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

• An effective multi-object C–V segmentation model is proposed.
• A rapid initial contour search is conducted.
• To achieve the multi-object segmentation the image is converted to a single level set.
• The evolution of the curve is controlled adaptively via the contour line gradient.
• The model takes the local characteristics of each target into account through the broad contours to avoid missing the target.

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.