Boundary Finding Combining Wavelet and Markov Random Field Segmentation Based on Maximum Entropy Theory

Boundary finding is one of the most important aspects in medical image processing. Wavelet edge detector becomes popular in recent years but is known to degrade in noisy situations. This study aimed to develop an advance precision image segmentation algorithm to enhance the blurred edges clearly for medical target definition. A new method of combining wavelet analysis with Markov random field (RBF) segmentations has been developed to improve the performance of boundary finding. We found that the resulting boundary is indeed much superior than using the wavelets or RBF segmentations performed alone. Experimental results of a magnetic resonance of imaging (MRI) proved the method shall have important practical values.