An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

• Published in: Scientific reports 2015-10, Vol.5 (1), p.14938-14938, Article 14938
• Main Authors: Chin Neoh, Siew, Srisukkham, Worawut, Zhang, Li, Todryk, Stephen, Greystoke, Brigit, Peng Lim, Chee, Alamgir Hossain, Mohammed, Aslam, Nauman
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/1564; 631/114/2164; Acute lymphoblastic leukemia; Algorithms; Blood; Cell Nucleus - metabolism; Classification; Cluster Analysis; Clustering; Cytoplasm; Cytoplasm - metabolism; Databases, Factual; Decision support systems; Diagnosis, Computer-Assisted - methods; Discriminant Analysis; Humanities and Social Sciences; Humans; Image processing; Leukemia; Leukemia - blood; Leukemia - diagnosis; Leukocytes - classification; Leukocytes - metabolism; Leukocytes - pathology; Lymphoblasts; Lymphocytes; Microscopy - methods; multidisciplinary; Nuclei; Reproducibility of Results; Science; Segmentation; Sensitivity and Specificity; Staining and Labeling - methods; Support Vector Machine
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep14938

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm and background regions. Subsequently, a total of eighty features consisting of shape, texture and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.