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.
In this paper, a SGNN (Self-Generating Neural Network)-based method is applied to image segmentation, which is
implemented automatically by autonomously clustering the pixels according to their gray values. The optimization of
SGNN is studied to further improve the accuracy and robustness, as well as to reduce the computational complexity of the
segmentation. The experimental results show that the optimized SGNN gets better segmentation results and outperforms
the existing methods for its distinguished advantages of perfect segmentation without any manual intervention, high
self-learning capacity, less computational complexity, robustness to noise, etc. What's more, the experimental results
suggest that the proposed method can be widely used in segmentation of all typical images, such as IR (Infrared) images,
visible images, X-ray images, and MR (Magnetic Resonance) Images.