Microscopic Image Analysis and Recognition On Pathological Cells

According to the features of the configuration and color information on the cancer cells, an adaptive automatic threshold segmentation based on the RGB and HIS color spaces is presented, which is available to segment suspected cancer cells and nucleus from the complex backgrounds in the microscopic images. The edges of the suspected cancer cells and nucleus are detected by using Canny operator. Using the technology of eight-chain code tracking, the feature values of suspected cancer cells are extracted. The feature values consists of the perimeter, the area, the height, the width, the circularity, the rectangularity, the extension and the area ratio between the nucleus and the cytolympth. Based on feature values, a two-round recognition scheme combined with morphologic and colourometry is proposed to recognize and classify the pathological and normal cells. The results show that the proposed algorithm can efficiently segment cell images and receive higher accuracy of cancer cell diagnosis.