Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

被引：0

作者：

Saravanan Alagarsamy ^{[1
]}

D. Nagarajan ^{[2
]}

Vishnuvardhan Govindaraj ^{[3
]}

机构：

[1] Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai (OMR), Tamilnadu, Chennai

[2] Department of Mathematics, Rajalakshmi Institute of Technology, Chennai

[3] Division of Data Science of School of Computing Science and Education, VIT Bhopal University, Kothrikalan, Madhya Pradesh, Sehore

来源：

Operations Research Forum | / 6卷 / 1期

关键词：

Crow Search (CS); Interval Type-II Fuzzy Logic System (IT2FLS); Magnetic Resonance Imaging (MRI); Tumor segmentation;

D O I：

10.1007/s43069-025-00433-0

中图分类号：

学科分类号：

摘要：

Accurate brain tumor prediction is essential when approaching the field of health care; wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

引用

共 29 条

[1]

Ahmad M., Abdullah M., Moon H., Yoo S.J., Han D., Image classification based on automatic neural architecture search using binary crow search algorithm, IEEE Access, 8, pp. 189891-189912, (2020)

[2]

Alagarsamy S., Govindaraj V., Senthilkumar A., Automated brain tumor segmentation for MR brain images using artificial bee colony combined with interval type-II fuzzy technique, IEEE Trans Industr Inf, 19, 11, pp. 11150-11159, (2023)

[3]

Huang G., Zhang Q., Han J., Han J., Wang Y., Yu Y., Exploring task structure for brain tumor segmentation from multi-modality MR images, IEEE Trans Image Process, 29, pp. 9032-9043, (2020)

[4]

Alagarsamy S., Zhang Y.D., Govindaraj V., Rajasekaran M.P., Sankaran S., Smart identification of topographically variant anomalies in brain magnetic resonance imaging using a fish school-based fuzzy clustering approach, IEEE Trans Fuzzy Syst, 29, 10, pp. 3165-3177, (2021)

[5]

Bai X., Zhang Y., Liu H., Wang Y., Intuitionistic center-free FCM clustering for MR brain image segmentation, IEEE J Biomed Health Inform, 23, 5, pp. 2168-2194, (2019)

[6]

Fang F., Yao Y., Zhou T., Xie G., Lu J., Self-supervised multi-modal hybrid fusion network for brain tumor segmentation, IEEE J Biomed Health Inform, 26, 11, pp. 5310-5320, (2022)

[7]

Liu Y., Mu F., Shi Y., Chen X., SF-Net: a multi-task model for brain tumor segmentation in multimodal MRI via image fusion, IEEE Signal Process Lett, 29, pp. 1799-1803, (2022)

[8]

Liu Z., Tong L., Chen L., Zhou F., Jiang Z., Zhang Q., CANet: context aware network for brain glioma segmentation, IEEE Trans Med Imaging, 40, 7, pp. 1763-1777, (2021)

[9]

Ma C., Luo G., Wang K., Concatenated and connected random forests with multi scale patch driven active contour model for automated brain segmentation of MR images, IEEE Trans Med Imaging, 37, 8, pp. 1943-1954, (2018)

[10]

Mekhmoukh A., Mokrani K., Improved fuzzy C-means based particle swarm optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation, Comput Methods Programs Biomed, 122, 2, pp. 266-281, (2015)

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