Analysis of the Use of K-Means Clustering Method in Brain Tumor MRI Segmentation
Main Article Content
Accurate measurement of brain tumor volume in MRI examinations is critical for diagnosis and treatment planning. Traditionally, linear measurement is the gold standard, but it is prone to errors due to subjectivity and fatigue, and only provides a rough estimate of tumor volume. This research aims to compare brain tumor volume calculation on MRI images using linear measurement and k-means clustering method on post-contrast T1WI sequences to evaluate their accuracy and clinical consistency. Using a quasi-experimental design with post-test only and no control group, 32 MRI images of brain tumors were analyzed. Tumor volumes were calculated using both methods, and the results were statistically compared. The average tumor volume was 39,304.55 mm³ for the linear method and 35,374.69 mm³ for the k-means clustering method. Statistical analysis using the Wilcoxon test showed no significant difference between the two methods (p = 0.082; p > 0.05). The results of this research suggest that although both methods produce comparable volume estimates, k-means clustering offers the advantage of reducing subjectivity, indicating its potential to improve consistency and reliability of measurements in clinical practice. The implication of this research is that the k-means clustering method may be a more reliable alternative in the measurement of brain tumor volume on MRI examinations, especially in reducing the subjectivity bias often present in linear measurement methods. This can help improve the accuracy and consistency of measurement results, which is crucial for more precise treatment planning and evaluation of patient therapy response.
Chaulagain, D., Smolanka, V., & Smolanka, A. (2021). Intracranial tumors: overview, histological types, symptoms and treatment plans. Int. J. Health Sci. Res. 2021. 11 (10). 133, 144.
Choi, Y. K., Lee, J. H., Kim, Y. S., Wee, C. W., Kim, Y. B., Park, S. J., Jung, W., Seol, K. H., & Choi, E. (2024). Mid-treatment MRI-based tumor response assessment for tumor recurrence and patient survival in locally advanced adenocarcinoma of the cervix: A retrospective multicenter research of KROG 23-03. Gynecologic Oncology, 190, 222–229. https://doi.org/10.1016/j.ygyno.2024.08.025
Chouhan, S. S., Kaul, A., & Singh, U. P. (2019). Image segmentation using computational intelligence techniques. Archives of Computational Methods in Engineering, 26, 533–596.
Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, 54, 764–771. https://doi.org/10.1016/j.procs.2015.06.090
Jindal, S. K., Banerjee, S., Patra, R., & Paul, A. (2022). Deep learning-based brain malignant neoplasm classification using MRI image segmentation assisted by bias field correction and histogram equalization. In Brain Tumor MRI Image Segmentation Using Deep Learning Techniques (pp. 135–161). Elsevier. https://doi.org/10.1016/B978-0-323-91171-9.00008-9
Kaya, I. E., Pehlivanl?, A. Ç., Sekizkarde?, E. G., & Ibrikci, T. (2017). PCA based clustering for brain tumor segmentation of T1w MRI images. Computer Methods and Programs in Biomedicine, 140, 19–28. https://doi.org/10.1016/j.cmpb.2016.11.011
Khalil, Y. Al, Ayaz, A., Lorenz, C., Weese, J., Pluim, J., & Breeuwer, M. (2024). Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation. Computerized Medical Imaging and Graphics, 112, 102332. https://doi.org/10.1016/j.compmedimag.2024.102332
Li, J., Erdt, M., Janoos, F., Chang, T., & Egger, J. (2021). Medical image segmentation in oral-maxillofacial surgery. In Computer-Aided Oral and Maxillofacial Surgery (pp. 1–27). Elsevier. https://doi.org/10.1016/B978-0-12-823299-6.00001-8
Mohammadi, S., Ghaderi, S., Ghaderi, K., Mohammadi, M., & Pourasl, M. H. (2023). Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm. International Journal of Surgery Case Reports, 111, 108818. https://doi.org/10.1016/j.ijscr.2023.108818
Nasor, M., & Obaid, W. (2020). Detection and Localization of Early?Stage Multiple Brain Tumors Using a Hybrid Technique of Patch?Based Processing, k?means Clustering and Object Counting. International Journal of Biomedical Imaging, 2020(1), 9035096.
Perkins, A., & Liu, G. (2016). Primary brain tumors in adults: diagnosis and treatment. American Family Physician, 93(3), 211-217B.
Putri, M. N., Adi, K., Katili, M. I., Hariyanto, S., & Rochmayanti, D. (2022). Comparison Measurement And Calculation Of Brain Tumor In Mri Modalities Utilising Spin-Echo Pulse Sequence T1-Weighted Contrast And Digital Image Processing Applications.
Sabaghian, S., Dehghani, H., Batouli, S. A. H., Khatibi, A., & Oghabian, M. A. (2020). Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm. Spinal Cord, 58(7), 811–820.
Sarris, A. L., Sidiropoulos, E., Paraskevopoulos, E., & Bamidis, P. (2023). Towards a Digital Twin in Human Brain: Brain Tumor Detection Using K-Means. In Caring is Sharing–Exploiting the Value in Data for Health and Innovation (pp. 1052–1056). IOS Press.
Sreenivasan, S. A., Madhugiri, V. S., Sasidharan, G. M., & Kumar, R. V. R. (2016). Measuring glioma volumes: a comparison of linear measurement based formulae with the manual image segmentation technique. Journal of Cancer Research and Therapeutics, 12(1), 161–168.
Vishnuvarthanan, G., Rajasekaran, M. P., Subbaraj, P., & Vishnuvarthanan, A. (2016a). An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Applied Soft Computing, 38, 190–212. https://doi.org/10.1016/j.asoc.2015.09.016
Vishnuvarthanan, G., Rajasekaran, M. P., Subbaraj, P., & Vishnuvarthanan, A. (2016b). An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Applied Soft Computing, 38, 190–212. https://doi.org/10.1016/j.asoc.2015.09.016
Wadhwa, A., Bhardwaj, A., & Singh Verma, V. (2019a). A review on brain tumor segmentation of MRI images. Magnetic Resonance Imaging, 61, 247–259. https://doi.org/10.1016/j.mri.2019.05.043
Wadhwa, A., Bhardwaj, A., & Singh Verma, V. (2019b). A review on brain tumor segmentation of MRI images. Magnetic Resonance Imaging, 61, 247–259. https://doi.org/10.1016/j.mri.2019.05.043
