An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

• DOI: 10.32604/cmc.2021.018472
• Publication Date: 2021-08-26T01:50:53Z
• Authors: P. Naga Srinivasu; Shakeel Ahmed; Abdulaziz Alhumam; Akash Bhoi Kumar; Muhammad Fazal Ijaz
• Keywords: Abnormality; Kernel (algebra)

In the digestion of amino acids, carbohydrates, and lipids, as well protein synthesis from consumed food, liver has many diverse responsibilities functions that are to be performed. Liver disease may impact hormonal nutritional balance in human body. The earlier diagnosis such critical conditions help treat patient effectively. A computationally efficient AW-HARIS algorithm is used this paper perform automated segmentation CT scan images identify abnormalities liver. proposed approach can recognize with better accuracy without training, unlike supervisory procedures requiring considerable computational efforts for training. stages, pre-processed through an Adaptive Multiscale Data Condensation Kernel normalize underlying noise enhance image’s contrast segmentation. Then, preliminary phase’s outcome being fed input Anisotropic Weighted–-Heuristic Algorithm Real-time Image Segmentation uses texture-related information, which resulted precise acceptable latency when compared its counterparts. It observed outperformed majority cases 78%. smart would medical staff accurately predict abnormality progression ailment stages.