An Efficient Segmentation Technique for Different Medical Image Modalities

In this paper a study on the segmentation of the medical image is carried out. Image segmentation is the process of splitting an image into a number of non-overlapped segments (sets of pixels, also known as image objects). The success of image analysis process depends on accuracy of segmentation process, but a successful segmentation of an image is generally a difficult problem. During an image preprocessing operation, the input given is an image and its output is an enhanced high-quality image as per the techniques used. This paper provides a solid introduction to image enhancement along with image segmentation technique fundamentals. Firstly, the local spatial information of the image is combined with fuzzy c-mean by introducing morphological reconstruction operation to ensure noise-immunity and image detail-protection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the modification of membership partition depends only on the spatial neighbors of membership partition instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed algorithm 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 image because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image multimodalities illustrate that the proposed algorithm can achieve better results, as well as it requires short time for the image segmentation process.