Baidya Nath Saha

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A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR(More)
BACKGROUND AND PURPOSE WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating(More)
An important measure in various stages of oil sand mining is particle size distribution (PSD). Currently PSD is found by time consuming manual inspection. An effective automation of PSD computation can play a significant role in improving the mining process. Toward this, we propose an algorithm (snake-PCA) to detect oil sands from conveyor belt images,(More)
We propose a novel framework for counting passengers in a railway station. The framework has three components: people detection, tracking and validation. We detect every person using Hough circle when he or she enters the field of view. The person is then tracked using optical flow until (s)he leaves the field of view. Finally, the tracker generated(More)
Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimer's disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects(More)
—We utilize outlier detection by principal component analysis (PCA) as an effective step to automate snakes/active contours for object detection. The principle of our approach is straightforward: we allow snakes to evolve on a given image and classify them into desired object and non-object classes. To perform the classification, an annular image band(More)
We consider here a change detection problem: to find regions of change on a test image with respect to a reference image. Unlike the state-of-the-art change detection and background subtraction algorithms that compute only local (pixel location-based) changes, we propose to minimize a novel region-based energy functional based on Bhattacharya coefficient(More)
We introduce a novel, robust data-driven regularization strategy called Adaptive Regularized Boosting (AR-Boost), motivated by a desire to reduce overfitting. We replace AdaBoost's hard margin with a regularized soft margin that trades-off between a larger margin, at the expense of misclassification errors. Minimizing this regularized exponential loss(More)