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A unified framework for a fully automated diagnostic system for cervical intraepithelial neoplasia (CIN) is proposed. CIN is a detectable and treatable precursor pathology of cancer of the uterine cervix. Algorithms based on mathematical morphology, and clustering based on Gaussian mixture modeling (GMM) in a joint color and geometric feature space, are used to segment macro regions. A non-parametric...
In the present work, a robust algorithm for automatic identification and segmentation of heart portion from cardiac Magnetic Resonance video Image (MRI) is presented. At first, an outline has been generated to get the region of interest (ROI) by employing the moving object criterion, which eventually reduces the processing time significantly. In the next step, Expectation Maximization (EM) algorithm...
Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are noninvasive neuroimaging technologies providing functional mapping of stimulus activated voxels and detailed connectivity structures in the brain, which are traditionally based on simplified linear models. Despite the unique functional and structural representations achievable by fMRI and DTI, respectively, both representations...
Mixture models are among the most popular and effective techniques for image segmentation. While Gaussian Mixture Models (GMM) are a reasonable choice, the number of components is not easy to determine. A non-parametric technique, based on the transformation and analysis of the D(R) (distortion- rate) curve is proposed for model order identification purposes. This curve is estimated via the popular...
Secure and efficient retrieval of multimedia information from archives of large biomedical images for teaching, research and development of diagnostic tools have become a necessity in today's global environment. Most traditional content-based image retrieval (CBIR) systems do not address the challenges involved in high fidelity and efficient retrieval of diverse classes of biomedical imagery through...
This paper explores the classification of texture patterns observed in digital images of the cervix. In particular, the problem of identifying and segmenting punctations and mosaic patterns is considered. First, the ability of large scale filter banks in characterizing punctations and mosaic structures is studied using texton models. However, texton-based models fail to consistently classify punctation...
This work aims at automated segmentation of major lesions observed in early stages of cervical cancer which is the second most common cancer among women worldwide. The purpose of segmentation is to automatically determine the location for a biopsy to be taken for diagnosis, a process that is currently done manually. The acetowhite region, a major indicator of abnormality in the cervix image, is first...
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