Namita Aggarwal

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In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of(More)
The goal in real-time cardiac MR imaging is to reconstruct a cardiac cine from MR data acquired without ECG gating. Since MR data acquisition speed is limited, it is usually not feasible to sample each point in k-space at the required Nyquist rate. Adaptive Dynamic Imaging (ADI), proposed in [1], is an approach that enables reconstruction of a high-spatial(More)
Alzheimer’s disease is the most common form of dementia occurring in the elderly persons. Its early diagnosis may help in providing proper treatment. To date, there is no appropriate technique available to automatically classify it using MR brain images. In this work, first-and-second-order-statistics (FSOS) was employed for classification of Alzheimer’s(More)
In this study, we propose a model which may assist in diagnosis of (AD) using T1 weighted MRI brain images. The proposed model involves construction of statistical features from multiple trans-axial slices from hippocampus and amygdala regions, which play a significant role in AD diagnosis. Features from multiple slices are then averaged, which resulted(More)
In this paper, we propose a three-phased method for diagnosis of Alzheimer's disease using the structural magnetic resonance imaging (MRI). In first phase, gray matter tissue probability map is obtained from every brain MRI volume. Further, five regions of interest (ROIs) are extracted as per prior knowledge. In second phase, features are extracted from(More)
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