NDLI: Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features

Content Provider	IET Digital Library
Author	Abbasi, Mohamadreza Kermani, Saeed Tajebib, Ardeshir Amin, Morteza Moradi Abbasi, Manije
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.
Starting Page	132
Ending Page	137
Page Count	6
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5910
Journal	IET Image Processing
Publisher Date	2019-10-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acute Lymphoblastic Leukaemia Automatic Detection Binary Types Biology And Medical Computing Biomedical Measurement And Imaging Blood Cancer Chaotic Features Computer Vision And Image Processing Technique Diagnosis Efficiency Feature Extraction Feature Space Fractal Dimension Fractals Image Classification Image Colour Analysis Image Denoising Image Enhancement Image Segmentation Knowledge Engineering Technique Medical Image Processing Microscopic Image Multiclass Types Multifractal Feature Nuclei Image Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Pattern Classification Principal Component Analysis Statistics Support Vector Machine Support Vector Machine Algorithm Watershed Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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