Active contour method with locally computed signed pressure force function: An application to brain MR image segmentation

Citations

SCOPUS

10

초록

This paper presents a region-based active contour method that embeds both region and gradient information. In the proposed algorithm area term practices a new region-based signed pressure force (SPF) function which utilizes the image local information obtained using the local binary fitted (LBF) energy model. By introducing the SPF function based on local fitted image (LFI), the proposed model is able to segment images with intensity in homogeneities. A Gaussian kernel is used to regularize the level set function which not only regularizes it but also removes the need of computationally expensive re-initialization. The proposed segmentation algorithm is applied to synthetic and real images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.

키워드

Active contour method; Local binary fitted; Local image fitted; Segmentation; SPF function; Active contour method; Brain MR image segmentation; Gradient informations; Local binary fitted; Local image fitted; Region based active contours; Segmentation algorithms; Signed pressure forces; Algorithms; Drag reduction; Image segmentation
제목
Active contour method with locally computed signed pressure force function: An application to brain MR image segmentation
저자
Akram, F.; Kim, J.H.; Choi, K.N.
DOI
10.1109/ICIG.2013.37
발행일
2013-07
유형
Conference Paper
저널명
Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013
페이지
154 ~ 159