Vol.73, No.2, 2022, pp.3455-3472, doi:10.32604/cmc.2022.028722
Hybrid Segmentation Approach for Different Medical Image Modalities
  • Walid El-Shafai1,2, Amira A. Mahmoud1, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Naglaa F. Soliman3, Amel A. Alhussan4,*, Fathi E. Abd El-Samie1
1 Department Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
2 Security Engineering Laboratory, Department of Computer Science, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Amel A. Alhussan. Email:
Received 15 February 2022; Accepted 23 March 2022; Issue published 16 June 2022
The segmentation process requires separating the image region into sub-regions of similar properties. Each sub-region has a group of pixels having the same characteristics, such as texture or intensity. This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization (PSO) and improved fast fuzzy C-means clustering (IFFCM) algorithms. An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques. The existing medical image segmentation techniques incorporate clustering, thresholding, graph-based, edge-based, active contour, region-based, and watershed algorithms. This paper extensively analyzes and summarizes the comparative investigation of these techniques. Finally, a prediction of the improvement involves the combination of these techniques is suggested. The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.
Image segmentation; ultrasonic images; X-ray images; CT images; PET images; MR images; fuzzy c-mean; morphological operations; active contour
Cite This Article
W. El-Shafai, A. A. Mahmoud, E. M. El-Rabaie, T. E. Taha, O. F. Zahran et al., "Hybrid segmentation approach for different medical image modalities," Computers, Materials & Continua, vol. 73, no.2, pp. 3455–3472, 2022.
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