Image Segmentation using Bias Correction Active Contoursopen access
- Authors
- Zia, Hamza; Soomro, Shafiullah; Choi, Kwang Nam
- Issue Date
- 2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Active Contour Model; Active contours; Adaptation models; Adaptive Function; Computational modeling; Distance Adjustment Term; Fitting; Gradient Approach; Image edge detection; Image segmentation; Level set; Non-uniform Intensity Segmentation
- Citation
- IEEE Access, v.12, pp 60641 - 60655
- Pages
- 15
- Journal Title
- IEEE Access
- Volume
- 12
- Start Page
- 60641
- End Page
- 60655
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73706
- DOI
- 10.1109/ACCESS.2024.3391052
- ISSN
- 2169-3536
- Abstract
- Deep learning-based image segmentation methods require densely annotated and massive datasets to produce effective results. On the other hand, active contours-based methods are excellent alternatives to the situation, producing acceptable segmentation results. Earlier active contour models, including local and global region information, struggle with their limitations, such as spurious contours appearing in inhomogeneous images. Bias correction is utilized to solve the bias field’s energy, considering the intensity inhomogeneity and the level set functions that suggest an image domain division. In our approach, we combine the advantages of local and global information in the image level set function, resulting in a combined energy function that aids in the efficient evolution of contours on images and can judge the relevance of the item and its surroundings. The proposed model computes data force by extracting local information from an in-homogeneous image using image-fitting energy and then computing all pixel values simultaneously. Objects with high differences between grey levels or more in-homogeneity can be segmented. The outcome demonstrates that our method is more dependable and computationally efficient than previous methods. Authors
- Files in This Item
-
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73706)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.