An Efficient Segmentation Technique for Different Medical Image Modalities

Document Type : Original Article

Authors

Depart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.

Abstract

In this paper, a study of the segmentation of medical images is presented. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. Firstly, the local spatial information of the image is enhanced with morphological operations to ensure noise-immunity and image detail-protection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, fuzzy c-means (FCM) clustering is used with modification of membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast, since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image modalities illustrate that the proposed technique can achieve good results, as well as short time and efficient image segmentation.

Keywords


[1] Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. “Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering”, IEEE Transactions on Fuzzy Systems, 2018.
[2] Gang Li, Yi Zhao, Ling Zhang, Xingwei Wang, Yueqin Zhang, and Fayun Guo, “Entropy-Based Global and Local Weight Adaptive Image Segmentation Models”, Tsinghua Science and Technology, IEEE, vol. 25, no. 1, DOI:10.26599/tst.2019.9010026, 2020.
[3] Mahapatra. “Semi-Supervised Learning and Graph Cuts for Consensus Based Medical Image Segmentation,” Pattern Recognit., vol. 63, pp. 700-709, Mar. 2017.4] Mengxuan Zhang, Licheng Jiao, Ronghua Shang, Xiangrong Zhang and Lingling Li “Unsupervised EA-Based Fuzzy Clustering for Image Segmentation”, IEEE Access, vol. 8, DOI: 10.1109/ACCESS.2019.2963363, 2020.
[5] Soudani, A., &Zagrouba, E. “Adaptive Region Based Active Contour Model for Image Segmentation” IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017.
[6] Xu, W., Yue, X., Chen, Y., & Reformat, M. “Ensemble of Active Contour-Based Image Segmentation”, IEEE International Conference on Image Processing (ICIP), 2017.
[7] Ali, H., Rada, L., & Badshah, N. “Image Segmentation for Intensity Inhomogeneity in Presence of High Noise”, IEEE Transactions on Image Processing, 27(8), pp. 3729–3738, 2018.
[8] Sasmal, P., Iwahori, Y., Bhuyan, M. K., & Kasugai, K. “Active contour segmentation of polyps in capsule endoscopic image”, 2018 International Conference on Signals and Systems (ICSigSys), 2018 [9] Pengcheng Li, Yue Zhao, Yang Liu, Qiaoyi Chen, Fangcen Liu and Chenqiang Gao “Temporally Consistent Segmentation of Brain Tissue from Longitudinal MR Data” Digital Object Identifier, IEEE Access, vol. 8, DOI: 10.1109/ACCESS.2019.2949078, 2020.
[10] Yao Yao, Changlin Xia, Jitao Li and Qiong Li, "HeadCT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs", Digital Object Identifier, IEEE Access, DOI: 10.1109/ACCESS.2019.2963714, 2020. [11] Fankui Hu, Haibing Chen and Xiaofei Wang,"An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm with Local Information for Power Equipment Image Segmentation", Digital Object Identifier, IEEE Access, vol. 8, DOI: 10.1109/ACCESS.2019.2963444 ,2020.
[12] Yanbo Li, Junping Wang “Novel Binary Adaptive Morphological Operators”, 12th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID), 2018.
[13] Amira A. Mahmoud, El-Sayed M. El-Rabaie, Taha E. Taha, Adel Elfishawy, Osama Zahran and Fathi E. Abd El-Samie, “ Medical Image Segmentation Techniques, a Literature Review, and Some Novel Trends”, MJEER, Vol. 27, no. 2, pp. 23-58, 2018.
[14] Soomro, S., and Choi, K. N. “Robust Active Contours for Mammogram Image Segmentation”, IEEE International Conference on Image Processing (ICIP), 2017. [15] Xiaojun Yang, Xiaoliang Jiang, Lingfei Zhou, Yong Wang and Yuliang Zhang, “Active Contours Driven by Local and Global Region-Based Information for Image Segmentation”, IEEE Access, vol. 8, DOI: 10.1109/ACCESS.2019.2963435, 2020.
[16] T. Dietenbeck, M. Alessandrini, D. Friboulet, and O. Bernard, ''Creaseg: A Free Software for The Evaluation of Image Segmentation Algorithms Based on Level-Set", in IEEE International Conference on Image Processing. Hong Kong, China, 2010.
[17] http://www.onlinemedicalimages.com/index.php/en/
[18] https://www.cancerimagingarchive.net/
[19] Dang N. H. Thanh "Image Segmentation Quality Scores", (https://www.mathworks.com/matlabcentral/fileexchange/71221, (image segmentation quality-scores), MATLAB Central File Exchange,