Medical image segmentation is an interdisciplinary research area and an important application of graphics computing and image processing in biomedical engineering. Medical image segmentation has important applications in diagnostic medicine, surgical planning and anatomy teaching. The aim of this paper is to investigate support vector machine based medical image segmentation. The main algorithms of image segmentation, the problems of Mr image segmentation, the development trend of medical image segmentation and the current status of support vector machine research are described. Different kernel functions are selected to generate different support vector machine classifiers for segmentation of MR brain images using Mr images as the research object, and the experimental results show that the cross-validation of Gaussian radial basis kernel functions has a high correct rate.

Yunbo Li. The Application of Support Vector Machines in Medical Image Segmentation. Machine Learning Theory and Practice (2022), Vol. 3, Issue 2: 32-39. https://doi.org/10.38007/ML.2022.030204.

[1] Julia Andresen, Timo Kepp, Jan Ehrhardt, Claus von der Burchard, Johann Roider, Heinz Handels: Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies. Int. J. Comput. Assist. Radiol. Surg. 17(4): 699-710 (2022) https://doi.org/10.1007/s11548-022-02577-4

[2] Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi: Medical image segmentation using deep learning: A survey. IET Image Process. 16(5): 1243-1267 (2022) https://doi.org/10.1049/ipr2.12419

[3] Mourtada Benazzouz, Mohammed Lamine Benomar, Youcef Moualek: Modified U-Net for cytological medical image segmentation. Int. J. Imaging Syst. Technol. 32(5): 1761-1773 (2022) https://doi.org/10.1002/ima.22732

[4] Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava: Ensemble-based deep meta learning for medical image segmentation. J. Intell. Fuzzy Syst. 42(5): 4307-4313 (2022) https://doi.org/10.3233/JIFS-219221

[5] Jiwoong Jason Jeong, Amara Tariq, Tobiloba Adejumo, Hari Trivedi, Judy W. Gichoya, Imon Banerjee: Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation. J. Digit. Imaging 35(2): 137-152 (2022) https://doi.org/10.1007/s10278-021-00556-w

[6] Yoav Goldstein, Martin Schätz, Mireille Avigal: Chest area segmentation in 3D images of sleeping patients. Medical Biol. Eng. Comput. 60(8): 2159-2172 (2022) https://doi.org/10.1007/s11517-022-02577-1

[7] Tin Tin Khaing, Pakinee Aimmanee, Stanislav S. Makhanov, Hideaki Haneishi: Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images. Medical Biol. Eng. Comput. 60(2): 421-437 (2022) https://doi.org/10.1007/s11517-021-02484-x

[8] Michaela Kulasekara, Vu Quang Dinh, Maria Fernandez-del-Valle, Jon D. Klingensmith: Comparison of two-dimensional and three-dimensional U-Net architectures for segmentation of adipose tissue in cardiac magnetic resonance images. Medical Biol. Eng. Comput. 60(8): 2291-2306 (2022) https://doi.org/10.1007/s11517-022-02612-1

[9] Rangu Srikanth, Kalagadda Bikshalu: Chaotic multi verse improved Harris hawks optimization (CMV-IHHO) facilitated multiple level set model with an ideal energy active contour for an effective medical image segmentation. Multim. Tools Appl. 81(15): 20963-20992 (2022) https://doi.org/10.1007/s11042-022-12344-x

[10] R. Premalatha, P. Dhanalakshmi: Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach. Neural Comput. Appl. 34(14): 11553-11569 (2022) https://doi.org/10.1007/s00521-022-07043-5

[11] Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov, Egor Krivov, Valery Kostjuchenko, Amayak Durgaryan, Mikhail Galkin, Andrey Golanov, Mikhail Belyaev: Systematic Clinical Evaluation of a Deep Learning Method for Medical Image Segmentation: Radiosurgery Application. IEEE J. Biomed. Health Informatics 26(7): 3037-3046 (2022) https://doi.org/10.1109/JBHI.2022.3153394

[12] Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger: Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty. IEEE Trans. Medical Imaging 41(3): 702-714 (2022) https://doi.org/10.1109/TMI.2021.3123461

[13] Suraj Mishra, Yizhe Zhang, Danny Z. Chen, X. Sharon Hu: Data-Driven Deep Supervision for Medical Image Segmentation. IEEE Trans. Medical Imaging 41(6): 1560-1574 (2022) https://doi.org/10.1109/TMI.2022.3143371

[14] Rangayya, Virupakshappa, Nagabhushan Patil: An enhanced segmentation technique and improved support vector machine classifier for facial image recognition. Int. J. Intell. Comput. Cybern. 15(2): 302-317 (2022) https://doi.org/10.1108/IJICC-08-2021-0172

[15] Sana Ullah Khan, Naveed Islam, Zahoor Jan, Khalid Haseeb, Syed Inayat Ali Shah, Muhammad Hanif: A machine learning-based approach for the segmentation and classification of malignant cells in breast cytology images using gray level co-occurrence matrix (GLCM) and support vector machine (SVM). Neural Comput. Appl. 34(11): 8365-8372 (2022) https://doi.org/10.1007/s00521-021-05697-1

[16] K. Sathish, Y. V. Narayana, Mahammad Shareef Mekala, Rizwan Patan, Suresh Kallam: Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines. Neural Comput. Appl. 34(9): 7199-7207 (2022) https://doi.org/10.1007/s00521-021-06769-y

[17] Mohamed Djerioui, Youcef Brik, Mohamed Ladjal, Bilal Attallah: Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction. Ingénierie des Systèmes d Inf. 24(6): 591-595 (2019) https://doi.org/10.18280/isi.240605

[18] Syed Muhammad Saqlain Shah, Muhammad Sher, Faiz Ali Shah, Imran Khan, Muhammad Usman Ashraf, Muhammad Awais, Anwar Ghani: Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl. Inf. Syst. 58(1): 139-167 (2019) https://doi.org/10.1007/s10115-018-1185-y

[19] Carl Leake, Hunter Johnston, Lidia Smith, Daniele Mortari: Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines Using the Theory of Functional Connections. Mach. Learn. Knowl. Extr. 1(4): 1058-1083 (2019) https://doi.org/10.3390/make1040060