Image Segmentation Approach for Acute and Chronic Leukaemia Based on Blood Sample Images

1st International Conference on Mechanical Electronic and Biosystem Engineering

Khairudin, N.A.A.^1 ; Ariff, F.N.M.^1 ; Nasir, A.S.A.^1 ; Mustafa, W.A.^1 ; Khairunizam, Wan^2 ; Jamlos, M.A.^1 ; Zunaidi, I.^3 ; Razlan, Z.M.^2 ; Shahriman, A.B.^2

Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar, Perlis

02100, Malaysia^1

Advanced Intelligent Computing and Sustainability (AICoS) Research Group, School of Mechatronics Engineering, Universiti Malaysia Perlis, Perlis Arau

02600, Malaysia^2

Faculty of Technology, University of Sunderland, St Peter's Campus, SR6 0DD, Sunderland, United Kingdom^3

关键词: Blood samples;    Contrast Enhancement;    Leukaemia cells;    Segmentation performance;    Segmentation procedure;    Segmentation techniques;    Sensitivity and specificity;    White blood cells;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/557/1/012008/pdf
DOI  :  10.1088/1757-899X/557/1/012008

来源: IOP

【 摘 要 】

The uncontrolled development of abnormal white blood cells (blast) in bone marrow is the starting point where leukaemia cancer begins. It does not die like it should. Instead, it goes on dividing and crowding out the healthy blood cells, making it difficult for these healthy bloods to function normally. The diagnosis process from haematologist consumes a lot of time. Therefore, a good segmentation procedure is necessary in order to successfully identify and diagnose leukaemia cells automatically from blood sample images. This paper proposes segmentation procedures which consist of contrast enhancement, extraction of hue component information, as well as segmentation based on Otsu's thresholding and watershed technique. The experimental results shows that the proposed segmentation procedure has successfully segmented 200 images consisting of acute and chronic leukaemia with average accuracy, sensitivity and specificity obtained of 98.90%, 82.14% and 99.49%, respectively. The result of segmentation performance achieved shows the significant of this approach. Based on results obtained, this segmentation technique is applicable to segment both acute and chronic leukaemia images with good segmentation performance.