Adaptive active contours based on variable kernel with constant initialisation

作者：Munir Asad; Soomro Shafiullah; Lee Chang Ha; Choi Kwang Nam

来源：IET Image Processing, 2018, 12(7): 1117-1123.

DOI：10.1049/iet-ipr.2017.0481

• medical image processing
• image segmentation
• partial differential equations
• set theory
• object detection
• skin
• cancer
• adaptive active contours
• variable kernel
• disease diagnosis
• medical image
• active contour methods
• image segmentation
• partial differential equation
• PDE
• object boundary
• regularisation term
• level set function smoothness
• edge term
• energy formulation
• iteration method
• local region information
• object detection
• complex background
• global image information

In this paper, a novel method of active contours based on the formulation of partial differential equation (PDE) is proposed for image segmentation. The evolution equation incorporates a force term that pushes the contour towards object boundary, a regularisation term which takes into account the smoothness of the level set function and an edge term which helps to stop the contour at required boundaries. The proposed method integrates an image convolved by a variable kernel into an energy formulation, where the width of the kernel varies in each iteration. Therefore, it takes local region information when the width of the kernel is small while for the larger width of the kernel, the proposed method considers global region information across the regions. Due to the use of both local and global image information, the method easily detects objects in the complex background and also segments the objects where intensity changes within the object. Moreover, the proposed method totally eliminates the need of the contour initialisation by using constant initialisation scheme. Experimental results on real and medical images prove the robustness of the proposed method. Finally, the authors validate their method on PH2 database for skin lesion segmentation.

• 出版日期2018-7