Liver cancer detection based on various sustainable segmentation techniques for CT images
by Reshma Jose; Shanty Chacko
International Journal of Environmental Technology and Management (IJETM), Vol. 25, No. 3, 2022

Abstract: Liver cancer remains the most common cause of cancer death worldwide; in recent decades, the epidemiology has improved. Commonly, endoscopic stomach biopsy is performed for early detection of liver cancer to minimise mortality. Picture segmentation is a key technique for comprehension and intensification of the medical image. The purpose of this study was to create a sustainable computer-aided estimating system to determine the risk of liver cancer development, achieved through image processing on a CT image. Initially, the image is enhanced by using anisotropic diffusion filtering with unsharp masking (ADF-USM) technique, and the computer-aided estimating method was developed based on fuzzy C-means clustering, Otsu's, region-dependent active contour and superpixel segmentation dependent iterative clustering (SSBIC). This sustainable approach will allow for the effective selection of high-risk liver cancer populations. The performed sustainable CAD device acts as an assistant to the radiologists, helping to identify the area of cancer in the CT scaffold images, take biopsies from those areas and make a better diagnosis.

Online publication date: Wed, 04-May-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Environmental Technology and Management (IJETM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com