A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

被引:38
作者
Anilkumar, K. K. [1 ]
Manoj, V. J. [1 ]
Sagi, T. M. [2 ]
机构
[1] Cochin Univ Sci & Technol, Cochin Univ, Coll Engn Kuttanad, Dept Elect & Commun, Pulincunnu PO, Kochi, Kerala, India
[2] Ctr Profess & Adv Studies, Sch Med Educ, Dept Med Lab Technol, Gandhinagar PO, Arpookara, Kerala, India
关键词
Leukemia; Segmentation; Blood smear; Leukocytes; Survey; Image processing;
D O I
10.1016/j.bbe.2020.08.010
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Leukemia is an abnormal proliferation of leukocytes in the bone marrow and blood and it is usually diagnosed by the pathologists by observing the blood smear under a microscope. The count of various cells and their morphological features are used by the pathologists to identify and classify leukemia. An abnormal increase in the count of immature leukocytes along with a reduced count of other blood cells may be an indication of leukemia. The Pathologist may then recommend for bone marrow examination to confirm and identify the specific type of leukemia. These conventional methods are time consuming and may be affected by the skill and expertise of the medical professionals involved in the diagnostic procedures. Image processing based methods can be used to analyze the microscopic smear images to detect the incidence of leukemia automatically and quickly. Image segmentation is one of the very important tasks in processing and analyzing medical images. In the proposed paper an attempt has been made to review the available works in the area of medical image processing of blood smear images, highlighting automated detection of leukemia. The available works in the related area are reviewed based on the segmentation method used. It is learnt that even though there are many studies for detection of acute leukemia only a very few studies are there for the detection of chronic leukemia. There are a few related review studies available in the literature but, none of the works classify the previous studies based on the segmentation method used. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:1406 / 1420
页数:15
相关论文
共 117 条
  • [1] Henry J.B., Clinical diagnosis and management by laboratory methods, (1989)
  • [2] Gonzalez R.C., Woods R.E., Digital image processing, (2002)
  • [3] Jain A.K., Fundamentals of digital image processing, (2003)
  • [4] Xian M., Yingtao, Cheng H.D., FeiXu, Zhang B., Ding J., Automatic breast ultrasound image segmentation: a survey, Pattern Recogn, 79, pp. 340-355, (2018)
  • [5] Garcia-Lamont F., Cervantes J., Lopez A., Rodrigues L., Segmentation of images by color features: a survey, Neurocomputing, 292, pp. 1-27, (2018)
  • [6] Saraswath M., Arya K.V., Automated microscopic image analysis for leukocytes identification: a survey, Micron, 65, pp. 20-33, (2014)
  • [7] Zaitoun N.M., Aqel M.J., Survey on image segmentation techniques, Proc International conference on Communication, Management and Information Technology (ICCMIT), Proceedia Computer Science, vol. 65, pp. 797-806, (2015)
  • [8] Hedge R.B., Prasad K., Hebbar H., Peripheral blood smear analysis using image processing approach for diagnostic purposes: a review, Biocybern Biomed Eng, 38, pp. 467-480, (2018)
  • [9] Alsalem M.A., Zaidan A.A., Zaidan B.B., hashim M., Madhloom H.T., Azeez N.D., A review of auotomatic detection and classification of acute leukemia: coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations, Comput Methods Programs Biomed, 158, pp. 93-112, (2018)
  • [10] Rodellar J., Alferez S., Acevedo A., Molina A., Merino A., Image processing and machine learning in the morphological analysis of blood cells, Int J Lab Hematol, 40, pp. 46-53, (2018)