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We present the use of multiscale Amplitude Modulation Frequency Modulation (AM-FM) methods for analyzing brain white matter lesions that are associated with disease progression. We analyze lesions and normal appearing white matter (NAWM) longitudinally (0 and 6 months) and also for progression of disease. We use the expanded disability status scale (EDSS) to assess disease progression. The findings...
Brain structural volumes can be used for automatically classifying subjects into categories like controls and patients. We aimed to automatically separate patients with temporal lobe epilepsy (TLE) with and without hippocampal atrophy on MRI, pTLE and nTLE, from controls, and determine the epileptogenic side. In the proposed framework 83 brain structure volumes are identified using multi-atlas segmentation...
In this paper, a new method that uses relative contrast is proposed for medical image segmentation problems. Generally, the absolute intensity values of different features are mapped into a comparable range with a normalization method, but the differences across patients are not considered. In order to utilize the patient-specific information from medical images, we use relative contrast between the...
Imaging methods to localize prostate cancer with sufficient accuracy are extremely useful in guiding biopsy, radiotherapy and surgery as well as to monitor disease progression. Imaging prostate cancer with multispectral magnetic resonance imaging (MRI) has shown a superior performance when compared to classical imaging modality transrectal ultrasound (TRUS). An important component of multispectral...
The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed...
The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. A computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI is developed and used for differential diagnosis. The proposed scheme...
In this work, we propose a kind of supervised classification - support vector machine (SVM) to segment magnetic resonance image (MRI). As a classifier, SVM can employ structural risk minimization principle and perform better in classification task. Based on those excellent capabilities of SVM, we conduct many detailed experiments on some standard simulated data and real data. According to the experiments...
Automated computer classification (ACC) techniques are needed to facilitate physician's diagnosis of complex diseases in individual patients. We provide an example of ACC using computational techniques within the context of cross-sectional analysis of magnetic resonance images (MRI) in neurodegenerative diseases, namely Alzheimer's dementia (AD). In this paper, the accuracy of our ACC methodology...
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