A multi-object image segmentation C-V model based on region division and gradient guide

摘要

The Chan-Vese (C-V) model is an ineffective method for processing images in which the intensity is inhomogeneous. This is especially true for multi-object segmentation, in which the target may be missed or excessively segmented. In addition, for images with rich texture information, the processing speed of the C-V is slow. To overcome these problems, this paper proposes an effective multi-object C-V segmentation model based on region division and gradient guide. First, a rapid initial contour search is conducted using Otsu's method. This contour line becomes the initial contour for our multi-object segmentation C-V model based on a gradient guide. To achieve the multi-object segmentation the image is then converted to a single level set whose evolution is controlled using an adaptive gradient. The feasibility of the proposed model is analyzed theoretically, and a number of simulation experiments are conducted to validate its effectiveness. (C) 2016 Elsevier Inc. All rights reserved.