Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widel...

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Bibliographic Details
Published in:Electronics (Basel) 2020-01, Vol.9 (1), p.188
Main Authors: Inbarani H., Hannah, Azar, Ahmad Taher, G, Jothi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Blood
Cancer
Cluster analysis
Clustering
Cytoplasm
Diagnostic systems
Feature extraction
Histograms
Image classification
Image processing
Image segmentation
Intelligence
Internet of Things
Leukemia
Machine learning
Medical imaging
Medical research
Neural networks
Optimization
Performance evaluation
Physicians
Set theory
Support vector machines
Vector quantization
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Summary:Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9010188