Adhish Prasoon

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Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a(More)
With continuous advancement of VLSI technology it has become possible to achieve any desired performance metric, but at a cost of increased system complexity. In this paper we present area optimal integer 2-D DCT architecture for H.264/AVC codecs. The 2-D DCT calculation is performed by utilizing the separability property, in such a way, 2-D DCT is divided(More)
Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining(More)
This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge amount of training data to cover sufficient biological(More)
This paper presents the design of the area optimized integer Two Dimensional Discrete Cosine Transform (2-D DCT) used in H.264/AVC codecs. The 2-D DCT calculation is performed by utilizing the separability property, in such a way that 2-D DCT is divided into two 1-D DCT calculation that are joined through a common memory. Due to its area optimized approach,(More)
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