Abstract—Image segmentation is a key step in medical image processing, since it affects the quality of the medical image in the follow-up steps. However, in the practice of processing MRI images, we find out that the segmentation process involves much difficulty due to the poorly defined boundaries of medical images, meanwhile, there are usually more than one target area. In this study, an improved algorithm based on the fuzzy connectedness framework for medical image is developed. The improved algorithm has involved an adaptive fuzzy connectedness segmentation combined with multiple seeds selection. Also, the algorithm can effectively overcome many problems when manual selection is used, such as the un-precise result of each target region segmented of the medical image and the difficulty of completion the segmentation when the areas are not connected. For testing the proposed method, some original real images, taken from a large hospital, were analyzed. The results have been evaluated with some rules, such as Dice’s coefficient, over segmentation rate, and under segmentation rate. The results show that the proposed method has an ideal segmentation boundary on medical images, meanwhile, it has a low time cost. In conclusion, the proposed method is superior to the traditional fuzzy connectedness segmentation methods for medical images.

Index Terms—Fuzzy connectedness, image segmentation, region growing, multiple seeds.

Yunping Zheng and Tong Chang are with School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China. Mudar Sarem is with School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.

Cite:Yunping Zheng, Tong Chang, Mudar Sarem, "Improved Fuzzy Connectedness Segmentation Method for Medical Images with Multiple Seeds in MRI," International Journal of Computer and Communication Engineering vol. 7, no. 3, pp. 32-44, 2018.