Automated Nucleus Segmentation of Leukemia Blast Cells : Color Spaces Study

Leukemia detection using computer vision algorithms is a significant step in computer-assisted diagnosis for a pathologist. In order to extract the blood cells, many color space models are used for image enhancement and as the preprocessing steps. The present work compares the effect of the green, saturation, Cb and M component of RGB, HSV, YCbCr and CMY color spaces for segmentation of nucleus of blast cells in a leukemia patient's blood smear. The segmentation result of each color space for every ten images is divided into three categories i.e. only WBC segmentation, WBC with peripheral cells and all blood cell segmentation. The study demonstrates that the performance of segmentation is negatively correlated with contrast and illuminance of the input image. HSV and CMY models obtained 85% segmentation accuracy. The present study would help researchers to narrow down their selection when choosing a color space model for segmenting the nucleus of leukemia blast cells.