TY - GEN

T1 - A novel trignometric energy functional for image segmentation in the presence of intensity in-homogeneity

AU - Hussain, Sajid

AU - Chun, Qi

AU - Asif, Muhammad Rizwan

AU - Khan, Muhammad Sohrab

AU - Zhaoqiang, Zhang

AU - Fareed, Muhammad Sadiq

AU - Zhe, Zhang

N1 - Publisher Copyright: © 2016 IEEE.

PY - 2016/8/25

Y1 - 2016/8/25

N2 - A novel region based Active Contour Model (ACM) for image segmentation is presented using image local information for intensity inhomogeneity images. A transcendental (trigonometric) energy functional based on Local Fitted Image (LFI) energy is suggested to extract the image local information. The difference between the original and fitting image is introduced as an angular constraint of the trigonometric function. This new approach has the ability to handle images with severe intensity inhomogeneity, multiple intensity classes and regions with non-homogeneous pixel intensities. To regularize and maintain the level set function, Gaussian filtering procedure is adopted that not only maintains the level set consistency but also eliminates re-initialization procedure to avoid computational complexity. Furthermore, the proposed model holds boundary regularization property and achieves sub-pixel accuracy. Experimental results on several real and synthetic images demonstrate that the proposed model has higher performance and better accuracy in comparison to the state-of-the-art models.

AB - A novel region based Active Contour Model (ACM) for image segmentation is presented using image local information for intensity inhomogeneity images. A transcendental (trigonometric) energy functional based on Local Fitted Image (LFI) energy is suggested to extract the image local information. The difference between the original and fitting image is introduced as an angular constraint of the trigonometric function. This new approach has the ability to handle images with severe intensity inhomogeneity, multiple intensity classes and regions with non-homogeneous pixel intensities. To regularize and maintain the level set function, Gaussian filtering procedure is adopted that not only maintains the level set consistency but also eliminates re-initialization procedure to avoid computational complexity. Furthermore, the proposed model holds boundary regularization property and achieves sub-pixel accuracy. Experimental results on several real and synthetic images demonstrate that the proposed model has higher performance and better accuracy in comparison to the state-of-the-art models.

KW - Active Contour

KW - Chan-Vese Model

KW - Image segmentation

KW - LBF Model

KW - level set

UR - http://www.scopus.com/inward/record.url?scp=84987619535&partnerID=8YFLogxK

U2 - 10.1109/ICME.2016.7552994

DO - 10.1109/ICME.2016.7552994

M3 - Article in proceedings

AN - SCOPUS:84987619535

T3 - Proceedings - IEEE International Conference on Multimedia and Expo

BT - 2016 IEEE International Conference on Multimedia and Expo, ICME 2016

PB - IEEE Computer Society

T2 - 2016 IEEE International Conference on Multimedia and Expo, ICME 2016

Y2 - 11 July 2016 through 15 July 2016

ER -