A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.

Measurement of image quality is very important for various applications such as image compression, restoration and enhancement. Conventional methods (e.g., mean squared error; MSE) use error summation to measure quality change pixel by pixel and do not correlate well with subjective quality measurement. This is due to the fact that human eyes extract structural information from the viewing field. In this study a new quality index using a Moran I statistics is proposed. The Moran statistic that measures the sharpness from a local area is a good index of quality as most image processing techniques alter the smoothness of the image. Preliminary results show that the new quality index outperforms the MSE significantly under various types of image distortions.

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