Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation

IEEE Access

Kwang Nam Choi¹  Shafiullah Soomro²  Asad Munir³  Asim Niaz⁴  Ehtesham Iqbal⁵  Asif Aziz Memon⁵ 

[1] degli Studi di Udine, Udine, Italy;Department of Computer Science, Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah, Pakistan;Department of Industrial and Information Engineering, Universit&x00E0;STARS Team, Inria Sophia Antipolis, Biot, France;School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea;

关键词: Active contour;    intensity inhomogeneity;    image segmentation;    region-based;    local and global intensity;   

DOI  :  10.1109/ACCESS.2020.3034908

来源: DOAJ

【 摘 要 】

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.

【 授权许可】

Unknown