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Magnetic resonance imagery and computed tomography (MRI/CT) scan is realized as a powerful technology for mapping soft tissue sections for diagnosis of internal human body parts since decades. These `maps' give different biomedical and anatomical information for early diagnosis of any disease. In the current work MRI scan data is utilized for building up of computer aided design (CAD) model for amputee's...
Heart failures are of increasing importance due to increasing life expectation. For clinical diagnosis parameters for the condition of hearts are needed and can be derived automatically by image processing. Accurate and fast image segmentation algorithms are of paramount importance for a wide range of medical imaging applications. Level set algorithms based on narrow band implementation have been...
We propose two new schemes for segmentation of images with uneven lighting conditions. These are based on adaptive window selection. The first one is a window merging method based on Lorentz information measure (LIM) but the second one is a window growing method using the notion of entropy. We propose two new window merging criterion where the window merging is carried out based on linear combination...
In this paper we propose an expectation-maximization (EM) algorithm with distance measure for color image segmentation. The probability distribution model used is the Gaussian mixture model. The concept of color distance measure is used in this algorithm to determine the region to which a particular pixel belongs. L *a* b color space is used to replace the more straightforward spaces such as the RGB...
In this paper, we present an OCR for handwritten Devnagari characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features...
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