Open AccessImproved image segmentation algorithms for detecting types of acute lymphatic leukaemia
Author(s) -
AlTahhan F.E.,
Sakr Ali A.,
Aladle Doaa A.,
Fares M.E.
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6515
Subject(s) - artificial intelligence , histogram , segmentation , pattern recognition (psychology) , discriminative model , computer science , computer vision , image processing , image segmentation , image (mathematics) , mathematics
A modified digital image processing technique is presented to accurately investigate the types of the acute lymphatic leukaemia (ALL). In this technique, three complementary steps are performed. In the first one, a colour segmentation procedure is used to obtain images including only the white blood cell. In the second step, the histogram equalisation and linear contrast stretching procedures are utilised to obtain images for the nucleus. In the third step, images for the cytoplasm only may be reconstructed from which the vacuoles may be detected. For accurate detection for ALL types, significant and discriminative parameters are introduced such as geometric shape of nucleus membrane, equivalent sizes for the nucleus and cytoplasm and their ratio when the shapes of nucleuses are regular or irregular. This method is applied to a blood smear images for real cases of ALL. To validate the present technique, a comparison is made between present results with their counterparts obtained by expert (manual) technique. Another assessment is performed by comparing the average accuracy of the present technique and the average accuracy of different image processing techniques in the literature. The assessment confirms the high efficiency of the present technique in detecting all types of ALL.