Simultaneous Sparsity Model for Histopathological

1. FeatureSpaceScaled 2D SegmentedSkinImages IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 33, NO. 5, MAY 2014 1163 Simultaneous Sparsity Model for Histopathological Image Representation and Classification

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3. Abstract • The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. • Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). • Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. • A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. • We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2

4. Proposed System • Psoriasis skin segmentation images were digitally captured under controlled environment in our proposed system. • This paper shows that certain normalization technique can be employed to distinguish the Markov random field (MRF) of psoriasis skin diseases infecting the further analysis symptoms of psoriasis. • In clinical diagnostic, dermatologist usually groups the psoriasis skin segmentation pattern in order to reduce the problem of segmenting in our proposed concept. • There are several pre-processing techniques, filtering techniques and morphological processing techniques employed in this image segmentation process to eradicate the affected areas of psoriasis

5. Existing System • In many cases, our existing system portrays that the early leg diseases (psoriasis) diagnosis is difficult and may be confusing, so hence it may affect treatments to the patients at a wide range of inflamed skin. • Usually this chronic inflammatory skin disease have a discrete psoriasis skin pattern with poor border definition, which may not be easy to analyze and figure out . • It has a variety of clinical presentations, most of which eventuate into assessment of psoriasis severity and often on the area where scaly patches of itchy skin presents.

6. System Requirements • Hardware Requirements: Processor : Core 2 duo Speed : 2.2GHZ RAM : 2GB Hard Disk : 160GB • Software Requirements:  Platform : DOTNET (VS2010) Dot net framework 4.0 Database : SQL Server 2008 R2

7. Architecture Diagram

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