Vol 1, No 2 (2017)

Authors: Saimar Khan S, Surya S R

Abstract: Precise and accordant detection and prognosis, CAD (computer aided diagnosis) plays a fair role in predicting the outcome of the treatment and planning of the therapy. Detection and segmentation of the cells are the important steps in a CAD. These steps are difficult due to touching cells, untidy background and variation in the shapes of the cell and changes inside the nuclei. In this paper, we present an analysis based on the textual features of the detected cell after the detection of the cell using adaptive dictionary selection and the sparse reconstruction technique with trivial template. The analysis is done on the basis of the first order and second order statistical features. The proposed method has been tested on a data set with 1000 cells extracted from 20 whole slide scanned images.

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