Learn 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)
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)
In this paper we propose a locally adaptive image threshold technique via variational energy minimization. The novelty of the proposed method is that from an image it automatically computes the weights on the data fidelity and the regularization terms in the energy functional, unlike many other previously proposed variational formulations that require(More)
– Tumor/abnormality segmentation from magnetic resonance imagery (MRI) can play a significant role in cancer research and clinical practice. Although accurate tumor segmentation by radiologists is ideal, it is extremely tedious. Experience shows that for MRI database indexing purposes approximate segmentations can be adequate. In this paper, we propose a(More)
In this paper we introduce an adaptive image thresholding technique via minimax optimization of a novel energy functional that consists of a non-linear convex combination of an edge sensitive data fidelity term and a regularization term. While the proposed data fidelity term requires the threshold surface to intersect the image surface only at places with(More)
Wepropose anovel framework for counting passengers in a railway station. The framework has three components: people detection, trackingand 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 trajectory(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 around(More)
An important measure in various stages of oil sand mining is particle size distribution (PSD) of oil sand particles. 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 goal we propose an algorithm (snake-PCA) to detect oil sands from(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 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)