Open AccessSegmentation Technique for Nucleus Detection in Blood Images for Chronic Leukaemia
Author(s) -
Normadiah Daud,
Rafikha Aliana A. Raof,
Muhammad Khusairi Osman,
Nor Hazlyna Harun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1755/1/012053
Subject(s) - thresholding , sobel operator , nucleus , segmentation , artificial intelligence , edge detection , computer science , chronic myeloid leukaemia , bone marrow , image segmentation , pattern recognition (psychology) , computer vision , image (mathematics) , image processing , medicine , pathology , cancer research , psychiatry
leukaemia is a disease which develops in the bone marrow, causing a large formation of abnormal cells and usually affected adults. The process of inspecting visually on the microscopic images is time consuming and a tiring process. The developed technique is aimed at assisting the haematologists upon identifying the presence of nucleus in blood cell images. Therefore, this technique is hoped to aid the haematologists in early and fast identification of leukaemia. This paper will be focusing on Acute Myeloid Leukaemia (AML). This research work proposed a combination of methods for the detection of Leukaemia using image processing techniques such as L*A*B colour-based thresholding algorithm, Sobel edge detection algorithm, and watershed distance transform to identify the nucleus blasts of leukaemia cells from blood cells image. The developed technique shows that it is able to produce the image of segmented nucleus blasts.