Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using RGB and HSV Color Spaces

  • Kamal A ElDahshan Professor of Computer Science, Dept. of Mathematics, Faculty of Science, AL-AZHAR University, Cairo, Egypt
  • Mohammed I Youssef Prof. Mohammed I. Youssef Dept. of Electronic Engineering, Faculty of Engineering, AL-AZHAR University, Cairo, Egypt
  • Emad H Masameer Assistant Professor of Computer Science, Dept. of Mathematics, Faculty of Science, AL-AZHAR University, Cairo, Egypt
  • Mohammed A Hassan Lecturer assistant of Computer Science, Dept. of MIS, Modern Academy for Computer Science and Information Technology, Cairo, Egypt
Keywords: Image Segmentation, Microscope Images, ALL, RGB, HSV

Abstract

Image segmentation process is considered the most essential step in image analysis especially in the medical field. In this paper, the color segmentation for acute lymphoblastic leukemia images (ALL) is applied to segment each leukemia image into two clearly defined regions: blasts and background. The ALL segmentation process is based on two different color spaces: RGB color space and HSV color space. The comparison performance between the segmentation methods based on RGB and HSV color spaces are investigated to find the best method to segment the acute lymphoblastic leukemia images. The experimental results show that the segmentation of ALL images based on HSV color space yield better accuracy than RGB color space when compared with the manual segmentation image made by medical experts. Using HSV color space, the shape of blasts in ALL blood samples is closely preserved with segmentation accuracy over 99.00%. However, segmentation based HSV color space was chosen as it produced the highest ALL segmentation rate.

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Published
2015-05-04