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Dynamic magnetic resonance imaging MRI acquisitions are used in the clinical assessment of the pelvic organs behaviour during an abdominal strain. The main organs (bladder, uterus-vagina, rectum) undergo deformations and intrinsic movements along a sequence. Anatomical references and measurements are generally used by clinicians to evaluate pathology grades. In this context, we have established quantitative...
In this work, we present a probabilistic information fusion approach for the diagnosis of dementia from cross-sectional magnetic resonance (MR) images. The approach relies on first mapping the outputs of a support vector classifier (SVM) trained on image features to probabilities and then on combining these probabilities with the class-conditional distributions of neuropsychiatric test scores, such...
Classification of complex motor activities from brain imaging is relatively new in the fields of neuroscience and brain-computer interfaces (BCIs). We report sign language classification results for a set of three contrasting pairs of signs. Executed sign accuracy was 93.3%, and imagined sign accuracy was 76.7%. For a full multiclass problem, we used a decision directed acyclic graph of pairwise support...
Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has to rule out cancer for each...
This paper deals with the development of a new texture analysis method based on both spatial and spectral information for texture classification purposes. The idea of Generalized Gray Level Difference Method (GGLDM) is to extend the concept of spatial Gray Level Difference Method(GLDM) by assuming texture joint information between spectral bands. In addition, new texture features measurement related...
This paper outlines a new method for automatic detection of microcalcification clusters in mammograms. The presence of microcalcification clusters, which appear as small bright spots in mammographic images, is considered a very important sign in breast cancer diagnosis. However, such clusters can be hard to detect due to their size and low contrast from surrounding normal tissue. This work presents...
A new and efficient method for casts recognition in urinary sediment microscopic images is proposed in this paper. It combines the shape and texture characteristics of casts, and accordingly, consists of two steps. In the first step, the casts' tube-like shape feature is expressed by a modified method stems from the traditional one which is based on the minimum bounding rectangle(MBR). Instead of...
The objective of this study concerns the classification of a scene observed by different types of images, which generates large amounts of data to be processed. We have therefore chosen to use the classification SVM (Support Vector Machines) who is known for treating high-dimensional data. Although different sources of information can provide additional information to address the ambiguities, they...
In recent years, developments in functional neuroimaging technologies have helped facilitate a clearer understanding of the activation of sites in the brain. This technology is applied to brain-computer interfaces (BCIs). Previous BCIs have primarily used information on the brain activity related to the motor system. In this study, we examined the possibility of controlling the decision-making abilities...
In this paper we propose a new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images, using Wavelets Transform (WT) as input to Genetic Algorithm (GA) and Support Vector Machine (SVM). The proposed method segregates MR brain images into normal and abnormal. Our contribution employs genetic algorithm for feature selection witch requires much lighter computational...
This paper concerns lung tissue classification using asymmetric-margin support vector machine (ASVM) to handle the imbalance of the positive and negative classes in a one-against-all multiclass classification problem. The hyperparameters of the algorithm are obtained using an optimization of the upper bound of the leave-one-out error of the ASVM. The ASVM is applied on the dataset with its original...
Next-generation ultrasound contrast agents, in the form of tiny gas bubbles, can be targeted to selectively adhere to cancer cells. The number of attached microbubbles could be correlated with the status of the cancer. Consequently, the estimation of bubble concentration can provide useful medical information in addition to ultrasound molecular imaging. In this paper, a method to obtain the ultrasound...
One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere...
In this paper, surface electromyographic signal is analyzed by wavelet transform. The feature vectors are built by extracting the singular value of the wavelet coefficients. The multi-class support vector machine classifier is designed by using four kinds of multi-class classification approaches, and completed the eight class surface EMG pattern classification. The SVM classifier is applied to the...
Computer aided diagnosis systems using machine learning techniques have been developed in order to assist radiologists' diagnosis and overcome inherent limitations of conventional mammography. Such systems base their diagnosis on image features extracted from mammograms, which are mainly related to the shape, the morphology, the texture and the position of the suspicious abnormality. Since the discrimination...
To improve the correct rate of liver B ultrasonic image classification, a method based on support vector machines is proposed. The gray level co-occurrence matrix is calculated to get the texture feature of liver B ultrasonic image. Then classification using the proposed method with different kernel functions is carried out. The classification results show that RBF kernel can give better performance...
Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented...
Wireless capsule endoscopy (WCE) is a great breakthrough for Gastrointestinal (GI) Tract diagnoses, and it can view the entire gastrointestinal tract, especially the small intestine without invasiveness and sedation. However, a tough problem associated with this new technology is that too many images to be inspected by naked eyes cause a huge burden to physicians, so it is significant to find an automatic...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
Glaucoma is the second leading cause of blindness worldwide. The risk of glaucoma can be determined by calculating the cup to disc ratio in retinal fundus images. To accurately detect the optic cup, kinks or bends in small and medium vessels are important indicators of the cup boundary. In this paper, we present a method of detecting such vessels, through the extraction of patches and generation of...
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