Image Enhancement of Colon Cancer Images using a Two-Stage Hybrid Approach of TV and Shift-Invariant Filtering
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Abstract
Medical imaging holds a critical position in both disease diagnosis and treatment strategies, including colon cancer. However, the quality of medical images can often be compromised by noise and artifacts, making accurate interpretation challenging. Here, we suggest a innovative two-stage hybrid method aimed at enhancing colon cancer images, leveraging the strengths of Total Variation (TV) denoising and shift-invariant filtering techniques. The primary objective of this study is to increase visual superiority as well as diagnostic accurateness of colon cancer image while preserving crucial anatomical information.The first stage of our approach employs Total Variation (TV) denoising to reduce noise and enhance image contrast. TV regularization is known for its ability to preserve edges and fine details, making it well-suited for medical image enhancement. In the second stage, we apply shift-invariant filtering to further enhance the image quality. This technique is designed to address the limitations of traditional filtering methods and adapt to the specific characteristics of colon cancer images.
To evaluate the effectiveness of our hybrid approach, we conducted a comprehensive set of experiments using a relevant dataset. We employed a range of quantitative metrics, including the Global Relative Error (EGRAS), Root Mean Squared Error (RMSE), Universal Image Quality Index (UQI), and Pixel-Based Visual Information Fidelity (VIFP), to assess the quality and fidelity of enhanced images. Our results demonstrate that the hybrid combination consistently outperforms existing methods, yielding superior image quality and diagnostic potential. This study makes a valuable contribution to the realm of medical imaging by introducing a robust and effective method to improve the quality of colon cancer images. Findings suggest that the proposed two-stage hybrid method holds promise for improving the accuracy of diagnosis and treatment planning. Further research in this direction may lead to advancements in medical image enhancement techniques, ultimately benefiting patient care and medical research.
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References
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