MAXIMUM A-POSTERIORI SPATIAL PROBABILITY SEGMENTATION

An image segmentation algorithm that performs pixel-by-pixel segmentat ion on an image with consideration of spatial information is described . The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probabili ty that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a post eriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such th at each pixel is segmented into a particular class when the a posterio ri spatial probability is maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series o f intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure i s chosen as the optimum segmented image. Results from segmenting synth etic and practical images demonstrate that the MASP algorithm is capab le of achieving better results when compared with other global thresho lding methods.