Detection of acute lymphoblastic leukemia using microscopic images of blood

Article history: Received 19 March 2017 Received in revised form 1 July 2017 Accepted 5 July 2017 Leukemia is a group of cancers that usually begin in the bone marrow and result in high number of abnormal white blood cells. Detection of leukaemia in early stages is necessary as this can reduce the rate of mortality and may lead to death. In a manual method of leukaemia detection Haematologists analyze the microscopic images and decide the severity. This is lengthy, cost effective and time taking process which depends on person’s expertise and may not lead to standard accuracy. Till date, a number of methods have been proposed for this Leukaemia detection using Image Processing. Unlike the previous methods, which solely depend upon the entire cell, in this paper we proposed a new method to separate the cell Nucleus from Cytoplasm to obtain more features. The proposed method achieves the better accuracy when compared to the other existing methods.

[1]  V. Piuri,et al.  Morphological classification of blood leucocytes by microscope images , 2004, 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA..

[2]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[3]  Himali P. Vaghela,et al.  Leukemia Detection using Digital Image Processing Techniques , 2015 .

[4]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[5]  Dipti Patra,et al.  An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images , 2013, Neural Computing and Applications.

[6]  P. Sonneveld,et al.  Recombinant human interleukin-3 (rH IL-3) in combination with remission induction chemotherapy in patients with relapsed acute myelogenous leukemia (AML): a phase I/II study. , 1996, Leukemia.

[7]  Chin-Teng Lin,et al.  LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction , 2011, IEEE Transactions on Fuzzy Systems.

[8]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[9]  Shubhada Sankararaman,et al.  Cells, tissues and disease: Principles of general pathology , 1997 .

[11]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[12]  Ashutosh Mishra,et al.  Automated Leukaemia Detection Using Microscopic Images , 2015 .

[13]  Sunil Kumar,et al.  Kernel Induced Rough c-means clustering for lymphocyte image segmentation , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[14]  A. Zelenetz,et al.  Acute lymphoblastic leukemia. , 2019, Journal of the National Comprehensive Cancer Network : JNCCN.

[15]  Dipti Patra,et al.  Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images , 2010, 2010 International Conference on Systems in Medicine and Biology.

[16]  Dipti Patra,et al.  Unsupervised Blood Microscopic Image Segmentation and Leukemia Detection using Color based Clustering , 2011 .

[17]  Adnan Khashman,et al.  Acute Lymphoblastic Leukemia Identification Using Blood Smear Images and a Neural Classifier , 2013, IWANN.

[18]  Bor-Chen Kuo,et al.  Nonparametric weighted feature extraction for classification , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[19]  J. Downing,et al.  Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.

[20]  Jaya Sharma,et al.  Leukemia Image Segmentation using K-Means Clustering and HSI Color Image Segmentation , 2014 .

[21]  Jian Yu,et al.  A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests , 2005, Pattern Recognit. Lett..