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

Science.gov (United States)

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

2018-04-01

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

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

Directory of Open Access Journals (Sweden)

T.-A. Teo

2016-06-01

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

Wilson’s disease: Atypical imaging features

Directory of Open Access Journals (Sweden)

Venugopalan Y Vishnu

2016-10-01

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

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.

Infrared image enhancement with learned features

Science.gov (United States)

Fan, Zunlin; Bi, Duyan; Ding, Wenshan

2017-11-01

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

Radionuclide imaging of musculoskeletal infection

International Nuclear Information System (INIS)

Palestr, Christopher J.; North Shore Long Island Jewish Health System, Manhasset and New Hyde Park, NY; Love, Charito

2007-01-01

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. (author)

Imaging features of aggressive angiomyxoma

International Nuclear Information System (INIS)

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

2003-01-01

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

Image Processing and Features Extraction of Fingerprint Images ...

African Journals Online (AJOL)

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

Radionuclide imaging of musculoskeletal infection

Energy Technology Data Exchange (ETDEWEB)

Palestr, Christopher J. [Albert Einstein College of Medicine, Bronx, NY (United States); North Shore Long Island Jewish Health System, Manhasset and New Hyde Park, NY (United States). Div. of Nuclear Medicine and Molecular Imaging; E-mail: palestro@lij.edu; Love, Charito [North Shore Long Island Jewish Health System, Manhasset and New Hyde Park, NY (United States). Div. of Nuclear Medicine and Molecular Imaging

2007-09-15

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. (author)

Feature hashing for fast image retrieval

Science.gov (United States)

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

2018-03-01

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

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.)

Imaging of musculoskeletal soft tissue infections

International Nuclear Information System (INIS)

Turecki, Marcin B.; Taljanovic, Mihra S.; Holden, Dean A.; Hunter, Tim B.; Rogers, Lee F.; Stubbs, Alana Y.; Graham, Anna R.

2010-01-01

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.)

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

Imaging infection and inflammation

International Nuclear Information System (INIS)

Buscombe, John

1997-01-01

Full text. The use of nuclear medicine techniques to image infection has been with us for over 20 years, indeed this year sees the 20th anniversary of the publication of Matthew Thakur's paper of the use of In-111 oxime labelled leukocytes in imaging infection. Without doubt this technique has stood the test of time and has been used to save many lives in infected patients worldwide. As we approach the 21st century we are faced with new problems which will need new solutions. Infections themselves have changed their very nature, HIV a benign virus which only infected monkeys in central Africa in 1977 has now spread throughout the globe and unfortunately few societies have remained free of its ravages. In its wake tuberculosis continues to infect both the poor and weak but also has started to re-infect more affluent societies. In its wake tuberculosis continues to infect both both the poor and weak but also has started to re-infect more affluent societies. The use of immuno suppressive therapy in many patients with transplants or cancer has lead to new infections in a wider group of patients. The wide spread use of antibiotics has lead to the emergence of antibiotic resistant organisms. The old approach of widespread antibiotic treatment in patients with suspected infection is not acceptable. If possible organisms must be isolated. Normally imaging is required to localize infection and it is important to realize that a combination or anatomical imaging with CT, ultrasound or MRI and nuclear medicine is often the only way to determine the site of infection. Allied to this a new educated public has demanded that diagnostic tests be accurate and non-invasive, particularly in non-fatal inflammatory disease. All these challenges has lead us to a new frontier in nuclear medicine. In some ways we have had to rediscover the old. For example the use of Ga-67 citrate in imaging tuberculosis or infection in patients with Aids. The use of Tc-99 m HMPAO labelled leukocytes in

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. .

W-transform method for feature-oriented multiresolution image retrieval

Energy Technology Data Exchange (ETDEWEB)

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

1995-07-01

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

Field application of feature-enhanced imaging

International Nuclear Information System (INIS)

Mucciardi, A.N.

1988-01-01

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

A comparison of image features for registering LWIR and visual images

CSIR Research Space (South Africa)

Cronje, J

2012-11-01

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

Superpixel-Based Feature for Aerial Image Scene Recognition

Directory of Open Access Journals (Sweden)

Hongguang Li

2018-01-01

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

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

Science.gov (United States)

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples

Solving jigsaw puzzles using image features

DEFF Research Database (Denmark)

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

2008-01-01

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

Feature Evaluation for Building Facade Images - AN Empirical Study

Science.gov (United States)

Yang, M. Y.; Förstner, W.; Chai, D.

2012-08-01

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

Imaging features of kaposiform lymphangiomatosis

International Nuclear Information System (INIS)

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

2016-01-01

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

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

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

2015-10-06

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

Computed tomographic features of apical infection of equine maxillary cheek teeth: a retrospective study of 49 horses.

Science.gov (United States)

Bühler, M; Fürst, A; Lewis, F I; Kummer, M; Ohlerth, S

2014-07-01

Computed tomographic (CT) studies evaluating the relevance of individual CT features of apical infection in maxillary cheek teeth are lacking. To study the prevalence and relationship of single CT features in horses with and without clinical evidence of apical infection in maxillary cheek teeth. Retrospective case-control study. Multislice CT scans of the head of 49 horses were evaluated retrospectively. Changes of the infundibulum, pulp, root, lamina dura, periodontal space and alveolar bone in maxillary cheek teeth were recorded. Single CT changes were much more prevalent in the 28 horses with clinical signs. However, infundibular changes and a nondetectable lamina dura were also common in the 21 horses without clinical evidence of apical infection. Computed tomographic abnormalities of the pulp, root, periapical bone and periodontal space and the presence of a tooth fracture were significantly related. Infundibular changes were not associated with other CT signs of apical infection. Although nondetectable lamina dura was the most frequent CT change in all teeth in both studied groups, it was most commonly a solitary feature in otherwise normal teeth. Apical infections, defined as ≥3 CT changes, occurred mainly in the 108/208, 109/209 and 110/210 (Triadan numbers) and were found only in horses with clinical evidence of apical infection, except in one horse without clinical signs that had one affected root. Combined CT changes of the pulp, root, lamina dura, periapical bone and periodontal space and the presence of a tooth fracture appear to be reliable features to diagnose apical infection in maxillary cheek teeth. As a solitary feature, a nondetectable lamina dura should be interpreted cautiously and may even be considered normal due to its minor thickness and/or too low resolution of the imaging modality. © 2013 EVJ Ltd.

An Effective Combined Feature For Web Based Image Retrieval

Directory of Open Access Journals (Sweden)

H.M.R.B Herath

2015-08-01

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

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

International Nuclear Information System (INIS)

Moreno J; Caicedo J Gonzalez F

2010-01-01

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

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

Directory of Open Access Journals (Sweden)

J Carlos Moreno

2010-09-01

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

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).

Smart Images Search based on Visual Features Fusion

International Nuclear Information System (INIS)

Saad, M.H.

2013-01-01

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

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.)

Dengue encephalitis with predominant cerebellar involvement: Report of eight cases with MR and CT imaging features

International Nuclear Information System (INIS)

Hegde, Vinay; Bhat, Maya; Prasad, Chandrajit; Gupta, A.K.; Saini, Jitender; Aziz, Zarina; Kumar, Sharath; Netravathi, M.

2015-01-01

CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis. (orig.)

Multispectral Image Feature Points

Directory of Open Access Journals (Sweden)

Cristhian Aguilera

2012-09-01

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

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.

Feature Detector and Descriptor for Medical Images

Science.gov (United States)

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

2009-02-01

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

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.

Adapting Local Features for Face Detection in Thermal Image

Directory of Open Access Journals (Sweden)

Chao Ma

2017-11-01

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

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.

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.

Imaging of bacterial infections of the sacroiliac joint

International Nuclear Information System (INIS)

Groves, C.; Cassar-Pullicino, V.

2004-01-01

Infection of the sacroiliac joint can be pyogenic or granulomatous and is usually unilateral. There are a number of predisposing conditions including drug abuse and intra articular steroid injection, but in 44% of cases, no definite predisposing factors can be identified. Considerable delay between presentation and diagnosis is recognized. The clinical picture may be non-specific and variable, and clinical suspicion may be low due to the relatively low incidence of the condition. This is compounded by difficulties in clinical examination of the SIJs. The diagnosis is based on a history suggestive of infection, clinical or radiographic localization to the SIJs, and a positive blood culture or joint aspirate. The pathology of pyogenic sacroiliitis is reviewed with respect to the anatomy of the SIJ, and the differential diagnoses considered. The imaging findings, and relative merits of all the modalities are discussed with particular consideration given to changes over the course of the disease. Imaging strategies are evaluated and proposed. As the commonest presenting symptom is low back pain, consideration should be given to the addition of a STIR sequence covering the SIJs on all routine lumbar spine MR examinations. MR imaging is the most sensitive and specific imaging modality, while CT-guided arthrocentesis improves diagnostic confidence. Tc 99 MDP blood pool imaging mirrors the clinical features of resolution, and scintigraphy may be the best method to monitor response to treatment. Targeted antibiotic therapy usually leads to a full recovery. A high incidence of clinical suspicion, with MR imaging at an early stage are the essential prerequisites to an accurate diagnosis of bacterial sacroiliitis. (orig.) [de

FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY

Directory of Open Access Journals (Sweden)

M. Y. Yang

2012-08-01

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

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.

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.

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.

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

Science.gov (United States)

Li, Baopu; Meng, Max Q-H

2012-05-01

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

Radionuclide imaging of spinal infections

International Nuclear Information System (INIS)

Gemmel, Filip; Dumarey, Nicolas; Palestro, Christopher J.

2006-01-01

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 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 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. [ 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 radiolabelled

Histological image classification using biologically interpretable shape-based features

International Nuclear Information System (INIS)

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

2013-01-01

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

Determination of the Image Complexity Feature in Pattern Recognition

Directory of Open Access Journals (Sweden)

Veacheslav L. Perju

2003-11-01

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

Congenital cytomegalovirus infection: contribution and best timing of prenatal MR imaging

Energy Technology Data Exchange (ETDEWEB)

Cannie, Mieke M. [University Hospital Brugmann, Universite Libre de Bruxelles, Department of Radiology, Brussels (Belgium); UZ Brussel, Vrije Universiteit Brussel, Department of Radiology, Brussel (Belgium); Devlieger, Roland; Catte, Luc de; Valk, Elise van der [University Hospitals Leuven, Department of Obstetrics and Gynecology, Leuven (Belgium); Leyder, Mina; Foulon, Walter [UZ Brussel, Vrije Universiteit Brussel, Department of Obstetrics, Brussel (Belgium); Claus, Filip [University Hospitals Leuven, Department of Radiology, Leuven (Belgium); Onze-Lieve-Vrouw Hospital, Department of Radiology, Aalst (Belgium); Leus, Astrid [UZ Brussel, Vrije Universiteit Brussel, Department of Radiology, Brussel (Belgium); Cossey, Veerle [University Hospitals Leuven, Neonatal Intensive Care Unit, Leuven (Belgium); Foulon, Ina [UZ Brussel, Vrije Universiteit Brussel, Department of Otolaryngology - Head and Neck Surgery, Brussel (Belgium); Cos, Teresa; Jani, Jacques C. [University Hospital Brugmann, Universite Libre de Bruxelles, Department of Obstetrics and Gynecology, Brussels (Belgium); Bernaert, Anja [Sint-Augustinus Hospital, Department of Radiology, Antwerp (Belgium); Oyen, Raymond [University Hospitals Leuven, Department of Radiology, Leuven (Belgium)

2016-10-15

To predict sensorineural hearing loss (SNHL) and neurological impairment in congenital cytomegalovirus (cCMV) infection using MR imaging and define the best timing in pregnancy for prenatal assessment. In 121 patients with confirmed cCMV infection, brain features at MR imaging were respectively graded from 1 to 5: normal; isolated frontal/parieto-occipital hyperintensity; temporal periventricular hyperintensity; temporal/occipital cysts and/or intraventricular septa; migration disorders. Grading was correlated with postnatal SNHL and neurological impairment using regression analysis. In 51 fetuses with MR examinations at 26.9 and 33.0 weeks, the predictive value of SNHL and neurological impairment was compared using ROC curves. Postnatal follow-up showed SNHL in 18 infants and neurological impairment in 10. MR grading was predictive of SNHL and of neurological impairment (P < 0.001). In grade 1 or 2, none had SNHL and 1/74 had neurological impairment. The areas under ROC curves for prediction of postnatal SNHL and of neurological impairment from first and second MR examination were comparable. Our data suggest that in cCMV infection, prediction of SNHL and neurological impairment is feasible by fetal MR imaging with a high negative predictive value and can equally be done at 27 or 33 weeks of gestation. (orig.)

Congenital cytomegalovirus infection: contribution and best timing of prenatal MR imaging

International Nuclear Information System (INIS)

Cannie, Mieke M.; Devlieger, Roland; Catte, Luc de; Valk, Elise van der; Leyder, Mina; Foulon, Walter; Claus, Filip; Leus, Astrid; Cossey, Veerle; Foulon, Ina; Cos, Teresa; Jani, Jacques C.; Bernaert, Anja; Oyen, Raymond

2016-01-01

To predict sensorineural hearing loss (SNHL) and neurological impairment in congenital cytomegalovirus (cCMV) infection using MR imaging and define the best timing in pregnancy for prenatal assessment. In 121 patients with confirmed cCMV infection, brain features at MR imaging were respectively graded from 1 to 5: normal; isolated frontal/parieto-occipital hyperintensity; temporal periventricular hyperintensity; temporal/occipital cysts and/or intraventricular septa; migration disorders. Grading was correlated with postnatal SNHL and neurological impairment using regression analysis. In 51 fetuses with MR examinations at 26.9 and 33.0 weeks, the predictive value of SNHL and neurological impairment was compared using ROC curves. Postnatal follow-up showed SNHL in 18 infants and neurological impairment in 10. MR grading was predictive of SNHL and of neurological impairment (P < 0.001). In grade 1 or 2, none had SNHL and 1/74 had neurological impairment. The areas under ROC curves for prediction of postnatal SNHL and of neurological impairment from first and second MR examination were comparable. Our data suggest that in cCMV infection, prediction of SNHL and neurological impairment is feasible by fetal MR imaging with a high negative predictive value and can equally be done at 27 or 33 weeks of gestation. (orig.)

Application of eigen value expansion to feature extraction from MRI images

International Nuclear Information System (INIS)

Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

1991-01-01

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

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

Science.gov (United States)

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

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Seyed Mostafa Mousavi Kahaki

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

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

Directory of Open Access Journals (Sweden)

O. Akcay

2017-05-01

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

Nuclear medicine imaging of bone infections

International Nuclear Information System (INIS)

Love, C.; Palestro, C.J.

2016-01-01

Osteomyelitis is a broad group of infectious diseases that involve the bone and/or bone marrow. It can arise haematogenously, via extension from a contiguous infection, or by direct inoculation during surgery or trauma. The diagnosis is not always obvious and imaging tests are frequently performed as part of the diagnostic work-up. Commonly performed radionuclide tests include technetium-99m ("9"9"mTc)-diphosphonate bone scintigraphy (bone), and gallium-67 ("6"7Ga) and in vitro labelled leukocyte (white blood cell; WBC) imaging. Although they are useful, each of these tests has limitations. Bone scintigraphy is sensitive but not specific, especially when underlying osseous abnormalities are present. "6"7Ga accumulates in tumour, trauma, and in aseptic inflammation; furthermore, there is typically an interval of 1–3 days between radiopharmaceutical injection of and imaging. Currently, this agent is used primarily for spinal infections. Except for the spine, WBC imaging is the nuclear medicine test of choice for diagnosing complicating osteomyelitis. The in vitro leukocyte labelling process requires skilled personnel, is laborious, and is not always available. Complementary marrow imaging is usually required to maximise accuracy. Not surprisingly, alternative radiopharmaceuticals are continuously being investigated. Radiolabelled anti-granulocyte antibodies and antibody fragments, investigated as in vivo leukocyte labelling agents, have their own limitations and are not widely available. "1"1"1In-biotin is useful for diagnosing spinal infections. Radiolabelled synthetic fragments of ubiquicidin, a naturally occurring human antimicrobial peptide that targets bacteria, have shown promise as infection specific radiopharmaceuticals. 2-["1"8F]-fluoro-2-deoxy-D-glucose (FDG) positron-emission tomography (PET) with or without computed tomography (CT) is very useful in musculoskeletal infection. Sensitivities of more than 95% and specificities ranging from 75–99% have

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

Science.gov (United States)

Han, Xian-Hua; Chen, Yen-Wei

2011-01-01

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

CT imaging features of tuberculous spondylitis in children

International Nuclear Information System (INIS)

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

2009-01-01

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

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

A visual perceptual descriptor with depth feature for image retrieval

Science.gov (United States)

Wang, Tianyang; Qin, Zhengrui

2017-07-01

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

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.

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

Science.gov (United States)

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

2018-04-01

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

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...

Hyperspectral image classifier based on beach spectral feature

International Nuclear Information System (INIS)

Liang, Zhang; Lianru, Gao; Bing, Zhang

2014-01-01

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

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.

Diffusion tensor image registration using hybrid connectivity and tensor features.

Science.gov (United States)

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

2014-07-01

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

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.)

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)

Infection imaging with radiopharmaceuticals in the 21st century

International Nuclear Information System (INIS)

Das, Satya S.; Wareham, David W.; Britton, Keith E.; Hall, Anne V.

2002-01-01

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)

MR Imaging Features of Fibrocystic Change of the Breast

Science.gov (United States)

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

2008-01-01

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

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.

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

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

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.

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

DEFF Research Database (Denmark)

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

2016-01-01

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

FEATURE MATCHING OF HISTORICAL IMAGES BASED ON GEOMETRY OF QUADRILATERALS

Directory of Open Access Journals (Sweden)

F. Maiwald

2018-05-01

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

Imaging of the neurological complications of infective endocarditis

International Nuclear Information System (INIS)

Kim, S.J.; Lee, J.Y.; Kim, T.H.; Kim, S.C.; Choi, Y.H.; Pai, H.; Choi, W.S.

1998-01-01

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.)

Hand and wrist arthritis of Behcet disease: Imaging features

International Nuclear Information System (INIS)

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

2010-01-01

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

Image Retrieval based on Integration between Color and Geometric Moment Features

International Nuclear Information System (INIS)

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

2012-01-01

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

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

Directory of Open Access Journals (Sweden)

Vibha Gupta

2018-02-01

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

Renal angiomyoadenomatous tumour: Imaging features

Science.gov (United States)

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

2012-01-01

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

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

Science.gov (United States)

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

2018-04-01

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

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

The role of imaging in adult acute urinary tract infection

International Nuclear Information System (INIS)

Webb, J.A.W.

1997-01-01

Imaging is required in only a minority of patients with urinary tract infection. Some patients who present with severe loin pain are imaged because ureteric colic is suspected. If urinary tract infection does not respond normally to antibiotics, imaging is undertaken to check for evidence of renal obstuction or sepsis. Finally, after the acute infection has been treated, imaging is required in some patients to check for factors pre-disposing to renal damage or to relapsing or recurrent infection. This review discusses the appropriate choice of imaging technique to use in each clinical situation and summarises the expected findings. (orig.). With 15 figs., 1 tab

Prototype Theory Based Feature Representation for PolSAR Images

OpenAIRE

Huang Xiaojing; Yang Xiangli; Huang Pingping; Yang Wen

2016-01-01

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

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.

Imaging features of renal complications after crizotinib treatment for non–small-cell lung cancer: a case report

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Wan Ying Chan, MBBS

2016-09-01

Full Text Available Crizotinib has been approved for the treatment of advanced ALK-positive non–small cell lung cancer. Its use is associated with the development of complex renal cysts. However, there is limited literature regarding imaging features of renal cystic disease during crizotinib therapy and its complications or progression. Here, we describe a case of a patient with ALK-positive advanced non–small cell lung cancer who developed complex renal cyst during crizotinib treatment. The renal cyst is complicated by infection and abscess formation. Subsequent renal biopsy, antibiotics treatment, and open drainage of loculated renal abscess showed no malignant cells and contributed to the diagnosis. The imaging features should be recognized as renal cystic disease of crizotinib treatment and not to be mistaken as new metastasis and disease progression.

Textural features for image classification

Science.gov (United States)

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

1973-01-01

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

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

Directory of Open Access Journals (Sweden)

Chia-Hung Chen

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

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

International Nuclear Information System (INIS)

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

2005-01-01

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

Image Mosaic Method Based on SIFT Features of Line Segment

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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.

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.

Indium-111 chloride imaging in the detection of infected prostheses

International Nuclear Information System (INIS)

Sayle, B.A.; Fawcett, H.D.; Wilkey, D.J.; Cierny, G. III; Mader, J.T.

1985-01-01

Thirty-three patients with painful joint prostheses and a suspicion of infection were imaged with [ 111 In]chloride. A final diagnosis was established by culture in 19. Of these, 12 were categorized as true positives and three as true negatives. There were two false-positive studies, occurring in patients with knee prostheses. In both, the culture was obtained by aspiration. The sensitivity was 86%, specificity 60%, and accuracy 79%. Seventeen of the proven cases had bone imaging prior to [ 111 In]chloride imaging. All 17 static images were positive and were not helpful in differentiating loosening from infection. Using increased uptake on the blood-pool image as a criteria for infection, the sensitivity was 89%, but the specificity was 0. Adding flow studies made little difference in interpreting the blood-pool images. This study shows that [ 111 In]chloride imaging is more accurate in evaluating infection in prosthesis than bone imaging

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

Science.gov (United States)

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

2014-06-01

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

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

International Nuclear Information System (INIS)

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

2002-01-01

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

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

Science.gov (United States)

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

2017-07-01

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

Leukocyte and bacteria imaging in prosthetic joint infection

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AWJM Glaudemans

2013-01-01

Full Text Available There has been a significant increase in the number of joint prosthesis replacements worldwide. Although relatively uncommon, complications can occur with the most serious being an infection. Various radiological and nuclear imaging techniques are available to diagnose prosthetic joint infections (PJI. In this review article, we describe the pathophysiology of PJI, the principles of nuclear medicine imaging and the differences between Single Photon Emission Computed Tomography (SPECT and Positron Emission Tomography (PET. The value of nuclear medicine techniques for clinical practice is also discussed.Then we provide an overview of the most often used radionuclide imaging techniques that may be helpful in diagnosing prosthetic joint infection: the 67Ga-citrate, labelled white blood cells in vitro and in vivo (monoclonal antibodies directed against specific targets on the leukocytes, and 18F-fluorodeoxyglucose (18F-FDG. We describe their working methods, the pitfalls, and the interpretation criteria. Furthermore, we review recent advances in imaging bacteria, a molecular imaging method that holds promises for the detection of occult infections. We conclude proposing two diagnostic flow-charts, based on data in the literature, that could help the clinicians to choose the best nuclear imaging method when they have a patient with suspicion of or with proven PJI.

Pulmonary infections in HIV-positive children

International Nuclear Information System (INIS)

George, Reena; Andronikou, Savvas; Theron, Salomine; Plessis, Jaco du; Hayes, Murray; Mapukata, Ayanda; Goussard, Pierre; Gie, Robert

2009-01-01

Infection of the lungs and airways by viral, bacterial, fungal and protozoal agents, often producing atypical radiographic features, is common in children with human immunodeficiency virus (HIV) infection. Conventional chest radiography and chest CT remain the most useful imaging modalities for evaluation of the immunocompromised patient presenting with a suspected pulmonary infection. In this review the radiological features of acute lung infections in this population are discussed. (orig.)

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.

Modern Views on Rotavirus Infection in Children: Epidemiological and Clinicopathogenetic Features

Directory of Open Access Journals (Sweden)

G.O. Lezhenko

2014-04-01

Full Text Available Based on the analysis of epidemiological, clinical and laboratory data characterizing the course of rotavirus infection in children of Zaporizhya region, there are provided the modern features of the disease. The authors marked dynamic changes in clinical and laboratory parameters of lactase deficiency, which occur during rotavirus infection in infants. The efficiency of Saccharomyces boulardii in complex treatment of rotavirus infection in children is shown.

Radionuclide imaging of infection: what the future holds

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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)

SOCIAL AND PSYCHOLOGICAL FEATURES OF HIV-INFECTED INDIVIDUALS

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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

Radionuclide imaging of non osseous infection

International Nuclear Information System (INIS)

Palestro, C.J.; New York, Yeshiva Univ., NY; Torres, M.A.

1999-01-01

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 18 FDG PET and 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

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.

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.

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.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion.

Science.gov (United States)

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

2016-01-29

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

MR imaging of masticator space infection

International Nuclear Information System (INIS)

Seong, Chang Kyu; Han, Moon Hee; Kim, Hong Dae; Park, Byung Kwan; Lee, In Hee; Chang, Kee Hyun; Kim, Sam Soo

1998-01-01

To identify the characteristic appearances of masticator space infection, as seen on magnetic resonance(MR) imaging. We retrospectively reviewed the MR images of 23 patients with clinically and bacteriologically proven masticator space infection, with attention to the involved structures, spread pattern, abscess formation, mandibular involvement, and etiology. The masseter muscle was involved in 21 of 23 cases, while the cases, medial pterygoid, lateral pterygoid, and temporalis muscles were involved in 14, 12 , and 13 cases, respectively. All muscles in the masticator space were involved in eight cases and only a single muscle in five. In all but one case, extension through the muscle plane was noted, and in 10 cases, transfascial extension was seen. Abscess formation was noted in seven cases. Mandibular involvement was seen in 16 cases, half of which showed focal cortical disruption. The source of infection was odontogenous in 15 cases, with frequent involvement of the mandible and masseter. Masticator space infection frequently originated from an odontogenous source. the characteristic MR appearances of this infection included extension through the muscle plane and frequent transfascial spread to adjacent deep cervical spaces, as well as common mandibular involvement with or without cortical disruption of focal pattern.=20

Classification of radiolarian images with hand-crafted and deep features

Science.gov (United States)

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

2017-12-01

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

Cryptogenic organizing pneumonia: typical and atypical imaging features on computed tomography; Typische und atypische Morphologie der kryptogen organisierenden Pneumonie in der Computertomografie

Energy Technology Data Exchange (ETDEWEB)

Hamer, O.W. [Inst. fuer Roentgendiagnostik, Klinikum der Univ. Regensburg (Germany); Silva, C.I.; Mueller, N.L. [Section of Chest Imaging, Dept. of Radiology, Vancouver General Hospital (Canada)

2008-07-15

Organizing pneumonia (OP) occurs without any identifiable cause (''cryptogenic organizing pneumonia'') as well as secondary to a multitude of disorders of various origins (''secondary organizing pneumonia''). Possible triggers are infections, drugs, collagen vascular disease, inflammatory bowel disease, transplantations, and radiation directed to the chest. The present manuscript provides an overview of the histopathological, clinical and CT imaging features of OP. Classic CT morphologies (peripheral and peribronchovascular consolidations and ground glass opacities) and atypical imaging features (nodules, crazy paving, lines and bands, perilobular consolidations and the reversed halo sign) are discussed. (orig.)

The future of infection imaging

International Nuclear Information System (INIS)

Buscombe, J.R.

2006-01-01

Over the past 10 years, there has been an unprecedented explosion in new agents suggested for imaging infection. These agents have been based on our understanding of the processes involved in invasive infection of the body by microorganisms and the body's response to them. Work with antibodies has traditionally yielded the most likely candidates, 3 of which have entered clinical use in Europe and the USA. However, the expense and limitations of producing antibodies have resulted in investigations into the use of cytokine and anti-microbial radiolabelled peptides. This work is very promising in that these types of tracers may be more specific for infection than labelled leukocytes. There remains a big question mark, however, as to whether or not these will progress from phase 1./2. trials to phase 3. trials and, ultimately, to clinical use. Finally, the investigation of radiolabelled anti-microbial continues and may prove to be most useful with anti-fungal especially in the immuno-suppressed host. We live in exciting times in the evolution of infection imaging; time and commercial constraints will determine which, if any of these tracers make it to the market and will they do so before PET rules all nuclear medicine?

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.

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

International Nuclear Information System (INIS)

Soufi, M; Arimura, H; Toyofuku, F; Nakamura, K; Hirose, T; Umezu, Y; Shioyama, Y

2016-01-01

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Soufi, M; Arimura, H; Toyofuku, F [Kyushu University, Fukuoka, Fukuoka (Japan); Nakamura, K [Hamamatsu University School of Medicine, Hamamatsu, Shizuoka (Japan); Hirose, T; Umezu, Y [Kyushu University Hospital, Fukuoka, Fukuoka (Japan); Shioyama, Y [Saga Heavy Ion Medical Accelerator in Tosu, Tosu, Saga (Japan)

2016-06-15

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

Distinguishing infected from noninfected abdominal fluid collections after surgery: an imaging, clinical, and laboratory-based scoring system.

Science.gov (United States)

Gnannt, Ralph; Fischer, Michael A; Baechler, Thomas; Clavien, Pierre-Alain; Karlo, Christoph; Seifert, Burkhardt; Lesurtel, Mickael; Alkadhi, Hatem

2015-01-01

Mortality from abdominal abscesses ranges from 30% in treated cases up to 80% to 100% in patients with undrained or nonoperated abscesses. Various computed tomographic (CT) imaging features have been suggested to indicate infection of postoperative abdominal fluid collections; however, features are nonspecific and substantial overlap between infected and noninfected collections exists. The purpose of this study was to develop and validate a scoring system on the basis of CT imaging findings as well as laboratory and clinical parameters for distinguishing infected from noninfected abdominal fluid collections after surgery. The score developmental cohort included 100 consecutive patients (69 men, 31 women; mean age, 58 ± 17 years) who underwent portal-venous phase CT within 24 hours before CT-guided intervention of postoperative abdominal fluid collections. Imaging features included attenuation (Hounsfield unit [HU]), volume, wall enhancement and thickness, fat stranding, as well as entrapped gas of fluid collections. Laboratory and clinical parameters included diabetes, intake of immunosuppressive drugs, body temperature, C-reactive protein, and leukocyte blood cell count. The score was validated in a separate cohort of 30 consecutive patients (17 men, 13 women; mean age, 51 ± 15 years) with postoperative abdominal fluid collections. Microbiologic analysis from fluid samples served as the standard of reference. Diabetes, body temperature, C-reactive protein, attenuation of the fluid collection (in HUs), wall enhancement and thickness of the wall, adjacent fat stranding, as well as entrapped gas within the fluid collection were significantly different between infected and noninfected collections (P collection (in HUs), as well as entrapped gas as significant independent predictors of infection (P collection, ≥ 20 HU: 4 points; entrapped gas: 3 points). The model was well calibrated (Hosmer-Lemeshow test, P = 0.36). In the validation cohort, scores of 2 or lower

Imaging features of musculoskeletal tuberculosis

International Nuclear Information System (INIS)

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

2003-01-01

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

Evaluation of localized bacterial infection using radioisotope-labeled nucleosides imaging modality

Energy Technology Data Exchange (ETDEWEB)

Jang, Su Jin; Kang, Joo Hyun; Lee, Yong Jin; Lee, Tae Sup; Kim, Kwang Il; Lee, Kyo Chul; An, Gwang II; Cheon, Gi Jeong; Lim, Sang Moo [KIRAMS, Seoul (Korea, Republic of); Lim, Sang Yong [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2011-10-15

Conventional diagnostic methods for infections are difficult to distinguish localized bacterial infections from sites of sterile inflammation. For this reason, the importance of developing methods to image bacterial infections is widely recognized. In this study to acquire bacterial infection imaging with radiolabeled nucleosides, in vitro bacterial thymidine kinase (tk) activities of Salmonella typhimurium with [{sup 18}F]FLT and [{sup 125}I]IVDU were measured and localized infections model in BALB/c mice was imaged with [{sup 18}F]FLT or [{sup 125}I]FIAU

Iris recognition based on key image feature extraction.

Science.gov (United States)

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

2008-01-01

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

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.

Online Feature Selection for Classifying Emphysema in HRCT Images

Directory of Open Access Journals (Sweden)

M. Prasad

2008-06-01

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

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

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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.

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Directory of Open Access Journals (Sweden)

Xi Wenfei

2017-07-01

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

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

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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.

Radionuclide imaging of musculoskeletal infection

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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

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

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Mei Yu

2012-01-01

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

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

Science.gov (United States)

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

2017-06-22

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

SAR Image Classification Based on Its Texture Features

Institute of Scientific and Technical Information of China (English)

LI Pingxiang; FANG Shenghui

2003-01-01

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

Histological assessment of granulomas in natural and experimental Schistosoma mansoni infections using whole slide imaging.

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Kátia B Amaral

Full Text Available The pathology of schistosomiasis mansoni, a neglected tropical disease of great clinical and socioeconomic importance, results from the parasite eggs that become trapped in host tissues, particularly in the liver and intestines. Continuous antigenic stimulation from these eggs leads to recruitment of inflammatory cells to the sites of infection with formation of periovular granulomas. These complex structures have variable size and composition and are the most striking histopathological feature of schistosomiasis mansoni. However, evaluation of granulomas by conventional microscopy methods is time-consuming and limited, especially in large-scale studies. Here, we used high resolution Whole Slide Imaging (WSI, which allows fast scanning of entire histological slides, and multiple morphometric evaluations, to assess the granulomatous response elicited in target organs (liver, small and large intestines of two models of schistosomiasis mansoni. One of the advantages of WSI, also termed virtual microscopy, is that it generates images that simultaneously offer high resolution and a wide field of observation. By using a model of natural (Nectomys squamipes, a wild reservoir captured from endemic areas in Brazil and experimental (Swiss mouse infection with Schistosoma mansoni, we provided the first detailed WSI characterization of granulomas and other pathological aspects. WSI and quantitative analyses enabled a fast and reliable assessment of the number, evolutional types, frequency and areas of granulomas and inflammatory infiltrates and revealed that target organs are differentially impacted by inflammatory responses in the natural and experimental infections. Remarkably, high-resolution analysis of individual eosinophils, key cells elicited by this helminthic infection, showed a great difference in eosinophil numbers between the two infections. Moreover, features such as the intestinal egg path and confluent granulomas were uncovered. Thus, WSI may

Derivative-based scale invariant image feature detector with error resilience.

Science.gov (United States)

Mainali, Pradip; Lafruit, Gauthier; Tack, Klaas; Van Gool, Luc; Lauwereins, Rudy

2014-05-01

We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.

Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

OpenAIRE

Engelke, Ulrich; Zepernick, Hans-Jürgen

2007-01-01

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

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

Science.gov (United States)

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

2017-10-06

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

Feature extraction from mammographic images using fast marching methods

International Nuclear Information System (INIS)

Bottigli, U.; Golosio, B.

2002-01-01

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

Nuclear Medicine imaging of infection and inflammation Part 3: Clinical applications

International Nuclear Information System (INIS)

Gnanasegaran, G.; Croasdale, J.; Buscombe, J.R.

2005-01-01

Prompt localization of infection sites is essential for initiating appropriate therapeutic measures. There have been major advances in the management of patients suffering from infective and/or inflammatory disorders as a result of introduction of newer drugs with high sensitivity and specificity. However diagnosis of infection / inflammation still remains a major clinical problem. Although the typical signs of infection and inflammation are useful in localizing the pathology at superficial sites, infection and inflammation of internal structures are often difficult to localize without the aid of imaging procedures. Radionuclide imaging is an important diagnostic tool for the evaluation of patho-physiological processes of infection and inflammation. A spectrum of radiopharmaceuticals and a number of imaging protocols have been used in clinical practice. However every method differs in its potential to gather information at the cellular and molecular level. This review addresses some of the applications of radionuclide infection imaging procedures in musculoskeletal system, lymphatic system, in immunocompromised patients and in patients suffering from fever of unknown origin. (author)

SPECIFIC CONSTITUTIONAL FEATURES OF CHILDREN INFECTED WITH TUBERCULOSIS

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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%.

Imaging features of juxtacortical chondroma in children

International Nuclear Information System (INIS)

Miller, Stephen F.

2014-01-01

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

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.)

Magnetic resonance imaging features of neuroinfections in patients with AIDS. Pablo Tobon Hospital, Medellin (2008-2009)

International Nuclear Information System (INIS)

Pulgarin R, Luis G; Gomez R, Susana; Sanchez Z, Jonathan A; Abreo L, Diego F; Hernandez J, Andres F; Donado G, Jorge H.

2010-01-01

Our purpose is to describe the clinical and magnetic resonance imaging (MRI) features of the central nervous system (CNS ) most common infectious diseases in patients with human immunodeficiency virus (HIV) and / or acquired immunodeficiency syndrome (AIDS ) that consulted to the Hospital Pablo Tobon Uribe in the period 2008-2009. We included four adult patients with HIV and / or AIDS from Hospital Pablo Tobon Uribe database, who were diagnosed having central nervous system infections by MRI and diagnostic microbiological tests. The most common MRI patterns of CNS infections in AIDS patients in our institution were irregular multifocal intra-axial space-occupying lesions with heterogeneous and variable signals on T1, T2 and FLAI R, with perilesional edema and rim postcontrast enhancement.

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

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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.

Imaging features of benign adrenal cysts

International Nuclear Information System (INIS)

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

2006-01-01

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

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.

Featured Image: Diamonds in a Meteorite

Science.gov (United States)

Kohler, Susanna

2018-04-01

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

Vaginal Masses: Magnetic Resonance Imaging Features with Pathologic Correlation

International Nuclear Information System (INIS)

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

2007-01-01

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

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

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

2018-04-01

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

A blur-invariant local feature for motion blurred image matching

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Tong, Qiang; Aoki, Terumasa

2017-07-01

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

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.)

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

Directory of Open Access Journals (Sweden)

Pattichis Marios S

2007-11-01

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

Imaging features of skeletal changes in children with Gaucher disease

International Nuclear Information System (INIS)

Zhang Ningning; Duan Xiaomin; Duan Yanlong

2011-01-01

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

SU-D-202-02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images

Energy Technology Data Exchange (ETDEWEB)

Lamichhane, N; Johnson, P; Chinea, F; Patel, V; Yang, F [University of Miami, Miami, FL (United States)

2016-06-15

Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of

Improved image retrieval based on fuzzy colour feature vector

Science.gov (United States)

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

2013-03-01

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

An image-processing methodology for extracting bloodstain pattern features.

Science.gov (United States)

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

2017-08-01

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

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis

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Ye Sun

2018-04-01

Full Text Available Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches were considered for classification by deep belief network (DBN and partial least squares discriminant analysis (PLSDA in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

Modality prediction of biomedical literature images using multimodal feature representation

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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.

Localized scleroderma: imaging features

International Nuclear Information System (INIS)

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

1994-01-01

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

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

Science.gov (United States)

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

2015-12-01

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

A unified framework for image retrieval using keyword and visual features.

Science.gov (United States)

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Face detection on distorted images using perceptual quality-aware features

Science.gov (United States)

Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

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

Science.gov (United States)

Toews, Matthew; Wells, William M

2013-04-01

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

Clinical Features and Outcomes of Fusobacterium Species Infections in a Ten-Year Follow-up

Directory of Open Access Journals (Sweden)

Garcia-Carretero Rafael

2017-10-01

Full Text Available Objective: Although uncommon, Fusobacterium infections have a wide clinical spectrum, ranging from local pharyngeal infections to septic shock. Our aim was to characterize and analyze the clinical features and outcomes in patients with Fusobacterium infections, and determine which variables were able to predict a poor outcome.

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

Science.gov (United States)

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

2017-12-28

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

Imaging experimental infective endocarditis with indium-111-labeled blood cellular components

International Nuclear Information System (INIS)

Riba, A.L.; Thakur, M.L.; Gottschalk, A.; Andriole, V.T.; Zaret, B.L.

1979-01-01

The capability of radionuclide imaging to detect experimental aortic valve infective endocarditis was assessed with indium-111 ( 111 In)-labeled blood cells. Sequential cardiac imaging and tissue distribution studies were obtained in 17 rabbits with infective endocarditis after administration of 111 In-platelets and in five after 111 In-polymorphonuclear leukocytes. Forty-eight to 72 hours after platelet administration, in vivo imaging demonstrated abnormal 111 In uptake in all animals in the region of the aortic valve in an anatomically distinct pattern. Images of the excised heart showed discrete cardiac uptake conforming to the in vivo image and gross pathological examination. 111 In-platelet uptake in vegetations from the 17 animals averaged 240 +- 41 times greater than that in normal myocardium and 99 +- 15 times greater uptake in blood. In contrast, 111 In-leukocyte cardiac imaging showed no abnormal aortic valve uptake 24 hours after tracer administration and the lesion myocardium activity ratio was only 5 +- 2 (3 +- 1 for lesion/blood activity). Four normal rabbits demonstrated neither positive 111 In-platelet scintigraphs nor abnormal cardiac tissue uptake. Likewise, noncellular 111 In was not concentrated to any significant extent in three animals with infective endocarditis. This study demonstrates that 111 In-platelet, but not leukocyte cardiac imaging, is a sensitive technique for detecting experimental infective endocarditis. The imaging data conform to the cellular pathology of the infective endocarditis vegetation

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

Science.gov (United States)

Yang, Jianping; Li, Jun

2018-02-01

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

Magnetic resonance imaging features of fibrocystic change of the breast.

Science.gov (United States)

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

2008-11-01

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

LEUKOCYTE AND BACTERIA IMAGING IN PROSTHETIC JOINT INFECTION

NARCIS (Netherlands)

Glaudemans, Andor W. J. M.; Galli, Filippo; Pacilio, Marta; Signore, Alberto

2013-01-01

There has been a significant increase in the number of joint prosthesis replacements worldwide. Although relatively uncommon, complications can occur with the most serious being an infection. Various radiological and nuclear imaging techniques are available to diagnose prosthetic joint infections

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

Tracking image features with PCA-SURF descriptors

CSIR Research Space (South Africa)

Pancham, A

2015-05-01

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

A comparative study of image low level feature extraction algorithms

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M.M. El-gayar

2013-07-01

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

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.

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

KAUST Repository

Wang, Jim Jing-Yan; Almasri, Islam

2012-01-01

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

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.)

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

International Nuclear Information System (INIS)

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

2009-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2009-12-15

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

Immunopathological Features of Canine Myocarditis Associated with Leishmania infantum Infection

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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.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

Science.gov (United States)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

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

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Soheila Gheisari

2018-01-01

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

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

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Sun Xun

2016-12-01

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

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

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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.

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

Science.gov (United States)

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

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Zichun Zhong

2016-01-01

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

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.

The fuzzy Hough Transform-feature extraction in medical images

International Nuclear Information System (INIS)

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

1994-01-01

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

Localized scleroderma: imaging features

Energy Technology Data Exchange (ETDEWEB)

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

1994-06-01

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

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

Science.gov (United States)

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

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

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

Science.gov (United States)

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

2018-04-01

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

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

2015-06-15

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

Image features for misalignment correction in medical flat-detector CT

International Nuclear Information System (INIS)

Wicklein, Julia; Kunze, Holger; Kalender, Willi A.; Kyriakou, Yiannis

2012-01-01

Purpose: Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment. Methods: Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry. Results: The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window). Conclusions: Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

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

Science.gov (United States)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

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

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.

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.)

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

Directory of Open Access Journals (Sweden)

Myung-Ho Ju

2013-10-01

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

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

Science.gov (United States)

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

2017-03-01

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

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

OpenAIRE

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

2014-01-01

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

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.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

Science.gov (United States)

Xie, Yaoqin; Xing, Lei; Gu, Jia; Liu, Wu

2013-06-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

International Nuclear Information System (INIS)

Xie Yaoqin; Gu Jia; Xing Lei; Liu Wu

2013-01-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow. (paper)

RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

Directory of Open Access Journals (Sweden)

Z. Wang

2017-09-01

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

Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance

Directory of Open Access Journals (Sweden)

Minh-Tan Pham

2017-10-01

Full Text Available A novel efficient method for content-based image retrieval (CBIR is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

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

Institute of Scientific and Technical Information of China (English)

Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

2017-01-01

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

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

Science.gov (United States)

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

2015-01-01

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

Infection Imaging With 18F-FDS and First-in-Human Evaluation

International Nuclear Information System (INIS)

Yao, Shaobo; Xing, Haiqun; Zhu, Wenjia; Wu, Zhanhong; Zhang, Yingqiang; Ma, Yanru; Liu, Yimin; Huo, Li; Zhu, Zhaohui; Li, Zibo; Li, Fang

2016-01-01

Purpose: The noninvasive imaging of bacterial infections is critical in order to reduce mortality and morbidity caused by these diseases. The recently reported 18 F-FDS ( 18 F-2-fluorodeoxy sorbitol) as a PET (positron emission tomography) tracer can be used to image Enterobacteriaceae-specific infections and provides a potential alternative to this problem compared with other probes for imaging infections. In this study, automatic synthesis, validation of 18 F-FDS and a first-in-human study were performed and discussed. Methods: A multifunctional synthesis module was employed for the radiosynthesis of 18 F-FDG ( 18 F-2-fluorodeoxy glucose) and 18 F-FDS starting from 18 F ion using two-pot three-step fully automated reactions. The behavior of 18 F-FDS as an in vivo imaging probe for infections was evaluated in an Escherichia coli mouse infection model. The first detailed pharmacokinetic and biodistribution parameters were obtained from healthy human volunteers. Results: The uptake of 18 F-FDS in an E. coli mouse-myositis infection model was easily differentiated from other organs and normal muscle. Intensive lesion uptake declined after antibiotic treatment. In the pilot human study, no adverse effects due to 18 F-FDS were observed up to 24 h post-injection. The radiotracer was rapidly cleared from the circulation and excreted mainly through the urinary system. Conclusion: We conclude that 18 F-FDS PET holds great potential for appropriate and effective for the imaging of bacterial infections in vivo. These preliminary results indicate that further clinical studies are warranted.

Imaging findings and significance of deep neck space infection

International Nuclear Information System (INIS)

Zhuang Qixin; Gu Yifeng; Du Lianjun; Zhu Lili; Pan Yuping; Li Minghua; Yang Shixun; Shang Kezhong; Yin Shankai

2004-01-01

Objective: To study the imaging appearance of deep neck space cellulitis and abscess and to evaluate the diagnostic criteria of deep neck space infection. Methods: CT and MRI findings of 28 cases with deep neck space infection proved by clinical manifestation and pathology were analyzed, including 11 cases of retropharyngeal space, 5 cases of parapharyngeal space infection, 4 cases of masticator space infection, and 8 cases of multi-space infection. Results: CT and MRI could display the swelling of the soft tissues and displacement, reduction, or disappearance of lipoid space in the cellulitis. In inflammatory tissues, MRI imaging demonstrated hypointense or isointense signal on T 1 WI, and hyperintense signal changes on T 2 WI. In abscess, CT could display hypodensity in the center and boundary enhancement of the abscess. MRI could display obvious hyperintense signal on T 2 WI and boundary enhancement. Conclusion: CT and MRI could provide useful information for deep neck space cellulitis and abscess

Adaptive Colour Feature Identification in Image for Object Tracking

Directory of Open Access Journals (Sweden)

Feng Su

2012-01-01

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

Imaging in childhood urinary tract infection.

Science.gov (United States)

Riccabona, Michael

2016-05-01

Urinary tract infection (UTI) is a common query in pediatric radiology. Imaging for and after UTI is still a heavily debated topic with different approaches, as thorough evidence to decide upon a definite algorithm is scarce. This review article tries to address the clinical rational of the various approaches (general imaging, top-down or bottom-up, selected and individualized imaging concepts…), describes the available imaging modalities and the respective findings in imaging children with UTI, and proposes an imaging algorithm for the work-up of children during and after UTI discussing the "pros and cons" of the different attitudes. In summary, imaging by US is generally considered for all infants and children with a febrile or complicated (upper) UTI, particularly without previously known urinary tract anatomy. The further work-up (searching for renal scarring and assessment of vesico-ureteric reflux) is then decided according to these initial findings as well as the clinical presentation, course, and scenario.

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

Science.gov (United States)

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

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

An adaptive clustering algorithm for image matching based on corner feature

Science.gov (United States)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

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

Science.gov (United States)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

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

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

Science.gov (United States)

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

2013-04-01

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

Bubble feature extracting based on image processing of coal flotation froth

Energy Technology Data Exchange (ETDEWEB)

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

2001-11-01

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

Imaging features of maxillary osteoblastoma and its malignant transformation

International Nuclear Information System (INIS)

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

1994-01-01

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

An efficient fractal image coding algorithm using unified feature and DCT

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2009-01-01

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

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.

Spinal infection: Evaluation with MR imaging and intraoperative spinal US

International Nuclear Information System (INIS)

Donovan Post, M.J.; Montalvo, B.M.; Quencer, R.M.; Katz, B.H.; Green, B.A.; Elsmont, F.

1987-01-01

MR spine images and/or intraoperative US scans in 15 patients were reviewed retrospectively and correlated with clinical and pathologic data to determine the diagnostic value of these modalities in spinal infection. In osteomyelitis and retrospinal abscess MR imaging was definitive; in myelitis it was positive but nonspecific. In epidural abscess concomitant with meningitis, myelography with CT and intraoperative US were superior to MR imaging. Intraoperative US could be used to distinguish these processes and to monitor surgical decompression. The authors recommend that MR imaging be performed at the screening examination in cases of spinal infection, accompanied by intraoperative US in all surgical cases

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

Directory of Open Access Journals (Sweden)

S. Eken

2017-11-01

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

Current Role of Imaging in Diagnosing Aortic Graft Infections

NARCIS (Netherlands)

Bruggink, Janneke L. M.; Slart, Riemer H. J. A.; Pol, Jillis A.; Reijnen, Michel M. P. J.; Zeebregts, Clark J.

2011-01-01

Vascular prosthetic graft infection is a rare but serious complication after aortic graft replacement, with high morbidity and mortality rates. Therefore, adequate diagnostics are needed to detect and treat these infections as early as possible. Several imaging modalities provide different

Hand infections: anatomy, types and spread of infection, imaging findings, and treatment options.

Science.gov (United States)

Patel, Dakshesh B; Emmanuel, Neelmini B; Stevanovic, Milan V; Matcuk, George R; Gottsegen, Christopher J; Forrester, Deborah M; White, Eric A

2014-01-01

Infections of the hand are common, particularly in immunocompromised patients, and can lead to significant morbidity, including amputation, if not treated properly. Hand infection can spread far and wide from the original site of inoculation through interconnections between the synovium-lined and nonsynovial potential spaces. Because surgery is the mainstay of treatment, knowledge of the pertinent anatomy is imperative for accurately describing the presence, location, and extent of infection. The authors review the pertinent anatomy of the spaces of the hand and describe different types of infection-including cellulitis, necrotizing fasciitis, paronychia, felon, pyogenic flexor tenosynovitis, deep space infections, septic arthritis, and osteomyelitis-and common causative organisms of these infections. They also describe various modes of spread; the common radiologic appearances of hand infections, with emphasis on findings at magnetic resonance imaging and ultrasonography; and the role of radiology in the management of these infections, along with a brief overview of treatment options. ©RSNA, 2014.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

Science.gov (United States)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Research of image retrieval technology based on color feature

Science.gov (United States)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

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

Illumination invariant feature point matching for high-resolution planetary remote sensing images

Science.gov (United States)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

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.

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.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

International Nuclear Information System (INIS)

Oliver, J; Budzevich, M; Moros, E; Zhang, G; Hunt, D

2015-01-01

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images), image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work shall

Feature Recognition of Froth Images Based on Energy Distribution Characteristics

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WU Yanpeng

2014-09-01

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

[The Role of Imaging in Central Nervous System Infections].

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Yokota, Hajime; Tazoe, Jun; Yamada, Kei

2015-07-01

Many infections invade the central nervous system. Magnetic resonance imaging (MRI) is the main tool that is used to evaluate infectious lesions of the central nervous system. The useful sequences on MRI are dependent on the locations, such as intra-axial, extra-axial, and spinal cord. For intra-axial lesions, besides the fundamental sequences, including T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery (FLAIR) images, advanced sequences, such as diffusion-weighted imaging, diffusion tensor imaging, susceptibility-weighted imaging, and MR spectroscopy, can be applied. They are occasionally used as determinants for quick and correct diagnosis. For extra-axial lesions, understanding the differences among 2D-conventional T1-weighted images, 2D-fat-saturated T1-weighted images, 3D-Spin echo sequences, and 3D-Gradient echo sequence after the administration of gadolinium is required to avoid wrong interpretations. FLAIR plus gadolinium is a useful tool for revealing abnormal enhancement on the brain surface. For the spinal cord, the sequences are limited. Evaluating the distribution and time course of the spinal cord are essential for correct diagnoses. We summarize the role of imaging in central nervous system infections and show the pitfalls, key points, and latest information in them on clinical practices.

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

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Jiang, Meng

2018-06-01

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

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

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

2009-09-01

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

Image feature extraction based on the camouflage effectiveness evaluation

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

2018-04-01

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

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

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Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

2013-03-01

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

Skull base chordoid meningioma: Imaging features and pathology

International Nuclear Information System (INIS)

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

2004-01-01

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

Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli.

Science.gov (United States)

Lee, Hoonsoo; Kim, Moon S; Qin, Jianwei; Park, Eunsoo; Song, Yu-Rim; Oh, Chang-Sik; Cho, Byoung-Kwan

2017-09-23

The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400-1800 cm -1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm -1 and 437 cm -1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods.

Estimating perception of scene layout properties from global image features.

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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.

Clinical and epidemiological features of coryneform skin infections at a tertiary hospital

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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.

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2010-07-15

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

Multimodal Image Alignment via Linear Mapping between Feature Modalities.

Science.gov (United States)

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

2017-01-01

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

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.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Feature Tracking for High Speed AFM Imaging of Biopolymers.

Science.gov (United States)

Hartman, Brett; Andersson, Sean B

2018-03-31

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

Tuberculous arthritis of the appendicular skeleton: MR imaging appearances

International Nuclear Information System (INIS)

Parmar, Hemant; Shah, Jeshil; Patkar, Deepak; Singrakhia, Manoj; Patankar, Tufail; Hutchinson, Charles

2004-01-01

Tuberculosis [TB] of the appendicular skeleton is an uncommon infection caused by the tuberculous bacilli and constitutes only 1-3% of all tuberculosis infections. MR imaging features of tuberculous arthritis include bone marrow oedema, cortical erosions, synovitis, joint effusion, tenosynovitis, soft tissue collections, and myositis. These imaging features are at times non-specific, but in the correct clinical context help in diagnosis of tuberculosis. We present the various pathological manifestations of TB arthritis involving the different joints of appendicular skeleton and discuss their MR imaging appearances

Tuberculous arthritis of the appendicular skeleton: MR imaging appearances

Energy Technology Data Exchange (ETDEWEB)

Parmar, Hemant E-mail: parurad@hotmail.com; Shah, Jeshil; Patkar, Deepak; Singrakhia, Manoj; Patankar, Tufail; Hutchinson, Charles

2004-12-01

Tuberculosis [TB] of the appendicular skeleton is an uncommon infection caused by the tuberculous bacilli and constitutes only 1-3% of all tuberculosis infections. MR imaging features of tuberculous arthritis include bone marrow oedema, cortical erosions, synovitis, joint effusion, tenosynovitis, soft tissue collections, and myositis. These imaging features are at times non-specific, but in the correct clinical context help in diagnosis of tuberculosis. We present the various pathological manifestations of TB arthritis involving the different joints of appendicular skeleton and discuss their MR imaging appearances.

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

Science.gov (United States)

Grunert, J H

2015-03-01

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

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

Science.gov (United States)

Tiwari, Mayank; Gupta, Bhupendra

2018-04-01

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

Robust and efficient method for matching features in omnidirectional images

Science.gov (United States)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

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

MR imaging features of hemispherical spondylosclerosis

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

2017-10-15

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

Feature Detection of Curve Traffic Sign Image on The Bandung - Jakarta Highway

Science.gov (United States)

Naseer, M.; Supriadi, I.; Supangkat, S. H.

2018-03-01

Unsealed roadside and problems with the road surface are common causes of road crashes, particularly when those are combined with curves. Curve traffic sign is an important component for giving early warning to driver on traffic, especially on high-speed traffic like on the highway. Traffic sign detection has became a very interesting research now, and in this paper will be discussed about the detection of curve traffic sign. There are two types of curve signs are discussed, namely the curve turn to the left and the curve turn to the right and the all data sample used are the curves taken / recorded from some signs on the Bandung - Jakarta Highway. Feature detection of the curve signs use Speed Up Robust Feature (SURF) method, where the detected scene image is 800x450. From 45 curve turn to the right images, the system can detect the feature well to 35 images, where the success rate is 77,78%, while from the 45 curve turn to the left images, the system can detect the feature well to 34 images and the success rate is 75,56%, so the average accuracy in the detection process is 76,67%. While the average time for the detection process is 0.411 seconds.

Nuclear medicine imaging of diabetic foot infection

International Nuclear Information System (INIS)

Capriotti, Gabriela; D'Alessandria, Calogero; Signore, Alberto; Chianelli, Marco; Prandini, Napoleone

2005-01-01

Full text: Osteomyelitis of the foot is the most frequent complication in diabetic patients. Nuclear medicine plays an important role in diagnosis and for therapy follow-up, using different tracers. We reviewed 57 papers on diabetic foot imaging (published from 1982 to 2004, 50 original papers and 7 reviews), for a total of 2889 lesions. Data analysis has been carried out to establish which imaging technique could be used as a 'gold standard' for diagnosis of infection and to evaluate the extent of disease and to monitor the efficacy of therapy. Data analysis revealed that three-phase bone-scan is sensitive but not specific whereas specificity and diagnostic accuracy of 99m Tc-WBC scintigraphy is higher than 111 In- WBC scintigraphy. In the forefoot leukocyte scintigraphy may be useful for diagnosis of osteomyelitis and for monitoring the response to medical treatment. In the mid/hind foot the leukocytes uptake is not related only to the presence of infected region, but it is attributed to inflammation, fractures and reparative processes. Other radiopharmaceuticals such as 99m Tc/ 111 In-HIG, radiolabelled antibody and their fragments, showed high sensibility, but lower specificity than WBC (96.8/66.5, 95.8/70.2, 91.3/62 vs 85.8/84.5). Conclusion: It emerged that in the forefoot when clinical suspicious of osteomyelitis is low and medical treatment is contemplated, three-phase bon scan is the procedure of choice. A positive test is not diagnostic for osteomyelitis, and radiolabelled WBC scintigraphy is necessary. In the mid/hind foot, diagnosis of neuropathic joint with infection is problematic. Radiolabelled WBC imaging is probably the most accurate test for determining the presence of infection. Although a negative study strongly indicate the absence of osteomyelitis, it is important to note that a positive result requires a complementary study with marrow agent. (author)

Tc-99m labeled Sparfloxacin: A specific infection imaging agent

International Nuclear Information System (INIS)

Singh, A.K.; Verma, J.; Bhatnagar, A.; Ali, A.

2003-01-01

Radiolabeled antibiotics are being used for the specific diagnosis of infection by exploiting their specific binding properties to the bacterial component, thereby making it possible to differentiate infection from sterile lesions. A new radiopharmaceutical, Tc-99m Sparfloxacin has been developed for infection imaging. Sparfloxacin is a quinolone based broad-spectrum antibiotic, which is more potent than Ciprofloxacin. Radiolabeling of Sparfloxacin with Tc-99m was standardized using direct labeling protocol. Labeling efficiency, in-vitro and in-vivo stability, blood kinetics and organ distribution studies (in balb/c mice and New Zealand White Rabbits at different time interval up to 24hrs) were carried out. Biological activity of Sparfloxacin after its labeling with Tc-99m was evaluated with S.aureus using Peptone water (DIFCO) as media. Turpentine oil (100 μl) in left thigh and S.aureus (100μl of 3x10 7 cells) in right thigh were injected intramuscularly to create sterile and infective inflammation respectively in six New Zealand white rabbits. The localization kinetics of the radiolabeled complex were studied in the animal model by injecting 70-75MBq of Tc-99m Sparfloxacin intravenously in the ear of rabbit and the images were taken with a Gamma-camera (ECIL) at different post-injection time intervals. Standardized protocol produced >95% labeled complex. About 8% of tracer leached out at 24 hrs when incubated in serum at 37 0 C, confirming high stability of the complex. Blood clearance in rabbit revealed biphasic pattern and 50% of the complex clears from the blood within 5 min. Biodistribution studies in balb/c mice showed hepatobiliary route of excretion. Presence of insignificant amount of tracer at 24 hrs in the stomach confirmed high in vivo stability of the complex. Imaging in rabbits showed significant concentration of tracer in lesions with infection. Typical imaging patterns revealed initial accumulation of radiotracer in both sterile inflammatory

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.

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

Science.gov (United States)

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

2018-03-01

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

Comparison on imaging features of central serous chorioretinopathy fundus

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Ji-Jin Zhang

2014-10-01

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

Imaging in acute renal infection in children

International Nuclear Information System (INIS)

Sty, J.R.; Wells, R.G.; Starshak, R.J.; Schroeder, B.A.

1987-01-01

Infection is the most common disease of the urinary tract in children, and various imaging techniques have been used to verify its presence and location. On retrospective analysis, 50 consecutive children with documented upper urinary tract infection had abnormal findings on renal cortical scintigraphy with 99mTc-glucoheptonate. The infection involved the renal poles only in 38 and the poles plus other renal cortical areas in eight. Four had abnormalities that spared the poles. Renal sonograms were abnormal in 32 of 50 children. Excretory urograms were abnormal in six of 23 children in whom they were obtained. Vesicoureteral reflux was found in 34 of 40 children in whom voiding cystourethrography was performed. These data show the high sensitivity of renal cortical scintigraphy with 99mTc-glucoheptonate in documenting upper urinary tract infection. The location of the abnormalities detected suggests that renal infections spread via an ascending mode and implies that intrarenal reflux is a major contributing factor

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

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Fang Yang

2017-01-01

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

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.

Imaging after urinary tract infection in older children and adolescents.

Science.gov (United States)

Kurtz, Michael P; Chow, Jeanne S; Johnson, Emilie K; Rosoklija, Ilina; Logvinenko, Tanya; Nelson, Caleb P

2015-05-01

There are few guidelines and little data on imaging after urinary tract infections in older children. We determined the clinical yield of renal and bladder ultrasound, and voiding cystourethrogram in older children and adolescents after urinary tract infection. We analyzed findings on voiding cystourethrogram, and renal and bladder ultrasound as well as the clinical history of patients who underwent the 2 studies on the same day between January 2006 and December 2010. We selected for study patients 5 to 18 years old who underwent imaging for urinary tract infection. Those with prior postnatal genitourinary imaging or prenatal hydronephrosis were excluded from analysis. We identified a cohort of 153 patients, of whom 74% were 5 to 8 years old, 21% were 8 to 12 years old and 5% were 12 to 18 years old. Of the patients 77% were female, 78% had a febrile urinary tract infection history and 55% had a history of recurrent urinary tract infections. Renal and bladder ultrasound findings revealed hydronephrosis in 7.8% of patients, ureteral dilatation in 3.9%, renal parenchymal findings in 20% and bladder findings in 12%. No patient had moderate or greater hydronephrosis. Voiding cystourethrogram showed vesicoureteral reflux in 34% of cases and bladder or urethral anomalies in 12%. Reflux was grade I, II-III and greater than III in 5.9%, 26% and 2% of patients, respectively. For any voiding cystourethrogram abnormality the sensitivity and specificity of any renal and bladder ultrasound abnormality were 0.49 (95% CI 0.37-0.62) and 0.76 (95% CI 0.66-0.84), respectively. Positive and negative predictive values were 0.58 (95% CI 0.44-0.71) and 0.69 (0.59-0.77), respectively. In older children with a history of urinary tract infection the imaging yield is significant. However, imaging revealed high grade hydronephrosis or high grade vesicoureteral reflux in few patients. Renal ultrasound is not reliable for predicting voiding cystourethrogram findings such as vesicoureteral

Feature Selection from a Facial Image for Distinction of Sasang Constitution

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Imhoi Koo

2009-01-01

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

Feature Selection from a Facial Image for Distinction of Sasang Constitution

Science.gov (United States)

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

2009-01-01

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

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

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2008-01-01

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

Role of radionuclide imaging for diagnosis of device and prosthetic valve infections

Institute of Scientific and Technical Information of China (English)

Jean-Fran?ois Sarrazin; Fran?ois Philippon; Mika?l Trottier; Michel Tessier

2016-01-01

Cardiovascular implantable electronic device（CIED） infection and prosthetic valve endocarditis（PVE） remain a diagnostic challenge.Cardiac imaging plays an important role in the diagnosis and management of patients with CIED infection or PVE.Over the past few years,cardiac radionuclide imaging has gained a key role in the diagnosis of these patients,and in assessing the need for surgery,mainly in the most difficult cases.Both 18F-fluorodeoxyglucose positron emission tomography/computed tomography(18F-FDG PET/CT) and radiolabelled white blood cell single-photon emission computed tomography/computed tomography（WBC SPECT/CT） have been studied in these situations.In their 2015 guidelines for the management of infective endocarditis,the European Society of Cardiology incorporated cardiac nuclear imaging as part of their diagnostic algorithm for PVE,but not CIED infection since the data were judged insufficient at the moment.This article reviews the actual knowledge and recent studies on the use of 18F-FDG PET/CT and WBC SPECT/CT in the context of CIED infection and PVE,and describes the technical aspects of cardiac radionuclide imaging.It also discusses their accepted and potential indications for the diagnosis and management of CIED infection and PVE,the limitations of these tests,and potential areas of future research.

Role of radionuclide imaging for diagnosis of device and prosthetic valve infections

Science.gov (United States)

Sarrazin, Jean-François; Philippon, François; Trottier, Mikaël; Tessier, Michel

2016-01-01

Cardiovascular implantable electronic device (CIED) infection and prosthetic valve endocarditis (PVE) remain a diagnostic challenge. Cardiac imaging plays an important role in the diagnosis and management of patients with CIED infection or PVE. Over the past few years, cardiac radionuclide imaging has gained a key role in the diagnosis of these patients, and in assessing the need for surgery, mainly in the most difficult cases. Both 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and radiolabelled white blood cell single-photon emission computed tomography/computed tomography (WBC SPECT/CT) have been studied in these situations. In their 2015 guidelines for the management of infective endocarditis, the European Society of Cardiology incorporated cardiac nuclear imaging as part of their diagnostic algorithm for PVE, but not CIED infection since the data were judged insufficient at the moment. This article reviews the actual knowledge and recent studies on the use of 18F-FDG PET/CT and WBC SPECT/CT in the context of CIED infection and PVE, and describes the technical aspects of cardiac radionuclide imaging. It also discusses their accepted and potential indications for the diagnosis and management of CIED infection and PVE, the limitations of these tests, and potential areas of future research. PMID:27721936

Gadolinium-DTPA: value in MR imaging of extraspinal musculoskeletal infections

International Nuclear Information System (INIS)

Haddad, M.C.; Sharif, H.S.; Aabed, M.Y.; Al Shahed, M.S.; Sammak, B.M.; Clark, D.C.

1993-01-01

To determine if paramagnetic contrast agents can improve the detection, delineation, and characterization of extraspinal musculoskeletal infections (MSI) on magnetic resonance (MR) imaging, 42 patients with clinical suspicion of MSI underwent MR imaging before and after intravenous administration of gadolinium-DTPA. The lesions consisted of 27 proven infections and 15 noninfective conditions. Specificity and accuracy in identifying infective lesions averaged 80% and 84%, respectively, on precontrast studies and 80% and 89% on the enhanced examinations, with no statistically significant difference. Rim enhancement around abscess loculi was the only pathognomonic sign of infection seen in ten patients with chronic osteomyelitis and pyogenic or tuberculous infections. In 17 patients with acute osteomyelitis, brucellosis, or mycetoma, detection and delineation of the lesions were best on precontrast studies, while postcontrast examinations resulted in underestimation of the extent of abnormalities in all cases. We conclude that intravenous gadolinium-DTPA has limited usefulness in the MR evaluation of extraspinal MSI. (orig.)

Gadolinium-DTPA: value in MR imaging of extraspinal musculoskeletal infections

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Haddad, M.C. [Dept. of Radiology, Riyadh Armed Forces Hospital, Riyadh (Saudi Arabia); Sharif, H.S. [Dept. of Radiology, Riyadh Armed Forces Hospital, Riyadh (Saudi Arabia); Aabed, M.Y. [Dept. of Radiology, Riyadh Armed Forces Hospital, Riyadh (Saudi Arabia); Al Shahed, M.S. [Dept. of Radiology, Riyadh Armed Forces Hospital, Riyadh (Saudi Arabia); Sammak, B.M. [Dept. of Radiology, Riyadh Armed Forces Hospital, Riyadh (Saudi Arabia); Clark, D.C. [Dept. of Radiology, Riyadh Armed Forces Hospital, Riyadh (Saudi Arabia)

1993-12-01

To determine if paramagnetic contrast agents can improve the detection, delineation, and characterization of extraspinal musculoskeletal infections (MSI) on magnetic resonance (MR) imaging, 42 patients with clinical suspicion of MSI underwent MR imaging before and after intravenous administration of gadolinium-DTPA. The lesions consisted of 27 proven infections and 15 noninfective conditions. Specificity and accuracy in identifying infective lesions averaged 80% and 84%, respectively, on precontrast studies and 80% and 89% on the enhanced examinations, with no statistically significant difference. Rim enhancement around abscess loculi was the only pathognomonic sign of infection seen in ten patients with chronic osteomyelitis and pyogenic or tuberculous infections. In 17 patients with acute osteomyelitis, brucellosis, or mycetoma, detection and delineation of the lesions were best on precontrast studies, while postcontrast examinations resulted in underestimation of the extent of abnormalities in all cases. We conclude that intravenous gadolinium-DTPA has limited usefulness in the MR evaluation of extraspinal MSI. (orig.)

Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging.

Science.gov (United States)

Lee, Hoonsoo; Kim, Moon S; Song, Yu-Rim; Oh, Chang-Sik; Lim, Hyoun-Sub; Lee, Wang-Hee; Kang, Jum-Soon; Cho, Byoung-Kwan

2017-03-01

There is a need to minimize economic damage by sorting infected seeds from healthy seeds before seeding. However, current methods of detecting infected seeds, such as seedling grow-out, enzyme-linked immunosorbent assays, the polymerase chain reaction (PCR) and the real-time PCR have a critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to evaluate the potential of visible/near-infrared (Vis/NIR) hyperspectral imaging system for detecting bacteria-infected watermelon seeds. A hyperspectral Vis/NIR reflectance imaging system (spectral region of 400-1000 nm) was constructed to obtain hyperspectral reflectance images for 336 bacteria-infected watermelon seeds, which were then subjected to partial least square discriminant analysis (PLS-DA) and a least-squares support vector machine (LS-SVM) to classify bacteria-infected watermelon seeds from healthy watermelon seeds. The developed system detected bacteria-infected watermelon seeds with an accuracy > 90% (PLS-DA: 91.7%, LS-SVM: 90.5%), suggesting that the Vis/NIR hyperspectral imaging system is effective for quarantining bacteria-infected watermelon seeds. The results of the present study show that it is possible to use the Vis/NIR hyperspectral imaging system for detecting bacteria-infected watermelon seeds. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Pneumococcal pneumonia: clinical features, diagnosis and management in HIV-infected and HIV noninfected patients.

Science.gov (United States)

Madeddu, Giordano; Fois, Alessandro Giuseppe; Pirina, Pietro; Mura, Maria Stella

2009-05-01

In this review, we focus on the clinical features, diagnosis and management of pneumococcal pneumonia in HIV-infected and noninfected patients, with particular attention to the most recent advances in this area. Classical clinical features are found in young adults, whereas atypical forms occur in immunocompromised patients including HIV-infected individuals. Bacteremic pneumococcal pneumonia is more frequently observed in HIV-infected and also in low-risk patients, according to the Pneumonia Severity Index (PSI). Pneumococcal pneumonia diagnostic process includes physical examination, radiologic findings and microbiologic diagnosis. However, etiologic diagnosis using traditional culture methods is difficult to obtain. In this setting, urinary antigen test, which recognizes Streptococcus pneumoniae cell wall C-polysaccharide, increases the probability of etiologic diagnosis. A correct management approach is crucial in reducing pneumococcal pneumonia mortality. The use of the PSI helps clinicians in deciding between inpatient and outpatient management in immunocompetent individuals, according to Infectious Diseases Society of America (IDSA)-American Thoracic Society (ATS) guidelines. Recent findings support PSI utility also in HIV-infected patients. Recently, efficacy of pneumococcal vaccine in reducing pneumococcal disease incidence has been evidenced in both HIV-infected and noninfected individuals. Rapid diagnosis and correct management together with implementation of preventive measures are crucial in order to reduce pneumococcal pneumonia related incidence and mortality in HIV-infected and noninfected patients.

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

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

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

Primary Neuroendocrine Tumor of the Breast: Imaging Features

International Nuclear Information System (INIS)

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

2013-01-01

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

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

Science.gov (United States)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

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.

Detection of acute synthetic vascular graft infection with IN-111 labeled leukocyte imaging

International Nuclear Information System (INIS)

Alazraki, N.; Dries, D.; Lawrence, P.; Murphy, K.; Kercher, J.; Datz, F.; Christian, P.; Taylor, A.

1985-01-01

Synthetic vascular graft infection is characterized by late diagnosis due to indolent and nonspecific symptoms. Reported data on accuracy of In-111 labeled leukocyte imaging to identify vascular graft infection is sparse and conflicting. The purpose of this animal study was to clarify the accuracy of detection of early graft infection using a mixed population of In-111 labeled leukocytes. Twelve mongrel dogs received dacron aortic interposition grafts. Seven grafts were contaminated at surgery by topical ATCC S. aureus, 10/sup 8/ organisms per ml. Six control animals received no graft contamination Mixed population In-111 homologous leukocyte labeling was performed followed by imaging at 24 and 48 hours following intravenous injection of 250 μCi In-111 leukocytes. Scans were done on Day 2 post-surgery. Infected dogs were sacrificed following Indium imaging; control dogs were rescanned at 3 weeks postop and sacrificed thereafter. Autopsy results were correlated with scans, yielding sensitivity 71%, specificity 100%, accuracy 85% for In-111 leukocyte imaging to detect early graft infection. False positive leukocyte imaging in the early postop period was not a problem. At autopsy all 5 dogs with infected grafts and positive scans had gross pus. The 2 dogs with false negative scans showed no gross pus at autopsy; cultures were positive for S. aureus in all 7 dogs. Scans at 2 days and 3 weeks post-surgery were true negatives in all 6 control dogs. These data suggest a high level of clinical reliability of leukocyte imaging for early graft infection detection

99mTc-Alafosfalin: an antibiotic peptide infection imaging agent

International Nuclear Information System (INIS)

Tsopelas, C.; Penglis, S.; Ruszkiewicz, A.; Bartholomeusz, F.D.L.

2003-01-01

The radiolabeled antibiotic peptide 99m Tc-alafosfalin was assessed as an infection imaging agent in a rat model by comparison with 99m Tc-DTPA and 99m Tc-leukocytes. 99m Tc-alafosfalin was prepared via an instant cold kit and 99m Tc-leukocytes were prepared using 99m Tc-stannous fluoride colloid in an ex vivo labeling procedure of whole blood. In separate experiments, the three radiotracers were administered to rats infected with staphylococcus aureus. Quantitative biodistribution studies were performed as well as scintigraphic images and histopathology. 99m Tc-alafosfalin is a stable product, obtained in high radiochemical purity (>95%). This agent was mainly renally excreted, with low liver, spleen and bone uptake, and resulted in a mean ratio of infected/non-infected thighs of 4.3/1.0 at 4 hr post radiotracer injection. 99m Tc-DTPA gave a corresponding ratio of 1.9/1.0 and 99m Tc-leukocytes gave 20.0/1.0 at the same time point. An in vitro assay found the level of 99m Tc-alafosfalin binding to staphylococcus aureas higher than 99m Tc-DTPA (10% versus 1% respectively). 99m Tc-alafosfalin accumulates at sites of infection in a rat model better than the perfusion molecule 99m Tc-DTPA, yet less than 99m Tc-leukocytes. The distribution characteristics of this 99m Tc-antibiotic peptide would be an advantage in imaging abdominal and soft tissue infection

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2015-11-15

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

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

Science.gov (United States)

Zhang, Jingfa; Qin, Qiming

2003-09-01

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

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

Science.gov (United States)

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

2018-04-01

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

Human gait recognition by pyramid of HOG feature on silhouette images

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Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

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.

Breast cancer mitosis detection in histopathological images with spatial feature extraction

Science.gov (United States)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

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Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Abdominal tuberculosis: Imaging features

International Nuclear Information System (INIS)

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

2005-01-01

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

Image-Processing Scheme to Detect Superficial Fungal Infections of the Skin

Directory of Open Access Journals (Sweden)

Ulf Mäder

2015-01-01

Full Text Available The incidence of superficial fungal infections is assumed to be 20 to 25% of the global human population. Fluorescence microscopy of extracted skin samples is frequently used for a swift assessment of infections. To support the dermatologist, an image-analysis scheme has been developed that evaluates digital microscopic images to detect fungal hyphae. The aim of the study was to increase diagnostic quality and to shorten the time-to-diagnosis. The analysis, consisting of preprocessing, segmentation, parameterization, and classification of identified structures, was performed on digital microscopic images. A test dataset of hyphae and false-positive objects was created to evaluate the algorithm. Additionally, the performance for real clinical images was investigated using 415 images. The results show that the sensitivity for hyphae is 94% and 89% for singular and clustered hyphae, respectively. The mean exclusion rate is 91% for the false-positive objects. The sensitivity for clinical images was 83% and the specificity was 79%. Although the performance is lower for the clinical images than for the test dataset, a reliable and fast diagnosis can be achieved since it is not crucial to detect every hypha to conclude that a sample consisting of several images is infected. The proposed analysis therefore enables a high diagnostic quality and a fast sample assessment to be achieved.

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

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Li, Xuan; Li, Shengyang

2018-05-01

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

Feature Extraction and Simplification from colour images based on Colour Image Segmentation and Skeletonization using the Quad-Edge data structure

DEFF Research Database (Denmark)

Sharma, Ojaswa; Mioc, Darka; Anton, François

2007-01-01

Region features in colour images are of interest in applications such as mapping, GIS, climatology, change detection, medicine, etc. This research work is an attempt to automate the process of extracting feature boundaries from colour images. This process is an attempt to eventually replace manua...

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.

Segmenting texts from outdoor images taken by mobile phones using color features

Science.gov (United States)

Liu, Zongyi; Zhou, Hanning

2011-01-01

Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

Science.gov (United States)

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

Science.gov (United States)

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

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

Directory of Open Access Journals (Sweden)

Shibin Wu

2013-01-01

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

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

CSIR Research Space (South Africa)

Pancham, Ardhisha

2016-10-01

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

Imaging features of intracranial solitary fibrous tumors

International Nuclear Information System (INIS)

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

2012-01-01

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

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

Science.gov (United States)

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-08-01

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

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

International Nuclear Information System (INIS)

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

2002-01-01

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

STUDY ON SHADOW EFFECTS OF VARIOUS FEATURES ON CLOSE RANGE THERMAL IMAGES

Directory of Open Access Journals (Sweden)

C. L. Liao

2012-07-01

Full Text Available Thermal infrared data become more popular in remote sensing investigation, for it could be acquired both in day and night. The change of temperature has special characteristic in natural environment, so the thermal infrared images could be used in monitoring volcanic landform, the urban development, and disaster prevention. Heat shadow is formed by reflecting radiating capacity which followed the objects. Because of poor spatial resolution of thermal infrared images in satellite sensor, shadow effects were usually ignored. This research focus on discussing the shadow effects of various features, which include metals and nonmetallic materials. An area-based thermal sensor, FLIR-T360 was selected to acquire thermal images. Various features with different emissivity were chosen as reflective surface to obtain thermal shadow in normal atmospheric temperature. Experiments found that the shadow effects depend on the distance between sensors and features, depression angle, object temperature and emissivity of reflective surface. The causes of shadow effects have been altered in the experiment for analyzing the variance in thermal infrared images. The result shows that there were quite different impacts by shadow effects between metals and nonmetallic materials. The further research would be produced a math model to describe the shadow effects of different features in the future work.

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

International Nuclear Information System (INIS)

Strojan-Flezar, Margareta; Lavrencak, Jaka; Zganec, Mario; Strojan, Primoz

2011-01-01

Image cytometry can measure numerous nuclear features which could be considered a surrogate end-point marker of molecular genetic changes in a nucleus. The aim of the study was to analyze image cytometric nuclear features in paired samples of primary tumor and neck metastasis in patients with inoperable carcinoma of the head and neck. Image cytometric analysis of cell suspensions prepared from primary tumor tissue and fine needle aspiration biopsy cell samples of neck metastases from 21 patients treated with concomitant radiochemotherapy was performed. Nuclear features were correlated with clinical characteristics and response to therapy. Manifestation of distant metastases and new primaries was associated (p<0.05) with several chromatin characteristics from primary tumor cells, whereas the origin of index cancer and disease response in the neck was related to those in the cells from metastases. Many nuclear features of primary tumors and metastases correlated with the TNM stage. A specific pattern of correlation between well-established prognostic indicators and nuclear features of samples from primary tumors and those from neck metastases was observed. Image cytometric nuclear features represent a promising candidate marker for recognition of biologically different tumor subgroups

Oral and dental infections with anaerobic bacteria: clinical features, predominant pathogens, and treatment.

Science.gov (United States)

Tanner, A; Stillman, N

1993-06-01

Microbial populations colonizing the teeth are a major source of pathogens responsible for oral and dental infections, including periodontal diseases, gingivitis, pericoronitis, endodontitis, peri-implantitis, and postextraction infections. Each entity has distinct clinical and microbial features. Bacterial species associated with oral infections include Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, Bacteroides forsythus, Campylobacter rectus, Eubacterium species, Fusobacterium nucleatum, Eikenella corrodens, and Peptostreptococcus micros. Treponema pallidum-related spirochetes have been associated with acute necrotizing ulcerative gingivitis. Porphyromonas endodontalis appears to be specifically related to endodontic infections. Oral infections in medically compromised patients, including those with AIDS, are associated with similar species and are usually complicated by superinfection with enteric and Candida species. Isolation of species causing oral infections requires the collection of appropriate samples and the use of strictly anaerobic techniques. Rapid selective culture, immunofluorescence, and DNA probe methods have been developed for the identification of these oral species. The varied measures required in the management of oral and dental infections may include antimicrobial therapy. Accurate microbiological diagnosis, including antibiotic susceptibility testing, is indicated for cases that do not respond to therapy.

High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

Science.gov (United States)

Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

2018-04-01

Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

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

Science.gov (United States)

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

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

Development of estimation system of knee extension strength using image features in ultrasound images of rectus femoris

Science.gov (United States)

Murakami, Hiroki; Watanabe, Tsuneo; Fukuoka, Daisuke; Terabayashi, Nobuo; Hara, Takeshi; Muramatsu, Chisako; Fujita, Hiroshi

2016-04-01

The word "Locomotive syndrome" has been proposed to describe the state of requiring care by musculoskeletal disorders and its high-risk condition. Reduction of the knee extension strength is cited as one of the risk factors, and the accurate measurement of the strength is needed for the evaluation. The measurement of knee extension strength using a dynamometer is one of the most direct and quantitative methods. This study aims to develop a system for measuring the knee extension strength using the ultrasound images of the rectus femoris muscles obtained with non-invasive ultrasonic diagnostic equipment. First, we extract the muscle area from the ultrasound images and determine the image features, such as the thickness of the muscle. We combine these features and physical features, such as the patient's height, and build a regression model of the knee extension strength from training data. We have developed a system for estimating the knee extension strength by applying the regression model to the features obtained from test data. Using the test data of 168 cases, correlation coefficient value between the measured values and estimated values was 0.82. This result suggests that this system can estimate knee extension strength with high accuracy.

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

Science.gov (United States)

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

2009-02-01

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

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

Directory of Open Access Journals (Sweden)

Pradipta Maji

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

Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

Science.gov (United States)

Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

2015-06-01

Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

Directory of Open Access Journals (Sweden)

Dat Tien Nguyen

2017-03-01

Full Text Available Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT, speed-up robust feature (SURF, local binary patterns (LBP, histogram of oriented gradients (HOG, and weighted HOG. Recently, the convolutional neural network (CNN method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Comparative imaging features of brucellar and tuberculous spondylitis

International Nuclear Information System (INIS)

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

1987-01-01

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

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.

Primary vs. secondary antibody deficiency: clinical features and infection outcomes of immunoglobulin replacement.

Directory of Open Access Journals (Sweden)

Sai S Duraisingham

Full Text Available Secondary antibody deficiency can occur as a result of haematological malignancies or certain medications, but not much is known about the clinical and immunological features of this group of patients as a whole. Here we describe a cohort of 167 patients with primary or secondary antibody deficiencies on immunoglobulin (Ig-replacement treatment. The demographics, causes of immunodeficiency, diagnostic delay, clinical and laboratory features, and infection frequency were analysed retrospectively. Chemotherapy for B cell lymphoma and the use of Rituximab, corticosteroids or immunosuppressive medications were the most common causes of secondary antibody deficiency in this cohort. There was no difference in diagnostic delay or bronchiectasis between primary and secondary antibody deficiency patients, and both groups experienced disorders associated with immune dysregulation. Secondary antibody deficiency patients had similar baseline levels of serum IgG, but higher IgM and IgA, and a higher frequency of switched memory B cells than primary antibody deficiency patients. Serious and non-serious infections before and after Ig-replacement were also compared in both groups. Although secondary antibody deficiency patients had more serious infections before initiation of Ig-replacement, treatment resulted in a significant reduction of serious and non-serious infections in both primary and secondary antibody deficiency patients. Patients with secondary antibody deficiency experience similar delays in diagnosis as primary antibody deficiency patients and can also benefit from immunoglobulin-replacement treatment.

Primary vs. Secondary Antibody Deficiency: Clinical Features and Infection Outcomes of Immunoglobulin Replacement

Science.gov (United States)

Duraisingham, Sai S.; Buckland, Matthew; Dempster, John; Lorenzo, Lorena; Grigoriadou, Sofia; Longhurst, Hilary J.

2014-01-01

Secondary antibody deficiency can occur as a result of haematological malignancies or certain medications, but not much is known about the clinical and immunological features of this group of patients as a whole. Here we describe a cohort of 167 patients with primary or secondary antibody deficiencies on immunoglobulin (Ig)-replacement treatment. The demographics, causes of immunodeficiency, diagnostic delay, clinical and laboratory features, and infection frequency were analysed retrospectively. Chemotherapy for B cell lymphoma and the use of Rituximab, corticosteroids or immunosuppressive medications were the most common causes of secondary antibody deficiency in this cohort. There was no difference in diagnostic delay or bronchiectasis between primary and secondary antibody deficiency patients, and both groups experienced disorders associated with immune dysregulation. Secondary antibody deficiency patients had similar baseline levels of serum IgG, but higher IgM and IgA, and a higher frequency of switched memory B cells than primary antibody deficiency patients. Serious and non-serious infections before and after Ig-replacement were also compared in both groups. Although secondary antibody deficiency patients had more serious infections before initiation of Ig-replacement, treatment resulted in a significant reduction of serious and non-serious infections in both primary and secondary antibody deficiency patients. Patients with secondary antibody deficiency experience similar delays in diagnosis as primary antibody deficiency patients and can also benefit from immunoglobulin-replacement treatment. PMID:24971644

MR imaging features of peritoneal adenomatoid mesothelioma: a case report

International Nuclear Information System (INIS)

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

2009-01-01

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

MR imaging features of peritoneal adenomatoid mesothelioma: a case report

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

2009-07-01

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

Radiological features of pulmonary tuberculosis in human immunodeficiency virus-infected patients: correlation with the blood CD4 cell count

International Nuclear Information System (INIS)

Isusi, M.; Eguidazu, J.; Oleaga, L.; Grande, D.

2000-01-01

To describe the radiological features of pulmonary tuberculosis (TB) in patients infected with human immunodeficiency virus (HIV) and its correlation with the blood CD4 cell count. We present 44 HIV+patients, 24 with CD4 cell counts of less than 200 cells/mm''3 (group A) and 20 in whom the CD4 counts surpassed this level (group B). We also assessed the chest x-ray images to determine whether or not there was any correlation with the blood CD4 cell counts. Fisher's exact test was used for the statistical study of the differences in the radiological findings in the two groups. The incidence of atypical features was significantly greater in the patients with CD4 cell counts of less than 200 cells/mm''3 (group A) than in those with CD4 counts of over 200 cells/mm''3 (group B). Among HIV+patients, those with a more intact immune status were more likely to present lung x-ray images typical of post-primary TB, with cavitary lesions in upper lobes. The group of patients in whom the immune deficiency was more marked showed a greater incidence of atypical pulmonary findings, more characteristics of primary TB. (Author)

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

Science.gov (United States)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Malignant round cell tumours of bone: atypical clinical and imaging features

International Nuclear Information System (INIS)

Saifuddin, A.; Whelan, J.; Pringle, J.A.S.; Cannon, S.R.

2000-01-01

Objective. To describe the clinical, radiological and MRI features of six atypical cases of histologically proven appendicular Ewing sarcoma/ primitive neuroectodermal tumour (PNET). Design. Retrospective review of case notes and available imaging was carried out. Patients. Six patients (4 male, 2 female; mean age 27 years, range 19-44 years), presenting over a 77-month period, were identified from the Bone Tumour Register. All had unusual clinical and imaging features for Ewing sarcoma/PNET.Results and conclusions. Four tumours were centred on the distal femoral metaphysis, one in the proximal tibial metaphysis and one in the distal tibial metaphysis. Plain radiographs were available in four cases and showed minor cortical changes. MRI demonstrated a relatively small, eccentrically located intraosseous component with a large, eccentric extraosseous component. Extension into the epiphysis was seen in three cases and into the adjacent joint in two cases. Intraosseous ''skip'' metastases were present in three cases. The clinical and imaging features were atypical for conventional intraosseous Ewing sarcoma/PNET and the exact site of origin (intraosseous, periosteal or soft-tissue) was unclear. (orig.)

Learning effective color features for content based image retrieval in dermatology

NARCIS (Netherlands)

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

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

Chest radiographic features of lymphocytic interstitial pneumonitis in HIV-infected children

Energy Technology Data Exchange (ETDEWEB)

Pitcher, R.D., E-mail: pitcher@iafrica.co [Division of Paediatric Radiology, Red Cross War Memorial Children' s Hospital, Department of Radiation Medicine, University of Cape Town (South Africa); Beningfield, S.J. [Division of Radiology, New Groote Schuur Hospital and University of Cape Town (South Africa); Zar, H.J. [Department of Paediatric Pulmonology, Red Cross War Memorial Children' s Hospital, School of Child and Adolescent Health, University of Cape Town (South Africa)

2010-02-15

Aim: To review the radiological features of biopsy-proven lymphocytic interstitial pneumonitis (LIP) in human immunodeficiency virus (HIV)-infected children and establish whether these are based on systematic radiological analysis, and to investigate whether more specific radiological diagnostic criteria can be developed. Materials and methods: A Medline search of English-language articles on the radiological features of biopsy-proven LIP in HIV-infected children was conducted for the period 1982 to 2007 inclusive. Radiological findings were compared with the Centers for Disease Control and Prevention (CDC) criteria for a presumptive diagnosis of LIP. Results: Pulmonary pathology was recorded as 'diffuse' and 'bilateral' in 125 (97.6%) of 128 reported cases of LIP. Twenty-five different terms were used to describe the pulmonary parenchyma. In 96 (75%), the terminology was consistent with CDC diagnostic criteria. Radiological evolution was documented in 43 (33.5%). Persistent focal opacification superimposed on diffuse pulmonary nodularity was demonstrated in 10 (7.8%). The method of radiological evaluation was described in six (4.6%). In no instance was the terminology defined. Conclusion: The radiological features of LIP have not been systematically analysed. However, CDC criteria remain reliable, allowing diagnosis of at least 75% of cases. The sensitivity of these criteria may be increased by including cases with persistent focal pulmonary opacification superimposed on diffuse nodularity. Longitudinal studies utilizing standardized radiographic analysis are needed to elucidate the natural history of LIP.

Chest radiographic features of lymphocytic interstitial pneumonitis in HIV-infected children

International Nuclear Information System (INIS)

Pitcher, R.D.; Beningfield, S.J.; Zar, H.J.

2010-01-01

Aim: To review the radiological features of biopsy-proven lymphocytic interstitial pneumonitis (LIP) in human immunodeficiency virus (HIV)-infected children and establish whether these are based on systematic radiological analysis, and to investigate whether more specific radiological diagnostic criteria can be developed. Materials and methods: A Medline search of English-language articles on the radiological features of biopsy-proven LIP in HIV-infected children was conducted for the period 1982 to 2007 inclusive. Radiological findings were compared with the Centers for Disease Control and Prevention (CDC) criteria for a presumptive diagnosis of LIP. Results: Pulmonary pathology was recorded as 'diffuse' and 'bilateral' in 125 (97.6%) of 128 reported cases of LIP. Twenty-five different terms were used to describe the pulmonary parenchyma. In 96 (75%), the terminology was consistent with CDC diagnostic criteria. Radiological evolution was documented in 43 (33.5%). Persistent focal opacification superimposed on diffuse pulmonary nodularity was demonstrated in 10 (7.8%). The method of radiological evaluation was described in six (4.6%). In no instance was the terminology defined. Conclusion: The radiological features of LIP have not been systematically analysed. However, CDC criteria remain reliable, allowing diagnosis of at least 75% of cases. The sensitivity of these criteria may be increased by including cases with persistent focal pulmonary opacification superimposed on diffuse nodularity. Longitudinal studies utilizing standardized radiographic analysis are needed to elucidate the natural history of LIP.

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

International Nuclear Information System (INIS)

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

1998-01-01

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

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.)

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.

MR imaging of skeletal soft tissue infection: utility of diffusion-weighted imaging in detecting abscess formation

International Nuclear Information System (INIS)

Harish, Srinivasan; Rebello, Ryan; Chiavaras, Mary M.; Kotnis, Nikhil

2011-01-01

Our objectives were to assess if diffusion-weighted imaging (DWI) can help identify abscess formation in the setting of soft tissue infection and to assess whether abscess formation can be diagnosed confidently with a combination of DWI and other unenhanced sequences. Eight cases of soft tissue infection imaged with MRI including DWI were retrospectively reviewed. Two male and six female patients were studied (age range 23-50 years). Unenhanced MRI including DWI was performed in all patients. Post-contrast images were obtained in seven patients. All patients had clinically or surgically confirmed abscesses. Abscesses demonstrated restricted diffusion. DWI in conjunction with other unenhanced imaging showed similar confidence levels as post-contrast images in diagnosing abscess formation in four cases. In two cases, although the combined use of DWI and other unenhanced imaging yielded the same confidence levels as post-contrast imaging, DWI was more definitive for demonstrating abscess formation. In one case, post-contrast images had a better confidence for suggesting abscess. In one case, DWI helped detected the abscess, where gadolinium could not be administered because of a contraindication. This preliminary study suggests that DWI is a useful adjunct in the diagnosis of skeletal soft tissue abscesses. (orig.)

Comparison of CT enterography and MR enterography imaging features of active Crohn disease in children and adolescents

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Gale, Heather I. [The Warren Alpert Medical School of Brown University, Department of Diagnostic Imaging, Rhode Island Hospital/Hasbro Children' s Children' s Hospital/Women and Infants Hospital, Providence, RI (United States); Sharatz, Steven M.; Nimkin, Katherine; Gee, Michael S. [MassGeneral Hospital for Children, Division of Pediatric Imaging, Department of Radiology, Harvard Medical School, Boston, MA (United States); Taphey, Mayureewan [Bumrungrad International Hospital, Bangkok (Thailand); Bradley, William F. [Cambridge Mobile Telematics, Cambridge, MA (United States)

2017-09-15

Assessment for active Crohn disease by CT enterography and MR enterography relies on identifying mural and perienteric imaging features. To evaluate the performance of established imaging features of active Crohn disease in children and adolescents on CT and MR enterography compared with histological reference. We included patients ages 18 years and younger who underwent either CT or MR enterography from 2007 to 2014 and had endoscopic biopsy within 28 days of imaging. Two pediatric radiologists blinded to the histological results reviewed imaging studies and scored the bowel for the presence or absence of mural features (wall thickening >3 mm, mural hyperenhancement) and perienteric features (mesenteric hypervascularity, edema, fibrofatty proliferation and lymphadenopathy) of active disease. We performed univariate analysis and multivariate logistic regression to compare imaging features with histological reference. We evaluated 452 bowel segments (135 from CT enterography, 317 from MR enterography) from 84 patients. Mural imaging features had the highest association with active inflammation both for MR enterography (wall thickening had 80% accuracy, 69% sensitivity and 91% specificity; mural hyperenhancement had 78%, 53% and 96%, respectively) and CT enterography (wall thickening had 84% accuracy, 72% sensitivity and 91% specificity; mural hyperenhancement had 76%, 51% and 91%, respectively), with perienteric imaging features performing significantly worse on MR enterography relative to CT enterography (P < 0.001). Mural features are predictors of active inflammation for both CT and MR enterography, while perienteric features can be distinguished better on CT enterography compared with MR enterography. This likely reflects the increased conspicuity of the mesentery on CT enterography and suggests that mural features are the most reliable imaging features of active Crohn disease in children and adolescents. (orig.)

Research on improving image recognition robustness by combining multiple features with associative memory

Science.gov (United States)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

CT imaging and histopathological features of renal epithelioid angiomyolipomas

International Nuclear Information System (INIS)

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

2012-01-01

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

Role of diffusion weighted imaging in musculoskeletal infections: Current perspectives

International Nuclear Information System (INIS)

Kumar, Yogesh; Khaleel, Mohammad; Boothe, Ethan; Awdeh, Haitham; Wadhwa, Vibhor; Chhabra, Avneesh

2017-01-01

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.)

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.)

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

International Nuclear Information System (INIS)

Mahon, R; Weiss, E; Karki, K; Hugo, G; Ford, J

2016-01-01

Purpose: To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. Methods: T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE-BC) and T2-weighted (T2-BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from the delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE-BC, T2-weighted, and T2-BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. Results: For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44–49%. The percentage dropped to 10–20% for the VIBE-BC images, and 12–14% for the T2-BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. Conclusion: We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

Energy Technology Data Exchange (ETDEWEB)

Mahon, R; Weiss, E; Karki, K; Hugo, G [Virginia Commonwealth University, Richmond, VA (United States); Ford, J [University of Miami Miller School of Medicine, Miami, FL (United States)

2016-06-15

Purpose: To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. Methods: T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE-BC) and T2-weighted (T2-BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from the delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE-BC, T2-weighted, and T2-BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. Results: For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44–49%. The percentage dropped to 10–20% for the VIBE-BC images, and 12–14% for the T2-BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. Conclusion: We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use

Clinical features of anaerobic orthopaedic infections.

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Lebowitz, Dan; Kressmann, Benjamin; Gjoni, Shpresa; Zenelaj, Besa; Grosgurin, Olivier; Marti, Christophe; Zingg, Matthieu; Uçkay, Ilker

2017-02-01

Some patient populations and types of orthopaedic surgery could be at particular risk for anaerobic infections. In this retrospective cohort study of operated adult patients with infections from 2004 to 2014, we assessed obligate anaerobes and considered first clinical infection episodes. Anaerobes, isolated from intra-operative samples, were identified in 2.4% of 2740 surgical procedures, of which half (33/65; 51%) were anaerobic monomicrobial infections. Propionibacterium acnes, a penicillin and vancomycin susceptible pathogen, was the predominantly isolated anaerobe. By multivariate analysis, the presence of fracture fixation plates was the variable most strongly associated with anaerobic infection (odds ratio: 2.1, 95% CI: 1.3-3.5). Anaerobes were also associated with spondylodesis and polymicrobial infections. In contrast, it revealed less likely in native bone or prosthetic joint infections and was not related to prior antibiotic use. In conclusion, obligate anaerobes in our case series of orthopaedic infections were rare, and mostly encountered in infections related to trauma with open-fracture fixation devices rather than clean surgical site infection. Anaerobes were often co-pathogens, and cultures most frequently recovered P. acnes. These observations thus do not support changes in current practices such as broader anaerobe coverage for perioperative prophylaxis.

Unusual acute encephalitis involving the thalamus: imaging features

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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.

Color Image Segmentation Based on Statistics of Location and Feature Similarity

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Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

Computer Aided Quantification of Pathological Features for Flexor Tendon Pulleys on Microscopic Images

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Yung-Chun Liu

2013-01-01

Full Text Available Quantifying the pathological features of flexor tendon pulleys is essential for grading the trigger finger since it provides clinicians with objective evidence derived from microscopic images. Although manual grading is time consuming and dependent on the observer experience, there is a lack of image processing methods for automatically extracting pulley pathological features. In this paper, we design and develop a color-based image segmentation system to extract the color and shape features from pulley microscopic images. Two parameters which are the size ratio of abnormal tissue regions and the number ratio of abnormal nuclei are estimated as the pathological progression indices. The automatic quantification results show clear discrimination among different levels of diseased pulley specimens which are prone to misjudgments for human visual inspection. The proposed system provides a reliable and automatic way to obtain pathological parameters instead of manual evaluation which is with intra- and interoperator variability. Experiments with 290 microscopic images from 29 pulley specimens show good correspondence with pathologist expectations. Hence, the proposed system has great potential for assisting clinical experts in routine histopathological examinations.

Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies

Directory of Open Access Journals (Sweden)

Bo Liu

2014-01-01

Full Text Available Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.

Imaging features of breast echinococcus granulosus

International Nuclear Information System (INIS)

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

2012-01-01

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

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

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

2018-03-01

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

The imaging features of neurologic complications of left atrial myxomas

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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.

The imaging features of neurologic complications of left atrial myxomas

International Nuclear Information System (INIS)

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

2015-01-01

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

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

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

2018-06-01

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