Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Abbasi, Mohamadreza; Kermani, Saeed; Tajebib, Ardeshir; Moradi Amin, Morteza; Abbasi, Manije
• Is Part Of: IET image processing. Volume 14:Issue 1(2020)
• Page Start: 132
• Page End: 137
• Publication Date: 2020-01-01
• Subjects: cancer -- image colour analysis -- blood -- pattern classification -- medical image processing -- image denoising -- feature extraction -- principal component analysis -- fractals -- image segmentation -- image enhancement -- image classification -- support vector machines; automatic detection -- acute lymphoblastic leukaemia -- multifractal features -- diagnosis efficiency -- microscopic images -- watershed algorithms -- chaotic features -- nuclei images -- fractal dimension -- binary types -- multiclass types -- support vector machine algorithm -- principal component analysis -- feature space; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5910
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the k ‐means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub‐images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six‐cell groups. The difference between the train and test errors was <3%, which proves that the classification performance had improved by using the multifractal features.