Biomedical Image Segmentation Using Integrated FCM Clustering Modified with Regularized Level Set Method

Biomedical image segmentation is used widely for various diagnosis of various diseases and other medicinal purposes and help the radiologist and doctor fraternity to reduce their work and help them concentrate more on their research for new diseases. Researchers and medical practitioners use applications based on image segmentation for detecting abnormalities as well as analyzing the effect of certain deformations or deviations quantitatively. However, there are various issues faced while carrying out this task. The primary reason is the presence of inherent noise, the non-uniform intensity of the pixels, and other artifacts. The presence of artifacts not only limits the process of image segmentation but also increases the computational time for the segmentation process. In biomedical images, the problem is more complicated and recurrent. This is due to the different anatomical structures and multi-modal systems available. In this paper, a new algorithm is proposed where a modified fuzzy C-means (MFCM) clustering algorithm is integrated with Regularized Level set method to enhance the efficiency of the image segmentation process which improves the analysis exercise of the image processing system. The approach encompasses two crucial steps. Initially, the image is segmented using the Modified FCM. The MFCM approach has two basic updates with respect to the conventional FCM [1]. Firstly, we introduce a factor to the conventional FCM and secondly, Euclidean distance is replaced with the kernel-dependent distance measure. The factor increases the speed of computation of the FCM algorithm. Replacing the Euclidean distance with a kernel-dependent distance measure makes the algorithm more robust. After the initial segmentation, the Regularized Level Set method was used to refine the result and track the variation boundaries. The regularized level set method solves the reinitialization problem faced in the conventional level set method and enhances the capability and efficiency of the level set method. The combined approach not only enhances the computational speed but also helps to overcome the artifacts mentioned above.