Non linear Image segmentation using fuzzy c means clustering method with thresholding for underwater images

The quality of underwater images is directly affected by water medium, atmosphere, 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. Clustering of numerical data forms the basis of many classification and system modelling algorithms. The purpose of clustering is to identify natural groupings of data from a large data set to produce a concise representation of a system's behaviour. In this paper we propose fuzzy c means clustering method with thresholding for underwater image segmentation. This paper focuses on comparison of fuzzy c means clustering algorithms with proposed method for underwater images. To evaluate the nonlinear image region segmentation, quantitative statistical measures have been used, such as the gray level energy, discrete entropy, relative entropy, mutual information and information redundancy. The assessment measures will further quantify the impact from image segmentation. The objective assessment approach has the potential to solve other image processing issues.The proposed method gives desirable results on the basis of energy, entropy, mutual information, redundancy, percentage of simplification and computer efficiency for underwater images.