Skip to content
Research Article Open access CC BY 4.0

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Noor Salah Hassan, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dathar A. Hasan

Asian Journal of Research in Computer Science · pp. 23–38 · Published 24 May 2021

10.9734/ajrcos/2021/v9i130212

Abstract

Early diagnosis is considered important for medical images of breast cancer, the rate of recovery and safety of affected women can be improved. It is also assisting doctors in their daily work by creating algorithms and software to analyze the medical images that can identify early signs of breast cancer. This review presents a comparison has been done in term of accuracy among many techniques used for detecting breast cancer in medical images. Furthermore, this work describes the imaging process, and analyze the advantages and disadvantages of the used techniques for mammography and ultrasound medical images. K-means clustering algorithm has been                       specifically used to analyze the medical image along with other techniques. The results                        of the K-means clustering algorithm are discussed and evaluated to show the capacity of this technique in the diagnosis of breast cancer and its reliability to identify a malignant from a benign tumor.

Medical images breast cancer pre-processing segmentation clustering K-means.

References (36)

  1. 1 Cluster analysis for gene expression data: a survey [DOI]
  2. 2 Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure [DOI]
  3. 3 Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm [DOI]
  4. 4 An optimizing BP neural network algorithm based on genetic algorithm [DOI]
  5. 5 Automatic Clustering Using an Improved Differential Evolution Algorithm [DOI]
  6. 6 The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties [DOI]
  7. 7 Improved J48 Classification Algorithm for the Prediction of Diabetes [DOI]
  8. 8 Image Segmentation Techniques [DOI]
  9. 9 Assessment of banana fruit maturity by image processing technique [DOI]
  10. 10 A Feature-Reduction Multi-View k-Means Clustering Algorithm [DOI]
  11. 11 Gene Selection and Classification of Microarray Data Using Convolutional Neural Network [DOI]
  12. 12 Machine learning and Region Growing for Breast Cancer Segmentation [DOI]
  13. 13 Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection [DOI]
  14. 14 Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer [DOI]
  15. 15 Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image [DOI]
  16. 16 Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features [DOI]
  17. 17 Survey of using GPU CUDA programming model in medical image analysis [DOI]
  18. 18 Medical image processing: A review [DOI]
  19. 19 Breast cancer detection in digital mammograms [DOI]
  20. 20 Robust watermarking scheme based LWT and SVD using artificial bee colony optimization [DOI]
  21. 21 Network Lifetime Enhancement of Homogeneous Sensor Network Using ART1 Neural Network [DOI]
  22. 22 A Study of Different Texture Features Based on Local Operator for Benign-malignant Mass Classification [DOI]
  23. 23 Automated Diagnosis of Breast Cancer Using Wavelet Based Entropy Features [DOI]
  24. 24 Segmentation of Breast Ultrasound image using Regularized K-Means (ReKM) clustering [DOI]
  25. 25 Performance Analysis of Segmentation Algorithms for the Detection of Breast Cancer [DOI]
  26. 26 Breast tumors segmentation using Otsu method and K-means [DOI]
  27. 27 Breast Cancer Prediction Based on K-Means and SOM Hybrid Algorithm [DOI]
  28. 28 Combination of Novel Enhancement Technique and Fuzzy C Means ClusteringTechnique in Breast Cancer Detection.
  29. 29 Lesion Detection from the Ultrasound Images Using K-Means Algorithm [DOI]
  30. 30 Automatic detection of suspicious lesions in mammograms by histogram-peak-analysis based K-means [DOI]
  31. 31 Screening and breast cancer: the role of breast awareness [DOI]
  32. 32 Detection of masses in mammography through redundant expansions of scale [DOI]
  33. 33 A Hybrid Multifarious Clustering Algorithm for the Analysis of Memmogram Images [DOI]
  34. 34 A Novel Approach for Clustering Based On Bayesian Network [DOI]
  35. 35 Detection and classification of gastrointestinal cancer and other pathologies through quantitative analysis of optical coherence tomography data and goniophotometry [DOI]
  36. 36 Breast Tumor Segmentation using Image Segmentation Algorithms [DOI]

Cited by 40

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Omar Sedqi Kareem, Ahmed Khorsheed AL-Sulaifanie, Dathar Abas Hasan, Dindar Mikaeel Ahmed · Asian Journal of Research in Computer Science · 2021

IU-Net-DDPC: A Novel Integrated U-Net and Dynamic Density Peaks Clustering Framework for Real-Time Medical Image Stream Analysis

Ghadeer Qasim, Al-jaberi, Ahmed Al-Shammari · Journal of Intelligent Decision Making and Information Science · 2026

Defect Characterization and Prediction for Femtosecond Laser Drilling of Carbon Fiber‐Reinforced Polymer Based on Image Processing and Machine Learning

Ping Liu, Xin-Miao Zhang, Ying Huang · Polymer Composites · 2026

Diffusion Model in Latent Space for Medical Image Segmentation Task

Ngoc Huynh Trinh, H. Nguyen, Sơn Bá Lương · arXiv.org · 2025

Distillation anomaly and fault detection based on clustering algorithms

F. Martínez-García, Á. Molina-García, Francisco Gómez-Donoso · Journal of Industrial Information Integration · 2025

The Role of AI for Improved Management of Breast Cancer: Enhanced Diagnosis and Health Disparity Mitigation

Oluwatunmise Akinniyi, Jose Dixon, Joseph Aina · Comput. Methods Programs Biomed. · 2025

MFAR-Net: Multi-level feature interaction and Dual-Dimension adaptive reinforcement network for breast lesion segmentation in ultrasound images

Guo-Qi Liu, Shaocong Dong, Yanan Zhou · Expert systems with applications · 2025

Classification of Malaria Parasite Plasmodium Falciparum Based on Blood Smear Images Using Support Vector Machine Approach

Nur Chamidah, T. Saifudin, R. Rulaningtyas · Data and Metadata · 2025

Deep clustering domain adaptation for fault diagnosis of rolling bearings in mining belt conveyors

Xin Li, Ziming Kou, Cong Han · Measurement · 2025

Classification of Medical Images Based on Unsupervised Algorithms: A Review

Imad Majed Zeebaree, A. Abdulazeez · International Journal of Research and Applied Technology · 2024

Showing 28 of 40 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

35

Page views

0

PDF downloads

3

Outbound clicks

40

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.