An active contour model reinforced by convolutional neural network and texture description

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Active contour models (ACMs)are popular and widely used for many image segmentationapplications and obtain promising results. However, these methods are unable to achieve the highest performance in the presence ofintensity inhomogeneity. To address this issue, this paper presents an ACM based on the combination of a convolutional neural network (CNN) and a texture descriptor approach.This study uses a CNN model to generate parameter maps more effectively for ACM. Compared to conventional global techniques, these parameter maps increase the speed of movement of the contour into the target. In this approach, the Local Word Directional Pattern (LWDP) is applied as the texture descriptor. LWDP is a texture descriptor that uses the angle between two gradients for exploring the texture structure inside the image. In the proposed method both the original image and the new image obtained bythe LWDP texture descriptor(encoded image) are provided as inputs to the CNN. The experimental outcomes show that the proposed strategy consistently outperforms thestate-of-the-art in accuracy and robustness for segmenting images with fuzzy boundaries and intensity inhomogeneity.