An overview of intelligent image segmentation using active contour models

The active contour model (ACM) approach in image segmentation is regarded as a research hotspot the area of computer vision, which widely applied different kinds applications practice, such medical processing. essence ACM to make use ofuse an enclosed and smooth curve signify target boundary, usually accomplished by minimizing associated energy function means ofthrough standard descent method. This paper presents overview ACMs for handling problems various fields. It begins with introduction briefly reviewing their pros cons. Then, some basic knowledge theory explained, several popular terms three categories, including region-based ACMs, edge-based hybrid are detailedly reviewed advantages disadvantages. After that, twelve chosen from literature conduct sets experiments segment images, compare efficiency accuracy methods. Next, two deep learning-based algorithms implemented types images results ACMs. Experimental confirm useful conclusions about sharing strengths weaknesses. Lastly, this points out promising directions that need be further studied future.