Enhancing Thorax Images Using Fuzzy Logic Based Techniques - Marfiana, Duwi Lufita (Journal Medical Informatics Technology, 2026)
Enhancing the quality of thoracic X-ray images is crucial for accurate medical diagnosis; however, conventional enhancement methods often struggle to reduce noise while preserving important edge structures and anatomical details. This study proposes a hybrid image enhancement framework that integrates median neighborhood filtering, convolution processing, fuzzy logic-based edge detection, and morphological operations to improve image clarity and structural definition. The proposed pipeline begins with median neighborhood filtering to reduce noise while preserving essential image structures. The filtered image is then processed using convolution to enhance feature representation and prepare the data for edge detection. Subsequently, fuzzy logic-based edge detection is applied to handle intensity variations and uncertainty, enabling adaptive detection of faint and overlapping edges. Finally, morphological operations are used to refine edge continuity and remove small artifacts, resulting in clearer anatomical boundaries. Experimental results demonstrate that the proposed method effectively reduces noise while maintaining structural integrity, as indicated by stable pixel value transformations after filtering and improved edge clarity in visual comparisons. The method shows better performance in preserving continuous edge structures and detecting subtle thoracic features compared to conventional approaches. In conclusion, the integration of median filtering, convolution processing, fuzzy logic-based edge detection, and morphological refinement provides an effective framework for enhancing thoracic medical images and supports more reliable interpretation in medical imaging applications.