Modified segmentation algorithm and its feature extraction of cancer affected white blood cells

The cancer cells are multiplicative in nature. Doctors face difficulties in counting the white blood cells (WBCs) at a particular stage due to crowding of cells. This paper proposes the robust segmentation algorithm that can reliably separate touching cells. Segmentation is the main important step in medical image processing. Precisely locating the area of interest in an image, in the presence of inherent uncertainty and ambiguity, is a challenging problem in medical imaging. Hence, one is often faced with a situation that demands proper segmentation. The algorithm is composed of two steps. It begins with a detecting and finding the cells in the region that utilizes level set algorithm. Next, the contour of big cell is obtained using modified level set active contour based on a piecewise smooth function. Feature extraction process follows Segmentation. The required information from the Geomentry and Texture features were obtained. The Feature Selection process is carried out by using Minimum-Redundancy And Maximum-Relevance(MRMR) technique. BPN is used as a classifier for the classification process. Finally, the proposed algorithm is compared with several images which aids in applications such as locating and identifying the tumours and other pathologies.