Abstract
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.
Highlights
Image segmentation is a principal research direction in image processing and is widely used in image analysis, medical imaging, and computer vision [1]–[3]
The active contour model (ACM), initially proposed by Kass et al.[19], can yield a closed and smooth curve to represent the edges of an object while constructing the energy function regarding continuous curves as the independent variable so that the segmentation process calculates the minimum value of the energy function [10]
The first column illustrates the original images with different initial contour positions, the second and third columns illustrate the contour moving toward the object rapidly within a few iterations, and the fourth column illustrates the segmentation results of the proposed method
Summary
Image segmentation is a principal research direction in image processing and is widely used in image analysis, medical imaging, and computer vision [1]–[3]. The ACM, initially proposed by Kass et al.[19], can yield a closed and smooth curve to represent the edges of an object while constructing the energy function regarding continuous curves as the independent variable so that the segmentation process calculates the minimum value of the energy function [10]. It can be expressed by calculating the Euler–Lagrange.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.