Fuzzy optimal thresholded multiscale morphological segmentation of digital images

A new fuzzy based multiscale morphological segmentation is proposed in this paper. The technique works satisfactorily on gray scale images containing bright and dark features of various scales even in an impulse corrupted environment. The segmentation algorithm involves three passes. In the first pass, the image is preprocessed by using an iterative adaptive switching median filter which reduces the impact of impulse that causes over segmentation. In the second pass the multiple scales of bright and dark features of different objects are extracted by the respective opening and closing of the preprocessed image. The resultant image is binarized using an optimum threshold, obtained by the fuzzy Gaussian measure. The process is repeated for multiple scales of the structuring element until all the features are extracted. In the last pass, valid segments of the bright top-hat and dark bottom-hat images are detected and the contours of these images are combined to give the final segmented image. The scheme is implemented on a set of test images and the performance of the algorithm is proved better both objectively and subjectively than the standard methods. The problems of over segmentation and under segmentation are also addressed by the proposed segmentation technique.