Welcome to the InfoSci Platform
Reference Hub4
Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images

Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images

Abdalla Mostafa, Aboul Ella Hassanien, Hesham A. Hefny
Copyright: © 2017 |Pages: 20
ISBN13: 9781522522294|ISBN10: 1522522298|EISBN13: 9781522522300
DOI: 10.4018/978-1-5225-2229-4.ch024
Cite Chapter Cite Chapter

MLA

Mostafa, Abdalla, et al. "Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images." Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, IGI Global, 2017, pp. 562-581. https://doi.org/10.4018/978-1-5225-2229-4.ch024

APA

Mostafa, A., Hassanien, A. E., & Hefny, H. A. (2017). Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images. In A. Hassanien & T. Gaber (Eds.), Handbook of Research on Machine Learning Innovations and Trends (pp. 562-581). IGI Global. https://doi.org/10.4018/978-1-5225-2229-4.ch024

Chicago

Mostafa, Abdalla, Aboul Ella Hassanien, and Hesham A. Hefny. "Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images." In Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, 562-581. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2229-4.ch024

Export Reference

Mendeley
Favorite

Abstract

In the recent days, a great deal of researches is interested in segmentation of different organs in medical images. Segmentation of liver is as an initial phase in liver diagnosis, it is also a challenging task due to its similarity with other organs intensity values. This paper aims to propose a grey wolf optimization based approach for segmenting liver from the abdomen CT images. The proposed approach combines three parts to achieve this goal. It combines the usage of grey wolf optimization, statistical image of liver, simple region growing and Mean shift clustering technique. The initial cleaned image is passed to Grey Wolf (GW) optimization technique. It calculated the centroids of a predefined number of clusters. According to each pixel intensity value in the image, the pixel is labeled by the number of the nearest cluster. A binary statistical image of liver is used to extract the potential area that liver might exist in. It is multiplied by the clustered image to get an initial segmented liver. Then region growing (RG) is used to enhance the segmented liver. Finally, mean shift clustering technique is applied to extract the regions of interest in the segmented liver. A set of 38 images, taken in pre-contrast phase, was used for liver segmentation and testing the proposed approach. For evaluation, similarity index measure is used to validate the success of the proposed approach. The experimental results of the proposed approach showed that the overall accuracy offered by the proposed approach, results in 94.08% accuracy.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.