Image Segmentation Using Bias Correction Active Contours

Abstract

Deep learning-based image segmentation methods require densely annotated and massive datasets to produce effective results. On the other hand, active contours-based methods are excellent alternatives to the situation, producing acceptable segmentation results. Earlier active contour models, including local and global region information, struggle with their limitations, such as spurious contours appearing in inhomogeneous images. Bias correction is utilized to solve the bias field’s energy, considering the intensity inhomogeneity and the level set functions that suggest an image domain division. In our approach, we combine the advantages of local and global information in the image level set function, resulting in a combined energy function that aids in the efficient evolution of contours on images and can judge the relevance of the item and its surroundings. The proposed model computes data force by extracting local information from an in-homogeneous image using image-fitting energy and then computing all pixel values simultaneously. Objects with high differences between grey levels or more in-homogeneity can be segmented. The outcome demonstrates that our method is more dependable and computationally efficient than previous methods.

Keywords

• Non-uniform intensity segmentation
• distance adjustment term
• active contour model
• gradient approach
• adaptive function