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Liver cancer detection based on various sustainable segmentation techniques for CT images

Author

Listed:
  • Reshma Jose
  • Shanty Chacko

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.

Suggested Citation

  • Reshma Jose & Shanty Chacko, 2022. "Liver cancer detection based on various sustainable segmentation techniques for CT images," International Journal of Environmental Technology and Management, Inderscience Enterprises Ltd, vol. 25(3), pages 166-179.
    • Handle: RePEc:ids:ijetma:v:25:y:2022:i:3:p:166-179
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