CT-Scan Image Segmentation of Liver Cancer Using the Active Contour Method
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Abstract
Liver cancer is one of the most common types of cancer both in Indonesia and throughout the world. The increase in liver cancer cases is thought to be related to the increase in hepatitis B and C virus infections. According to statistical data from Globocan, in 2022 there will be 866,000 cases of liver cancer recorded worldwide, with 758,000 deaths due to this disease. To overcome this challenge, image processing plays an important role in improving image quality and performing segmentation to separate objects based on certain characteristics. In this research, the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Contrast Stretching methods are used to improve image quality, while the Active Contour method is applied for image segmentation. Validation of segmentation results was carried out using Receiver Operating Characteristic (ROC) calculations. Testing was conducted on 21 CT-scan images of liver cancer, yielding an accuracy of 97.16%, a sensitivity of 75.69%, and a specificity of 98.85%. This research was conducted using the MATLAB application (R2015a). These findings demonstrate the effectiveness of the methods used in supporting the diagnosis and treatment of liver cancer
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