entailsaccelerated processing to be enabled.GPUs, which were originally designed to provide efficient visualization,arebeing massively employed forcomputation intensive parallel processing environments. Image processing in general and hence automatedfeatureextraction, is highly computation intensive, where performance improvements have a direct impacton societal needs. In this context, an algorithm has been formulated for automated feature extraction froma panchromatic or multispectral image based on image processing techniques.Two Laplacian of Guassian(LoGmasks were applied on the image individually followed by detection of zero crossing points andextracting the pixels based on their standard deviationwiththe surrounding pixels. The two extractedimages with different LoG masks were combined together which resulted in an image withthe extractedfeatures and edges.Finally the user is at liberty to apply the image smoothing step depending on the noisecontent in the extracted image.The image ispassed through a hybrid median filter toremove the salt andpepper noise from the image.This paper discusses theaforesaidalgorithmforautomated featureextraction, necessity of deployment of GPUs for thesame;system-level challenges and quantifies thebenefits of integrating GPUs in such environment. The

  10. Weighted feature fusion for content-based image retrieval

    Science.gov (United States)

    Soysal, Omurhan A.; Sumer, Emre

    2016-07-01

    The feature descriptors such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) are known as the most commonly used solutions for the content-based image retrieval problems. In this paper, a novel approach called "Weighted Feature Fusion" is proposed as a generic solution instead of applying problem-specific descriptors alone. Experiments were performed on two basic data sets of the Inria in order to improve the precision of retrieval results. It was found that in cases where the descriptors were used alone the proposed approach yielded 10-30% more accurate results than the ORB alone. Besides, it yielded 9-22% and 12-29% less False Positives compared to the SIFT alone and SURF alone, respectively.

  11. A flower image retrieval method based on ROI feature

    Institute of Scientific and Technical Information of China (English)

    洪安祥; 陈刚; 李均利; 池哲儒; 张亶

    2004-01-01

    Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

  12. A flower image retrieval method based on ROI feature

    Institute of Scientific and Technical Information of China (English)

    洪安祥; 陈刚; 李均利; 池哲儒; 张亶

    2004-01-01

    Flower image retrieval is a very important step for computer-aided plant species recognition.In this paper,we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images.For flower retrieval,we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets,Centroid-Contour Distance(CCD)and Angle Code Histogram(ACH),to characterize the shape features of a flower contour.Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions.Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest(ROD based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard(1991)and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

  13. 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.

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

    Science.gov (United States)

    Schwingel, Ricardo; Reis, Fabiano; Zanardi, Veronica A; Queiroz, Luciano S; França, Marcondes C

    2012-02-01

    This paper aimed at studying presentations of the central nervous system (CNS) lymphoma using structural images obtained by magnetic resonance imaging (MRI). 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. 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. Presentations of lymphoma are very pleomorphic, but some of them should point to this diagnostic possibility.

  15. Feature Based Correspondence: A Comparative Study on Image Matching Algorithms

    Directory of Open Access Journals (Sweden)

    Munim Tanvir

    2016-03-01

    Full Text Available Image matching and recognition are the crux of computer vision and have a major part to play in everyday lives. From industrial robots to surveillance cameras, from autonomous vehicles to medical imaging and from missile guidance to space exploration vehicles computer vision and hence image matching is embedded in our lives. This communication presents a comparative study on the prevalent matching algorithms, addressing their restrictions and providing a criterion to define the level of efficiency likely to be expected from an algorithm. The study includes the feature detection and matching techniques used by these prevalent algorithms to allow a deeper insight. The chief aim of the study is to deliver a source of comprehensive reference for the researchers involved in image matching, regardless of specific applications.

  16. Adaptive feature-specific imaging: a face recognition example.

    Science.gov (United States)

    Baheti, Pawan K; Neifeld, Mark A

    2008-04-01

    We present an adaptive feature-specific imaging (AFSI) system and consider its application to a face recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. Using sequential hypothesis testing, we compare AFSI with static-FSI (SFSI) and static or adaptive conventional imaging in terms of the number of measurements required to achieve a specified probability of misclassification (Pe). The AFSI system exhibits significant improvement compared to SFSI and conventional imaging at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses and desired Pe=10(-2), AFSI requires 100 times fewer measurements than the adaptive conventional imager at SNR= -20 dB. We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage, resulting in an optimal value of integration time (equivalent to SNR) per measurement.

  17. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves

    Directory of Open Access Journals (Sweden)

    Chuanqi Xie

    2016-05-01

    Full Text Available This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm. Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation based on gray level co-occurrence matrix (GLCM were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.

  18. 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.)

  19. Infection imaging with radiopharmaceuticals in the 21st century

    Energy Technology Data Exchange (ETDEWEB)

    Das, Satya S.; Wareham, David W. [St. Bartholomew' s Hospital, London (United Kingdom). Dept. of Medical Microbiology; Britton, Keith E. [St. Bartholomew' s Hospital, London (United Kingdom). Dept. of Nuclear Medicine; Hall, Anne V. [Harefield Hospital, Middlesex (United Kingdom). Microbiology Dept.

    2002-09-01

    Infection continues to be a major cause of morbidity and mortality worldwide. Nuclear medicine has an important role in aiding the diagnosis of particularly deep-seated infections such as abscesses, osteomyelitis, septic arthritis, endocarditis, and infections of prosthetic devices. Established techniques such as radiolabelled leucocytes are sensitive and specific for inflammation but do not distinguish between infective and non-infective inflammation. The challenge for Nuclear Medicine in infection imaging in the 21st century is to build on the recent trend towards the development of more infection specific radiopharmaceuticals, such as radiolabelled anti-infectives (e.g. 99 m Tc ciprofloxacin). In addition to aiding early diagnosis of infection, through serial imaging these agents might prove very useful in monitoring the response to and determining the optimum duration of anti-infective therapy. This article reviews the current approach to infection imaging with radiopharmaceuticals nd the future direction it might take. (author)

  20. Infection imaging with radiopharmaceuticals in the 21st century

    Directory of Open Access Journals (Sweden)

    Das Satya S.

    2002-01-01

    Full Text Available Infection continues to be a major cause of morbidity and mortality worldwide. Nuclear medicine has an important role in aiding the diagnosis of particularly deep-seated infections such as abscesses, osteomyelitis, septic arthritis, endocarditis, and infections of prosthetic devices. Established techniques such as radiolabelled leucocytes are sensitive and specific for inflammation but do not distinguish between infective and non-infective inflammation. The challenge for Nuclear medicine in infection imaging in the 21st century is to build on the recent trend towards the development of more infection specific radiopharmaceuticals, such as radiolabelled anti-infectives (e.g. 99mTc- ciprofloxacin. In addition to aiding early diagnosis of infection, through serial imaging these agents might prove very useful in monitoring the response to and determining the optimum duration of anti-infective therapy. This article reviews the current approach to infection imaging with radiopharmaceuticals and the future direction it might take.

  1. Electronic image stabilization system based on global feature tracking

    Institute of Scientific and Technical Information of China (English)

    Zhu Juanjuan; Guo Baolong

    2008-01-01

    A new robust electronic image stabilization system is presented, which involves feature-point, tracking based global motion estimation and Kalman filtering based motion compensation. First, global motion is estimated from the local motions of selected feature points. Considering the local moving objects or the inevitable mismatch,the matching validation, based on the stable relative distance between the points set is proposed, thus maintaining high accuracy and robustness. Next, the global motion parameters are accumulated for correction by Kalman filter-ation. The experimental result illustrates that the proposed system is effective to stabilize translational, rotational,and zooming jitter and robust to local motions.

  2. The fuzzy Hough transform-feature extraction in medical images.

    Science.gov (United States)

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

    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 (improved) estimate of the true borders with other (subsequently used) image processing techniques. They 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 procedure 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.

  3. 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.

  4. Incorporating global information in feature-based multimodal image registration

    Science.gov (United States)

    Li, Yong; Stevenson, Robert

    2014-03-01

    A multimodal image registration framework based on searching the best matched keypoints and the incorporation of global information is proposed. It comprises two key elements: keypoint detection and an iterative process. Keypoints are detected from both the reference and test images. For each test keypoint, a number of reference keypoints are chosen as mapping candidates. A triplet of keypoint mappings determine an affine transformation that is evaluated using a similarity metric between the reference image and the transformed test image by the determined transformation. An iterative process is conducted on triplets of keypoint mappings, keeping track of the best matched reference keypoint. Random sample consensus and mutual information are applied to eliminate outlier keypoint mappings. The similarity metric is defined to be the number of overlapped edge pixels over the entire images, allowing for global information to be incorporated in the evaluation of triplets of mappings. The performance of the framework is investigated with keypoints extracted by scale invariant feature transform and partial intensity invariant feature descriptor. Experimental results show that the proposed framework can provide more accurate registration than existing methods.

  5. 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.)

  6. Imaging features of intraosseous ganglia: a report of 45 cases

    Energy Technology Data Exchange (ETDEWEB)

    Williams, H.J.; Davies, A.M.; Allen, G.; Evans, N. [Royal Orthopaedic Hospital, Department of Radiology, Birmingham (United Kingdom); Mangham, D.C. [Royal Orthopaedic Hospital, Department of Pathology, Birmingham (United Kingdom)

    2004-10-01

    The aim of this study is to report the spectrum of imaging findings of intraosseous ganglia (IG) with particular emphasis on the radiographic and magnetic resonance (MR) features. Forty-five patients with a final diagnosis of IG were referred to a specialist orthopaedic oncology service with the presumptive diagnosis of either a primary or secondary bone tumour. The diagnosis was established by histology in 25 cases. In the remainder, the imaging features were considered characteristic and the lesion was stable on follow-up radiographic examination. Radiographs were available for retrospective review in all cases and MR imaging in 29. There was a minor male preponderance with a wide adult age range. Three quarters were found in relation to the weight-bearing long bones of the lower limb, particularly round the knee. On radiographs all were juxta-articular and osteolytic; 74% were eccentric in location, 80% had a sclerotic endosteal margin and 60% of cases showed a degree of trabeculation. Periosteal new bone formation and matrix mineralization were not present. Of the 29 cases that underwent MR imaging, 66% were multiloculated. On T1-weighted images the IG contents were isointense or mildly hypointense in 90% cases. Forty-one per cent of the cases showed a slightly hyperintense rim lining that enhanced with a gadolinium chelate. Thirty-eight per cent were associated with soft tissue extension and 17% with a defect of the adjacent articular cortex. Fifty-five per cent showed surrounding marrow oedema on T2-weighted or STIR images and two cases (7%) a fluid-fluid level prior to any surgical intervention. The authors contend that it is semantics to differentiate between an IG and a degenerate subchondral cyst as, while the initial pathogenesis may vary, the histological endpoint is identical, as are the imaging features apart from the degree of associated degenerative joint disease. IGs, particularly when large, may be mistaken for a bone tumour. Correlation of the

  7. 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.

  8. Iris image enhancement for feature recognition and extraction

    CSIR Research Space (South Africa)

    Mabuza, GP

    2012-10-01

    Full Text Available 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... for performing such tasks and yielding better accuracy (Gonzalez & Woods, 2002). METHODOLOGY The block diagram in Figure 2 demonstrates the processes followed to achieve the results. Figure 2: Methodology flow chart Iris image enhancement for feature...

  9. 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.

  10. Multiwavelets domain singular value features for image texture classification

    Institute of Scientific and Technical Information of China (English)

    RAMAKRISHNAN S.; SELVAN S.

    2007-01-01

    A new approach based on multiwavelets transformation and singular value decomposition (SVD) is proposed for the classification of image textures. Lower singular values are truncated based on its energy distribution to classify the textures in the presence of additive white Gaussian noise (AWGN). The proposed approach extracts features such as energy, entropy, local homogeneity and max-min ratio from the selected singular values of multiwavelets transformation coefficients of image textures.The classification was carried out using probabilistic neural network (PNN). Performance of the proposed approach was compared with conventional wavelet domain gray level co-occurrence matrix (GLCM) based features, discrete multiwavelets transformation energy based approach, and HMM based approach. Experimental results showed the superiority of the proposed algorithms when compared with existing algorithms.

  11. Design Approach for Content-based Image Retrieval using Gabor-Zernike features

    Directory of Open Access Journals (Sweden)

    Abhinav Deshpande

    2012-04-01

    Full Text Available The process of extraction of different features from an image is known as Content-based Image Retrieval.Color,Texture and Shape are the major features of an image and play a vital role in the representation of an image..In this paper, a novel method is proposed to extract the region of interest(ROI from an image,prior to extraction of salient features of an image.The image is subjected to normalization so that the noise components due to Gaussian or other types of noises which are present in the image are eliminated and thesuccessfull extraction of various features of an image can be accomplished. Gabor Filters are used to extract the texture feature from an image whereas Zernike Moments can be used to extract the shape feature.The combination of Gabor feature and Zernike feature can be combined to extract Gabor-Zernike Features from an image.

  12. Hyperspectral image classification based on NMF Features Selection Method

    Science.gov (United States)

    Abe, Bolanle T.; Jordaan, J. A.

    2013-12-01

    Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.

  13. Feature-Enhanced, Model-Based Sparse Aperture Imaging

    Science.gov (United States)

    2008-03-01

    obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition ( SVD ) of the data...application in a variety of problems, including image reconstruction and restoration [5], wavelet denoising [6], feature selection in machine learning...on the singular value decomposition ( SVD ) to combine multiple samples and the use of second-order cone programming for optimization of the resulting

  14. Spinal cord ischemia: aetiology, clinical syndromes and imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Weidauer, Stefan [Frankfurt Univ., Sankt Katharinen Hospital Teaching Hospital, Frankfurt am Main (Germany). Dept. of Neurology; Hattingen, Elke; Berkefeld, Joachim [Frankfurt Univ., Frankfurt am Main (Germany). Inst. of Neuroradiology; Nichtweiss, Michael

    2015-03-01

    The purpose of this study was to analyse MR imaging features and lesion patterns as defined by compromised vascular territories, correlating them to different clinical syndromes and aetiological aspects. In a 19.8-year period, clinical records and magnetic resonance imaging (MRI) features of 55 consecutive patients suffering from spinal cord ischemia were evaluated. Aetiologies of infarcts were arteriosclerosis of the aorta and vertebral arteries (23.6 %), aortic surgery or interventional aneurysm repair (11 %) and aortic and vertebral artery dissection (11 %), and in 23.6 %, aetiology remained unclear. Infarcts occurred in 38.2 % at the cervical and thoracic level, respectively, and 49 % of patients suffered from centromedullar syndrome caused by anterior spinal artery ischemia. MRI disclosed hyperintense pencil-like lesion pattern on T2WI in 98.2 %, cord swelling in 40 %, enhancement on post-contrast T1WI in 42.9 % and always hyperintense signal on diffusion-weighted imaging (DWI) when acquired. The most common clinical feature in spinal cord ischemia is a centromedullar syndrome, and in contrast to anterior spinal artery ischemia, infarcts in the posterior spinal artery territory are rare. The exclusively cervical location of the spinal sulcal artery syndrome seems to be a likely consequence of anterior spinal artery duplication which is observed preferentially here. (orig.)

  15. Photoacoustic imaging features of intraocular tumors: Retinoblastoma and uveal melanoma

    Science.gov (United States)

    Xu, Guan; Xue, Yafang; Özkurt, Zeynep Gürsel; Slimani, Naziha; Hu, Zizhong; Wang, Xueding; Xia, Kewen; Ma, Teng; Zhou, Qifa; Demirci, Hakan

    2017-01-01

    The purpose of this study is to examine the capability of photoacoustic (PA) imaging (PAI) in assessing the unique molecular and architectural features in ocular tumors. A real-time PA and ultrasonography (US) parallel imaging system based on a research US platform was developed to examine retinoblastoma in mice in vivo and human retinoblastoma and uveal melanoma ex vivo. PA signals were generated by optical illumination at 720, 750, 800, 850, 900 and 950 nm delivered through a fiber optical bundle. The optical absorption spectra of the tumors were derived from the PA images. The optical absorption spectrum of each tumor was quantified by fitting to a polynomial model. The microscopic architectures of the tumors were quantified by frequency domain analysis of the PA signals. Both the optical spectral and architectural features agree with the histological findings of the tumors. The mouse and human retinoblastoma showed comparable total optical absorption spectra at a correlation of 0.95 (p<0.005). The quantitative PAI features of human retinoblastoma and uveal melanoma have shown statistically significant difference in two tailed t-tests (p<0.05). Fully compatible with the concurrent procedures, PAI could be a potential tool complementary to other diagnostic modalities for characterizing intraocular tumors. PMID:28231293

  16. Local features in natural images via singularity theory

    CERN Document Server

    Damon, James; Haslinger, Gareth

    2016-01-01

    This monograph considers a basic problem in the computer analysis of natural images, which are images of scenes involving multiple objects that are obtained by a camera lens or a viewer’s eye. The goal is to detect geometric features of objects in the image and to separate regions of the objects with distinct visual properties. When the scene is illuminated by a single principal light source, we further include the visual clues resulting from the interaction of the geometric features of objects, the shade/shadow regions on the objects, and the “apparent contours”. We do so by a mathematical analysis using a repertoire of methods in singularity theory. This is applied for generic light directions of both the “stable configurations” for these interactions, whose features remain unchanged under small viewer movement, and the generic changes which occur under changes of view directions. These may then be used to differentiate between objects and determine their shapes and positions.

  17. Nasopharyngeal adenoid cystic carcinoma: magnetic resonance imaging features in ten cases

    Institute of Scientific and Technical Information of China (English)

    Xue-Wen Liu; Pei-Hong Wu; Chuan-Miao Xie; Hui Li; Rong Zhang; Zhi-Jun Geng; Yun-Xian Mo; Jing Zhao; Mu-Yan Cai; Yan-Chun Lv

    2012-01-01

    Nasopharyngeal adenoid cystic carcinoma (NACC) is a rare malignancy with high local invasiveness.To date,there is no consensus on the imaging characteristics of NACC.To address this,we retrospectively reviewed 10 cases of NACC and summarized the magnetic resonance imaging (MRI) features.MR images of 10 patients with histologically validated NACC were reviewed by two experienced radiologists.The location,shape,margin,signal intensity,lesion texture,contrast enhancement patterns,local invasion,and cervical lymphadenopathy of all tumors were evaluated.Clinical and pathologic records were also reviewed.No patients were positive for antibodies against Epstein-Barr virus (EBV).The imaging patterns of primary tumors were classified into two types as determined by location,shape,and margin.Of all patients,7 had tumors with a type 1 imaging pattern and 3 had tumors with a type 2 imaging pattern.The 4 tubular NACCs were all homogeneous tumors,whereas 3 (60%) of 5 cribriform NACCs and the sole solid NACC were heterogeneous tumors with separations or central necrosis on MR images.Five patients had perineural infiltration and intracranial involvement,and only 2 had cervical lymphadenopathy.Based on these results,we conclude that NACC is a local,aggressive neoplasm that is often negative for EBV infection and associated with a low incidence of cervical lymphadenopathy.Furthermore,MRI features of NACC vary in locations and histological subtypes.

  18. Featured Image: A New Look at Malin 1

    Science.gov (United States)

    Kohler, Susanna

    2016-01-01

    Monochrome, inverted version of Malin 1. [Adapted from Galaz et al. 2015]The above image of Malin 1, the faintest and largest low-surface-brightness galaxy ever observed, was obtained with an instrument called Megacam on the 6.5m Magellan/Clay telescope. Gaspar Galaz (Pontifical Catholic University of Chile) and collaborators used Megacam to obtain deep optical observations of Malin 1. They then used novel noise-reduction and image-processing techniques to create this spectacular image of the spiral galaxy located roughly 1.2 billion light-years away. This new view of Malin 1 reveals details weve never before seen, including a stream within the disk that may have been caused by a past interaction between Malin 1 and another galaxy near it. Check outthe image to the rightfor a monochrome, inverted version thatmakes it a little easier to see some of Malin 1s features. To see the full original images and to learn more about what the images reveal about Malin 1, see the paper below.CitationGaspar Galaz et al 2015 ApJ 815 L29. doi:10.1088/2041-8205/815/2/L29

  19. 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.

  20. MR imaging features of focal liver lesions in Wilson disease.

    Science.gov (United States)

    Dohan, Anthony; Vargas, Ottavia; Dautry, Raphael; Guerrache, Youcef; Woimant, France; Hamzi, Lounis; Boudiaf, Mourad; Poujois, Aurelia; Faraoun, Sid Ahmed; Soyer, Philippe

    2016-09-01

    Hepatic involvement in Wilson disease (WD) manifests as a diffuse chronic disease in the majority of patients. However, in a subset of patients focal liver lesions may develop, presenting with a wide range of imaging features. The majority of focal liver lesions in patients with WD are benign nodules, but there are reports that have described malignant liver tumors or dysplastic nodules in these patients. Because of the possibility of malignant transformation of liver nodules, major concerns have been raised with respect to the management and follow-up of patients with WD in whom focal liver lesions have been identified. The assessment of liver involvement in patients with WD is generally performed with ultrasonography. However, ultrasonography conveys limited specificity so that magnetic resonance (MR) imaging is often performed to improve lesion characterization. This review was performed to illustrate the spectrum of MR imaging features of focal liver lesions that develop in patients with WD. It is assumed that familiarity with the MR imaging presentation of focal liver lesions in WD may help clarify the actual nature of hepatic nodules in patients with this condition.

  1. Medical Image Fusion Based on Feature Extraction and Sparse Representation.

    Science.gov (United States)

    Fei, Yin; Wei, Gao; Zongxi, Song

    2017-01-01

    As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

  2. FEATURE EXTRACTION OF RETINAL IMAGE FOR DIAGNOSIS OF ABNORMAL EYES

    Directory of Open Access Journals (Sweden)

    S. Praveenkumar

    2011-05-01

    Full Text Available Currently, medical image processing draws intense interests of scien- tists and physicians to aid in clinical diagnosis. The retinal Fundus image is widely used in the diagnosis and treatment of various eye diseases such as Diabetic Retinopathy, glaucoma etc. If these diseases are detected and treated early, many of the visual losses can be pre- vented. This paper presents the methods to detect main features of Fundus images such as optic disk, fovea, exudates and blood vessels. To determine the optic Disk and its centre we find the brightest part of the Fundus. The candidate region of fovea is defined an area circle. The detection of fovea is done by using its spatial relationship with optic disk. Exudates are found using their high grey level variation and their contours are determined by means of morphological recon- struction techniques. The blood vessels are highlighted using bottom hat transform and morphological dilation after edge detection. All the enhanced features are then combined in the Fundus image for the detection of abnormalities in eye.

  3. 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.

  4. Pro duct Image Classification Based on Fusion Features

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-hui; LIU Jing-jing; YANG Li-jun

    2015-01-01

    Two key challenges raised by a product images classification system are classi-fication precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/eBay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21%and the average classification time is reduced by 2/3.

  5. Image Watermarking Using Visual Perception Model and Statistical Features

    Directory of Open Access Journals (Sweden)

    Mrs.C.Akila

    2010-06-01

    Full Text Available This paper presents an effective method for the image watermarking using visual perception model based on statistical features in the low frequency domain. In the image watermarking community watermark resistance to geometric attacks is an important issue. Most countermeasures proposed in the literature usually focus on the problem of global affine transforms such as rotation, scaling and translation (RST, but few are resistant to challenging cropping and random bending attacks (RBAs. Normally in the case of watermarking there may be an occurrence of distortion in the form of artifacts. A visual perception model is proposed to quantify the localized tolerance to noise for arbitrary imagery which achieves the reduction of artifacts. As a result, the watermarking system provides a satisfactory performance for those content-preserving geometric deformations and image processing operations, including JPEG ompression, low pass filtering, cropping and RBAs.

  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. 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.

  8. SYSTEMIC LUPUS ERYTHEMATOSUS AND OPPORTUNISTIC INFECTIONS: PREVALENCE, CLINICAL FEATURES

    Directory of Open Access Journals (Sweden)

    O N Egorova

    2008-01-01

    Subjects and methods. Sixty-seven patients with a 1-to-7 history of SLE who received first-line therapy were examined. Results. The analysis of the history data and the results of a serological survey identified 3 groups of patients: 1 35 patients with viral infection, of them 9 had mixed viral-and-bacterial infections; 2 14 with bacterial infections and 3 18 patients without viral-and-bacterial complications. The analysis of clinical symptoms established a correlation of high titers of antibodies to cytomegalovirus (CMV and Epstein-Barr virus (EBV with symptoms, such as fever, arthritis, lymphadenopathy, carditis, hepatomegaly and erythema migrans eruption. However, having the similar clinical manifestations, CMV and EBV infections had some organ specificity. In SLE, concomitant comorbid infection, viral infection in particular, contributed to the development of the clinical picture polymorphism with the protracted, remitting inflammatory process and the inadequate efficiency of glucocorticoid and immunosuppressive therapy.

  9. SYSTEMIC LUPUS ERYTHEMATOSUS AND OPPORTUNISTIC INFECTIONS: PREVALENCE, CLINICAL FEATURES

    Directory of Open Access Journals (Sweden)

    O N Egorova

    2008-12-01

    Subjects and methods. Sixty-seven patients with a 1-to-7 history of SLE who received first-line therapy were examined. Results. The analysis of the history data and the results of a serological survey identified 3 groups of patients: 1 35 patients with viral infection, of them 9 had mixed viral-and-bacterial infections; 2 14 with bacterial infections and 3 18 patients without viral-and-bacterial complications. The analysis of clinical symptoms established a correlation of high titers of antibodies to cytomegalovirus (CMV and Epstein-Barr virus (EBV with symptoms, such as fever, arthritis, lymphadenopathy, carditis, hepatomegaly and erythema migrans eruption. However, having the similar clinical manifestations, CMV and EBV infections had some organ specificity. In SLE, concomitant comorbid infection, viral infection in particular, contributed to the development of the clinical picture polymorphism with the protracted, remitting inflammatory process and the inadequate efficiency of glucocorticoid and immunosuppressive therapy.

  10. Feature extraction for target identification and image classification of OMIS hyperspectral image

    Institute of Scientific and Technical Information of China (English)

    DU Pei-jun; TAN Kun; SU Hong-jun

    2009-01-01

    In order to combine feature extraction operations with specific hyperspectrai remote sensing information processing objectives, two aspects of feature extraction were explored. Based on clustering and decision tree algorithm, spectral absorption index (SAI), continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of dif-ferent targets, and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA), minimum noise fraction (MNF), grouping PCA, and derivate spectral analysis, the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM, and SVM outperforms traditional SAM and MLC classifiers for OMIS data.

  11. Shape Adaptive, Robust Iris Feature Extraction from Noisy Iris Images

    Science.gov (United States)

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

    2013-01-01

    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. PMID:24696801

  12. A feature-enriched completely blind image quality evaluator.

    Science.gov (United States)

    Lin Zhang; Lei Zhang; Bovik, Alan C

    2015-08-01

    Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinion-unaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk/~cslzhang/IQA/ILNIQE/ILNIQE.htm.

  13. 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.

  14. MRI and PET Image Fusion Using Fuzzy Logic and Image Local Features

    Directory of Open Access Journals (Sweden)

    Umer Javed

    2014-01-01

    to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.

  15. EPIDEMIOLOGICAL AND CLINICAL FEATURES OF COMBINED RESPIRATORY INFECTIONS IN CHILDREN

    Directory of Open Access Journals (Sweden)

    V. V. Shkarin

    2017-01-01

    Full Text Available Presents a review of publications on the problem of combined respiratory infections among children. Viral-bacterial associations are registered  in a group of often ill children in 51.7%. More than half of the patients have herpesvirus infection in various combinations. The presence of a combined acute respiratory viral infection among children in the group from 2 to 6 years was noted in 44.2% of cases, among which, in addition to influenza viruses, RS-, adeno-, etc., metapneumovirus and bocavirus plays an important role.The increase in severity of acute respiratory viral infection with combined  infection, with chlamydia  and mycoplasma infection is shown. A longer and more severe course of whooping cough was observed when combined with respiratory viruses.The revealed facts of frequency of distribution of combined  respiratory infections in children, the severity and duration of their course with the development of various complications and the formation of chronic pathology dictate the need to improve diagnosis and treatment tactics of these forms of infections.

  16. Appendiceal mucocele: clinical and imaging features of 14 cases.

    Science.gov (United States)

    Malya, F Umit; Hasbahceci, M; Serter, A; Cipe, G; Karatepe, O; Kocakoc, E; Muslumanoglu, M

    2014-01-01

    Appendiceal mucocele as a cystic dilatation filled with mucinous material is a very rare disease of the appendix vermiformis. Its preoperative diagnosis is still acking behind common use of imaging techniques. Retrospective analysis of the patients with a pathological diagnosis of appendiceal mucocele with regard to clinical and imaging features. The study group included 14 patients with a mean age of 51 years (range from 17 to 82 years). Predominant symptoms were pain and feeling of fullness in the right iliac fossa in 9(64%) and 5 (36%) patients, respectively. For imaging purposes, use of computed tomography resulted in preoperative diagnosis of appendiceal mucocele in half of the patients(50%). 93% of the cases underwent appendectomy, and righth emicolectomy was performed in one patient (7%). Mucocele and cystadenoma were detected in 11 (79%) and 3 (21%)patients, respectively. Presence of acute appendicitis and coloncarcinoma were confirmed afterwards histologically in 4 (29%)and one (7%) patients, respectively. Despite the common use of imaging studies,preoperative diagnosis of appendiceal mucocele is still not possible in most of the cases. During surgical treatment,which is tailored according to imaging and intraoperative findings, precautionary measures to avoid intraperitoneal rupture and dissemination should be taken. Celsius.

  17. A novel point cloud registration using 2D image features

    Science.gov (United States)

    Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

    2017-01-01

    Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

  18. Feature detection on 3D images of dental imprints

    Science.gov (United States)

    Mokhtari, Marielle; Laurendeau, Denis

    1994-09-01

    A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.

  19. Sparse coding based feature representation method for remote sensing images

    Science.gov (United States)

    Oguslu, Ender

    In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further

  20. Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

    Science.gov (United States)

    Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

    2017-03-01

    Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

  1. 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

  2. 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.

  3. Aggressive primary thyroid lymphoma: imaging features of two elderly patients

    Directory of Open Access Journals (Sweden)

    Eu Hyun Kim

    2014-10-01

    Full Text Available

    We report two cases of aggressive thyroid lymphoma in elderly patients that presented as Epub ahead of print large infiltrative thyroid masses with extensive invasion to adjacent structures including trachea, esophagus, and common carotid artery. Ultrasonography displayed irregular shaped, heterogeneous hypoechoic mass, mimicking anaplastic carcinoma. Computed tomography showed heterogeneously enhancing mass compared to surrounding muscles without calcification and hemorrhage. After biopsy, the masses were histopathologically diagnosed as lymphoma. Aggressive primary thyroid lymphoma is rare; therefore, here we report its image features, with emphasis on ultrasonographic findings, and discuss its differential diagnosis.

  4. Aggressive primary thyroid lymphoma: imaging features of two elderly patients

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Eu Hyun; Kim, Jee Young; Kim, Tae Jung [Yeouido St. Mary' s Hospital, The Catholic University College of Medicine, Seoul (Korea, Republic of)

    2014-12-15

    We report two cases of aggressive thyroid lymphoma in elderly patients that presented as Epub ahead of print large infiltrative thyroid masses with extensive invasion to adjacent structures including trachea, esophagus, and common carotid artery. Ultrasonography displayed irregular shaped, heterogeneous hypoechoic mass, mimicking anaplastic carcinoma. Computed tomography showed heterogeneously enhancing mass compared to surrounding muscles without calcification and hemorrhage. After biopsy, the masses were histopathologically diagnosed as lymphoma. Aggressive primary thyroid lymphoma is rare; therefore, here we report its image features, with emphasis on ultrasonographic findings, and discuss its differential diagnosis.

  5. Imaging features of constrictive pericarditis: beyond pericardial thickening

    Energy Technology Data Exchange (ETDEWEB)

    Napolitano, G.; Pressacco, J.; Paquet, E. [Dept. of Radiology, Montreal Heart Inst., Montreal, Quebec (Canada)], E-mail: napolitanog@hotmail.com

    2009-02-15

    Constrictive pericarditis is caused by adhesions between the visceral and parietal layers of the pericardium and progressive pericardial fibrosis that restricts diastolic filling of the heart. Later on, the thickened pericardium may calcify. Despite a better understanding of the pathophysiologic basis of the imaging findings in constrictive pericarditis and the recent advent of magnetic resonance imaging (MRI) technology, which has dramatically improved the visualization of the pericardium, the diagnosis of constrictive pericarditis remains a challenge in many cases. In patients with clinical suspicion of underlying constrictive pericarditis, the most important radiologic diagnostic feature is abnormal pericardial thickening, which can be shown readily by computed tomography (CT) and especially by MRI, and is highly suggestive of constrictive pericarditis. Nevertheless, a thickened pericardium does not always indicate constrictive pericarditis. Furthermore, constrictive pericarditis can occur without pericardial thickening. (author)

  6. Morphological features of adenohypophysis stillborn from HIV-infected mothers

    OpenAIRE

    Sherstiuk S.A.; Sorokina I.V

    2012-01-01

    Adenohypophysis stillborn from HIV-infected mothers had a high functional stress for long periods of fetal devel-opment, leading to inhibition of its functional state, as evidenced by the decrease in the diameter of cells, reducing their se-cretory granules, and increased nuclear-cytoplasmic index. In the antenatal period of development in the adenohypophysis of stillbirths from HIV-infected mothers apparently failed embryo genesis, which appeared violation of a population growth of acidophi...

  7. Specific endoscopic features of ulcerative colitis complicated by cytomegalovirus infection

    Institute of Scientific and Technical Information of China (English)

    Hideyuki; Suzuki; Jun; Kato; Motoaki; Kuriyama; Sakiko; Hiraoka; Kenji; Kuwaki; Kazuhide; Yamamoto

    2010-01-01

    AIM:To identify specific colonoscopic findings in patients with ulcerative colitis (UC) complicated by cyto-megalovirus (CMV) infection.METHODS: Among UC patients who were hospitalized due to exacerbation of symptoms, colonoscopic findings were compared between 15 CMV-positive patients and 58 CMV-negative patients. CMV infection was determined by blood test for CMV antigenemia. Five aspects of mucosal changes were analyzed (loss of vascular pattern, erythema, mucosal edema, easy bleeding, and mucinous exuda...

  8. Osteosarcoma of the jaws: demographic and CT imaging features

    Science.gov (United States)

    Wang, S; Shi, H; Yu, Q

    2012-01-01

    Objective The aim of this study was to evaluate the patient demographic and CT imaging findings of primary osteosarcoma of the jaws. Methods 88 primary osteosarcomas of the jaws histopathologically diagnosed during 1997–2007 were reviewed. 21 cases of CT images were reviewed. Results Of 88 patients, 51 (58%) had tumours in the mandible and 37 (42%) in the maxilla. The mean age was 37.8 years (range 9–80 years). The male-to-female ratio was 1.32:1. The mean age of patients with mandibular lesions was 41.04 years and in those with maxillary lesions it was 33.3 years. CT imaging findings were available in 21 patients. In the maxilla (n = 9), all tumours (100%) arose from the alveolar ridge. In the mandible (n = 12), most tumours (9 cases, 75%), arose from the ramus and/or condyle. All except two lesions had the epicentrum within the medullary cavity of the involved bone. The presence of periosteal reaction was demonstrated in 13 cases (62%). Soft-tissue extension was present in 18 lesions (86%), with calcification identified in 13 (72%). Conclusions This study provides age, sex distribution, location and CT imaging features of primary osteosarcoma of the jaws. PMID:22074870

  9. Feature identification for image-guided transcatheter aortic valve implantation

    Science.gov (United States)

    Lang, Pencilla; Rajchl, Martin; McLeod, A. Jonathan; Chu, Michael W.; Peters, Terry M.

    2012-02-01

    Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery, and is critically dependent on imaging for accurate placement of the new valve. Augmented image-guidance for TAVI can be provided by registering together intra-operative transesophageal echo (TEE) ultrasound and a model derived from pre-operative CT. Automatic contour delineation on TEE images of the aortic root is required for real-time registration. This study develops an algorithm to automatically extract contours on simultaneous cross-plane short-axis and long-axis (XPlane) TEE views, and register these features to a 3D pre-operative model. A continuous max-flow approach is used to segment the aortic root, followed by analysis of curvature to select appropriate contours for use in registration. Results demonstrate a mean contour boundary distance error of 1.3 and 2.8mm for the short and long-axis views respectively, and a mean target registration error of 5.9mm. Real-time image guidance has the potential to increase accuracy and reduce complications in TAVI.

  10. COURSE FEATURES EPIDEMIC PROCESS HIV INFECTION IN KHARKIV REGION

    Directory of Open Access Journals (Sweden)

    Nikolaeva LG

    2016-03-01

    Full Text Available Introduction. In the context of the transformation of the spheres of human living epidemic HIV-infection continues. According to the intensity of the epidemic process of HIV-infection, Ukraine takes one of the first places among the European countries. The epidemic process of the infection is concentrated mainly on the high-risk groups, and there is uneven prevalence. Besides in most cases this distribution can not be explained by the social and economic characteristics of certain territories. Kharkiv region belongs to the territory of Ukraine with the lowest prevalence level of HIV-infection. Though in terms of the social and economic crisis due to hostilities in the east of the country, which the region borders, the epidemic situation may significantly become worse. Work objective: to study the peculiarities of the course of the epidemic process of HIV-infection for the period from 1987 till 2015 in Kharkiv region that will improve the epidemiological surveillance of the infection and develop appropriate preventive measures in modern conditions. Material & methods. The studies were conducted in Kharkiv region, which is a big industrial and administrative center. The city of Kharkiv is located at the crossroads of drug trafficking from Asia and Russia. The reportings and analytics of the Kharkiv regional center for prevention and control of AIDS and the Ministry of Health of Ukraine for the period of 1987 – 2015 were used in the research. The analysis of incidence of HIV prevalence, structure of transmission routes and sex-age groups were carried out using descriptive and evaluative and analytical ways of epidemiological research method. Results & discussion. During 1987 – 2015 in Kharkiv region there were officially registered 7868 cases of HIV-infection what was equal to 4.0 % of the registered cases in Ukraine. Since 1996 a marked upward tendency of the incidence of HIV infection in Kharkiv region (growth rate – +7.0 %, and on the

  11. Image Retrieval based on combined features of image sub-blocks

    OpenAIRE

    Ch.Kavitha,; Dr. B.Prabhakara Rao,; Dr. A. Govardhan3

    2011-01-01

    In this paper we propose a new and efficient technique to retrieve images based on sum of the values of Local Histogram and GLCM (Gray Level Co-occurrence Matrix) texture of image sub-blocks to enhance theretrieval performance. The image is divided into sub blocks of equal size. Then the color and texture features of each sub-block are computed. Most of the image retrieval techniques used Histograms for indexing. Histograms describe global intensity distribution. They are very easy to compute...

  12. Feature selection applied to ultrasound carotid images segmentation.

    Science.gov (United States)

    Rosati, Samanta; Molinari, Filippo; Balestra, Gabriella

    2011-01-01

    The automated tracing of the carotid layers on ultrasound images is complicated by noise, different morphology and pathology of the carotid artery. In this study we benchmarked four methods for feature selection on a set of variables extracted from ultrasound carotid images. The main goal was to select those parameters containing the highest amount of information useful to classify the pixels in the carotid regions they belong to. Six different classes of pixels were identified: lumen, lumen-intima interface, intima-media complex, media-adventitia interface, adventitia and adventitia far boundary. The performances of QuickReduct Algorithm (QRA), Entropy-Based Algorithm (EBR), Improved QuickReduct Algorithm (IQRA) and Genetic Algorithm (GA) were compared using Artificial Neural Networks (ANNs). All methods returned subsets with a high dependency degree, even if the average classification accuracy was about 50%. Among all classes, the best results were obtained for the lumen. Overall, the four methods for feature selection assessed in this study return comparable results. Despite the need for accuracy improvement, this study could be useful to build a pre-classifier stage for the optimization of segmentation performance in ultrasound automated carotid segmentation.

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

    Energy Technology Data Exchange (ETDEWEB)

    Kauffman, W.M. [Dept. of Diagnostic Imaging, St. Jude Children`s Research Hospital, Memphis, TN (United States); Jenkins, J.J. III [Dept. of Pathology, St. Jude Children`s Research Hospital, Memphis, TN (United States); Helton, K. [Dept. of Diagnostic Imaging, Vanderbilt Univ. Medical Center, Nashville, TN (United States); Rao, B.N. [Dept. of Surgery, St. Jude Children`s Research Hospital, Memphis, TN (United States); Winer-Muram, H.T. [Dept. of Diagnostic Imaging, St. Jude Children`s Research Hospital, Memphis, TN (United States); Pratt, C.B. [Dept. of Hematology-Oncology, St. Jude Children`s Research Hospital, Memphis, TN (United States)

    1995-06-01

    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.)

  14. Texture features analysis for coastline extraction in remotely sensed images

    Science.gov (United States)

    De Laurentiis, Raimondo; Dellepiane, Silvana G.; Bo, Giancarlo

    2002-01-01

    The accurate knowledge of the shoreline position is of fundamental importance in several applications such as cartography and ships positioning1. Moreover, the coastline could be seen as a relevant parameter for the monitoring of the coastal zone morphology, as it allows the retrieval of a much more precise digital elevation model of the entire coastal area. The study that has been carried out focuses on the development of a reliable technique for the detection of coastlines in remotely sensed images. An innovative approach which is based on the concepts of fuzzy connectivity and texture features extraction has been developed for the location of the shoreline. The system has been tested on several kind of images as SPOT, LANDSAT and the results obtained are good. Moreover, the algorithm has been tested on a sample of a SAR interferogram. The breakthrough consists in the fact that the coastline detection is seen as an important features in the framework of digital elevation model (DEM) retrieval. In particular, the coast could be seen as a boundary line all data beyond which (the ones representing the sea) are not significant. The processing for the digital elevation model could be refined, just considering the in-land data.

  15. [X-ray features of disseminated pulmonary tuberculosis at late stages of HIV infection].

    Science.gov (United States)

    Babaeva, I Iu; Frolova, O P; Demikhova, O V

    2006-01-01

    The paper analyzes the results of a study of X-ray features of disseminated pulmonary tuberculosis in HIV infection on the basis of a retrospective analysis of the case histories of 65 HIV-infected patients with disseminated pulmonary tuberculosis and 60 patients with disseminated tuberculosis without HIV infection, who have been followed up in the Krasnodar Territory. X-ray changes characteristic for patients with disseminated tuberculosis in HIV infection and their difference from those with disseminated tuberculosis without HIV infection have been ascertained, which assists in timely establishing the diagnosis of tuberculosis in HIV infection.

  16. Robust image analysis with sparse representation on quantized visual features.

    Science.gov (United States)

    Bao, Bing-Kun; Zhu, Guangyu; Shen, Jialie; Yan, Shuicheng

    2013-03-01

    Recent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with l(0) norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex l(0) norm optimization into a convex l(1) norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.

  17. Analysis and Reliability Performance Comparison of Different Facial Image Features

    Directory of Open Access Journals (Sweden)

    J. Madhavan

    2014-11-01

    Full Text Available This study performs reliability analysis on the different facial features with weighted retrieval accuracy on increasing facial database images. There are many methods analyzed in the existing papers with constant facial databases mentioned in the literature review. There were not much work carried out to study the performance in terms of reliability and also how the method will perform on increasing the size of the database. In this study certain feature extraction methods were analyzed on the regular performance measure and also the performance measures are modified to fit the real time requirements by giving weight ages for the closer matches. In this study four facial feature extraction methods are performed, they are DWT with PCA, LWT with PCA, HMM with SVD and Gabor wavelet with HMM. Reliability of these methods are analyzed and reported. Among all these methods Gabor wavelet with HMM gives more reliability than other three methods performed. Experiments are carried out to evaluate the proposed approach on the Olivetti Research Laboratory (ORL face database.

  18. Schizencephaly: clinical and imaging features in 30 infantile cases.

    Science.gov (United States)

    Denis, D; Chateil, J F; Brun, M; Brissaud, O; Lacombe, D; Fontan, D; Flurin, V; Pedespan, J

    2000-12-01

    Schizencephaly is an uncommon structural disorder of cerebral cortical development, characterized by congenital clefts spanning the cerebral hemispheres from the pial surface to the lateral ventricles and lined by cortical gray matter. Either an antenatal environmental incident or a genetic origin could be responsible for this lesion which occurs between the third and fourth month of gestation. We report the clinical and cranial imaging features of 30 children, of whom 15 had unilateral and 15 had bilateral lesions. Their ages at the time of the first presentation ranged from 1 month to 10 years. They were thoroughly studied from clinical, epileptical, imaging and electroencephalographic (EEG) viewpoints. Five patients were investigated by cranial computed tomography (CT), eight by cranial magnetic resonance (MR) imaging, and 17 by both methods. The clinical features consisted of mild hemiparesis in 17 cases (57%), 12/17 were related to a unilateral phenotype (80% of all unilateral forms) and 5/17 to a bilateral phenotype. A tetraparesis was present in nine cases, all of which were due to a bilateral cleft. Bilateral forms were significantly associated with tetraparesis, whereas unilateral forms were associated with hemiparesis. Mental retardation was observed in 17 cases (57%), and was observed significantly more often in bilateral clefts (80%). When both hemispheres are involved, an absence of reorganization of the brain function between the two hemispheres leads to severe mental deficits, in addition to the cerebral anomaly itself. Eleven patients had seizures (seven from unilateral and three from bilateral forms). The degree of malformation was not related to the severity of epilepsy. Migration disorders, such as dysplasia or heterotopia, were observed in 30% of cases and are also important etiopathogenetic factors. The septum pellucidum was absent in 13 cases (43%), with septo-optical dysplasia in two cases. Corpus callosum dysgenesis was noted in 30% of cases

  19. SOFT COMPUTING BASED MEDICAL IMAGE RETRIEVAL USING SHAPE AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    M. Mary Helta Daisy

    2014-01-01

    Full Text Available Image retrieval is a challenging and important research applications like digital libraries and medical image databases. Content-based image retrieval is useful in retrieving images from database based on the feature vector generated with the help of the image features. In this study, we present image retrieval based on the genetic algorithm. The shape feature and morphological based texture features are extracted images in the database and query image. Then generating chromosome based on the distance value obtained by the difference feature vector of images in the data base and the query image. In the selected chromosome the genetic operators like cross over and mutation are applied. After that the best chromosome selected and displays the most similar images to the query image. The retrieval performance of the method shows better retrieval result.

  20. 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.)

  1. 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

  2. Nontyphoid salmonella infection: microbiology, clinical features, and antimicrobial therapy.

    Science.gov (United States)

    Chen, Hung-Ming; Wang, Yue; Su, Lin-Hui; Chiu, Cheng-Hsun

    2013-06-01

    Nontyphoid Salmonella is the most common bacterial pathogen causing gastrointestinal infection worldwide. Most nontyphoid Salmonella infection is limited to uncomplicated gastroenteritis that seldom requires antimicrobial treatment. Nevertheless, invasive infections, such as bacteremia, osteomyelitis, and meningitis, may occur and require antimicrobial therapy. Continuous genetic and genomic evolution in Salmonella leading to increased virulence and resistance to multiple drugs are of significant public health concern. Two major changes in the epidemiology of nontyphoid salmonellosis in Europe and in the USA occurred in the second half of the 20(th) century: the emergence of foodborne human infections caused by Salmonella enterica serotype Enteriditis and by multidrug-resistant strains of Salmonella enterica serotype Typhimurium. In the 21(st) century, a worsening situation is the increasing resistance to fluoroquinolones and third-generation cephalosporins in nontyphoid Salmonella. Clinical isolates showing carbapenem resistance also have been identified. Although antimicrobial therapy is usually not indicated for uncomplicated Salmonella gastroenteritis, recent studies indicated that a short-course ceftriaxone therapy (3-5 days) for patients with severe gastroenteritis would lead to a faster clinical recovery. Continuous surveillance of Salmonella in both humans and animals is mandatory. A better understanding of the mechanisms that lead to the emergence of antimicrobial resistance in Salmonella may help in the devising of better interventional strategies to reduce the spread of resistant Salmonella between humans and reservoirs along the food chain.

  3. FEATURES OF THE IMMUNE RESPONSE DURING VIRAL INFECTION

    Directory of Open Access Journals (Sweden)

    G. A. Borisov

    2015-01-01

    Full Text Available The aim of the investigation was to select using cluster analysis and comparatively characterize immune disorders types in acute and chronic viral infections. Patients with acute and chronic viral infections (n = 896 were examined: 77 patients with acute viral hepatitis B, 94 — chronic viral hepatitis B, 119 — chronic hepatitis C, 531 — recurrent herpes, 75 — human papillomavirus infection. Healthy persons (n = 466 were examined as control. The research of blood lymphocyte phenotype was performed by flow cytometry. Four-color immunophenotyping were used in the following panels: Т-lymphocytes (CD3+CD19–CD16/56–CD45+, Т-helpers (CD3+CD4+CD45+, cytotoxic Т-cells (CD3+CD8+CD45+, NKcells (CD3–CD16/56+CD45+, B-lymphocytes (CD3–CD19+CD16/56+CD45+. Absolute values were obtained on a dualplatform technology using the results of haematological analysis. The immunoglobulin concentrations were determined by ELISA. The clustering was performed by a single linkage method. The number of clusters was determined on the basis of calculating the values of the Euclidean distance between the mean group values. It was found that the parameters, characterizing the functional state of the various parts of the immune system in acute and chronic viral infections, considerable diversity values. Custer analysis allows to allocate 6 immunotypes defined different states of innate and adaptive immunity: characterized by activation of the innate (increasing the number of neutrophils and NK-cells and adaptive immunity humoral response (increasing the concentration of IgG, characterized by hyperreaction of adaptive immunity (a significant increase in the concentration of IgG, discoordinated (multidirectional changes in the values of immunological parameters, immunodeficiency and unresponsiveness (did not differ from the control parameters immunotypes. It is proved that in patients with viral infections most often determined by the

  4. Robust Colour Image Watermarking Scheme Based on Feature Points and Image Normalization in DCT Domain

    Directory of Open Access Journals (Sweden)

    Ibrahim Alsonosi Nasir

    2014-04-01

    Full Text Available Geometric attacks can desynchronize the location of the watermark and hence cause incorrect watermark detection. This paper presents a robust c olour image watermarking scheme based on visually significant feature points and image norma lization technique. The feature points are used as synchronization marks between watermark emb edding and detection. The watermark is embedded into the non overlapped normalized circula r regions in the luminance component or the blue component of a color image. The embedding of the watermark is carried out by modifying the DCT coefficients values in selected b locks. The original unmarked image is not required for watermark extraction Experimental results show that the proposed scheme successfully makes the watermark perceptually invis ible as well as robust to common signal processing and geometric attacks.

  5. 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.

  6. Features of direct implantation in chronic foci of odontogenic infection

    Directory of Open Access Journals (Sweden)

    Gudaryan А.А.

    2016-12-01

    Full Text Available The article presents the results of the developed therapeutic and prophylactic complex and efficacy of immediate implantation in 66 patients with chronic foci of odontogenic infection in the periapical region. Objective: to increase preventive measures of inflammatory and infectious complications and optimization of osteo-integrative processes in immediate implantation after tooth extraction, with periapical foci of chronic infection. It was found that the use of the developed medical complex of following up direct implantation includes the use of local photodynamic therapy, platelet, rich in fibrin as a injection and membranes; this allowed to create a favorable background for the prevention of inflammatory and infectious complications in the peri-implant area and created a favorable background for the osseo-integration of implants in 97.1% of the investigated.

  7. Ultrastructural features of PPRV infection in Vero cells

    Institute of Scientific and Technical Information of China (English)

    Xuelian; Meng; Yongxi; Dou; Xuepeng; Cai

    2014-01-01

    <正>Dear Editor,The peste des petits ruminants virus(PPRV)causes an increasingly important viral disease of livestock that predominantly infects small ruminants such as goats and sheep.It belongs to the Paramyxoviridae family and is classified as the fourth member of the genus Morbillivirus because of its genetic similarity with other members of this genus,which includes measles virus(MV),rinderpest virus(RPV),canine distemper virus

  8. Magnetic Resonance Imaging and DWI Features of Orbital Rhabdomyosarcoma

    Institute of Scientific and Technical Information of China (English)

    Xuetao Mu; Hong Wang; Yueyue Li; Yuwen Hao; Chunnan Wu; Lin Ma

    2014-01-01

    Purpose:.To describe the magnetic resonance imaging (MRI) features of orbital rhabdomyosarcoma (RMS). Methods:.Thirty-nine patients with histopathologically con-firmed orbital RMS were retrospectively reviewed. All patients underwent orbital conventional MRI, including axial,.sagittal, and coronal T1-weighted,.T2-weighted,.and postcontrast T1-weighted sequences. The location, shape, margin, and MRI signal of the 39 lesions were reviewed..DWI in 15 patients and susceptibility weighted imaging (SWI) in 2 patients were also analyzed. Results:.Orbital MRI was available in 39 patients and re-vealed a soft tissue mass in the orbital region in all cases..Of the 39 patients,.the primary tumor sites were limited to the orbital proper in 31 cases, while 28 cases had extraocular muscle invasion and 8 cases had extraorbital invasion..All le-sions were unilateral..Thirty-three cases were well-defined soft tissue masses and 6 cases appeared as less well-defined soft-tissue masses..Thirty-four cases showed homogeneous isoin-tense or slightly hypointense signals on T1-weighted imaging (T1WI) and hyperintense signal on T2-weighted imaging (T2WI) compared with extraocular muscles. Five cases had heterogeneous signals with focal areas of increased signal on T1WI or decreased signal on T2WI, including 1 case with hy-pointense signal on SWI..The mean apparent diffusion coeffi-cient (ADC) value of the viable part of tumors was (0.925 ± 0.09)×10-3 mm2/s. All cases showed moderate to marked en-hancement after contrast administration. Conclusion:Several MRI features-including homogeneous isointense or slightly hypointense signal on T1WI and slightly hyperintense signal on T2WI, relative low ADC values, and moderate to marked enhancement,.extraocular muscles inva-sion, and extraorbital extensionare helpful in the diagnosis of orbital RMS. (Eye Science 2014; 29:6-11).

  9. An image-tracking algorithm based on object center distance-weighting and image feature recognition

    Institute of Scientific and Technical Information of China (English)

    JIANG Shuhong; WANG Qin; ZHANG Jianqiu; HU Bo

    2007-01-01

    Areal-time image-tracking algorithm is proposed.which gives small weights to pixels farther from the object center and uses the quantized image gray scales as a template.It identifies the target's location by the mean-shift iteration method and arrives at the target's scale by using image feature recognition.It improves the kernel-based algorithm in tracking scale-changing targets.A decimation mcthod is proposed to track large-sized targets and real-time experimental results verify the effectiveness of the proposed algorithm.

  10. Imaging trace element distributions in single organelles and subcellular features

    Science.gov (United States)

    Kashiv, Yoav; Austin, Jotham R.; Lai, Barry; Rose, Volker; Vogt, Stefan; El-Muayed, Malek

    2016-02-01

    The distributions of chemical elements within cells are of prime importance in a wide range of basic and applied biochemical research. An example is the role of the subcellular Zn distribution in Zn homeostasis in insulin producing pancreatic beta cells and the development of type 2 diabetes mellitus. We combined transmission electron microscopy with micro- and nano-synchrotron X-ray fluorescence to image unequivocally for the first time, to the best of our knowledge, the natural elemental distributions, including those of trace elements, in single organelles and other subcellular features. Detected elements include Cl, K, Ca, Co, Ni, Cu, Zn and Cd (which some cells were supplemented with). Cell samples were prepared by a technique that minimally affects the natural elemental concentrations and distributions, and without using fluorescent indicators. It could likely be applied to all cell types and provide new biochemical insights at the single organelle level not available from organelle population level studies.

  11. Registration of image feature points using differential evolution

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hao; HUANG Zhan-hua; YU Dao-ying

    2005-01-01

    This paper introduces a robust global nonlinear optimizer-differential evolution(DE),which is a simple evolution algorithm to search for an optimal transformation that makes the best alignment of two sets of feature points.To map the problem of matching into the framework of DE,the objective function is proportional to the registration error which is measured by Hausdorff distance,while the parameters of transformation are encoded in floating-point as the functional variables.Three termination criteria are proposed for DE.A simulation of 2-dimensional point sets and a similarity transformation are presented to compare the robustness and convergence properties of DE with genetic algorithm's (GA).And the registration of an object and its contour model have been demonstrated by using of DE to natural images.

  12. Single-system ureteroceles in infants and children: imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Zerin, J.M.; Baker, D.R. [Dept. of Radiology, Indiana University Medical Center, James Whitcomb Riley Hospital for Children, Indianapolis, IN (United States); Casale, J.A. [Dept. of Urology, Indiana University Medical Center, James Whitcomb Riley Hospital for Children, Indianapolis, IN (United States)

    2000-03-01

    Purpose. The purpose of this manuscript is to describe the clinical and imaging findings in children who have single-system ureteroceles.Materials and methods. We reviewed the urology records and imaging studies in 32 consecutive infants and children who were diagnosed in our department with single-system ureteroceles.Results. There were 35 ureteroceles in the 32 patients - 29 were unilateral (14 right-sided, 15 left-sided) and 3 were bilateral. Twenty-five patients were boys (78 %) and 7 girls. Mean age at presentation was 0.7 years (0-9.2 years). Prenatally detected hydronephrosis or cystic renal dysplasia was the most common presentation (24 patients). Four presented with urinary infection, 2 with abdominal mass, 1 had myelomeningocele, and 1 had hypospadias. Three patients also had multiple non-urologic, congenital anomalies. Thirty-three ureteroceles were intravesical, and 2 were ectopic to the bladder neck. Twenty-four ureteroceles were associated with ipsilateral hydroureteronephrosis and 10 with ipsilateral multicystic dysplastic kidney. One patient had a normal ipsilateral kidney and a contralateral multicystic dysplastic kidney. The ureterocele was identified on at least one imaging study in each patient. Sixteen ureteroceles (47 %) everted at VCUG, mimicking paraureteral diverticula. Other variations included ureterocele prolapse and inadvertent ureterocele catheterization (1 each).Conclusions. Single-system ureterocele is an important, although uncommon cause of hydronephrosis and renal dysplasia in infants and children. Single-system ureterocele is distinguished clinically from the more common duplex-system ureterocele by its frequent occurrence in boys and its association with multicystic dysplastic kidney. Because these ureteroceles are frequently small and have a propensity to evert at VCUG, they can be mistaken for paraureteral diverticula. (orig.)

  13. Simian Immunodeficiency Virus Infection of Chimpanzees (Pan troglodytes Shares Features of Both Pathogenic and Non-pathogenic Lentiviral Infections.

    Directory of Open Access Journals (Sweden)

    Edward J D Greenwood

    2015-09-01

    Full Text Available The virus-host relationship in simian immunodeficiency virus (SIV infected chimpanzees is thought to be different from that found in other SIV infected African primates. However, studies of captive SIVcpz infected chimpanzees are limited. Previously, the natural SIVcpz infection of one chimpanzee, and the experimental infection of six chimpanzees was reported, with limited follow-up. Here, we present a long-term study of these seven animals, with a retrospective re-examination of the early stages of infection. The only clinical signs consistent with AIDS or AIDS associated disease was thrombocytopenia in two cases, associated with the development of anti-platelet antibodies. However, compared to uninfected and HIV-1 infected animals, SIVcpz infected animals had significantly lower levels of peripheral blood CD4+ T-cells. Despite this, levels of T-cell activation in chronic infection were not significantly elevated. In addition, while plasma levels of β2 microglobulin, neopterin and soluble TNF-related apoptosis inducing ligand (sTRAIL were elevated in acute infection, these markers returned to near-normal levels in chronic infection, reminiscent of immune activation patterns in 'natural host' species. Furthermore, plasma soluble CD14 was not elevated in chronic infection. However, examination of the secondary lymphoid environment revealed persistent changes to the lymphoid structure, including follicular hyperplasia in SIVcpz infected animals. In addition, both SIV and HIV-1 infected chimpanzees showed increased levels of deposition of collagen and increased levels of Mx1 expression in the T-cell zones of the lymph node. The outcome of SIVcpz infection of captive chimpanzees therefore shares features of both non-pathogenic and pathogenic lentivirus infections.

  14. Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer.

    Science.gov (United States)

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

    2011-11-01

    Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an "optimal" diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis.

  15. Hemicrania Continua: Functional Imaging and Clinical Features With Diagnostic Implications.

    Science.gov (United States)

    Sahler, Kristen

    2013-04-10

    This review focuses on summarizing 2 pivotal articles in the clinical and pathophysiologic understanding of hemicrania continua (HC). The first article, a functional imaging project, identifies both the dorsal rostral pons (a region associated with the generation of migraines) and the posterior hypothalamus (a region associated with the generation of cluster and short-lasting unilateral neuralgiform headache with conjunctival injection and tearing [SUNCT]) as active during HC. The second article is a summary of the clinical features seen in a prospective cohort of HC patients that carry significant diagnostic implications. In particular, they identify a wider range of autonomic signs than what is currently included in the International Headache Society criteria (including an absence of autonomic signs in a small percentage of patients), a high frequency of migrainous features, and the presence of aggravation and/or restlessness during attacks. Wide variations in exacerbation length, frequency, pain description, and pain location (including side-switching pain) are also noted. Thus, a case is made for widening and modifying the clinical diagnostic criteria used to identify patients with HC.

  16. Automated Image Retrieval of Chest CT Images Based on Local Grey Scale Invariant Features.

    Science.gov (United States)

    Arrais Porto, Marcelo; Cordeiro d'Ornellas, Marcos

    2015-01-01

    Textual-based tools are regularly employed to retrieve medical images for reading and interpretation using current retrieval Picture Archiving and Communication Systems (PACS) but pose some drawbacks. All-purpose content-based image retrieval (CBIR) systems are limited when dealing with medical images and do not fit well into PACS workflow and clinical practice. This paper presents an automated image retrieval approach for chest CT images based local grey scale invariant features from a local database. Performance was measured in terms of precision and recall, average retrieval precision (ARP), and average retrieval rate (ARR). Preliminary results have shown the effectiveness of the proposed approach. The prototype is also a useful tool for radiology research and education, providing valuable information to the medical and broader healthcare community.

  17. Middle ear infection (otitis media) (image)

    Science.gov (United States)

    Otitis media is an inflammation or infection of the middle ear. Acute otitis media (acute ear infection) occurs when there is ... which causes production of fluid or pus. Chronic otitis media occurs when the eustachian tube becomes blocked ...

  18. Epidemiological features of dengue and chikungunya infections in Burma.

    Science.gov (United States)

    Thaung, U; Ming, C K; Swe, T; Thein, S

    1975-06-01

    A serological survey for antibody to dengue and chikungunya was carried out in all 14 divisions and states and 2 border towns in Burma during 1973-74. Dengue HI antibody prevalence rate of less than 10% was observed in Arakan and Shan States, 10 to 30% in the Irrawaddy, Pegu, Mandalay Divisions and Kachin, Mon and Karen States, 31 to 60% in Sagaing Division, and over 60% in Rangoon, Magwe and Tenasserim Divisions. Similarly, chikungunya HI antibody prevalence rate of less than 10% was observed in Arakan State, 10 to 30% in the Irrawaddy, Pegu, Mandalay and Sagaing Divisions and Kachin State, 31 to 60% in Rangoon Division and Mon State. Both dengue and chikungunya antibodies were detected where Aedes aegypti mosquitoes were prevalent but the antibody prevalent rates were not directly proportional to the premises index. No HI antibody to dengue nor chikungunya was detected in Aedes aegypti free hilly areas, Chin and Kayah States, but was detected in the Shan State, Dengue and chikungunya infections were observed both in rural and urban populations. Dengue and chikungunya infections affected all socioeconomic classes in Rangoon equally but in Mandalay high socioeconomic class was nearly 3 times less affected than lower socioeconomic class. The infrequencies of dengue and chikungunya infections were observed to be 2 to 3 times higher in residents of Rangoon City than those of other towns. In Rangoon the antibody prevalence rates to dengue increased progressively with age while in other towns no appreciable increase in rates with age was observed. Both sexes were equally affected. This study provides strong circumstantial evidence that dengue and chikungunya viruses are highly and widely distributed throughout Burma, and that new outbreaks of haemorrhagic fever could occur in previously free areas following introduction of dengue viruses into populations previously exposed to one type of dengue.

  19. 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

  20. A New Method of Semantic Feature Extraction for Medical Images Data

    Institute of Scientific and Technical Information of China (English)

    XIE Conghua; SONG Yuqing; CHANG Jinyi

    2006-01-01

    In order to overcome the disadvantages of color, shape and texture-based features definition for medical images, this paper defines a new kind of semantic feature and its extraction algorithm. We firstly use kernel density estimation statistical model to describe the complicated medical image data, secondly, define some typical representative pixels of images as feature and finally, take hill-climbing strategy of Artificial Intelligence to extract those semantic features. Results of a content-based medial image retrieve system show that our semantic features have better distinguishing ability than those color, shape and texture-based features and can improve the ratios of recall and precision of this system smartly.

  1. Feature Based Image Mosaic Using Steerable Filters and Harris Corner Detector

    Directory of Open Access Journals (Sweden)

    Mahesh

    2013-05-01

    Full Text Available Image mosaic is to be combine several views of a scene in to single wide angle view. This paper proposes the feature based image mosaic approach. The mosaic image system includes feature point detection, feature point descriptor extraction and matching. A RANSAC algorithm is applied to eliminate number of mismatches and obtain transformation matrix between the images. The input image is transformed with the correct mapping model for image stitching and same is estimated. In this paper, feature points are detected using steerable filters and Harris, and compared with traditional Harris, KLT, and FAST corner detectors.

  2. Multi-scale contrast enhancement of oriented features in 2D images using directional morphology

    Science.gov (United States)

    Das, Debashis; Mukhopadhyay, Susanta; Praveen, S. R. Sai

    2017-01-01

    This paper presents a multi-scale contrast enhancement scheme for improving the visual quality of directional features present in 2D gray scale images. Directional morphological filters are employed to locate and extract the scale-specific image features with different orientations which are subsequently stored in a set of feature images. The final enhanced image is constructed by weighted combination of these feature images with the original image. While construction, the feature images corresponding to progressively smaller scales are made to have higher proportion of contribution through the use of progressively larger weights. The proposed method has been formulated, implemented and executed on a set of real 2D gray scale images with oriented features. The experimental results visually establish the efficacy of the method. The proposed method has been compared with other similar methods both on subjective and objective basis and the overall performance is found to be satisfactory.

  3. Two-level evaluation on sensor interoperability of features in fingerprint image segmentation.

    Science.gov (United States)

    Yang, Gongping; Li, Ying; Yin, Yilong; Li, Ya-Shuo

    2012-01-01

    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.

  4. Branhamella catarrhalis: significance in pulmonary infections and bacteriological features.

    Science.gov (United States)

    Christensen, J J; Gadeberg, O; Bruun, B

    1986-04-01

    A three-month survey revealed 29 patients at our hospital with symptoms of acute pulmonary infection, from whom Branhamella catarrhalis was isolated from lower respiratory tract specimens, in 18 cases in pure culture. Approximately 2% of all respiratory tract specimens examined during the period yielded growth of B. catarrhalis. All except one patient suffered from chronic pulmonary disease, notably chronic bronchitis. A phenotypic comparison was made between 55 strains of B. catarrhalis, of which 50 were recent isolates from lower respiratory tract specimens, and 23 Neisseria strains representing Neisseria meningitidis, Neisseria gonorrhoeae, Neisseria cinerea, Neisseria flavescens, Neisseria mucosa, Neisseria pharyngis, and Neisseria lactamica. The morphology of B. catarrhalis colonies is very characteristic, and when the diagnosis is suspected, testing for the ability to hydrolyze tributyrin may confirm it within hours. Ability to produce deoxyribonuclease is another property which differentiates B. catarrhalis from the Neisseria species. Otherwise, the combination of nitrate reduction and failure to produce acid from glucose, maltose, and sucrose establishes the diagnosis.

  5. Infections caused by carbapenemase-producing Enterobacteriaceae: risk factors, clinical features and prognosis.

    Science.gov (United States)

    Paño Pardo, José Ramón; Serrano Villar, Sergio; Ramos Ramos, Juan Carlos; Pintado, Vicente

    2014-12-01

    Infections caused by carbapenem-producing Enterobacteriaceae (CPE) can present as several infectious syndromes, but they primarily present as respiratory, urinary and blood stream infections (primary or catheter-related) that are usually found as nosocomial or healthcare-associated infections. The risk of CPE infection is influenced by individual factors, such as the length of the hospital stay and their exposure to invasive procedures and/or to antimicrobials. Of note, exposure to several antimicrobials, not only carbapenems, has been linked to CPE colonization; the duration of antibiotic exposure is one of the primary drivers of CPE acquisition. Individual risk factors must be considered jointly with the local epidemiology of these microorganisms in healthcare institutions. Overall, these infections have a high associated mortality. Mortality is influenced by host factors (e.g., age, comorbidity and immune deficiency), infection-related variables (e.g., type and severity of the infection) and treatment-related factors such as the delay in the initiation of appropriate antimicrobial therapy and the use or monotherapy or combined antimicrobial therapy. Gaining knowledge concerning the epidemiology, clinical features and prognostic features of CPE infection could be useful for improving infection prevention and for the management of patients with infections caused by these microorganisms.

  6. Imaging of the neurological complications of infective endocarditis

    Energy Technology Data Exchange (ETDEWEB)

    Kim, S.J.; Lee, J.Y.; Kim, T.H.; Kim, S.C.; Choi, Y.H. [Department of Radiology, Dankook University College of Medicine, Chungnam (Korea, Republic of); Pai, H. [Department of Internal Medicine, Dankook University College of Medicine, Chungnam (Korea, Republic of); Choi, W.S. [Department of Radiology, Kyung Hee University College of Medicine, Seoul (Korea, Republic of)

    1998-02-01

    We describe the findings on CT or MRI in five patients with neurological symptoms and underlying infective endocarditis (IE). We noted the size, number, and distribution of lesions, the presence or absence of haemorrhage, and contrast enhancement patterns. The number of lesions ranged from 4 to more than 10 in each patient. Their size varied from punctate to 6 cm; they were distributed throughout the brain. The lesions could be categorized into four patterns based on imaging features. A cortical infarct pattern was seen in all patients. Patchy lesions, which did not enhance, were found in the white matter or basal ganglia in three. Isolated, tiny, nodular or ring-enhancing white matter lesions were seen in three patients, and parenchymal haemorrhages in four. In addition to the occurrence of multiple lesions with various patterns in the same patient, isolated, tiny, enhancing lesions in the white matter seemed to be valuable features which could help to differentiate the neurological complications of IE from other thromboembolic infarcts. (orig.) With 4 figs., 2 tabs., 11 refs.

  7. Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

    Science.gov (United States)

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

    2016-07-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

  8. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

    Science.gov (United States)

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

    2015-01-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

  9. A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features

    Science.gov (United States)

    Li, Ang; Li, Changyang; Wang, Xiuying; Eberl, Stefan; Feng, Dagan; Fulham, Michael

    2016-08-01

    Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities. We evaluated our approach on the PROMISE-12 dataset with 50 prostate MR image volumes. Our approach achieved a mean dice similarity coefficient (DSC) of 0.90  ±  0.02, which surpassed the five best prior-based methods in the PROMISE-12 segmentation challenge.

  10. Image Processing Based Customized Image Editor and Gesture Controlled Embedded Robot Coupled with Voice Control Features

    Directory of Open Access Journals (Sweden)

    Somnath Kar

    2015-11-01

    Full Text Available In modern sciences and technologies, images gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data like microarray data in genetic research, or real-time multi-asset portfolio trading in finance etc. In this paper, a proposal has been presented to implement a Graphical User Interface (GUI consisting of various MATLAB functions related to image processing and using the same to create a basic image processing editor having different features like, viewing the red, green and blue components of a color image separately, color detection and various other features like noise addition and removal, edge detection, cropping, resizing, rotation, histogram adjust, brightness control that is used in a basic image editor along with object detection and tracking. This has been further extended to provide reliable and a more natural technique for the user to navigate a robot in the natural environment using gestures based on color tracking. Additionally, Voice control technique has been employed to navigate the robot in various directions in the Cartesian plane employing normal Speech recognition techniques available in Microsoft Visual Basic.

  11. IMAGE LABELING FOR LIDAR INTENSITY IMAGE USING K-NN OF FEATURE OBTAINED BY CONVOLUTIONAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Umemura

    2016-06-01

    Full Text Available We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS using K-Nearest Neighbor (KNN of feature obtained by Convolutional Neural Network (CNN. Image labeling assigns labels (e.g., road, cross-walk and road shoulder to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images and test sets (8 images. We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.

  12. Image Labeling for LIDAR Intensity Image Using K-Nn of Feature Obtained by Convolutional Neural Network

    Science.gov (United States)

    Umemura, Masaki; Hotta, Kazuhiro; Nonaka, Hideki; Oda, Kazuo

    2016-06-01

    We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS) using K-Nearest Neighbor (KNN) of feature obtained by Convolutional Neural Network (CNN). Image labeling assigns labels (e.g., road, cross-walk and road shoulder) to semantic regions in an image. Since CNN is effective for various image recognition tasks, we try to use the feature of CNN (Caffenet) pre-trained by ImageNet. We use 4,096-dimensional feature at fc7 layer in the Caffenet as the descriptor of a region because the feature at fc7 layer has effective information for object classification. We extract the feature by the Caffenet from regions cropped from images. Since the similarity between features reflects the similarity of contents of regions, we can select top K similar regions cropped from training samples with a test region. Since regions in training images have manually-annotated ground truth labels, we vote the labels attached to top K similar regions to the test region. The class label with the maximum vote is assigned to each pixel in the test image. In experiments, we use 36 LIDAR intensity images with ground truth labels. We divide 36 images into training (28 images) and test sets (8 images). We use class average accuracy and pixel-wise accuracy as evaluation measures. Our method was able to assign the same label as human beings in 97.8% of the pixels in test LIDAR intensity images.

  13. Magnetic resonance imaging features of asymptomatic bipartite patella

    Energy Technology Data Exchange (ETDEWEB)

    O' Brien, J., E-mail: juliemobrien@gmail.com [Department of Radiology, Adelaide and Meath Incorporating National Children' s Hospital, Tallaght, Dublin 24 (Ireland); Murphy, C.; Halpenny, D.; McNeill, G.; Torreggiani, W.C. [Department of Radiology, Adelaide and Meath Incorporating National Children' s Hospital, Tallaght, Dublin 24 (Ireland)

    2011-06-15

    Objective: The purpose of our study was to describe the magnetic resonance imaging (MRI) features of bipartite patella in asymptomatic patients. Materials and methods: The study was prospective in type and performed following institutional ethical committees approval. In total, 25 subjects were recruited into the study and informed consent obtained in each case. The local radiology database was utilised in conjunction with a clinical questionnaire to identify patients who had asymptomatic bipartite patella. Any patient with a history of trauma or symptomatic disease was excluded from the study. MRI imaging was performed in each case on a 1.5 T system using a dedicated knee coil and a standardised knee protocol. The images obtained were then analysed by two musculoskeletal radiologists in consensus. Results: Of the 25 subjects, there were 8 females and 17 males. The mean age was 34.6 years. All but one of the bipartite fragments were located on the superolateral aspect of the patella. In 23 cases, one fragment was identified. The average transverse diameter of the fragment was 12.8 mm. The average distance between the fragment and the adjacent patella in the axial plane was 1.46 mm. In addition, the cartilage overlying the patella and accessory fragment was intact in all cases. The average thickness of the patella cartilage at its border to the fragment was 2.4 mm with an average ratio of the cartilage thickness of the fragment as compared with the cartilage thickness of the patella of 0.72. There was no evidence of high signal or bone marrow oedema on fluid sensitive sequences within either the patella or the fragment in any of the patients. Fluid was identified in the cleft between the patella and the fragment in the majority of cases. Conclusions: Asymptomatic bipartite patella is characterised by intact but thinned cartilage along the border between the patella and the fragment, fluid between the cleft and a lack of any bone marrow oedema or high signal within

  14. An Open Source Agenda for Research Linking Text and Image Content Features.

    Science.gov (United States)

    Goodrum, Abby A.; Rorvig, Mark E.; Jeong, Ki-Tai; Suresh, Chitturi

    2001-01-01

    Proposes methods to utilize image primitives to support term assignment for image classification. Proposes to release code for image analysis in a common tool set for other researchers to use. Of particular focus is the expansion of work by researchers in image indexing to include image content-based feature extraction capabilities in their work.…

  15. Detection of Brain Tumor and Extraction of Texture Features using Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Prof. Dilip Kumar Gandhi

    2012-10-01

    Full Text Available Brain Cancer Detection system is designed. Aim of this paper is to locate the tumor and determine the texture features from a Brain Cancer affected MRI. A computer based diagnosis is performed in order to detect the tumors from given Magnetic Resonance Image. Basic image processing techniques are used to locate the tumor region. Basic techniques consist of image enhancement, image bianarization, and image morphological operations. Texture features are computed using the Gray Level Co-occurrence Matrix. Texture features consists of five distinct features. Selective features or the combination of selective features will be used in the future to determine the class of the query image. Astrocytoma type of Brain Cancer affected images are used only for simplicity

  16. KEYWORD AND IMAGE CONTENT FEATURES FOR IMAGE INDEXING AND RETRIEVAL WITHIN COMPRESSED DOMAIN

    Directory of Open Access Journals (Sweden)

    Irianto .

    2009-01-01

    Full Text Available The central problem of most Content Based Image Retrieval approaches is poor quality in terms of sensitivity (recall and specificity (precision. To overcome this problem, the semantic gap between high-level concepts and low-level features has been acknowledged. In this paper we introduce an approach to reduce the impact of the semantic gap by integrating high-level (semantic and low-level features to improve the quality of Image Retrieval queries. Our experiments have been carried out by applying two hierarchical procedures. The first approach is called keyword-content, and the second content-keyword. Our proposed approaches show better results compared to a single method (keyword or content based in term of recall and precision. The average precision has increased by up to 50%.

  17. A Novel Feature Extraction Scheme for Medical X-Ray Images

    OpenAIRE

    Prachi.G.Bhende; Dr.A.N.Cheeran

    2016-01-01

    X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform reliable matching between different views of an obje...

  18. Feature-point-extracting-based automatically mosaic for composite microscopic images

    Institute of Scientific and Technical Information of China (English)

    YIN YanSheng; ZHAO XiuYang; TIAN XiaoFeng; LI Jia

    2007-01-01

    Image mosaic is a crucial step in the three-dimensional reconstruction of composite materials to align the serial images. A novel method is adopted to mosaic two SiC/Al microscopic images with an amplification coefficient of 1000. The two images are denoised by Gaussian model, and feature points are then extracted by using Harris corner detector. The feature points are filtered through Canny edge detector. A 40x40 feature template is chosen by sowing a seed in an overlapped area of the reference image, and the homologous region in floating image is acquired automatically by the way of correlation analysis. The feature points in matched templates are used as feature point-sets. Using the transformational parameters acquired by SVD-ICP method, the two images are transformed into the universal coordinates and merged to the final mosaic image.

  19. 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.

  20. An Algorithm of Image Contrast Enhancement Based on Pixels Neighborhood’s Local Feature

    Directory of Open Access Journals (Sweden)

    Chen Yan

    2013-12-01

    Full Text Available In this study, we proposed an algorithm of Image Contrast enhancement based on local feature to acquire edge information of image, remove Ray Imaging noise and overcome edge blurry and other defects. This method can extract edge features and finish contrast enhancement in varying degrees for pixels neighborhood with different characteristics by using neighborhood local variance and complexity function, which can achieve local features enhancement. The stimulation shows that the method can not only enhance the contrast of the entire image, but also effectively preserves image edge information and improve image quality.

  1. Topographic Feature Extraction for Bengali and Hindi Character Images

    CERN Document Server

    Bag, Soumen; 10.5121/sipij.2011.2215

    2011-01-01

    Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shape-based graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi...

  2. Image mining and Automatic Feature extraction from Remotely Sensed Image (RSI using Cubical Distance Methods

    Directory of Open Access Journals (Sweden)

    S.Sasikala

    2013-04-01

    Full Text Available Information processing and decision support system using image mining techniques is in advance drive with huge availability of remote sensing image (RSI. RSI describes inherent properties of objects by recording their natural reflectance in the electro-magnetic spectral (ems region. Information on such objects could be gathered by their color properties or their spectral values in various ems range in the form of pixels. Present paper explains a method of such information extraction using cubical distance method and subsequent results. Thismethod is one among the simpler in its approach and considers grouping of pixels on the basis of equal distance from a specified point in the image or selected pixel having definite attribute values (DN in different spectral layers of the RSI. The color distance and the occurrence pixel distance play a vital role in determining similarobjects as clusters aid in extracting features in the RSI domain.

  3. 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.

  4. The changes of chest imaging and clinical features in patients with severe infection of new influenza A (H1N1)%新型甲型H1N1流感重症患者肺部影像学变化及临床特点

    Institute of Scientific and Technical Information of China (English)

    罗宏; 范梦柏; 宋承平; 杜春丽; 毕慧君

    2010-01-01

    目的 探讨新型甲型H1N1流感(简称甲型流感)危重病例肺部影像学的变化及其临床特点. 方法 山西省太原市第四人民医院2009年10月20日至11月22日收治的10例重症甲型流感确诊患者,男6例,女4例,年龄5~41岁,平均19.3岁.采用常规技术检测血常规、血气指标、肝肾功能及心肌酶学变化,并行胸部X线及CT检查;给予奥司他韦、吸氧、呼吸支持、抗感染及对症支持等综合治疗. 结果 患者均以发热、咳嗽、呼吸困难为主要症状,肺部病灶表现多种多样,表现为磨玻璃影及实变影、肺不张、液气胸及胸腔积液等征象,进展快,同时存在急性肺炎和急性间质性肺炎的影像学改变,10例中5例发展为急性肺损伤,3例发展为ARDS;奥司他韦、呼吸支持及抗感染治疗有效. 结论 甲型流感危重病例的影像学表现为急性肺炎和急性间质性肺炎改变,临床表现为病情重、进展快,可发展为ARDS.%Objective To study the clinical features and the pulmonary imaging changes of severe cases of new influenza A(H1N1). Methods This study included 10 severe cases with new influenza A (H1N1)infection in the Forth People's Hospital of Taiyuan from 20 Oct. 2009 to 22 Nov 2009.Six patients were males and 4 were females,with an average age of 19.3 years(range 5-41 years).The laboratory study included blood routine.blood gas analysis,liver and renal function tests,myocardium enzymology,chest radiograph and CT. Results The prominent clinical features included fever.cough and dyspnea.The pulmonary imaging changes were varied,including ground-glass opacity,consolidation,atelectasis,fluid pneumothorax and pleural effusion,and manifestations of acute pneumonia and interstitial pneumonia simultaneously.Extensive pulmonary infiltration developed quickly,and acute lung injury(ALI)Occurred in 5 patients and acute respiratory distress syndrome(ARDS)in 3 cases.Oseltamivir,oxygen therapy and antibiotic therapy were

  5. FEATURE DIMENSION REDUCTION FOR EFFICIENT MEDICAL IMAGE RETRIEVAL SYSTEM USING UNIFIED FRAMEWORK

    Directory of Open Access Journals (Sweden)

    Yogapriya Jaganathan

    2013-01-01

    Full Text Available Feature dimensionality reduction problem is a major issue in Content Based Medical Image Retrieval (CBMIR for the effective management of medical images with the support of visual features for the purpose of diagnosis and educational research field. However, high dimensional features would be an origin for substantial challenges in retrieval. The proposed CBMIR is used a unified approach based on extraction of visual features, optimized feature selection, classification of optimized features and similarity measurements. However, high dimensional features would be an origin for substantial challenges in retrieval. The Texture features are selected using Gray Level Co-occurrence Matrix (GLCM, Tamura Features (TF and Gabor Filter (GF in which pull out of features are formed a feature vector database. Fuzzy based PSO (FPSO is applied for Feature selection to overcome the difficulty of feature vectors being surrounded in local optima of original PSO. This procedure also integrates a smart policymaking structure of ACO procedure into the novel FPSO where the global optimum position to be exclusive for every feature particle. The Fuzzy based Particle Swarm Optimization and Ant Colony Optimization (FPSO-ACO technique is used to trim down the feature vector dimensionality and classification is accomplished using an extensive Fuzzy based Relevance Vector Machine (FRVM to form collections of relevant image features that would provide an accepted way to classify dimensionally concentrated feature vectors of images. The Euclidean Distance (ED is recognized as finest for similarity measurement between the medical query image and the medical image database. This proposed approach can acquire the query from the user and had retrieved the desired images from the database. The retrieval performance would be assessed based on precision and recall. This proposed CBMIR is used to provide comfort to the physician to obtain more assurance in their decisions for

  6. Performance Evaluation of Content Based Image Retrieval on Feature Optimization and Selection Using Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Kirti Jain

    2016-03-01

    Full Text Available The diversity and applicability of swarm intelligence is increasing everyday in the fields of science and engineering. Swarm intelligence gives the features of the dynamic features optimization concept. We have used swarm intelligence for the process of feature optimization and feature selection for content-based image retrieval. The performance of content-based image retrieval faced the problem of precision and recall. The value of precision and recall depends on the retrieval capacity of the image. The basic raw image content has visual features such as color, texture, shape and size. The partial feature extraction technique is based on geometric invariant function. Three swarm intelligence algorithms were used for the optimization of features: ant colony optimization, particle swarm optimization (PSO, and glowworm optimization algorithm. Coral image dataset and MatLab software were used for evaluating performance.

  7. 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.

  8. Radionuclide imaging of infection: what the future holds

    Energy Technology Data Exchange (ETDEWEB)

    Palestro, Christopher J. [Yeshiva University, NY (United States). Albert Einstein College of Medicine]. E-mail: palestro@lij.edu

    2008-12-15

    Nuclear Medicine plays an important role in the evaluation of patients suspected of harboring infection. Gallium imaging is especially useful for opportunistic infections and spinal osteomyelitis. In vitro labeled leukocyte imaging is the current radionuclide gold standard for imaging most infections, in immunocompetent patients, including cardiovascular, postoperative, and musculoskeletal infections (except spinal osteomyelitis). Several in-vivo leukocyte labeling methods have been investigated, but none are widely used. Results obtained with radiolabeled antibiotics have been disappointing. Data on FDG are still emerging, but this agent appears to be especially valuable in fever of unknown origin, spinal osteomyelitis, vasculitis and sarcoidosis. It is conceivable that in the near future, FDG-PET and PET/CT will replace gallium for many indications. Investigators also are studying ways to label leukocytes with positron emitters in order to combine the advantages of PET with those of labeled leukocytes. (author)

  9. Content-Based Digital Image Retrieval based on Multi-Feature Amalgamation

    Directory of Open Access Journals (Sweden)

    Linhao Li

    2013-12-01

    Full Text Available In actual implementation, digital image retrieval are facing all kinds of problems. There still exists some difficulty in measures and methods for application. Currently there is not a unambiguous algorithm which can directly shown the obvious feature of image content and satisfy the color, scale invariance and rotation invariance of feature simultaneously. So the related technology about image retrieval based on content is analyzed by us. The research focused on global features such as seven HU invariant moments, edge direction histogram and eccentricity. The method for blocked image is also discussed. During the process of image matching, the extracted image features are looked as the points in vector space. The similarity of two images is measured through the closeness between two points and the similarity is calculated by Euclidean distance and the intersection distance of histogram. Then a novel method based on multi-features amalgamation is proposed, to solve the problems in retrieval method for global feature and local feature. It extracts the eccentricity, seven HU invariant moments and edge direction histogram to calculate the similarity distance of each feature of the images, then they are normalized. Contraposing the interior of global feature the weighted feature distance is adopted to form similarity measurement function for retrieval. The features of blocked images are extracted with the partitioning method based on polar coordinate. Finally by the idea of hierarchical retrieval between global feature and local feature, the results are output through global features like invariant moments etc. These results will be taken as the input of local feature match for the second-layer retrieval, which can improve the accuracy of retrieval effectively.

  10. Hierarchical Multi-modal Image Registration by Learning Common Feature Representations.

    Science.gov (United States)

    Ge, Hongkun; Wu, Guorong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    2015-10-05

    Mutual information (MI) has been widely used for registering images with different modalities. Since most inter-modality registration methods simply estimate deformations in a local scale, but optimizing MI from the entire image, the estimated deformations for certain structures could be dominated by the surrounding unrelated structures. Also, since there often exist multiple structures in each image, the intensity correlation between two images could be complex and highly nonlinear, which makes global MI unable to precisely guide local image deformation. To solve these issues, we propose a hierarchical inter-modality registration method by robust feature matching. Specifically, we first select a small set of key points at salient image locations to drive the entire image registration. Since the original image features computed from different modalities are often difficult for direct comparison, we propose to learn their common feature representations by projecting them from their native feature spaces to a common space, where the correlations between corresponding features are maximized. Due to the large heterogeneity between two high-dimension feature distributions, we employ Kernel CCA (Canonical Correlation Analysis) to reveal such non-linear feature mappings. Then, our registration method can take advantage of the learned common features to reliably establish correspondences for key points from different modality images by robust feature matching. As more and more key points take part in the registration, our hierarchical feature-based image registration method can efficiently estimate the deformation pathway between two inter-modality images in a global to local manner. We have applied our proposed registration method to prostate CT and MR images, as well as the infant MR brain images in the first year of life. Experimental results show that our method can achieve more accurate registration results, compared to other state-of-the-art image registration

  11. 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.

  12. An Imaging-Based Approach to Spinal Cord Infection.

    Science.gov (United States)

    Talbott, Jason F; Narvid, Jared; Chazen, J Levi; Chin, Cynthia T; Shah, Vinil

    2016-10-01

    Infections of the spinal cord, nerve roots, and surrounding meninges are uncommon, but highly significant given their potential for severe morbidity and even mortality. Prompt diagnosis can be lifesaving, as many spinal infections are treatable. Advances in imaging technology have now firmly established magnetic resonance imaging (MRI) as the gold standard for spinal cord imaging evaluation, enabling the depiction of infectious myelopathies with exquisite detail and contrast. In this article, we aim to provide an overview of MRI findings for spinal cord infections with special focus on imaging patterns of infection that are primarily confined to the spinal cord, spinal meninges, and spinal nerve roots. In this context, we describe and organize this review around 5 distinct patterns of transverse spinal abnormality that may be detected with MRI as follows: (1) extramedullary, (2) centromedullary, (3) eccentric, (4) frontal horn, and (5) irregular. We seek to classify the most common presentations for a wide variety of infectious agents within this image-based framework while realizing that significant overlap and variation exists, including some infections that remain occult with conventional imaging techniques.

  13. Cardiac tumors: CT and MR imaging features; Tumeurs cardiaques: aspects en scanner et en IRM

    Energy Technology Data Exchange (ETDEWEB)

    Moskovitch, G.; Chabbert, V.; Escourrou, G.; Desloques, L.; Otal, P.; Glock, Y.; Rousseau, H. [Centre Hospitalier Universitaire de Rangueil, Service de Radiologie Generale, 31 - Toulouse (France)

    2010-09-15

    The CT and MR imaging features of the main cardiac tumors will be reviewed. Cross-sectional imaging features may help differentiate between cardiac tumors and pseudo-tumoral lesions and identify malignant features. Based on clinical features, imaging findings are helpful to further characterize the nature of the lesion. CT and MR imaging can demonstrate the relationship of the tumor with adjacent anatomical structures and are invaluable in the pre-surgical work-up and post-surgical follow-up. (authors)

  14. Importance of the texture features in a query from a spectral image database

    Science.gov (United States)

    Kohonen, Oili; Hauta-Kasari, Markku

    2006-01-01

    A new, semantically meaningful technique for querying the images from a spectral image database is proposed. The technique is based on the use of both color- and texture features. The color features are calculated from spectral images by using the Self-Organizing Map (SOM) when methods of Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are used for constructing the texture features. The importance of texture features in a querying is seen in experimental results, which are given by using a real spectral image database. Also the differences between the results gained by the use of co-occurrence matrix and LBP are introduced.

  15. 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.

  16. Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy.

    Directory of Open Access Journals (Sweden)

    Feng Jin

    Full Text Available Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS. The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume.

  17. Feature extraction for the analysis of colon status from the endoscopic images

    Directory of Open Access Journals (Sweden)

    Krishnan Shankar M

    2003-04-01

    Full Text Available Abstract Background Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. Methods Endoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA and are evaluated using Backpropagation Neural Network (BPNN. Results Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (τ = 1%, is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, τ = 1% or color (90.52%, τ = 1% features. Conclusion In conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method.

  18. Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer

    Directory of Open Access Journals (Sweden)

    Jasmine A. Oliver

    2015-12-01

    Full Text Available Radiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D] and respiratory-gated (RG positron emission tomography (PET/computed tomography (CT images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order, sum entropy, information measure of correlation 2, Short Run Emphasis (SRE, Long Run Emphasis (LRE, and Run Percentage (RPC; and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS, Short Run Emphasis (SRE, and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion.

  19. 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.

  20. The role of imaging in urinary tract infections.

    Science.gov (United States)

    Johansen, Truls E Bjerklund

    2004-11-01

    The aim of imaging in urinary tract infections (UTI) is to detect conditions that must be corrected to avoid imminent deterioration of kidney function, or to prevent recurrent infections and long-term kidney damage. The most threatening conditions are obstruction of an infected upper tract and abscesses of the genitourinary system. An image-guided percutaneous drainage can be lifesaving. The role of imaging in small children with UTI is controversial in terms of the importance of anatomical and functional disorders in relation to the preventive measures to be taken. In newborns identified with hydronephrosis during pregnancy or by neonatal screening, vesicoureteral reflux (VUR) and renal scarring are congenital and not caused by infection. Most of these patients are males and the VUR is of a higher grade than VUR detected in girls after the first UTI. Imaging in children with UTI should only be ordered after a thorough evaluation of the risk of renal damage and the benefits of preventive measures. In adult females, no imaging is necessary in cystitis, while ultrasonography and plain films are recommended in acute pyelonephritis. Since uncomplicated UTI in men is rare, diagnostic imaging should be started early to rule out complicating factors in the urinary tract. In prostatitis, vesiculitis, epididymitis and orchitis the role of imaging is to rule out abscess formation and testicular malignancies.

  1. Age Estimation Based on AAM and 2D-DCT Features of Facial Images

    Directory of Open Access Journals (Sweden)

    Asuman Günay

    2015-02-01

    Full Text Available This paper proposes a novel age estimation method - Global and Local feAture based Age estiMation (GLAAM - relying on global and local features of facial images. Global features are obtained with Active Appearance Models (AAM. Local features are extracted with regional 2D-DCT (2- dimensional Discrete Cosine Transform of normalized facial images. GLAAM consists of the following modules: face normalization, global feature extraction with AAM, local feature extraction with 2D-DCT, dimensionality reduction by means of Principal Component Analysis (PCA and age estimation with multiple linear regression. Experiments have shown that GLAAM outperforms many methods previously applied to the FG-NET database.

  2. Fast Image Retrieval of Textile Industrial Accessory Based on Multi-Feature Fusion

    Institute of Scientific and Technical Information of China (English)

    沈文忠; 杨杰

    2004-01-01

    A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessory coordination) is used to filter the image database to decouple the image search scope. After the accessory contour information and region information are extracted, the fusion multi-feature of the centroid distance Fourier descriptor and distance distribution histogram is adopted to finish image retrieval accurately. All the features above are invariable under translation, scaling and rotation. Results from the test on the image database including 1,000 accessory images demonstrate that the method is effective and practical with high accuracy and fast speed.

  3. [Research on non-rigid medical image registration algorithm based on SIFT feature extraction].

    Science.gov (United States)

    Wang, Anna; Lu, Dan; Wang, Zhe; Fang, Zhizhen

    2010-08-01

    In allusion to non-rigid registration of medical images, the paper gives a practical feature points matching algorithm--the image registration algorithm based on the scale-invariant features transform (Scale Invariant Feature Transform, SIFT). The algorithm makes use of the image features of translation, rotation and affine transformation invariance in scale space to extract the image feature points. Bidirectional matching algorithm is chosen to establish the matching relations between the images, so the accuracy of image registrations is improved. On this basis, affine transform is chosen to complement the non-rigid registration, and normalized mutual information measure and PSO optimization algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.

  4. Feature Extraction with Ordered Mean Values for Content Based Image Classification

    Directory of Open Access Journals (Sweden)

    Sudeep Thepade

    2014-01-01

    Full Text Available Categorization of images into meaningful classes by efficient extraction of feature vectors from image datasets has been dependent on feature selection techniques. Traditionally, feature vector extraction has been carried out using different methods of image binarization done with selection of global, local, or mean threshold. This paper has proposed a novel technique for feature extraction based on ordered mean values. The proposed technique was combined with feature extraction using discrete sine transform (DST for better classification results using multitechnique fusion. The novel methodology was compared to the traditional techniques used for feature extraction for content based image classification. Three benchmark datasets, namely, Wang dataset, Oliva and Torralba (OT-Scene dataset, and Caltech dataset, were used for evaluation purpose. Performance measure after evaluation has evidently revealed the superiority of the proposed fusion technique with ordered mean values and discrete sine transform over the popular approaches of single view feature extraction methodologies for classification.

  5. Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

    Science.gov (United States)

    Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

    2017-03-01

    This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

  6. Herpes Simplex Virus Type 1 infection: overview on relevant clinico-pathological features.

    Science.gov (United States)

    Arduino, Paolo G; Porter, Stephen R

    2008-02-01

    Herpes Simplex Virus Type 1 (HSV-1) is a nuclear replicating enveloped virus, usually acquired through direct contact with infected lesions or body fluids (typically saliva). The prevalence of HSV-1 infection increases progressively from childhood, the seroprevalence being inversely related to socioeconomic background. Primary HSV-1 infections in children are either asymptomatic or following an incubation period of about 1 week gives rise to mucocutaneous vesicular eruptions. Herpetic gingivostomatitis typically affects the tongue, lips, gingival, buccal mucosa and the hard and soft palate. Most primary oro-facial HSV infection is caused by HSV-1, infection by HSV-2 is increasingly common. Recurrent infections, which occur at variable intervals, typically give rise to vesiculo-ulcerative lesions at mucocutaneous junctions particularly the lips (herpes labialis). Recurrent HSV-1 infection within the mouth is uncommon in otherwise healthy patients, although in immunocompromised patients, recurrent infection can be more extensive and/or aggressive. The diagnosis of common herpetic infection can usually be based upon the clinical history and presenting features. Confirmatory laboratory diagnosis is, however, required when patients are, or may be, immunocompromised.

  7. Multiple Myeloma: A Review of Imaging Features and Radiological Techniques

    Directory of Open Access Journals (Sweden)

    C. F. Healy

    2011-01-01

    Full Text Available The recently updated Durie/Salmon PLUS staging system published in 2006 highlights the many advances that have been made in the imaging of multiple myeloma, a common malignancy of plasma cells. In this article, we shall focus primarily on the more sensitive and specific whole-body imaging techniques, including whole-body computed tomography, whole-body magnetic resonance imaging, and positron emission computed tomography. We shall also discuss new and emerging imaging techniques and future developments in the radiological assessment of multiple myeloma.

  8. Multiple myeloma: a review of imaging features and radiological techniques.

    Science.gov (United States)

    Healy, C F; Murray, J G; Eustace, S J; Madewell, J; O'Gorman, P J; O'Sullivan, P

    2011-01-01

    The recently updated Durie/Salmon PLUS staging system published in 2006 highlights the many advances that have been made in the imaging of multiple myeloma, a common malignancy of plasma cells. In this article, we shall focus primarily on the more sensitive and specific whole-body imaging techniques, including whole-body computed tomography, whole-body magnetic resonance imaging, and positron emission computed tomography. We shall also discuss new and emerging imaging techniques and future developments in the radiological assessment of multiple myeloma.

  9. Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang

    2016-06-01

    Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

  10. 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.

  11. Evaluation of clinico-pathological features and Helicobacter pylori infection in gastric inflammatory fibroid polyps.

    Science.gov (United States)

    Albuquerque, Andreia; Rios, Elisabete; Carneiro, Fátima; Macedo, Guilherme

    2014-12-01

    Inflammatory fibroid polyps are rare mesenchymal lesions. The frequency of Helicobacter pylori infection in the gastric mucosa overlying inflammatory fibroid polyps and its relation with the histologic features of the polyps are undetermined. The clinico-pathological features of inflammatory fibroid polyps, the frequency of Helicobacter pylori infection in the overlying gastric mucosa, and its putative impact on the phenotype of the polyps were evaluated. Gastric inflammatory fibroid polyps diagnosed in our Hospital from 1998 to 2012 were reviewed and the histological. The histological sections were stained with hematoxylin and eosin and modified Giemsa for the evaluation of Helicobacter pylori infection. Inconclusive cases were further analyzed by immunohistochemistry with anti-Helicobacter pylori antibody. Diagnosis was confirmed in 54 polyps, 85 % developed in females, mean age 63 ± 11 years. Most polyps were sessile (74 %), with a mean size of 15 ± 12 mm, 96 % were located in the antrum and 85 % were removed by snare polypectomy. Helicobacter pylori infection was identified in 48 % of the polyps. Most inflammatory fibroid polyps developed in the submucosa, and mucosal extension was observed in 96 % of the cases. Chronic gastritis was observed in all cases (63 % with activity, 31 % with intestinal metaplasia, and 61 % with foveolar hyperplasia). Erosion and ulceration of the overlying gastric mucosa was observed in 48 % and 11 % of the polyps, respectively. Onion skin features were present in 52 % of the polyps and were more frequently observed in cases without evidence of Helicobacter pylori infection. Background changes in gastric mucosa were not distinctive according to Helicobacter pylori infection. Chronic atrophic gastritis with intestinal metaplasia was associated with the presence of perivascular onion skin lesions. To our knowledge, this is the second largest series of gastric inflammatory fibroid polyps. Helicobacter pylori infection was

  12. An Adequate Approach to Image Retrieval Based on Local Level Feature Extraction

    Directory of Open Access Journals (Sweden)

    Sumaira Muhammad Hayat Khan

    2010-10-01

    Full Text Available Image retrieval based on text annotation has become obsolete and is no longer interesting for scientists because of its high time complexity and low precision in results. Alternatively, increase in the amount of digital images has generated an excessive need for an accurate and efficient retrieval system. This paper proposes content based image retrieval technique at a local level incorporating all the rudimentary features. Image undergoes the segmentation process initially and each segment is then directed to the feature extraction process. The proposed technique is also based on image?s content which primarily includes texture, shape and color. Besides these three basic features, FD (Fourier Descriptors and edge histogram descriptors are also calculated to enhance the feature extraction process by taking hold of information at the boundary. Performance of the proposed method is found to be quite adequate when compared with the results from one of the best local level CBIR (Content Based Image Retrieval techniques.

  13. Application of Fisher Score and mRMR Techniques for Feature Selection in Compressed Medical Images

    Directory of Open Access Journals (Sweden)

    Vamsidhar Enireddy

    2015-12-01

    Full Text Available In nowadays there is a large increase in the digital medical images and different medical imaging equipments are available for diagnoses, medical professionals are increasingly relying on computer aided techniques for both indexing these images and retrieving similar images from large repositories. To develop systems which are computationally less intensive without compromising on the accuracy from the high dimensional feature space is always challenging. In this paper an investigation is made on the retrieval of compressed medical images. Images are compressed using the visually lossless compression technique. Shape and texture features are extracted and best features are selected using the fisher technique and mRMR. Using these selected features RNN with BPTT was utilized for classification of the compressed images.

  14. 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.

  15. Clinical and epidemiological features of patients with chronic hepatitis C co-infected with HIV

    Directory of Open Access Journals (Sweden)

    Braga Eduardo Lorens

    Full Text Available Co-infection with hepatitis C virus (HCV and human immunodeficiency virus (HIV is increasingly common and affects the clinical course of chronic hepatitis C. Highly active antiretroviral therapy has improved the life expectancy of HIV infected patients, but, by extending survival, it permits the development of HCV cirrhosis. This study tried to evaluate clinical and epidemiological features of patients with chronic hepatitis C co-infected with HIV. We evaluated 134 HCV-infected patients: i group A - 65 co-infected HCV/HIV patients, ii group B - 69 mono-infected HCV patients. The impact of HIV infection on HCV liver disease was analyzed using Child's score, ultrasound findings and liver histology. Patients were subjected to HCV genotyping and anti-HBs dosage. Patients mean age was 42.4 years (±9.1 and 97 (72.4% were males. Injected drug use and homo/bisexual practice were more frequently encountered in the co-infected group: 68.3% and 78.0%, respectively. Antibodies against hepatitis B virus (anti-HBs were found in only 38.1% of the patients (66.7% group A x 33.3% group B. Ten out of 14 individuals (71.4% who had liver disease (Child B or C and 25 out of 34 (73.5% who showed ultrasound evidence of chronic liver disease were in the co-infection group. HCV genotype-2/3 was more frequently encountered in co-infected patients (36.9% group A vs. 21.8% group B. Conclusions: a HIV infection seems to adversely affect the clinical course of chronic hepatitis C, b injected drug use, bi/homosexual practice and genotype-2/3 were more frequently encountered in co-infected patients, c immunization against HBV should be encouraged in these patients.

  16. Clinical and epidemiological features of patients with chronic hepatitis C co-infected with HIV

    Directory of Open Access Journals (Sweden)

    Braga Eduardo Lorens

    2006-02-01

    Full Text Available Co-infection with hepatitis C virus (HCV and human immunodeficiency virus (HIV is increasingly common and affects the clinical course of chronic hepatitis C. Highly active antiretroviral therapy has improved the life expectancy of HIV infected patients, but, by extending survival, it permits the development of HCV cirrhosis. This study tried to evaluate clinical and epidemiological features of patients with chronic hepatitis C co-infected with HIV. We evaluated 134 HCV-infected patients: i group A - 65 co-infected HCV/HIV patients, ii group B - 69 mono-infected HCV patients. The impact of HIV infection on HCV liver disease was analyzed using Child's score, ultrasound findings and liver histology. Patients were subjected to HCV genotyping and anti-HBs dosage. Patients mean age was 42.4 years (±9.1 and 97 (72.4% were males. Injected drug use and homo/bisexual practice were more frequently encountered in the co-infected group: 68.3% and 78.0%, respectively. Antibodies against hepatitis B virus (anti-HBs were found in only 38.1% of the patients (66.7% group A x 33.3% group B. Ten out of 14 individuals (71.4% who had liver disease (Child B or C and 25 out of 34 (73.5% who showed ultrasound evidence of chronic liver disease were in the co-infection group. HCV genotype-2/3 was more frequently encountered in co-infected patients (36.9% group A vs. 21.8% group B. Conclusions: a HIV infection seems to adversely affect the clinical course of chronic hepatitis C, b injected drug use, bi/homosexual practice and genotype-2/3 were more frequently encountered in co-infected patients, c immunization against HBV should be encouraged in these patients.

  17. Image feature meaning for automatic key-frame extraction

    Science.gov (United States)

    Di Lecce, Vincenzo; Guerriero, Andrea

    2003-12-01

    Video abstraction and summarization, being request in several applications, has address a number of researches to automatic video analysis techniques. The processes for automatic video analysis are based on the recognition of short sequences of contiguous frames that describe the same scene, shots, and key frames representing the salient content of the shot. Since effective shot boundary detection techniques exist in the literature, in this paper we will focus our attention on key frames extraction techniques to identify the low level visual features of the frames that better represent the shot content. To evaluate the features performance, key frame automatically extracted using these features, are compared to human operator video annotations.

  18. Nuclear imaging for musculoskeletal infections in children

    Energy Technology Data Exchange (ETDEWEB)

    Herndon, W.A.; Alexieva, B.T.; Schwindt, M.L.; Scott, K.N.; Shaffer, W.O.

    1985-05-01

    The authors retrospectively reviewed all patients who underwent bone scanning for possible osteomyelitis at the Naval Regional Medical Center (Portsmouth, VA, U.S.A.) between 1980 and 1983. Among 63 children, there were 20 sites of osteomyelitis. They were able to conclude that a high proportion of neonates with septic arthritis will have osteomyelitis and that bone scan is not helpful in this age group. Nuclear imaging of the foot was less reliable than imaging of the remainder of the extremities. The bone scan can be a useful adjunct in the diagnosis of osteomyelitis in certain children, but is not a substitute for an accurate clinical examination and appropriate workup.

  19. Nuclear imaging for musculoskeletal infections in children.

    Science.gov (United States)

    Herndon, W A; Alexieva, B T; Schwindt, M L; Scott, K N; Shaffer, W O

    1985-01-01

    We retrospectively reviewed all patients who underwent bone scanning for possible osteomyelitis at the Naval Regional Medical Center (Portsmouth, VA, U.S.A.) between 1980 and 1983. Among 63 children, there were 20 sites of osteomyelitis. We were able to conclude that a high proportion of neonates with septic arthritis will have osteomyelitis and that bone scan is not helpful in this age group. Nuclear imaging of the foot was less reliable than imaging of the remainder of the extremities. The bone scan can be a useful adjunct in the diagnosis of osteomyelitis in certain children, but is not a substitute for an accurate clinical examination and appropriate workup.

  20. Human neurocysticercosis: immunological features involved in the host's susceptibility to become infected and to develop disease.

    Science.gov (United States)

    Sciutto, Edda; Cárdenas, Graciela; Adalid-Peralta, Laura; Fragoso, Gladis; Larralde, Carlos; Fleury, Agnes

    2013-06-01

    Human neurocysticercosis (NC) is a clinically and radiologically heterogeneous disease caused by the establishment of Taenia solium larvae in the central nervous system. Herein, the immunological and endocrinological features involved in resistance to infection and severe forms of the disease are reviewed, and their clinical relevance is discussed.

  1. Some features of immune status of animals infected with bovine leukosis background unbalanced on feeding

    OpenAIRE

    TURKO I.; SEMANYUK V.; PELENYO R.; KULYABA O.; TURKO YA.; VERHOLYUK M.

    2012-01-01

    The features of protein metabolism and immunity in cows with leukemia by unbalanced feeding of animals. The peculiarities of the dynamics of total protein, protein fractions, immunoglobulins, Tand B-lymphocytes in cows under violation of the sugar-protein ratio of diet and infection with a virus leukemia.

  2. Essential features of Chiari II malformation in MR imaging : an interobserver reliability study-part 1

    NARCIS (Netherlands)

    Geerdink, Niels; van der Vliet, Ton; Rotteveel, Jan J.; Feuth, Ton; Roeleveld, Nel; Mullaart, Reinier A.

    2012-01-01

    Brain MR imaging is essential in the assessment of Chiari II malformation in clinical and research settings concerning spina bifida. However, the interpretation of morphological features of the malformation on MR images may not always be straightforward. In an attempt to select those features that u

  3. Essential features of Chiari II malformation in MR imaging: an interobserver reliability study--part 1.

    NARCIS (Netherlands)

    Geerdink, N.; Vliet, T. van der; Rotteveel, J.J.; Feuth, T.; Roeleveld, N.; Mullaart, R.A.

    2012-01-01

    PURPOSE: Brain MR imaging is essential in the assessment of Chiari II malformation in clinical and research settings concerning spina bifida. However, the interpretation of morphological features of the malformation on MR images may not always be straightforward. In an attempt to select those featur

  4. A HYBRID APPROACH BASED MEDICAL IMAGE RETRIEVAL SYSTEM USING FEATURE OPTIMIZED CLASSIFICATION SIMILARITY FRAMEWORK

    Directory of Open Access Journals (Sweden)

    Yogapriya Jaganathan

    2013-01-01

    Full Text Available For the past few years, massive upgradation is obtained in the pasture of Content Based Medical Image Retrieval (CBMIR for effective utilization of medical images based on visual feature analysis for the purpose of diagnosis and educational research. The existing medical image retrieval systems are still not optimal to solve the feature dimensionality reduction problem which increases the computational complexity and decreases the speed of a retrieval process. The proposed CBMIR is used a hybrid approach based on Feature Extraction, Optimization of Feature Vectors, Classification of Features and Similarity Measurements. This type of CBMIR is called Feature Optimized Classification Similarity (FOCS framework. The selected features are Textures using Gray level Co-occurrence Matrix Features (GLCM and Tamura Features (TF in which extracted features are formed as feature vector database. The Fuzzy based Particle Swarm Optimization (FPSO technique is used to reduce the feature vector dimensionality and classification is performed using Fuzzy based Relevance Vector Machine (FRVM to form groups of relevant image features that provide a natural way to classify dimensionally reduced feature vectors of images. The Euclidean Distance (ED is used as similarity measurement to measure the significance between the query image and the target images. This FOCS approach can get the query from the user and has retrieved the needed images from the databases. The retrieval algorithm performances are estimated in terms of precision and recall. This FOCS framework comprises several benefits when compared to existing CBMIR. GLCM and TF are used to extract texture features and form a feature vector database. Fuzzy-PSO is used to reduce the feature vector dimensionality issues while selecting the important features in the feature vector database in which computational complexity is decreased. Fuzzy based RVM is used for feature classification in which it increases the

  5. Integrating Color and Spatial Feature for Content-Based Image Retrieval

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.

  6. Fuzzy zoning for feature matching technique in 3D reconstruction of nasal endoscopic images.

    Science.gov (United States)

    Rattanalappaiboon, Surapong; Bhongmakapat, Thongchai; Ritthipravat, Panrasee

    2015-12-01

    3D reconstruction from nasal endoscopic images greatly supports an otolaryngologist in examining nasal passages, mucosa, polyps, sinuses, and nasopharyx. In general, structure from motion is a popular technique. It consists of four main steps; (1) camera calibration, (2) feature extraction, (3) feature matching, and (4) 3D reconstruction. Scale Invariant Feature Transform (SIFT) algorithm is normally used for both feature extraction and feature matching. However, SIFT algorithm relatively consumes computational time particularly in the feature matching process because each feature in an image of interest is compared with all features in the subsequent image in order to find the best matched pair. A fuzzy zoning approach is developed for confining feature matching area. Matching between two corresponding features from different images can be efficiently performed. With this approach, it can greatly reduce the matching time. The proposed technique is tested with endoscopic images created from phantoms and compared with the original SIFT technique in terms of the matching time and average errors of the reconstructed models. Finally, original SIFT and the proposed fuzzy-based technique are applied to 3D model reconstruction of real nasal cavity based on images taken from a rigid nasal endoscope. The results showed that the fuzzy-based approach was significantly faster than traditional SIFT technique and provided similar quality of the 3D models. It could be used for creating a nasal cavity taken by a rigid nasal endoscope.

  7. Pulmonary fungal infection: Imaging findings in immunocompetent and immunocompromised patients

    Energy Technology Data Exchange (ETDEWEB)

    Chong, Semin [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Lee, Kyung Soo [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of)]. E-mail: kyungs.lee@samsung.com; Yi, Chin A [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Chung, Myung Jin [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Kim, Tae Sung [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Han, Joungho [Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710 (Korea, Republic of)

    2006-09-15

    Histoplasmosis is the most common endemic mycosis in North America, and is followed by coccidioidomycosis and blastomycosis. Although the majority of these infections in immunocompetent persons are self-limited, some patients can develop severe pneumonitis or various forms of chronic pulmonary infection. Cryptococcoci, Aspergillus, Candidas, and Mucorals are ubiquitous organisms, which may affect immunocompromised patients. Specific imaging findings can be expected, depending on the organisms involved, underlying patients' conditions (immune status), and specific situations after immune depleting procedures.

  8. Face image analysis using a multiple features fitting strategy

    OpenAIRE

    Romdhani, Sami

    2005-01-01

    The main contribution of this thesis is a novel algorithm for fitting a Three-Dimensional Morphable Model of faces to a 2D input image. This fitting algorithm enables the estimation of the 3D shape, the texture, the 3D pose and the light direction from a single input image. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as ...

  9. Kernel Density Feature Points Estimator for Content-Based Image Retrieval

    CERN Document Server

    Zuva, Tranos; Ojo, Sunday O; Ngwira, Seleman M

    2012-01-01

    Research is taking place to find effective algorithms for content-based image representation and description. There is a substantial amount of algorithms available that use visual features (color, shape, texture). Shape feature has attracted much attention from researchers that there are many shape representation and description algorithms in literature. These shape image representation and description algorithms are usually not application independent or robust, making them undesirable for generic shape description. This paper presents an object shape representation using Kernel Density Feature Points Estimator (KDFPE). In this method, the density of feature points within defined rings around the centroid of the image is obtained. The KDFPE is then applied to the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Density Histogram Feature Points (DHFP) method. Analytic analysis is done to justify our m...

  10. 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.

  11. 3D Elastic Registration of Ultrasound Images Based on Skeleton Feature

    Institute of Scientific and Technical Information of China (English)

    LI Dan-dan; LIU Zhi-Yan; SHEN Yi

    2005-01-01

    In order to eliminate displacement and elastic deformation between images of adjacent frames in course of 3D ultrasonic image reconstruction, elastic registration based on skeleton feature was adopt in this paper. A new automatically skeleton tracking extract algorithm is presented, which can extract connected skeleton to express figure feature. Feature points of connected skeleton are extracted automatically by accounting topical curvature extreme points several times. Initial registration is processed according to barycenter of skeleton. Whereafter, elastic registration based on radial basis function are processed according to feature points of skeleton. Result of example demonstrate that according to traditional rigid registration, elastic registration based on skeleton feature retain natural difference in shape for organ's different part, and eliminate slight elastic deformation between frames caused by image obtained process simultaneously. This algorithm has a high practical value for image registration in course of 3D ultrasound image reconstruction.

  12. STATISTICAL PROBABILITY BASED ALGORITHM FOR EXTRACTING FEATURE POINTS IN 2-DIMENSIONAL IMAGE

    Institute of Scientific and Technical Information of China (English)

    Guan Yepeng; Gu Weikang; Ye Xiuqing; Liu Jilin

    2004-01-01

    An algorithm for automatically extracting feature points is developed after the area of feature points in 2-dimensional (2D) imagebeing located by probability theory, correlated methods and criterion for abnormity. Feature points in 2D image can be extracted only by calculating standard deviation of gray within sampled pixels area in our approach statically. While extracting feature points, the limitation to confirm threshold by tentative method according to some a priori information on processing image can be avoided. It is proved that the proposed algorithm is valid and reliable by extracting feature points on actual natural images with abundant and weak texture, including multi-object with complex background, respectively. It can meet the demand of extracting feature points of 2D image automatically in machine vision system.

  13. 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.

  14. Non-rigid registration of medical images based on ordinal feature and manifold learning

    Science.gov (United States)

    Li, Qi; Liu, Jin; Zang, Bo

    2015-12-01

    With the rapid development of medical imaging technology, medical image research and application has become a research hotspot. This paper offers a solution to non-rigid registration of medical images based on ordinal feature (OF) and manifold learning. The structural features of medical images are extracted by combining ordinal features with local linear embedding (LLE) to improve the precision and speed of the registration algorithm. A physical model based on manifold learning and optimization search is constructed according to the complicated characteristics of non-rigid registration. The experimental results demonstrate the robustness and applicability of the proposed registration scheme.

  15. Image Analysis of Soil Micromorphology: Feature Extraction, Segmentation, and Quality Inference

    Directory of Open Access Journals (Sweden)

    Petros Maragos

    2004-06-01

    Full Text Available We present an automated system that we have developed for estimation of the bioecological quality of soils using various image analysis methodologies. Its goal is to analyze soilsection images, extract features related to their micromorphology, and relate the visual features to various degrees of soil fertility inferred from biochemical characteristics of the soil. The image methodologies used range from low-level image processing tasks, such as nonlinear enhancement, multiscale analysis, geometric feature detection, and size distributions, to object-oriented analysis, such as segmentation, region texture, and shape analysis.

  16. 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.)

  17. clinical features and patterns of imaging in cerebral venous sinus ...

    African Journals Online (AJOL)

    2013-09-01

    Sep 1, 2013 ... study was conducted at Kenyatta National Hospital. Clinical and imaging records ... loss of memory, abdominal pain and senile dementia. Aetiological factors .... with a large study by Khealani in Pakistan and. Middle East that ...

  18. An Image Retrieval Method Based on Color and Texture Features

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The technique of image retrieval is widely used in science experiment, military affairs, public security,advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD(Earth Mover's Distance) distance and texture distance. EMD distance is the distance between the histograms of two images in HSV(Hue, Saturation, Value) color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.

  19. Examplers based image fusion features for face recognition

    CERN Document Server

    James, Alex Pappachen

    2012-01-01

    Examplers of a face are formed from multiple gallery images of a person and are used in the process of classification of a test image. We incorporate such examplers in forming a biologically inspired local binary decisions on similarity based face recognition method. As opposed to single model approaches such as face averages the exampler based approach results in higher recognition accu- racies and stability. Using multiple training samples per person, the method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET, 99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face databases. In addition to face recognition, the method also detects the natural variability in the face images which can find application in automatic tagging of face images.

  20. Change detection in high resolution SAR images based on multiscale texture features

    Science.gov (United States)

    Wen, Caihuan; Gao, Ziqiang

    2011-12-01

    This paper studied on change detection algorithm of high resolution (HR) Synthetic Aperture Radar (SAR) images based on multi-scale texture features. Firstly, preprocessed multi-temporal Terra-SAR images were decomposed by 2-D dual tree complex wavelet transform (DT-CWT), and multi-scale texture features were extracted from those images. Then, log-ratio operation was utilized to get difference images, and the Bayes minimum error theory was used to extract change information from difference images. Lastly, precision assessment was done. Meanwhile, we compared with the result of method based on texture features extracted from gray-level cooccurrence matrix (GLCM). We had a conclusion that, change detection algorithm based on multi-scale texture features has a great more improvement, which proves an effective method to change detect of high spatial resolution SAR images.

  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......-recognition are considered in the light of the increasing availability of hyper-spectral images that are difficult to analyse using visual inspection alone....

  2. 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.

  3. Bioluminescence imaging of Chlamydia muridarum ascending infection in mice.

    Science.gov (United States)

    Campbell, Jessica; Huang, Yumeng; Liu, Yuanjun; Schenken, Robert; Arulanandam, Bernard; Zhong, Guangming

    2014-01-01

    Chlamydial pathogenicity in the upper genital tract relies on chlamydial ascending from the lower genital tract. To monitor chlamydial ascension, we engineered a luciferase-expressing C. muridarum. In cells infected with the luciferase-expressing C. muridarum, luciferase gene expression and enzymatic activity (measured as bioluminescence intensity) correlated well along the infection course, suggesting that bioluminescence can be used for monitoring chlamydial replication. Following an intravaginal inoculation with the luciferase-expressing C. muridarum, 8 of 10 mice displayed bioluminescence signal in the lower with 4 also in the upper genital tracts on day 3 after infection. By day 7, all 10 mice developed bioluminescence signal in the upper genital tracts. The bioluminescence signal was maintained in the upper genital tract in 6 and 2 mice by days 14 and 21, respectively. The bioluminescence signal was no longer detectable in any of the mice by day 28. The whole body imaging approach also revealed an unexpected airway infection following the intravaginal inoculation. Although the concomitant airway infection was transient and did not significantly alter the genital tract infection time courses, caution should be taken during data interpretation. The above observations have demonstrated that C. muridarum can not only achieve rapid ascending infection in the genital tract but also cause airway infection following a genital tract inoculation. These findings have laid a foundation for further optimizing the C. muridarum intravaginal infection murine model for understanding chlamydial pathogenic mechanisms.

  4. Bioluminescence imaging of Chlamydia muridarum ascending infection in mice.

    Directory of Open Access Journals (Sweden)

    Jessica Campbell

    Full Text Available Chlamydial pathogenicity in the upper genital tract relies on chlamydial ascending from the lower genital tract. To monitor chlamydial ascension, we engineered a luciferase-expressing C. muridarum. In cells infected with the luciferase-expressing C. muridarum, luciferase gene expression and enzymatic activity (measured as bioluminescence intensity correlated well along the infection course, suggesting that bioluminescence can be used for monitoring chlamydial replication. Following an intravaginal inoculation with the luciferase-expressing C. muridarum, 8 of 10 mice displayed bioluminescence signal in the lower with 4 also in the upper genital tracts on day 3 after infection. By day 7, all 10 mice developed bioluminescence signal in the upper genital tracts. The bioluminescence signal was maintained in the upper genital tract in 6 and 2 mice by days 14 and 21, respectively. The bioluminescence signal was no longer detectable in any of the mice by day 28. The whole body imaging approach also revealed an unexpected airway infection following the intravaginal inoculation. Although the concomitant airway infection was transient and did not significantly alter the genital tract infection time courses, caution should be taken during data interpretation. The above observations have demonstrated that C. muridarum can not only achieve rapid ascending infection in the genital tract but also cause airway infection following a genital tract inoculation. These findings have laid a foundation for further optimizing the C. muridarum intravaginal infection murine model for understanding chlamydial pathogenic mechanisms.

  5. Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

    Science.gov (United States)

    Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

    2016-01-01

    The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

  6. The molecular imaging approach to image infections and inflammation by nuclear medicine techniques

    NARCIS (Netherlands)

    Signore, Alberto; Glaudemans, Andor W. J. M.

    2011-01-01

    Inflammatory and infectious diseases are a heterogeneous class of diseases that may be divided into infections, acute inflammation and chronic inflammation. Radiological imaging techniques have, with the exception of functional MRI, high sensitivity but lack in specificity. Nuclear medicine techniqu

  7. The molecular imaging approach to image infections and inflammation by nuclear medicine techniques

    NARCIS (Netherlands)

    Signore, Alberto; Glaudemans, Andor W. J. M.

    2011-01-01

    Inflammatory and infectious diseases are a heterogeneous class of diseases that may be divided into infections, acute inflammation and chronic inflammation. Radiological imaging techniques have, with the exception of functional MRI, high sensitivity but lack in specificity. Nuclear medicine

  8. Spectrum Feature Retrieval and Comparison of Remote Sensing Images Using Improved ISODATA Algorithm

    Institute of Scientific and Technical Information of China (English)

    刘磊; 敬忠良; 肖刚

    2004-01-01

    Due to the large quantities of data and high relativity of the spectra of remote sensing images, K-L transformation is used to eliminate the relativity. An improved ISODATA(Interative Self-Organizing Data Analysis Technique A) algorithm is used to extract the spectrum features of the images. The computation is greatly reduced and the dynamic arguments are realized. The comparison of features between two images is carried out, and good results are achieved in simulation.

  9. ENTEROVIRUS INFECTION IN CHILDREN: CLINICAL AND EPIDEMIOLOGICAL FEATURES AT THE CURRENT STAGE

    Directory of Open Access Journals (Sweden)

    G. P. Martynova

    2016-01-01

    Full Text Available The article presents the current clinical and epidemiological features of enterovirus infection in children of Krasnoyarsk Territory. A retrospective analysis of the incidence of enterovirus infection and enterovirus meningitis in the period 2014—2015 according to the forms of state statistical reporting №2 «Information on infectious and parasitic diseases». Clinical and epidemiological analysis of enterovirus infection in 454 children who were treated at MBUZ «City Children's Infectious Hospital №1» in the period of seasonal rise of morbidity in 2014 revealed a prevalence of etiological structure of enteroviruses Coxsackie B, Coxsackie B5, Coxsackie B3, Coxsackie B4. The region recorded the different clinical forms of enterovirus infection (rash, myalgia, diarrhea, gerpangina, the structure of which is still, aseptic meningitis prevails.

  10. Hyperspectral Image Classification Based on the Weighted Probabilistic Fusion of Multiple Spectral-spatial Features

    Directory of Open Access Journals (Sweden)

    ZHANG Chunsen

    2015-08-01

    Full Text Available A hyperspectral images classification method based on the weighted probabilistic fusion of multiple spectral-spatial features was proposed in this paper. First, the minimum noise fraction (MNF approach was employed to reduce the dimension of hyperspectral image and extract the spectral feature from the image, then combined the spectral feature with the texture feature extracted based on gray level co-occurrence matrix (GLCM, the multi-scale morphological feature extracted based on OFC operator and the end member feature extracted based on sequential maximum angle convex cone (SMACC method to form three spectral-spatial features. Afterwards, support vector machine (SVM classifier was used for the classification of each spectral-spatial feature separately. Finally, we established the weighted probabilistic fusion model and applied the model to fuse the SVM outputs for the final classification result. In order to verify the proposed method, the ROSIS and AVIRIS image were used in our experiment and the overall accuracy reached 97.65% and 96.62% separately. The results indicate that the proposed method can not only overcome the limitations of traditional single-feature based hyperspectral image classification, but also be superior to conventional VS-SVM method and probabilistic fusion method. The classification accuracy of hyperspectral images was improved effectively.

  11. Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images

    Directory of Open Access Journals (Sweden)

    Zhang Zenghui

    2016-02-01

    Full Text Available Sparse microwave imaging using sparse priors of observed scenes in space, time, frequency, or polarization domain and echo data with sampling rate smaller than the traditional Nyquist rate as well as optimization algorithms for reconstructing the microwave images of observed scenes has many advantages over traditional microwave imaging systems. In sparse microwave imaging, image acquisition and representation vary; therefore, new feature analysis and cognitive interpretation theories and methods should be developed based on current research results. In this study, we analyze the statistical properties of sparse Synthetic Aperture Radar (SAR images and changes in point, line and regional features induced by sparse reconstruction. For SAR images recovered by the spatial sparse model, the statistical distribution degrades, whereas points and lines can be accurately extracted by low sampling rates. Furthermore, the target detection method based on sparse SAR images is studied. Owing to a weak background noise, target detection is easier using sparse SAR images than traditional ones.

  12. Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

    Science.gov (United States)

    Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

    2016-01-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

  13. Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Subodh

    2015-01-01

    A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images. PMID:27006938

  14. Use of Wavelet-Fuzzy Features with PCA for Image Registration

    Directory of Open Access Journals (Sweden)

    Safia Sadruddin

    2014-01-01

    Full Text Available In this paper, we discuss an Image Registration system based on neural network, which uses Wavelet-fuzzy features of an image. In this system, Wavelet-fuzzy features are extracted from an image and then reduced using Principal Component Analysis (PCA. The reduced feature set is then used for training the neural network for image registration. The geometric transformation between the reference and sensed image sets are evaluated using affine transformation parameters. The trained neural network produces registration parameters (translation, rotation and scaling with respect to reference and sensed image. Two parameters namely Mean Absolute Registration Error and Mutual Information are used as evaluation parameters. Experimentally, we show that the proposed technique for image registration is accurate and robust for distorted and noisy inputs.

  15. The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    LI Shao-kun; SUO Xing-mei; BAI Zhong-ying; QI Zhi-li; Liu Xiao-hong; GAO Shi-ju; ZHAO Shuang-ning

    2002-01-01

    Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixei labeling was used to segment image and extract feature. (3)A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.

  16. 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.

  17. Featured Image: A Galaxy Plunges Into a Cluster Core

    Science.gov (United States)

    Kohler, Susanna

    2015-10-01

    The galaxy that takes up most of the frame in this stunning image (click for the full view!) is NGC 1427A. This is a dwarf irregular galaxy (unlike the fortuitously-located background spiral galaxy in the lower right corner of the image), and its currently in the process of plunging into the center of the Fornax galaxy cluster. Marcelo Mora (Pontifical Catholic University of Chile) and collaborators have analyzed observations of this galaxy made by both the Very Large Telescope in Chile and the Hubble Advanced Camera for Surveys, which produced the image shown here as a color composite in three channels. The team worked to characterize the clusters of star formation within NGC 1427A identifiable in the image as bright knots within the galaxy and determine how the interactions of this galaxy with its cluster environment affect the star formation within it. For more information and the original image, see the paper below.Citation:Marcelo D. Mora et al 2015 AJ 150 93. doi:10.1088/0004-6256/150/3/93

  18. 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.

  19. Color and Texture Feature for Remote Sensing - Image Retrieval System: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Retno Kusumaningrum

    2011-09-01

    Full Text Available In this study, we proposed score fusion technique to improve the performance of remote sensing image retrieval system (RS-IRS using combination of several features. The representation of each feature is selected based on their performance when used as single feature in RS-IRS. Those features are color moment using L*a*b* color space, edge direction histogram extracted from Saturation channel, GLCM and Gabor Wavelet represented using standard deviation, and local binary pattern using 8-neighborhood. The score fusion is performed by computing the value of image similarity between an image in the database and query, where the image similarity value is sum of all features similarity, where each of feature similarity has been divided by SVD value of feature similarity between all images in the database and query from related feature. The feature similarity is measured by histogram intersection for local binary pattern, whereas the color moment, edge direction histogram, GLCM, and Gabor are measured by Euclidean Distance. The final result shows that the best performance of remote sensing image retrieval in this study is a system which uses the combination of color and texture features (i.e. color moment, edge direction histogram, GLCM, Gabor wavelet, and local binary pattern and uses score fusion in measuring the image similarity between query and images in the database. This system outperforms the other five individual feature with average precision rates 3%, 20%, 13%, 11%, and 9%, respectively, for color moment, edge direction histogram, GLCM, Gabor wavelet, and LBP. Moreover, this system also increase 17% compared to system without score fusion, simple-sum technique.

  20. Ensemble classification of colon biopsy images based on information rich hybrid features.

    Science.gov (United States)

    Rathore, Saima; Hussain, Mutawarra; Aksam Iftikhar, Muhammad; Jalil, Abdul

    2014-04-01

    In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the

  1. Similar Reference Image Quality Assessment: A New Database and A Trial with Local Feature Matching

    Science.gov (United States)

    Lu, Qingbo; Zhou, Wengang; Li, Houqiang

    2016-12-01

    Conventionally, the reference image for image quality assessment (IQA) is completely available (full-reference IQA) or unavailable (no-reference IQA). Even for reduced-reference IQA, the features that are used to predict image quality are still extracted from the pristine reference image. However, the pristine reference image is always unavailable in many real scenarios. In contrast, it is convenient to obtain a number of similar reference images via retrieval from the Internet. These similar reference images may share similar contents and scenes with the image to be assessed. In this paper, we attempt to discuss the image quality assessment problem from the view of similar images, i.e. similar reference IQA. Although the similar reference images share similar contents with the degraded image, the difference between them still cannot be ignored. Therefore, we propose an IQA framework based on local feature matching, which can help to identify the similar regions and structures. Then the IQA features are computed only from these similar regions to predict the final image quality score. Besides, since there is no IQA databases for the similar reference IQA problem, we establish a novel IQA database that consists of 272 images from four scenes. The experiments demonstrate that the performance of our scheme goes beyond state-of-the-art no-reference IQA methods and some full-reference IQA algorithms.

  2. Image indexing using composite color and shape invariant features

    NARCIS (Netherlands)

    Gevers, Th.; Smeulders, A.W.M.

    1998-01-01

    New sets of color models are proposed for object recognition invariant to a change in view point, object geometry and illumination. Further, computational methods are presented to combine color and shape invariants to produce a high-dimensional invariant feature set for discriminatory object recogni

  3. A new method to extract stable feature points based on self-generated simulation images

    Science.gov (United States)

    Long, Fei; Zhou, Bin; Ming, Delie; Tian, Jinwen

    2015-10-01

    Recently, image processing has got a lot of attention in the field of photogrammetry, medical image processing, etc. Matching two or more images of the same scene taken at different times, by different cameras, or from different viewpoints, is a popular and important problem. Feature extraction plays an important part in image matching. Traditional SIFT detectors reject the unstable points by eliminating the low contrast and edge response points. The disadvantage is the need to set the threshold manually. The main idea of this paper is to get the stable extremums by machine learning algorithm. Firstly we use ASIFT approach coupled with the light changes and blur to generate multi-view simulated images, which make up the set of the simulated images of the original image. According to the way of generating simulated images set, affine transformation of each generated image is also known. Instead of the traditional matching process which contain the unstable RANSAC method to get the affine transformation, this approach is more stable and accurate. Secondly we calculate the stability value of the feature points by the set of image with its affine transformation. Then we get the different feature properties of the feature point, such as DOG features, scales, edge point density, etc. Those two form the training set while stability value is the dependent variable and feature property is the independent variable. At last, a process of training by Rank-SVM is taken. We will get a weight vector. In use, based on the feature properties of each points and weight vector calculated by training, we get the sort value of each feature point which refers to the stability value, then we sort the feature points. In conclusion, we applied our algorithm and the original SIFT detectors to test as a comparison. While in different view changes, blurs, illuminations, it comes as no surprise that experimental results show that our algorithm is more efficient.

  4. The relationship study between image features and detection probability based on psychology experiments

    Science.gov (United States)

    Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

    2011-04-01

    Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

  5. Relationship between Hyperuricemia and Haar-Like Features on Tongue Images

    Directory of Open Access Journals (Sweden)

    Yan Cui

    2015-01-01

    Full Text Available Objective. To investigate differences in tongue images of subjects with and without hyperuricemia. Materials and Methods. This population-based case-control study was performed in 2012-2013. We collected data from 46 case subjects with hyperuricemia and 46 control subjects, including results of biochemical examinations and tongue images. Symmetrical Haar-like features based on integral images were extracted from tongue images. T-tests were performed to determine the ability of extracted features to distinguish between the case and control groups. We first selected features using the common criterion P<0.05, then conducted further examination of feature characteristics and feature selection using means and standard deviations of distributions in the case and control groups. Results. A total of 115,683 features were selected using the criterion P<0.05. The maximum area under the receiver operating characteristic curve (AUC of these features was 0.877. The sensitivity of the feature with the maximum AUC value was 0.800 and specificity was 0.826 when the Youden index was maximized. Features that performed well were concentrated in the tongue root region. Conclusions. Symmetrical Haar-like features enabled discrimination of subjects with and without hyperuricemia in our sample. The locations of these discriminative features were in agreement with the interpretation of tongue appearance in traditional Chinese and Western medicine.

  6. Malformations of cortical development: 3T magnetic resonance imaging features

    Science.gov (United States)

    Battal, Bilal; Ince, Selami; Akgun, Veysel; Kocaoglu, Murat; Ozcan, Emrah; Tasar, Mustafa

    2015-01-01

    Malformation of cortical development (MCD) is a term representing an inhomogeneous group of central nervous system abnormalities, referring particularly to embriyological aspect as a consequence of any of the three developmental stages, i.e., cell proliferation, cell migration and cortical organization. These include cotical dysgenesis, microcephaly, polymicrogyria, schizencephaly, lissencephaly, hemimegalencephaly, heterotopia and focal cortical dysplasia. Since magnetic resonance imaging is the modality of choice that best identifies the structural anomalies of the brain cortex, we aimed to provide a mini review of MCD by using 3T magnetic resonance scanner images. PMID:26516429

  7. Feature exploration for biometric recognition using millimetre wave body images

    OpenAIRE

    2015-01-01

    The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13640-015-0084-3 The use of millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. Furthermore, the security community has started using millimetre wave screening scanners in order to detect concealed objects. We believe we can exploit the use of these devices by incorporating b...

  8. Malformations of cortical development:3T magnetic resonance imaging features

    Institute of Scientific and Technical Information of China (English)

    Bilal; Battal; Selami; Ince; Veysel; Akgun; Murat; Kocaoglu; Emrah; Ozcan; Mustafa; Tasar

    2015-01-01

    Malformation of cortical development(MCD) is a term representing an inhomogeneous group of central nervous system abnormalities, referring particularly to embriyological aspect as a consequence of any of the three developmental stages, i.e., cell proliferation, cell migration and cortical organization. These include cotical dysgenesis, microcephaly, polymicrogyria, schizencephaly, lissencephaly, hemimegalencephaly, heterotopia and focal cortical dysplasia. Since magnetic resonance imaging is the modality of choice that best identifies the structural anomalies of the brain cortex, we aimed to provide a mini review of MCD by using 3T magnetic resonance scanner images.

  9. X-ray image enhancement via determinant based feature selection.

    Science.gov (United States)

    Tappenden, R; Hegarty, J; Broughton, R; Butler, A; Coope, I; Renaud, P

    2013-12-01

    Previous work has investigated the feasibility of using Eigenimage-based enhancement tools to highlight abnormalities on chest X-rays (Butler et al in J Med Imaging Radiat Oncol 52:244-253, 2008). While promising, this approach has been limited by computational restrictions of standard clinical workstations, and uncertainty regarding what constitutes an adequate sample size. This paper suggests an alternative mathematical model to the above referenced singular value decomposition method, which can significantly reduce both the required sample size and the time needed to perform analysis. Using this approach images can be efficiently separated into normal and abnormal parts, with the potential for rapid highlighting of pathology.

  10. Manifold-based feature point matching for multi-modal image registration.

    Science.gov (United States)

    Hu, Liang; Wang, Manning; Song, Zhijian

    2013-03-01

    Images captured using different modalities usually have significant variations in their intensities, which makes it difficult to reveal their internal structural similarities and achieve accurate registration. Most conventional feature-based image registration techniques are fast and efficient, but they cannot be used directly for the registration of multi-modal images because of these intensity variations. This paper introduces the theory of manifold learning to transform the original images into mono-modal modalities, which is a feature-based method that is applicable to multi-modal image registration. Subsequently, scale-invariant feature transform is used to detect highly distinctive local descriptors and matches between corresponding images, and a point-based registration is executed. The algorithm was tested with T1- and T2-weighted magnetic resonance (MR) images obtained from BrainWeb. Both qualitative and quantitative evaluations of the method were performed and the results compared with those produced previously. The experiments showed that feature point matching after manifold learning achieved more accurate results than did the similarity measure for multi-modal image registration. This study provides a new manifold-based feature point matching method for multi-modal medical image registration, especially for MR images. The proposed method performs better than do conventional intensity-based techniques in terms of its registration accuracy and is suitable for clinical procedures. Copyright © 2012 John Wiley & Sons, Ltd.

  11. Medical image retrieval based on texture and shape feature co-occurrence

    Science.gov (United States)

    Zhou, Yixiao; Huang, Yan; Ling, Haibin; Peng, Jingliang

    2012-03-01

    With the rapid development and wide application of medical imaging technology, explosive volumes of medical image data are produced every day all over the world. As such, it becomes increasingly challenging to manage and utilize such data effectively and efficiently. In particular, content-based medical image retrieval has been intensively researched in the past decade or so. In this work, we propose a novel approach to content-based medical image retrieval utilizing the co-occurrence of both the texture and the shape features in contrast to most previous algorithms that use purely the texture or the shape feature. Specifically, we propose a novel form of representation for the co-occurrence of the texture and the shape features in an image, i.e., the gray level and edge direction co-occurrence matrix (GLEDCOM). Based on GLEDCOM, we define eleven features forming a feature vector that is used to measure the similarity between images. As a result, it consistently yields outstanding performance on both images rich in texture (e.g., image of brain) and images with dominant smooth regions and sharp edges (e.g., image of bladder). As demonstrated by experiments, the mean precision of retrieval with GLEDCOM algorithm outperforms a set of representative algorithms including the gray level co-occurrence matrix (GLCM) based, the Hu's seven moment invariants (HSMI) based, the uniformity estimation method (UEM) based and the the modified Zernike moments (MZM) based algorithms by 10%-20%.

  12. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

    Science.gov (United States)

    Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

    2016-02-01

    Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

  13. Qualitative research methods: key features and insights gained from use in infection prevention research.

    Science.gov (United States)

    Forman, Jane; Creswell, John W; Damschroder, Laura; Kowalski, Christine P; Krein, Sarah L

    2008-12-01

    Infection control professionals and hospital epidemiologists are accustomed to using quantitative research. Although quantitative studies are extremely important in the field of infection control and prevention, often they cannot help us explain why certain factors affect the use of infection control practices and identify the underlying mechanisms through which they do so. Qualitative research methods, which use open-ended techniques, such as interviews, to collect data and nonstatistical techniques to analyze it, provide detailed, diverse insights of individuals, useful quotes that bring a realism to applied research, and information about how different health care settings operate. Qualitative research can illuminate the processes underlying statistical correlations, inform the development of interventions, and show how interventions work to produce observed outcomes. This article describes the key features of qualitative research and the advantages that such features add to existing quantitative research approaches in the study of infection control. We address the goal of qualitative research, the nature of the research process, sampling, data collection and analysis, validity, generalizability of findings, and presentation of findings. Health services researchers are increasingly using qualitative methods to address practical problems by uncovering interacting influences in complex health care environments. Qualitative research methods, applied with expertise and rigor, can contribute important insights to infection prevention efforts.

  14. Diagnostic value of imaging in infective endocarditis: a systematic review.

    Science.gov (United States)

    Gomes, Anna; Glaudemans, Andor W J M; Touw, Daan J; van Melle, Joost P; Willems, Tineke P; Maass, Alexander H; Natour, Ehsan; Prakken, Niek H J; Borra, Ronald J H; van Geel, Peter Paul; Slart, Riemer H J A; van Assen, Sander; Sinha, Bhanu

    2017-01-01

    Sensitivity and specificity of the modified Duke criteria for native valve endocarditis are both suboptimal, at approximately 80%. Diagnostic accuracy for intracardiac prosthetic material-related infection is even lower. Non-invasive imaging modalities could potentially improve diagnosis of infective endocarditis; however, their diagnostic value is unclear. We did a systematic literature review to critically appraise the evidence for the diagnostic performance of these imaging modalities, according to PRISMA and GRADE criteria. We searched PubMed, Embase, and Cochrane databases. 31 studies were included that presented original data on the performance of electrocardiogram (ECG)-gated multidetector CT angiography (MDCTA), ECG-gated MRI, (18)F-fluorodeoxyglucose ((18)F-FDG) PET/CT, and leucocyte scintigraphy in diagnosis of native valve endocarditis, intracardiac prosthetic material-related infection, and extracardiac foci in adults. We consistently found positive albeit weak evidence for the diagnostic benefit of (18)F-FDG PET/CT and MDCTA. We conclude that additional imaging techniques should be considered if infective endocarditis is suspected. We propose an evidence-based diagnostic work-up for infective endocarditis including these non-invasive techniques. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Global image feature extraction using slope pattern spectra

    CSIR Research Space (South Africa)

    Toudjeu, IT

    2008-06-01

    Full Text Available of coffee beans. Granulometries were also used to estimate the dominant width of the white patterns in the X-ray images of welds [7]. Due to the computational load associated with the calculation of granulometries, Vincent [6], building on the work...

  16. Magnetic resonance imaging features of chloroma of the shoulder

    Energy Technology Data Exchange (ETDEWEB)

    Gomez, N. [Department of Radiology, Hospital de la Princesa, Madrid (Spain)]|[Department of Radiology, Hospital de la Princesa, Madrid (Spain); Ocon, E. [Department of Radiology, Hospital de la Princesa, Madrid (Spain); Friera, A. [Department of Radiology, Hospital de la Princesa, Madrid (Spain); Penarrubia, M.J. [Department of Hematology, Hospital de la Princesa, Madrid (Spain); Acevedo, A. [Department of Pathology, Hospital de la Princesa, Madrid (Spain)

    1997-01-01

    A patient with a history of essential thrombocytosis presented with diffuse skeletal pain and restricted motion of the left shoulder. Magnetic resonance imaging (MRI) of the left glenohumeral joint showed a soft tissue mass that displaced the rotator cuff. Biopsy of the mass revealed chloroma. MRI is the method that best characterizes this lesion. (orig.). With 5 figs.

  17. 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.

  18. 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

  19. Monitoring Therapeutic Treatments against Burkholderia Infections Using Imaging Techniques

    Directory of Open Access Journals (Sweden)

    Tiffany M. Mott

    2013-05-01

    Full Text Available Burkholderia mallei, the etiologic agent of glanders, are Category B select agents with biothreat potential, and yet effective therapeutic treatments are lacking. In this study, we showed that CpG administration increased survival, demonstrating protection in the murine glanders model. Bacterial recovery from infected lungs, liver and spleen was significantly reduced in CpG-treated animals as compared with non-treated mice. Reciprocally, lungs of CpG-treated infected animals were infiltrated with higher levels of neutrophils and inflammatory monocytes, as compared to control animals. Employing the B. mallei bioluminescent strain CSM001 and the Neutrophil-Specific Fluorescent Imaging Agent, bacterial dissemination and neutrophil trafficking were monitored in real-time using multimodal in vivo whole body imaging techniques. CpG-treatment increased recruitment of neutrophils to the lungs and reduced bioluminescent bacteria, correlating with decreased bacterial burden and increased protection against acute murine glanders. Our results indicate that protection of CpG-treated animals was associated with recruitment of neutrophils prior to infection and demonstrated, for the first time, simultaneous real time in vivo imaging of neutrophils and bacteria. This study provides experimental evidence supporting the importance of incorporating optimized in vivo imaging methods to monitor disease progression and to evaluate the efficacy of therapeutic treatment during bacterial infections.

  20. Content-Based Image Retrieval using Color Moment and Gabor Texture Feature

    Directory of Open Access Journals (Sweden)

    K. Hemachandran

    2012-09-01

    Full Text Available Content based image retrieval (CBIR has become one of the most active research areas in the past few years. Many indexing techniques are based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. In this paper, we propose a content-based image retrieval method which combines color and texture features. To improve the discriminating power of color indexing techniques, we encode a minimal amount of spatial information in the color index. As its color features, an image is divided horizontally into three equal non-overlapping regions. From each region in the image, we extract the first three moments of the color distribution, from each color channel and store them in the index i.e., for a HSV color space, we store 27 floating point numbers per image. As its texture feature, Gabor texture descriptors are adopted. We assign weights to each feature respectively and calculate the similarity with combined features of color and texture using Canberra distance as similarity measure. Experimental results show that the proposed method has higher retrieval accuracy than other conventional methods combining color moments and texture features based on global features approach.

  1. Second order Statistical Texture Features from a New CSLBPGLCM for Ultrasound Kidney Images Retrieval

    Directory of Open Access Journals (Sweden)

    Chelladurai CALLINS CHRISTIYANA

    2013-12-01

    Full Text Available This work proposes a new method called Center Symmetric Local Binary Pattern Grey Level Co-occurrence Matrix (CSLBPGLCM for the purpose of extracting second order statistical texture features in ultrasound kidney images. These features are then feed into ultrasound kidney images retrieval system for the point of medical applications. This new GLCM matrix combines the benefit of CSLBP and conventional GLCM. The main intention of this CSLBPGLCM is to reduce the number of grey levels in an image by not simply accumulating the grey levels but incorporating another statistical texture feature in it. The proposed approach is cautiously evaluated in ultrasound kidney images retrieval system and has been compared with conventional GLCM. It is experimentally proved that the proposed method increases the retrieval efficiency, accuracy and reduces the time complexity of ultrasound kidney images retrieval system by means of second order statistical texture features.

  2. Trypanosoma cruzi: single cell live imaging inside infected tissues

    Science.gov (United States)

    Ferreira, Bianca Lima; Orikaza, Cristina Mary; Cordero, Esteban Mauricio

    2016-01-01

    Summary Although imaging the live Trypanosoma cruzi parasite is a routine technique in most laboratories, identification of the parasite in infected tissues and organs has been hindered by their intrinsic opaque nature. We describe a simple method for in vivo observation of live single‐cell Trypanosoma cruzi parasites inside mammalian host tissues. BALB/c or C57BL/6 mice infected with DsRed‐CL or GFP‐G trypomastigotes had their organs removed and sectioned with surgical blades. Ex vivo organ sections were observed under confocal microscopy. For the first time, this procedure enabled imaging of individual amastigotes, intermediate forms and motile trypomastigotes within infected tissues of mammalian hosts. PMID:26639617

  3. 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.

  4. Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

    Science.gov (United States)

    Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

    2016-09-12

    In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

  5. 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.

  6. Spinal dural arteriovenous fistula: Imaging features and its mimics

    Energy Technology Data Exchange (ETDEWEB)

    Jeog, Ying; Ting, David Yen; Hsu, Hui Ling; Huang, Yen Lin; Chen, Chi Jen; Tseng, Ting Chi [Dept. of Radiology, aipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan (China)

    2015-10-15

    Spinal dural arteriovenous fistula (SDAVF) is the most common spinal vascular malformation, however it is still rare and underdiagnosed. Magnetic resonance imaging findings such as spinal cord edema and dilated and tortuous perimedullary veins play a pivotal role in the confirmation of the diagnosis. However, spinal angiography remains the gold standard in the diagnosis of SDAVF. Classic angiographic findings of SDAVF are early filling of radicular veins, delayed venous return, and an extensive network of dilated perimedullary venous plexus. A series of angiograms of SDAVF at different locations along the spinal column, and mimics of serpentine perimedullary venous plexus on MR images, are demonstrated. Thorough knowledge of SDAVF aids correct diagnosis and prevents irreversible complications.

  7. Edge-Based Feature Extraction Method and Its Application to Image Retrieval

    Directory of Open Access Journals (Sweden)

    G. Ohashi

    2003-10-01

    Full Text Available We propose a novel feature extraction method for content-bases image retrieval using graphical rough sketches. The proposed method extracts features based on the shape and texture of objects. This edge-based feature extraction method functions by representing the relative positional relationship between edge pixels, and has the advantage of being shift-, scale-, and rotation-invariant. In order to verify its effectiveness, we applied the proposed method to 1,650 images obtained from the Hamamatsu-city Museum of Musical Instruments and 5,500 images obtained from Corel Photo Gallery. The results verified that the proposed method is an effective tool for achieving accurate retrieval.

  8. Two-dimensional electrophoresis analysis of proteomics based on image feature and mathematical morphology

    Institute of Scientific and Technical Information of China (English)

    SHEN Peng; FAN Xiaohui; ZENG Zhen; CHENG Yiyu

    2005-01-01

    In this paper, a novel method to automatically detect protein spots on a two-dimensional (2-D) electrophoresis gel image is proposed to implement proteomics analysis of complex analyte.On the basis of the identifying spots results based on color variation and spot size features, morphological feature is introduced as a new criterion to distinguish protein spots from non-protein spots.Image-sharpening, edge-detecting and morphological feature extraction methods were consequently combined to detect protein spots on a 2-D electrophoresis gel image subject to strong disturbance.The proposed method was applied to detect the protein spots of proteomic gel images from E.coli cell, human kidney tissue and human serum.The results demonstrated that this method is more accurate and reliable than previous methods such as PDQuest 7.2 and ImageMaster 5.0 software for detecting protein spots on gel images with strong interferences.

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

    Science.gov (United States)

    Xi, Wenfei; Shi, Zhengtao; Li, Dongsheng

    2017-07-01

    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.

  10. Angioleiomyoma: magnetic resonance imaging features in ten cases

    Energy Technology Data Exchange (ETDEWEB)

    Gupte, Cynthia; Butt, Sajid H.; Saifuddin, Asif [Royal National Orthopaedic Hospital, Department of Radiology, Middlesex (United Kingdom); Tirabosco, Roberto [Royal National Orthopaedic Hospital, Department of Histopathology, Middlesex (United Kingdom)

    2008-11-15

    Angioleiomyoma is a rare, benign smooth muscle tumour arising from the tunica media of small veins and arteries and can occur anywhere in the body. The histological appearances are well documented, but there are relatively few descriptions of the magnetic resonance imaging (MRI) findings. A retrospective study of the clinical presentation, MRI appearances and histological findings of ten angioleiomyomas presenting as extremity soft tissue masses. MRI typically demonstrated a well-defined, oval mass located superficial to the fascia with the commonest sites being the hand (three cases) and ankle/foot (five cases). The lesion was isointense to muscle on T1-weighted spin echo images with heterogeneous increased internal T2W/short tau inversion recovery (STIR) signal intensity, which commonly appeared as multiple linear or branching areas of hyperintensity. Enhancement after IV gadolinium ranged from diffuse to heterogeneous. In a single case, central fat signal intensity was seen, while a further case showed marked T2W/STIR hypointensity due to diffuse hyalinisation within the lesion. This is the largest reported MRI series of extremity musculoskeletal angioleiomyoma. Angioleiomyoma should be considered in the differential diagnosis of a superficial mass in the hand or foot, particularly when characteristic linear or branching hyperintensity is seen on T2W or STIR images. (orig.)

  11. Featured Image: Violent History of the Toothbrush Cluster

    Science.gov (United States)

    Kohler, Susanna

    2016-03-01

    This stunning composite image shows the components of the galaxy cluster RX J0603.3+4214, located at a redshift of z=0.225. This image contains Chandra X-ray data (red), radio data from the Giant Metrewave Radio Telescope (green), and optical from the Subaru Telescope (background). The shape of the enormous (6.5 million light-years across!) radio relic, shown in green, gives this collection of galaxies its nickname: the Toothbrush Cluster. A team of scientists led by Myungkook James Jee (Yonsei University and University of California, Davis) used Hubble and Subaru to study weak gravitational lensing by the Toothbrush Cluster, in order to determine how the clusters mass is distributed. Jee and collaborators found that most of the dark-matter mass is located in two large clumps on a north-south axis (shown by the white contours overlaid on the image), suggesting that the Toothbrush Cluster is the result of a past merger between two clusters. This violent merger is likely what caused the enormous Toothbrush radio relic. Check out the paper below for more information!CitationM. James Jee et al 2016 ApJ 817 179. doi:10.3847/0004-637X/817/2/179

  12. Hepatic CT Image Query Based on Threshold-based Classification Scheme with Gabor Features

    Institute of Scientific and Technical Information of China (English)

    JIANG Li-jun; LUO Yong-zing; ZHAO Jun; ZHUANG Tian-ge

    2008-01-01

    Hepatic computed tomography (CT) images with Gabor function were analyzed.Then a thresholdbased classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments,a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme,support vector machines (SVM) scheme and threshold-based scheme.

  13. Role of diffusion weighted imaging in musculoskeletal infections: Current perspectives

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Yogesh [Yale New Haven Health System at Bridgeport Hospital, Department of Radiology, Bridgeport, CT (United States); Khaleel, Mohammad [UT Southwestern Medical Center, Department of Orthopaedic Surgery, Dallas, TX (United States); Boothe, Ethan; Awdeh, Haitham [UT Southwestern Medical Center, Department of Radiology, Dallas, TX (United States); Wadhwa, Vibhor [University of Arkansas for Medical Sciences, Department of Radiology, Little Rock, AR (United States); Chhabra, Avneesh [UT Southwestern Medical Center, Department of Orthopaedic Surgery, Dallas, TX (United States); UT Southwestern Medical Center, Department of Radiology, Dallas, TX (United States)

    2017-01-15

    Accurate diagnosis and prompt therapy of musculoskeletal infections are important prognostic factors. In most cases, clinical history, examination and laboratory findings help one make the diagnosis, and routine magnetic resonance imaging (MRI) is useful to identify the extent of the disease process. However, in many situations, a routine MRI may not be specific enough especially if the patient cannot receive contrast intravenously, thereby delaying the appropriate treatment. Diffusion-weighted imaging (DWI) can help in many such situations by providing additional information, accurate characterization and defining the extent of the disease, so that prompt treatment can be initiated. In this article, we illustrate the imaging findings of the spectrum of musculoskeletal infections, emphasizing the role of DWI in this domain. (orig.)

  14. [Classification technique for hyperspectral image based on subspace of bands feature extraction and LS-SVM].

    Science.gov (United States)

    Gao, Heng-zhen; Wan, Jian-wei; Zhu, Zhen-zhen; Wang, Li-bao; Nian, Yong-jian

    2011-05-01

    The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM (least squares support vector machine). The LS-SVM uses the features extracted from subspace of bands (SOB). The maximum noise fraction (MNF) method is adopted as the feature extraction method. The spectral correlations of the hyperspectral image are used in order to divide the feature space into several SOBs. Then the MNF is used to extract characteristic features of the SOBs. The extracted features are combined into the feature vector for classification. So the strong bands correlation is avoided and the spectral redundancies are reduced. The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved. The proposed method optimizes spectral information by feature extraction and reduces the spectral noise. The classifier performance is improved. Experimental results show the superiorities of the proposed algorithm.

  15. A Content based CT Lung Image Retrieval by DCT Matrix and Feature Vector Technique

    Directory of Open Access Journals (Sweden)

    J.Bridget Nirmala

    2012-03-01

    Full Text Available Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. Image Retrieval technique to retrieve similar and relevant Computed Tomography (CT images of lung from a large database of images. During the process of retrieval, a query image which contains the affected area / abnormal region is given as an input to retrieve similar images which contain affected area/abnormal region from the database. DCT Matrix (DCTM is a kind of commonly used color feature representation in image retrieval. This paper describes a content based image retrieval (CBIR that represent each image in database by a vector of feature values called DCT vector matrix(8x8. Using this DCTM row and column feature vector values considered as a query image which is compared with existing database to cull out more similar and relevant images. The experimental result shows that 97% of images can be retrieved correctly using this technique

  16. A PCA Based Automatic Image Categorization Approach Using Dominant Color Features

    Institute of Scientific and Technical Information of China (English)

    WUChunming; QIANHui; WANGDonghui

    2005-01-01

    Automatic Image categorization is a universal problem in area of Content-based image retrieval (CBIR). The goal of automatic image categorization is to find a mapping between images and the predefined image categories. The difficulty of this problem is that how to describe image content and incorporate low-level features into semantic categories. As a solution, we propose a Principal component analysis (PCA) based approach. This approach assumes that the images in the same semantic category have the similar spatial distribution of color components and treats the images in the same category as a linear combination of a fixed set of dominant color blocks with special textural information. A three-step algorithm is designed: (1) extracting Dominant colors (DC) of images, which describe the major color information in an image; (2) Establishing a feature space based on DC blocks and its textural information; (3) using PCA to reduce dimensionality of feature space and using the basis vectors to categorize images. An experimental database containing nine categories including cars, flowers, houses, portraits, fish, bark, sunshine, leaves and fresco is constructed to test the algorithm based on our image categorization approach. The results show that this approach is effective and a reasonable compromise between accuracy and speed in practice.

  17. 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.

  18. Clinical and Immunological Features of Bronchial Asthma in Children on the Background of Persistent Intracellular Infections

    Directory of Open Access Journals (Sweden)

    O.Ye. Chernyshova

    2015-03-01

    Full Text Available The article presents information on the impact of persistent intracellular infections on the course of the di­sease and the state of immune system in children with bronchial asthma. Clinical features of bronchial asthma, the degree of sensitization, level of proinflammatory (IL-1, IL-2, IL-6, ­IL-8, IFN-α and IFN-γ, TNF-α and antiinflammatory (IL-4, ­IL-10 cytokines in the blood serum are described.

  19. Clinical and microbiologic features of Shigella and enteroinvasive Escherichia coli infections detected by DNA hybridization.

    OpenAIRE

    Taylor, D N; Echeverria, P.; Sethabutr, O.; Pitarangsi, C; Leksomboon, U; Blacklow, N R; Rowe, B.; R. Gross; Cross, J.

    1988-01-01

    To determine the clinical and microbiologic features of Shigella and enteroinvasive Escherichia coli (EIEC) infections, we investigated 410 children with diarrhea and 410 control children without diarrhea who were seen at Children's Hospital, Bangkok, Thailand, from January to June 1985. Shigella spp. were isolated from 96 (23%) and EIEC were isolated from 17 (4%) of 410 children with diarrhea and from 12 (3%) and 6 (1%) of 410 control children, respectively. The isolation rates of both patho...

  20. 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.

  1. Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

    Science.gov (United States)

    Kiyuna, Tomoharu; Saito, Akira; Marugame, Atsushi; Yamashita, Yoshiko; Ogura, Maki; Cosatto, Eric; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2013-03-01

    Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

  2. [CT imaging features of pulmonary involvement in connective tissue disorders].

    Science.gov (United States)

    Brillet, P Y; Mama, N; Nunes, H; Uzunhan, Y; Abbad, S; Brauner, M W

    2009-11-01

    Connective tissue disorders correspond to a heterogeneous group of inflammatory diseases characterized by abnormal immune system activity leading to connective tissue alterations in multiple parts of the body. In adults, connective tissue disorders include rheumatoid arthritis, progressive systemic sclerosis, Sjögren syndrome, systemic lupus erythematosus, dermatomyositis and polymyositis, ankylosing spondylitis, and mixed connective tissue disease. Broncho-pulmonary involvement may be variable with involvement of all anatomical components of the lung. Involvement of other intrathoracic structures (pleura, respiratory muscles, heart, rib cage) is frequent. The most specific manifestations include interstitial lung diseases and pulmonary hypertension. During follow-up, progressive respiratory diseases may occur due to the treatment, infections, pulmonary embolism or neoplasms.

  3. Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework

    Directory of Open Access Journals (Sweden)

    Chen Shaokang

    2011-01-01

    Full Text Available Although automatic faces recognition has shown success for high-quality images under controlled conditions, for video-based recognition it is hard to attain similar levels of performance. We describe in this paper recent advances in a project being undertaken to trial and develop advanced surveillance systems for public safety. In this paper, we propose a local facial feature based framework for both still image and video-based face recognition. The evaluation is performed on a still image dataset LFW and a video sequence dataset MOBIO to compare 4 methods for operation on feature: feature averaging (Avg-Feature, Mutual Subspace Method (MSM, Manifold to Manifold Distance (MMS, and Affine Hull Method (AHM, and 4 methods for operation on distance on 3 different features. The experimental results show that Multi-region Histogram (MRH feature is more discriminative for face recognition compared to Local Binary Patterns (LBP and raw pixel intensity. Under the limitation on a small number of images available per person, feature averaging is more reliable than MSM, MMD, and AHM and is much faster. Thus, our proposed framework—averaging MRH feature is more suitable for CCTV surveillance systems with constraints on the number of images and the speed of processing.

  4. Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory.

    Science.gov (United States)

    Velayutham, C; Thangavel, K

    2012-01-01

    Feature Selection (FS) is a process, which attempts to select features, which are more informative. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features, which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised FS. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ziereisen, France; Damry, Nash; Christophe, Catherine [Queen Fabiola Children' s University Hospital, Department of Radiology, Brussels (Belgium); Dan, Bernard [Queen Fabiola Children' s University Hospital, Department of Neurology, Brussels (Belgium); Azzi, Nadira; Ferster, Alina [Queen Fabiola Children' s University Hospital, Department of Paediatrics, Brussels (Belgium)

    2006-03-15

    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.)

  6. Clinical features of Vibrio vulnificus infections in the coastal areas of the Ariake Sea, Japan.

    Science.gov (United States)

    Matsumoto, Kouichi; Ohshige, Kenji; Fujita, Naohiro; Tomita, Yukiko; Mitsumizo, Shinji; Nakashima, Mikio; Oishi, Hirotaka

    2010-08-01

    Vibrio vulnificus infection can result in necrotizing fasciitis and sepsis and is associated with high mortality. Most patients infected with this microbe have liver dysfunction as an underlying disease. However, because of the sporadic nature of outbreaks and unidentified cases, extensive evaluation of clinical features and identification of factors affecting prognosis have not been performed. We retrospectively analyzed 37 cases in Japan from 1984 to 2008 to review clinical features and to identify risk factors associated with prognosis. Statistical differences between clinical features (patient's characteristics, initial clinical laboratory data, symptoms upon admission, and other risk indicators) and prognosis were analyzed by use of the chi(2) test or the Mann-Whitney U test. Multivariate logistic regression analysis was also performed to assess factors which potentially affect hospital mortality. The mortality rate was 64.9%. An underlying liver disease was observed in 91.6% of the patients. The presence of liver cirrhosis tended to be related to hospital mortality; however, statistical significance was not achieved. Advanced age, lower platelet counts, and the presence of extensive skin lesions at onset affected outcomes with statistical significance. The prognosis of this disease is poor, because septic shock and necrotizing fasciitis often develop within a few days. Early diagnosis and treatment are needed to improve the prognosis of V. vulnificus infection.

  7. 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.

  8. Automatic extraction of disease-specific features from Doppler images

    Science.gov (United States)

    Negahdar, Mohammadreza; Moradi, Mehdi; Parajuli, Nripesh; Syeda-Mahmood, Tanveer

    2017-03-01

    Flow Doppler imaging is widely used by clinicians to detect diseases of the valves. In particular, continuous wave (CW) Doppler mode scan is routinely done during echocardiography and shows Doppler signal traces over multiple heart cycles. Traditionally, echocardiographers have manually traced such velocity envelopes to extract measurements such as decay time and pressure gradient which are then matched to normal and abnormal values based on clinical guidelines. In this paper, we present a fully automatic approach to deriving these measurements for aortic stenosis retrospectively from echocardiography videos. Comparison of our method with measurements made by echocardiographers shows large agreement as well as identification of new cases missed by echocardiographers.

  9. Clinical Features of Bacterial Vaginosis in a Murine Model of Vaginal Infection with Gardnerella vaginalis

    Science.gov (United States)

    Gilbert, Nicole M.; Lewis, Warren G.; Lewis, Amanda L.

    2013-01-01

    Bacterial vaginosis (BV) is a dysbiosis of the vaginal flora characterized by a shift from a Lactobacillus-dominant environment to a polymicrobial mixture including Actinobacteria and Gram-negative bacilli. BV is a common vaginal condition in women and is associated with increased risk of sexually transmitted infection and adverse pregnancy outcomes such as preterm birth. Gardnerella vaginalis is one of the most frequently isolated bacterial species in BV. However, there has been much debate in the literature concerning the contribution of G. vaginalis to the etiology of BV, since it is also present in a significant proportion of healthy women. Here we present a new murine vaginal infection model with a clinical isolate of G. vaginalis. Our data demonstrate that this model displays key features used clinically to diagnose BV, including the presence of sialidase activity and exfoliated epithelial cells with adherent bacteria (reminiscent of clue cells). G. vaginalis was capable of ascending uterine infection, which correlated with the degree of vaginal infection and level of vaginal sialidase activity. The host response to G. vaginalis infection was characterized by robust vaginal epithelial cell exfoliation in the absence of histological inflammation. Our analyses of clinical specimens from women with BV revealed a measureable epithelial exfoliation response compared to women with normal flora, a phenotype that, to our knowledge, is measured here for the first time. The results of this study demonstrate that G. vaginalis is sufficient to cause BV phenotypes and suggest that this organism may contribute to BV etiology and associated complications. This is the first time vaginal infection by a BV associated bacterium in an animal has been shown to parallel the human disease with regard to clinical diagnostic features. Future studies with this model should facilitate investigation of important questions regarding BV etiology, pathogenesis and associated complications

  10. Clinical features of bacterial vaginosis in a murine model of vaginal infection with Gardnerella vaginalis.

    Directory of Open Access Journals (Sweden)

    Nicole M Gilbert

    Full Text Available Bacterial vaginosis (BV is a dysbiosis of the vaginal flora characterized by a shift from a Lactobacillus-dominant environment to a polymicrobial mixture including Actinobacteria and gram-negative bacilli. BV is a common vaginal condition in women and is associated with increased risk of sexually transmitted infection and adverse pregnancy outcomes such as preterm birth. Gardnerella vaginalis is one of the most frequently isolated bacterial species in BV. However, there has been much debate in the literature concerning the contribution of G. vaginalis to the etiology of BV, since it is also present in a significant proportion of healthy women. Here we present a new murine vaginal infection model with a clinical isolate of G. vaginalis. Our data demonstrate that this model displays key features used clinically to diagnose BV, including the presence of sialidase activity and exfoliated epithelial cells with adherent bacteria (reminiscent of clue cells. G. vaginalis was capable of ascending uterine infection, which correlated with the degree of vaginal infection and level of vaginal sialidase activity. The host response to G. vaginalis infection was characterized by robust vaginal epithelial cell exfoliation in the absence of histological inflammation. Our analyses of clinical specimens from women with BV revealed a measureable epithelial exfoliation response compared to women with normal flora, a phenotype that, to our knowledge, is measured here for the first time. The results of this study demonstrate that G. vaginalis is sufficient to cause BV phenotypes and suggest that this organism may contribute to BV etiology and associated complications. This is the first time vaginal infection by a BV associated bacterium in an animal has been shown to parallel the human disease with regard to clinical diagnostic features. Future studies with this model should facilitate investigation of important questions regarding BV etiology, pathogenesis and

  11. 基于图像重建的Zernike矩形状特征评价%Image Zernike Moments Shape Feature Evaluation Based on Image Reconstruction

    Institute of Scientific and Technical Information of China (English)

    刘茂福; 何炎祥; 叶斌

    2007-01-01

    The evaluation approach to the accuracy of the image feature descriptors plays an important role in image feature extraction. We point out that the image shape feature can be described by the Zernike moments set while briefly introducing the basic concept of the Zernike moment. After talking about the image reconstruction technique based on the inverse transformation of Zernike moment, the evaluation approach to the accuracy of the Zernike moments shape feature via the dissimilarity degree and the reconstruction ratio between the original image and the reconstructed image is proposed. The experiment results demonstrate the feasibility of this evaluation approach to image Zernike moments shape feature.

  12. A Novel Feature Extraction Scheme for Medical X-Ray Images

    Directory of Open Access Journals (Sweden)

    Prachi.G.Bhende

    2016-02-01

    Full Text Available X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray images belonging to IRMA (Image Retrieval in Medical applications database that can be used to perform reliable matching between different views of an object or scene. GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. The LBP features are invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A HOG feature vector represents local shape of an object, having edge information at plural cells. These features have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

  13. 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.

  14. Featured Image: A Pulsar Is Obscured by a Solar Explosion

    Science.gov (United States)

    Kohler, Susanna

    2016-12-01

    This series of images (click for the full view!), taken by the Solar and Heliospheric Observatory satellite (SOHO) in August 2015, reveals a tremendous outburst of plasma and magnetic field from the Sun: a coronal mass ejection (CME). If you look closely, youll note that as the CME expands from the Suns surface, it passes in front of a dot highlighted in yellow. This dot marks the location of a distant background pulsar, PSR B0950+08. In a recent study led by Tim Howard (Southwest Research Institute), a team of scientists studied the change observed in the radio emission of this pulsar as the CME passed by in the foreground. The team used these observations to estimate the CMEs density and magnetic field measurements that can tell us more about the nature of the magnetic field in the Suns corona and the solar wind.You can check out the animation of this CME, also taken with SOHOs LASCO instrument, below (the CME starts around 20 seconds in), or you can find out more from the original paper!http://cdn.iopscience.com/images/0004-637X/831/2/208/Full/apjaa35ecf1_video.mp4CitationT. A. Howard et al 2016 ApJ 831 208. doi:10.3847/0004-637X/831/2/208

  15. Design of vector quantizer for image compression using self-organizing feature map and surface fitting.

    Science.gov (United States)

    Laha, Arijit; Pal, Nikhil R; Chanda, Bhabatosh

    2004-10-01

    We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization.

  16. 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)

  17. 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.

  18. MR Imaging Features of a Solitary Subcutaneous Metastasis from a Gastric Adenocarcinoma: A Case Report

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jung Im; Choi, Jung Ah; Choi, Ja Young; Hong, Sung Hwan; Kang, Heung Sik [Seoul National University, Seoul (Korea, Republic of); Chung, Jin Haeng; Oh, Joo Han [Seoul National University Bundang Hospital, Seungnam (Korea, Republic of)

    2009-03-15

    Subcutaneous tissue is an unusual site for a metastasis from a gastric carcinoma. We present a case of a patient with a nodular solitary subcutaneous metastasis from a gastric cancer and describe its magnetic resonance imaging (MRI) features

  19. INFECTED HALLER CELL. RADIOLOGY IMAGE OF THE ISSUE

    Directory of Open Access Journals (Sweden)

    Balasubramanian Thiagarajan

    2012-08-01

    Full Text Available Haller cells are also known as infraorbital ethmoidal cells / maxilla ethmoidal cells. These cellsextend into the inferomedial portion of orbital floor. They are seen in 40% of patients. 1 This article discusses the imaging features of haller cell as seen in coronal CT scan.

  20. Glioma grading using cell nuclei morphologic features in digital pathology images

    Science.gov (United States)

    Reza, Syed M. S.; Iftekharuddin, Khan M.

    2016-03-01

    This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

  1. Digital Library ImageRetrieval usingScale Invariant Feature and Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Hongtao Zhang

    2014-10-01

    Full Text Available With the advance of digital library, the digital content develops with rich information connotation. Traditional information retrieval methods based on external characteristic and text description are unable to sufficientlyreveal and express the substance and semantic relation of multimedia information, and unable to fully reveal and describe the representative characteristics of information. Because of the abundant connotation of image content and the people’s abstract subjectivity in studying image content, the visual feature of the image is difficult to be described by key words. Therefore, this method not always can meet people’s needs, and the study of digital library image retrieval technique based on content is important to both academic research and application. At present, image retrieval methods are mainly based on the text and content, etc. But these existing algorithms have shortages, such as large errors and slow speeds. Motivated by the above fact, we in this paper propose a new approach based on relevance vector machine (RVM. The proposed approach first extracts the patch-level scale invariant image feature (SIFT, and then constructs the global features for images. The image feature is then delivered into RVM for retrieval. We evaluate the proposed approach on Corel dataset. The experimental result shows that the proposed method in this text has high accuracy when retrieves images.

  2. Unsupervised Skin cancer detection by combination of texture and shape features in dermoscopy images

    Directory of Open Access Journals (Sweden)

    Hamed aghapanah rudsari

    2014-05-01

    Full Text Available In this paper a novel unsupervised feature extraction method for detection of melanoma in skin images is presented. First of all, normal skin surrounding the lesion is removed in a segmentation process. In the next step, some shape and texture features are extracted from the output image of the first step: GLCM, GLRLM, the proposed directional-frequency features, and some parameters of Ripplet transform are used as texture features; Also, NRL features and Zernike moments are used as shape features. Totally, 63 texture features and 31 shape features are extracted. Finally, the number of extracted features is reduced using PCA method and a proposed method based on Fisher criteria. Extracted features are classified using the Perceptron Neural Networks, Support Vector Machine, 4-NN, and Naïve Bayes. The results show that SVM has the best performance. The proposed algorithm is applied on a database that consists of 160 labeled images. The overall results confirm the superiority of the proposed method in both accuracy and reliability over previous works.

  3. Featured Image: H I Gas in the Triangulum Galaxy

    Science.gov (United States)

    Kohler, Susanna

    2017-08-01

    These spectacular images are of M33, otherwise known as the Triangulum Galaxy a spiral galaxy roughly 3 million light-years away. The views on the left and in the center are different Wide-field Infrared Survey Explorer (WISE) filters, and the view on the right is a full-resolution look at the H I gas distribution in M33s inner disk, made with data from the Dominion Radio Astrophysical Observatory (DRAO) Synthesis Telescope and Arecibo. In a new study, a team of authors led by Zacharie Sie Kam (University of Ouagadougou, Burkina Faso; University of Montreal, Canada) uses the H I gas observations to explore how the mass is distributed throughout M33 and how the gas moves as the galaxys disk rotates. To read more about what they learned, check out the paper below.CitationS. Z. Kam et al 2017 AJ 154 41. doi:10.3847/1538-3881/aa79f3

  4. Featured Image: A Search for Stellar Bow Shock Nebulae

    Science.gov (United States)

    Kohler, Susanna

    2017-02-01

    These dynamic infrared images (click for the full view!) reveal what are known as bow shock nebulae nebulae that form at the interface between the interstellar medium and the stellar wind from a high-speed star zipping through the galaxy (the arrows show the direction of motion of the star). When the relative speed between the two is supersonic, an arc-shaped bow shock forms ahead of the star, like the six prototypical ones pictured here. A team of scientists led by Henry Kobulnicky (University of Wyoming) has recently searched through survey data from the Spitzer Space Telescope and the Wide Field Infrared Explorer (WISE) to build a catalog of more than 700 such bow-shock nebula candidates, the vast majority of which are new discoveries. To find out more about their sample, check out the paper below!CitationHenry A. Kobulnicky et al 2016 ApJS 227 18. doi:10.3847/0067-0049/227/2/18

  5. Extraction of Geometric Features of Wear Particles in Color Ferrograph Images Based on RGB Color Space

    Institute of Scientific and Technical Information of China (English)

    CHEN Gui-ming; WANG Han-gong; ZHANG Bao-jun; PAN Wei

    2003-01-01

    This paper analyzes the potential color formats of ferrograph images, and presents the algorithms of converting the formats to RGB(Red, Green, Blue) color space. Through statistical analysis of wear par-ticles' geometric features of color ferrograph images in the RGB color space, we give the differences of ferro-graph wear panicles' geometric features among RGB color spaces and gray scale space, and calculate their respective distributions.

  6. STUDY ON THE TECHNIQUE TO DETECT TEXTURE FEATURES IN SAR IMAGES

    Institute of Scientific and Technical Information of China (English)

    Fu Yusheng; Ding Dongtao; Hou Yinming

    2004-01-01

    This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.

  7. Image quality assessment method based on nonlinear feature extraction in kernel space

    Institute of Scientific and Technical Information of China (English)

    Yong DING‡; Nan LI; Yang ZHAO; Kai HUANG

    2016-01-01

    To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.

  8. 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

    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...

  9. 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...

  10. Orientation Modeling for Amateur Cameras by Matching Image Line Features and Building Vector Data

    Science.gov (United States)

    Hung, C. H.; Chang, W. C.; Chen, L. C.

    2016-06-01

    With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

  11. Neuro-imaging findings in infants with congenital cytomegalovirus infection : Relation to trimester of infection

    NARCIS (Netherlands)

    Oosterom, Natanja; Nijman, Joppe; Gunkel, Julia; Wolfs, Tom F W; Groenendaal, Floris; Verboon-Maciolek, Malgosia A.; De Vries, Linda S.

    2015-01-01

    Background: Congenital cytomegalovirus (cCMV) infection early in pregnancy may result in major disabilities. Cerebral abnormalities detected using cranial ultrasound (cUS) and magnetic resonance imaging (MRI) have been related to neurological sequelae. Objective: To evaluate the additional value of

  12. Multilevel Wavelet Feature Statistics for Efficient Retrieval, Transmission, and Display of Medical Images by Hybrid Encoding

    Science.gov (United States)

    Yang, Shuyu; Mitra, Sunanda; Corona, Enrique; Nutter, Brian; Lee, DJ

    2003-12-01

    Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.

  13. A Novel Semi-blind Watermarking Algorithm Based on Fractal Dimension and Image Feature

    Institute of Scientific and Technical Information of China (English)

    NIRongrong; RUANQiuqi

    2004-01-01

    This paper presents a novel semi-blind watermarking algorithm based on fractal dimension and image feature. An original image is divided into blocks with fixed size. According to the idea of the second generation watermarking[1], the image is analyzed using fractal dimension to attain its feature blocks containing edges and textures that are used in the later embedding process and used to form a feature label. The watermark that is the fusion of the feature label and a binary copyright symbol not only represents the copyright symbol, but also reflects the feature of the image. Arnold iteration transform is employed to increase the security of watermark. Then,DCT (Discrete cosine transform) is applied to the feature blocks. The secure watermark that is adaptive to the individual image is embedded into the relations between middle-frequency coefficients and corresponding DC coefficients. The detection and extraction procedure is a semiblind one which does not use the original image but the watermark. Only those who have the original watermarkand the key can detect and extract the right watermark.This makes the approach authentic and have high securitylevel. Experimental results show that this algorithm can get good perceptual invisibility, adaptability and security.And it is robust against cropping, scribbling, low or highpass filtering, adding noise and JPEG compression.

  14. Infective endocarditis detection through SPECT/CT images digital processing

    Science.gov (United States)

    Moreno, Albino; Valdés, Raquel; Jiménez, Luis; Vallejo, Enrique; Hernández, Salvador; Soto, Gabriel

    2014-03-01

    Infective endocarditis (IE) is a difficult-to-diagnose pathology, since its manifestation in patients is highly variable. In this work, it was proposed a semiautomatic algorithm based on SPECT images digital processing for the detection of IE using a CT images volume as a spatial reference. The heart/lung rate was calculated using the SPECT images information. There were no statistically significant differences between the heart/lung rates values of a group of patients diagnosed with IE (2.62+/-0.47) and a group of healthy or control subjects (2.84+/-0.68). However, it is necessary to increase the study sample of both the individuals diagnosed with IE and the control group subjects, as well as to improve the images quality.

  15. 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.

  16. [Clinical feature of cryptosporidium infection in HIV/AIDS patients with chronic diarrhea].

    Science.gov (United States)

    Jiao, Bing-xin; Wang, Hui-zhu; Liu, Ying; Li, Juan; Guo, Jie; Li, Min; Wan, Gang; Hua, Wen-hao

    2011-10-11

    To investigate the clinical feature of cryptosporidium infection in HIV/AIDS patients with chronic diarrhea. 253 Stool samples were collected from HIV/AIDS patients with chronic diarrhea during Nov.2009 to Dec.2010. The samples were concentrated by Formalin-Ethyl Acetate Sedimentation technique and stained by Modified acid-fast stain (AFS) for the identification of oocysts by microscopy. Divided the cases into three groups according to their CD4 cell counts (AIDS patients was 12.6% in 253 cases. CD4(+) T-lymphocyte counts was related to the infection rates of cryptosporidium, the difference was statistically significant (χ(2) = 10.33, P difference was no statistically significant (P > 0.05). HIV/AIDS patients with chronic diarrhea who progressed during asymptomatic period, pre-AIDS period, AIDS period, had the infection rate of 0(0/7), 21.3% (19/89), 8.3% (13/157) respectively, the difference was statistically significant (χ(2) = 9.822, P HIV patients were diagnosed with enteritis, the infection rate in urban area and rural area was 6.5% (7/104) and 16.8% (25/149) separately, the difference was statistically significant (χ(2) = 5.596, P different age groups, Cryptosporidium infection status were separately 7.3% (4/55), 13.4% (22/164), 17.6% (6/34). Each group's comparative difference was no statistically significant (χ(2) = 2.29, P > 0.05). The infection rate of cryptosporidium and clinical severity of cryptosporidium infection are statistically correlated with CD4(+) T-lymphocyte counts, with AIDS stage, with HIV associated proctitis.

  17. Contrast-enhanced breast ultrasonography: imaging features with histopathologic correlation.

    Science.gov (United States)

    Liu, He; Jiang, Yu-Xin; Liu, Ji-Bin; Zhu, Qing-Li; Sun, Qiang; Chang, Xiao-Yan

    2009-07-01

    The purpose of this study was to identify histopathologic correlates for the varied appearances of breast masses on contrast-enhanced ultrasonography (CEUS). Contrast-enhanced ultrasonography was performed in 104 patients (age range, 19-86 years) after administration of a sulfur hexafluoride microbubble contrast agent, and enhancement patterns were classified as no enhancement, peripheral enhancement, homogeneous enhancement, regional enhancement, and heterogeneous enhancement. All patients' histologic slides were reviewed and correlated with CEUS findings. In malignant masses, heterogeneous enhancement corresponded to tumor cell cords or clusters in a variable amount of desmoplastic stroma. Homogeneous enhancement corresponded to hypercellularity in the whole mass, or ductal carcinoma in situ (DCIS) was predominant. Regional enhancement corresponded to a DCIS component. Peripheral enhancement corresponded to a DCIS component, hypercellularity or adenosis at the periphery, and low-degree cellularity, degeneration, fibrosis, or necrosis in the center. No enhancement was present in 1 case of low-grade DCIS. In benign masses, heterogeneous enhancement corresponded to loose cell proliferation in a more sclerotic stroma. Homogeneous enhancement corresponded to diffuse hypercellularity, an inflammatory cell infiltrate, or intraductal papilloma. Regional enhancement corresponded to focal hypercellularity or intraductal papilloma within a dilated duct. No enhancement corresponded to desmoplastic stroma. Peripheral enhancement was shown in 1 case of granulomatous mastitis with an inflammatory infiltrate at the periphery and necrosis in the center. Breast mass CEUS findings correlated with histologic features.

  18. Quantitative image analysis of HIV-1 infection in lymphoid tissue

    Energy Technology Data Exchange (ETDEWEB)

    Haase, A.T.; Zupancic, M.; Cavert, W. [Univ. of Minnesota Medical School, Minneapolis, MN (United States)] [and others

    1996-11-08

    Tracking human immunodeficiency virus-type 1 (HIV-1) infection at the cellular level in tissue reservoirs provides opportunities to better understand the pathogenesis of infection and to rationally design and monitor therapy. A quantitative technique was developed to determine viral burden in two important cellular compartments in lymphoid developed to determine viral burden in two important cellular compartments in lymphoid tissues. Image analysis and in situ hybridization were combined to show that in the presymptomatic stages of infection there is a large, relatively stable pool of virions on the surfaces of follicular dendritic cells and a smaller pool of productivity infected cells. Despite evidence of constraints on HIV-1 replication in the infected cell population in lymphoid tissues, estimates of the numbers of these cells and the virus they could produce are consistent with the quantities of virus that have been detected in the bloodstream. The cellular sources of virus production and storage in lymphoid tissues can now be studied with this approach over the course of infection and treatment. 22 refs., 2 figs., 2 tabs.

  19. Clinical and epidemiological features of coryneform skin infections at a tertiary hospital

    Directory of Open Access Journals (Sweden)

    Malcolm Pinto

    2016-01-01

    Full Text Available Background: Skin infections caused by coryneform bacteria are common dermatological conditions. However, to the best of our knowledge, no studies are available on the clinical characteristics and epidemiological features of this group of disorders as one entity from India and abroad. Aims: To study the clinical and epidemiological features of coryneform skin infections Methods: A total of 75 patients presenting with clinically distinctive lesions of pitted keratolysis, erythrasma and trichobacteriosis to our hospital were included in the study. Cases were interviewed with particular emphasis on epidemiological features and the various clinical findings were recorded. Investigations like Gram's stain, Wood's light examination, 10% KOH scrapings, were done in selected cases to ascertain the diagnosis. Results: Pitted keratolysis was more common in the age group of 31-40 years (40% with a male preponderance (76.7%, most commonly affecting pressure bearing areas of the soles with malodour (86.7% and frequent contact with water (58.3% constituting the most important presenting symptom and provocating factor respectively. Erythrasma affected both male and female patients equally and was more commonly detected in patients with a BMI > 23kg/m2 (62.5% and in diabetics (50%. All patients with trichobacteriosis presented with yellow coloured concretions in the axillae. Bromhidrosis (71.4% and failure to regularly use an axillary deodorant (71.4% were the most common presenting symptom and predisposing factor respectively. Conclusion: Coryneform skin infections are common dermatological conditions, though epidemiological data are fragmentary. Hyperhidrosis is a common predisposing factor to all three coryneform skin infections. Asymmetrical distribution of pits has been reported in our study. Diabetic status needs to be evaluated in all patients with erythrasma. Woods lamp examination forms an indispensible tool to diagnose erythrasma and trichobacteriosis.

  20. Clinical and epidemiological features of coryneform skin infections at a tertiary hospital

    Science.gov (United States)

    Pinto, Malcolm; Hundi, Ganesh Kamath; Bhat, Ramesh Marne; Bala, Nanda Kishore; Dandekeri, Sukumar; Martis, Jacintha; Kambil, Srinath M.

    2016-01-01

    Background: Skin infections caused by coryneform bacteria are common dermatological conditions. However, to the best of our knowledge, no studies are available on the clinical characteristics and epidemiological features of this group of disorders as one entity from India and abroad. Aims: To study the clinical and epidemiological features of coryneform skin infections Methods: A total of 75 patients presenting with clinically distinctive lesions of pitted keratolysis, erythrasma and trichobacteriosis to our hospital were included in the study. Cases were interviewed with particular emphasis on epidemiological features and the various clinical findings were recorded. Investigations like Gram's stain, Wood's light examination, 10% KOH scrapings, were done in selected cases to ascertain the diagnosis. Results: Pitted keratolysis was more common in the age group of 31-40 years (40%) with a male preponderance (76.7%), most commonly affecting pressure bearing areas of the soles with malodour (86.7%) and frequent contact with water (58.3%) constituting the most important presenting symptom and provocating factor respectively. Erythrasma affected both male and female patients equally and was more commonly detected in patients with a BMI > 23kg/m2 (62.5%) and in diabetics (50%). All patients with trichobacteriosis presented with yellow coloured concretions in the axillae. Bromhidrosis (71.4%) and failure to regularly use an axillary deodorant (71.4%) were the most common presenting symptom and predisposing factor respectively. Conclusion: Coryneform skin infections are common dermatological conditions, though epidemiological data are fragmentary. Hyperhidrosis is a common predisposing factor to all three coryneform skin infections. Asymmetrical distribution of pits has been reported in our study. Diabetic status needs to be evaluated in all patients with erythrasma. Woods lamp examination forms an indispensible tool to diagnose erythrasma and trichobacteriosis. PMID

  1. Improving Image steganalysis performance using a graph-based feature selection method

    Directory of Open Access Journals (Sweden)

    Amir Nouri

    2016-05-01

    Full Text Available Steganalysis is the skill of discovering the use of steganography algorithms within an image with low or no information regarding the steganography algorithm or/and its parameters. The high-dimensionality of image data with small number of samples has presented a difficult challenge for the steganalysis task. Several methods have been presented to improve the steganalysis performance by feature selection. Feature selection, also known as variable selection, is one of the fundamental problems in the fields of machine learning, pattern recognition and statistics. The aim of feature selection is to reduce the dimensionality of image data in order to enhance the accuracy of Steganalysis task. In this paper, we have proposed a new graph-based blind steganalysis method for detecting stego images from the cover images in JPEG images using a feature selection technique based on community detection. The experimental results show that the proposed approach is easy to be employed for steganalysis purposes. Moreover, performance of proposed method is better than several recent and well-known feature selection-based Image steganalysis methods.

  2. Introduction: feature issue on phantoms for the performance evaluation and validation of optical medical imaging devices.

    Science.gov (United States)

    Hwang, Jeeseong; Ramella-Roman, Jessica C; Nordstrom, Robert

    2012-06-01

    The editors introduce the Biomedical Optics Express feature issue on "Phantoms for the Performance Evaluation and Validation of Optical Medical Imaging Devices." This topic was the focus of a technical workshop that was held on November 7-8, 2011, in Washington, D.C. The feature issue includes 13 contributions from workshop attendees.

  3. On the selection of optimal feature region set for robust digital image watermarking.

    Science.gov (United States)

    Tsai, Jen-Sheng; Huang, Win-Bin; Kuo, Yau-Hwang

    2011-03-01

    A novel feature region selection method for robust digital image watermarking is proposed in this paper. This method aims to select a nonoverlapping feature region set, which has the greatest robustness against various attacks and can preserve image quality as much as possible after watermarked. It first performs a simulated attacking procedure using some predefined attacks to evaluate the robustness of every candidate feature region. According to the evaluation results, it then adopts a track-with-pruning procedure to search a minimal primary feature set which can resist the most predefined attacks. In order to enhance its resistance to undefined attacks under the constraint of preserving image quality, the primary feature set is then extended by adding into some auxiliary feature regions. This work is formulated as a multidimensional knapsack problem and solved by a genetic algorithm based approach. The experimental results for StirMark attacks on some benchmark images support our expectation that the primary feature set can resist all the predefined attacks and its extension can enhance the robustness against undefined attacks. Comparing with some well-known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

  4. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images.

    Science.gov (United States)

    Acharya, U Rajendra; Sree, S Vinitha; Muthu Rama Krishnan, M; Krishnananda, N; Ranjan, Shetty; Umesh, Pai; Suri, Jasjit S

    2013-12-01

    Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.

  5. FURTHER EVALUATION OF QUANTITATIVE NUCLEAR IMAGE FEATURES FOR CLASSIFICATION OF LUNG CARCINOMAS

    NARCIS (Netherlands)

    THUNNISSEN, FBJM; DIEGENBACH, PC; VANHATTUM, AH; TOLBOOM, J; VANDERSLUIS, DM; SCHAAFSMA, W; HOUTHOFF, HJ; BAAK, JPA

    1992-01-01

    The usefulness of quantitative nuclear image features (QNI) for the histological classification of lung carcinomas was investigated. As no clear distinction could be established between the distributions of these features for the nuclei of squamous cell, adenocarcinoma, and large cell carcinoma, the

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

    NARCIS (Netherlands)

    van Randen, A.; Lameris, 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

  7. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Galavis, Paulina E.; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert (Dept. of Medical Physics, Univ. of Wisconsin, Madison, WI (United States)), E-mail: galavis@wisc.edu; Hollensen, Christian (Dept. of Informatics and Mathematical Models, Technical Univ. of Denmark, Copenhagen (Denmark))

    2010-10-15

    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 [18F]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

  9. Featured Image: The Simulated Collapse of a Core

    Science.gov (United States)

    Kohler, Susanna

    2016-11-01

    This stunning snapshot (click for a closer look!) is from a simulation of a core-collapse supernova. Despite having been studied for many decades, the mechanism driving the explosions of core-collapse supernovae is still an area of active research. Extremely complex simulations such as this one represent best efforts to include as many realistic physical processes as is currently computationally feasible. In this study led by Luke Roberts (a NASA Einstein Postdoctoral Fellow at Caltech at the time), a core-collapse supernova is modeled long-term in fully 3D simulations that include the effects of general relativity, radiation hydrodynamics, and even neutrino physics. The authors use these simulations to examine the evolution of a supernova after its core bounce. To read more about the teams findings (and see more awesome images from their simulations), check out the paper below!CitationLuke F. Roberts et al 2016 ApJ 831 98. doi:10.3847/0004-637X/831/1/98

  10. Amebiasis in HIV-1-Infected Japanese Men: Clinical Features and Response to Therapy

    Science.gov (United States)

    Watanabe, Koji; Gatanaga, Hiroyuki; Cadiz, Aleyla Escueta-de; Tanuma, Junko; Nozaki, Tomoyoshi; Oka, Shinichi

    2011-01-01

    Invasive amebic diseases caused by Entamoeba histolytica are increasing among men who have sex with men and co-infection of ameba and HIV-1 is an emerging problem in developed East Asian countries. To characterize the clinical and epidemiological features of invasive amebiasis in HIV-1 patients, the medical records of 170 co-infected cases were analyzed retrospectively, and E. histolytica genotype was assayed in 14 cases. In this series of HIV-1-infected patients, clinical presentation of invasive amebiasis was similar to that described in the normal host. High fever, leukocytosis and high CRP were associated with extraluminal amebic diseases. Two cases died from amebic colitis (resulting in intestinal perforation in one and gastrointestinal bleeding in one), and three cases died from causes unrelated to amebiasis. Treatment with metronidazole or tinidazole was successful in the other 165 cases. Luminal treatment was provided to 83 patients following metronidazole or tinidazole treatment. However, amebiasis recurred in 6 of these, a frequency similar to that seen in patients who did not receive luminal treatment. Recurrence was more frequent in HCV-antibody positive individuals and those who acquired syphilis during the follow-up period. Various genotypes of E. histolytica were identified in 14 patients but there was no correlation between genotype and clinical features. The outcome of metronidazole and tinidazole treatment of uncomplicated amebiasis was excellent even in HIV-1-infected individuals. Luminal treatment following metronidazole or tinidazole treatment does not reduce recurrence of amebiasis in high risk populations probably due to amebic re-infection. PMID:21931875

  11. Amebiasis in HIV-1-infected Japanese men: clinical features and response to therapy.

    Directory of Open Access Journals (Sweden)

    Koji Watanabe

    2011-09-01

    Full Text Available Invasive amebic diseases caused by Entamoeba histolytica are increasing among men who have sex with men and co-infection of ameba and HIV-1 is an emerging problem in developed East Asian countries. To characterize the clinical and epidemiological features of invasive amebiasis in HIV-1 patients, the medical records of 170 co-infected cases were analyzed retrospectively, and E. histolytica genotype was assayed in 14 cases. In this series of HIV-1-infected patients, clinical presentation of invasive amebiasis was similar to that described in the normal host. High fever, leukocytosis and high CRP were associated with extraluminal amebic diseases. Two cases died from amebic colitis (resulting in intestinal perforation in one and gastrointestinal bleeding in one, and three cases died from causes unrelated to amebiasis. Treatment with metronidazole or tinidazole was successful in the other 165 cases. Luminal treatment was provided to 83 patients following metronidazole or tinidazole treatment. However, amebiasis recurred in 6 of these, a frequency similar to that seen in patients who did not receive luminal treatment. Recurrence was more frequent in HCV-antibody positive individuals and those who acquired syphilis during the follow-up period. Various genotypes of E. histolytica were identified in 14 patients but there was no correlation between genotype and clinical features. The outcome of metronidazole and tinidazole treatment of uncomplicated amebiasis was excellent even in HIV-1-infected individuals. Luminal treatment following metronidazole or tinidazole treatment does not reduce recurrence of amebiasis in high risk populations probably due to amebic re-infection.

  12. Microbiological Characteristics and Clinical Features of Cardiac Implantable Electronic Device Infections at a Tertiary Hospital in China

    Science.gov (United States)

    Wang, Ruobing; Li, Xuebin; Wang, Qi; Zhang, Yawei; Wang, Hui

    2017-01-01

    The incidence of cardiac implantable electronic device (CIED) infections is rapidly increasing worldwide. However, the microbiological characteristics and clinical features of symptomatic CIED infections are not well described. The present study included patients with CIED infections in China, and their pocket tissues were collected for clinical microbiological determination. A total of 219 patients with CIED infections were investigated; of these patients, 145 (66.2%) were positive for CIED infection in pocket tissue cultures and 24 (11.0%) were positive in both blood and pocket tissue cultures. Patients with recurrent infections and patients with systemic infections tended to have higher rates of positive cultures from pocket tissue. In addition, patients with lung diseases were more likely to have early CIED infections than late CIED infections, while patients with liver diseases were more susceptible to systemic infections than local infections. Staphylococcus species were the most common cause of CIED infections; coagulase-negative staphylococci was the predominant type (accounting for 45.2% in all cases and 68.3% in culture-positive cases). None of the Staphylococcus isolates were resistant to gentamicin, linezolid or vancomycin. Gram-negative bacilli accounted for 9.1% of all cases and 13.8% of culture-positive cases. Significant differences in the distribution of different pathogens were identified between primary infections and recurrent infections, between local infections and systemic infections, and between early infections and late infections. Our data describe the microbiological characteristics and clinical features of CIED infections, and provide evidence for advisory guidelines on the management of CIED infections in China.

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

    NARCIS (Netherlands)

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

    2011-01-01

    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 favorabl

  14. 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.

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

    CSIR Resear