Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 14 December 2021
Issue publication date: 6 July 2022
Abstract
Purpose
The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.
Design/methodology/approach
After collecting the ultrasound images, contrast-limited adaptive histogram equalization approach (CLAHE) is applied as preprocessing, in order to enhance the visual quality of the images that helps in better segmentation. Then, adaptively regularized kernel-based fuzzy C means (ARKFCM) is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.
Findings
The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost. The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient, dice coefficient, precision, Matthews correlation coefficient, f-score and accuracy. The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value, which is better than the existing algorithms.
Practical implications
From the experimental analysis, the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm. However, the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.
Originality/value
The image preprocessing is carried out using CLAHE algorithm. The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm. In this research, the proposed algorithm has advantages such as independence of clustering parameters, robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.
Keywords
Acknowledgements
The authors would like to thank their guru (mentor) Late Dr. Basavaraj Amarapur, former HOD, Electrical and Electronics Engineering Department, PDA College of engineering Kalaburagi for their continuous guidance and support.
Citation
Uplaonkar, D.S., Virupakshappa and Patil, N. (2022), "Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm", International Journal of Intelligent Computing and Cybernetics, Vol. 15 No. 3, pp. 438-453. https://doi.org/10.1108/IJICC-10-2021-0223
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited