Segmentation of medical images using mean value guided contour

Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments' results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries.