Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Classification of Chronic Myeloid Leukemia Cell Subtypes Based on Microscopic Image Analysis



Ghane N1 ; Vard A2 ; Talebi A3 ; Nematollahy P3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: EXCLI Journal Published:2019

Abstract

This paper presents a simple and efficient computer-aided diagnosis method to classify Chronic Myeloid Leukemia (CML) cells based on microscopic image processing. In the proposed method, a novel combination of both typical and new features is introduced for classification of CML cells. Next, an effective decision tree classifier is proposed to classify CML cells into eight groups. The proposed method was evaluated on 1730 CML cell images containing 714 cells of non-cancerous bone marrow aspiration and 1016 cells of cancerous peripheral blood smears. The performance of the proposed classification method was compared to manual labels made by two experts. The average values of accuracy, specificity and sensitivity were 99.0 %, 99.4 % and 98.3 %, respectively for all groups of CML. In addition, Cohen's kappa coefficient demonstrated high conformity, 0.99, between joint diagnostic results of two experts and the obtained results of the proposed approach. According to the obtained results, the suggested method has a high capability to classify effective cells of CML and can be applied as a simple, affordable and reliable computer-aided diagnosis tool to help pathologists to diagnose CML. © 2019, Leibniz Research Centre for Working Environment and Human Factors. All rights reserved.
Related Docs
View other Related Docs
1. Classification of Effective Cells in Diagnosis of Chronic Myeloid Leukemia (Cml) Using Semi-Automatic Image Processing of Microscopic Images, Journal of Isfahan Medical School (2017)
2. Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm, Journal of Medical Signals and Sensors (2017)
3. Extraction and Recognition of Myeloma Cell in Microscopic Bone Marrow Aspiration Images, Journal of Isfahan Medical School (2014)
4. Computer Aided Detection and Classification of Acute Lymphoblastic Leukemia Cell Subtypes Based on Microscopic Image Analysis, Microscopy Research and Technique (2016)
5. Segmentation of Effective Cells in Multiple Myeloma Cancer Using Deformable Models and K-Means Clustering, Journal of Isfahan Medical School (2017)
6. Automatic Recognition of Myeloma Cells in Microscopic Images Using Bottleneck Algorithm, Modified Watershed and Svm Classifier, Journal of Microscopy (2016)
7. Plasmacell Cad: A Computer-Aided Diagnosis Software Tool for Plasma Cell Recognition and Characterization in Microscopic Images, International Journal of Medical Informatics (2025)
8. A Novel and More Efficient Approach for Automatic Diagnosis of Acute Lymphoblastic Leukemic Cells Based on Combining Geometrical and Statistical Features of Blood Cells, Journal of Isfahan Medical School (2017)
Experts (# of related papers)
View all Related Experts
Ardeshir Talebi (17)
Alireza Mehri-Dehnavi (11)
Other Related Docs
9. Automatic Detection of Acute Lymphoblastic Leukaemia Based on Extending the Multifractal Features, IET Image Processing (2020)
10. Detecting Different Sub-Types of Acute Myelogenous Leukemia Using Dictionary Learning and Sparse Representation, Proceedings - International Conference on Image Processing, ICIP (2015)
11. A Simple and Accurate Method for White Blood Cells Segmentation Using K-Means Algorithm, IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation (2015)
12. Recognition of Acute Lymphoblastic Leukemia Cells in Microscopic Images Using K-Means Clustering and Support Vector Machine Classifier, Journal of Medical Signals and Sensors (2015)
13. Prediction of Blood Cancer Using Leukemia Gene Expression Data and Sparsity-Based Gene Selection Methods, Iranian Journal of Pediatric Hematology and Oncology (2023)
14. Computer-Aided Diagnosis Software for Vulvovaginal Candidiasis Detection From Pap Smear Images, Microscopy Research and Technique (2018)
15. Automatic Boundary Extraction of Leishman Bodies in Bone Marrow Samples From Patients With Visceral Leishmaniasis, Journal of Isfahan Medical School (2014)
16. Analyzing Features by Swlda for the Classification of Hep-2 Cell Images Using Gmm, Pattern Recognition Letters (2016)
17. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data, Iranian Journal of Radiology (2015)
18. Diagnosis of Vulvovaginal Candidiasis Via Automatic Extraction of Candida Fungus From Pap Smear Images, Journal of Isfahan Medical School (2014)
19. Classification of Three Types of Red Blood Cells in Peripheral Blood Smear Based on Morphology, International Conference on Signal Processing Proceedings, ICSP (2010)
20. Circlet Based Framework for Red Blood Cells Segmentation and Counting, IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation (2015)
21. Automatic Diagnosis of Vulvovaginal Candidiasis From Pap Smear Images, Journal of Microscopy (2017)
22. Classification and Staging of Brain Glioma Tumors Using Magnetic Resonance Imaging and Machine Learning Algorithms, Journal of Isfahan Medical School (2022)
23. Diagnosis of Cervical Cancer Using Texture and Morphological Features in Pap Smear Images, Journal of Isfahan Medical School (2020)