A fast and reliable noise-resistant medical image segmentation and bias field correction model

In recent years, with the rapid development of modern medical image technology, the medical image processing technology is becoming more important. In particular, the accurate segmentation of medical images is significant for doctors to diagnose and analyze the etiology. However, the false contours appearing in medical images due to fuzzy image boundary, intensity inhomogeneity and random noise, may lead to the inaccurate segmentation results. In this paper, an improved active contour model based on global image information is proposed, which can accurately segment images disturbed by intensity inhomogeneities and serious noise. We give the two-phase energy functional and multi-phase energy functional of our model, and apply it to segment magnetic resonance (MR) images, ultrasound (US) images and synthetic images. Experimental results and comparisons with other models have shown that our model has the advantages of higher accuracy, higher efficiency and robustness in dealing with the intensity inhomogeneity and serious noise in image segmentation.