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Automatic Detection of Acute Lymphoblastic Leukaemia Based on Extending the Multifractal Features Publisher



Abbasi M1 ; Kermani S2 ; Tajebib A3 ; Amin MM4 ; Abbasi M1
Authors
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Authors Affiliations
  1. 1. Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461, Iran
  2. 2. Faculty of Advanced Technologies, Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461, Iran
  3. 3. School of Medicine, Isfahan University of Medical Sciences, P.O. Box 319, Hezar-Jerib Ave., Isfahan, 81746 73461, Iran
  4. 4. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
  5. 5. Islamic Azad University, North Tehran Branch, Vafadar Blvd., Shahid Sadoughi St., Hakimiyeh Exit, Shahid Babaee Highway, Tehran, 1651153311, Iran

Source: IET Image Processing Published:2020


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. © The Institution of Engineering and Technology 2019
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