Objective assesment of different segmentation algorithm for underwater images

The quality of underwater images is directly affected by water medium, atmosphere medium, pressure and Temperature. This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image segmentation is the most practical approach among virtually all automated image recognition systems. Feature extraction and recognition have numerous applications on telecommunication, weather forecasting, environment exploration and medical diagnosis. Different segmentation techniques are available in the literature for segmenting or simplifying the underwater images. The performance of an image segmentation algorithm depends on its simplification of image. In this paper, different segmentation algorithms namely, edge based image segmentation, adaptive image thresolding, K-means segmentation, Fuzzy c means(FCM), and Fuzzy C Means with thresholding (FCMT) are implemented for underwater images and they are compared using objective assesment parameter like Energy, Discrete Entropy, Relative Entropy, Mutual Information and Redundancy. Out of the above methods the experimental results show that Fuzzy C means with Thresholding (FCMT) algorithm performs better than other methods in processing underwater images.