Novel adaptive multi threshold image segmentation algorithm

A novel adaptive multi threshold image segmentation algorithm is proposed in this paper. This proposed segmentation algorithm has two unique characteristics: it fits the 1-D graylevel histogram of the image by potential base function and thereby adaptively determines the classification number by potential function clustering; based on the graylevel co-occurrence matrix, it acquires the multi segmentation thresholds which makes the shape connectivity maximum according to the shape connectivity criterion. Both theoretical analysis and simulation results indicate that the performance of this new adaptive multi threshold segmentation algorithm is superior to those of the conventional threshold segmentation algorithms. And it has not only a low computing cost, but also shows quite good segmentation effect. Besides, it is insensitive to noises and interferences.