Brain Tumor Segmentation using Community Detection Algorithm

• Published in: Proceedings (International Conference on Cyberworlds. Online) pp. 57 - 63
• Main Authors: Gammoudi, Islem, Mahjoub, Mohamed Ali
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2021
• Subjects: community detection; complex netwoks; dataset (BRATS); graph partitioning; Image segmentation; Medical images; random feature extraction(RFE)
• ISSN: 2642-3596
• DOI: 10.1109/CW52790.2021.00016

Segmentation is one of the most important subjects in image analysis due to its good performance in a wide range of applications. It is the task of clustering parts of an image together, which belong to the same object class. Using medical images for tumor growth modeling involves the improvement of all tasks of image processing, most importantly the segmentation. We introduce the tumor segmentation framework based on traditional machine learning and community detection algorithm. In This paper, we propose a novel approach for image segmentation, which is based on community detection algorithms existing in social networks. In this regard, we propose a method based on super-pixels and algorithms for community detection in graphs. The super-pixel method reduces the number of nodes in the graph while community detection algorithms provide more accurate segmentation than traditional approaches. We compare our method with the image segmentation method based on the deep learning approach and our previous work. Experimental results have shown that our method provides more precise segmentation.