Segmentation of Medical Ultrasound Image Based on Markov Random Field
Lihua Li, Lin Jiang-li, Deyu Li, Wang Tianfu · 2007
Segmentation is a most important but difficult step in ultrasound image analysis. For the speckle noise and the tissue intensity inhomogeneities in the medical ultrasound images, the conventional segmentation approaches based on intensity or intensity-statistics do not work well. Current studies to reduce the speckle noise are failed in boundary preserving. And the researches on intensity inhomogeneites can not obtain the complete structure. In this paper, a new segmental method combined Markov random field (MRF) model with morphological image processing is proposed to cover the shortages above. MRF step is used to estimate the label image and morphological image processing makes the region-of-interest (ROI) complete to get a complete tissue. This algorithm is insensitive to speckle noise. Experimental results on synthetic images and ultrasound images show that this algorithm works successfully in MRF model and can correctly identify the tissues in the medical ultrasound images.