Leukemia Cell Segmentation from Microscopic Blood Smear Image Using C-Mode

Abstract

Blood-related diseases such as leukemia are very dreadful diseases and detection of such diseases must be carried out at very early stage. In manual method of leukemia detection, experts check the microscopic images. This is time-consuming process which depends on the person’s skill and does not have standard accuracy. The automated leukemia detection system analyzes the microscopic blood smear image and overcomes these drawbacks of manual detection. Many literature surveys are done, and it was found that average accuracy up to 84–87% is achieved to date. In this technique for automating leukemia, we are applying a new algorithm called C-mode as a method of segmentation which is required in the process of detecting early symptoms of diseases via medical imaging analysis. Improving the present method by application of soft computing to provide accuracy is the focus of the paper. Next, we can achieve by applying C-mode on RGB image, this removes number of steps which was earlier needed to process. Third, the execution speed increases as C-mode reduces the number of iteration. The result obtained provides more accuracy and is used for feature extraction.