Shashank Mujumdar

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Diffusion Weighted MR Imaging (DWI) is routinely used for early detection of cerebral ischemic stroke. DWI with higher b-values (b=2000) provide improved sensitivity, higher conspicuity and reduced artifacts and thus improve the detectability of smallest infarcts than conventional DWI (b=1000). We propose a novel framework for accurately detecting stroke(More)
Diffusion Weighted Magnetic Resonance Imaging (DWI) is routinely used for early detection of cerebral ischemic changes in acute stroke. Fast acquisition with a standard echoplanar imaging technique generally compromises the image signal-to-noise ratio and in-plane resolution resulting in a reduction of the conspicuity and definition of lesions in the(More)
During the past decade, the number of mobile electronic devices equipped with cameras has increased dramatically and so has the number of real-world applications for image classification. In many of these applications, the image data is captured in an uncontrolled manner and in complex environments and conditions under which existing image classification(More)
Stroke is a chronic disease which often leads to death. Different medical imaging modalities enable diagnosis for stroke after the onset of symptoms. Time is of the essence during stroke analysis since the window of therapy is very small (< 3 hrs after the onset of symptoms). Recent clinical studies have shown the usefulness and significance of diagnosing(More)
Magnetic resonance (MR) images are displayed at low contrast due to the inability of a device to display the complete range of values under a single window. Generic contrast enhancement algorithms cannot be used in MR due to its characteristic variability of voxel values that represent the same object. Consequently, enhancement works better when driven from(More)
With the recent dramatic increase in the popularity of mobile electronic devices equipped with cameras, there is a growing number of real-world applications for image classification. Nevertheless, some of these real-world applications aim to classify images captured in an unconstrained manner and in complex environments where existing image classification(More)
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