Efficient Segmentation Approach for Different Medical Image Modalities

This paper presents a study of the segmentation of medical images. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. In the first step, the morphological operations are employed to ensure image detail protection and noise-immunity. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the Fuzzy C-Means (FCM) clustering algorithm is used to modify 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 efficiently improve the membership partition matrix. Simulation results are performed on different medical image modalities. Ultrasonic (Us), X-ray (Mammogram), Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance (MR) images are the main medical image modalities used in this work. The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation. Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%, 99.71%, 99.83%, 99.85%, and 99.74% for Us, Mammogram, CT, PET, and MRI images, respectively.