Journal of New Technology and Materials
Volume 8, Numéro 3, Pages 10-15
Edge-based active contour models have been one of the most prominent and inﬂuential approaches in image segmentation. It has been proven that they are very effective when they are applied on images with inhomogeneous intensity. Traditional edge-stop functions (ESFs) are usually used when edges are deﬁned by the image gradient. They often produce weak edges because they fail to stop at the precise boundary. In this work, a new approach integrating machine learning algorithm with edge-based model using a level set method (LSM) is proposed. The ESF is constructed from a convolutional neural network. Then it is applied to an edge-based active contour model. The proposed method has been applied on medical images. Obtained results have been compared to those given by k-nearest neighbors and support vector machine to conﬁrm the effectiveness of the proposed method.
Convolutional neural network ; edge-based active contour ; image segmentation ; medical images