Innovative Approach to Lung Nodule Detection Using Random Walker Segmentation and Texture Analysis on CT Images

Lung cancer remains a significant global health concern, necessitating advancements in early detection and diagnosis. This research presents a comprehensive approach to lung cancer detection using computed tomography (CT) images and advanced image processing techniques. The proposed methodology encompasses image enhancement through Block-Matching 3D (BM3D) filtering, precise segmentation using Random Walker segmentation, and comprehensive feature extraction, incorporating Gray-Level Co-occurrence Matrix (GLCM) analysis and Haralick texture features. The study leverages the Lung Image Database Consortium (LIDC) database to evaluate effectiveness of the proposed approach. The pre-processing stage employs BM3D filtering to attenuate noise inherent in CT images, enhancing the subsequent analysis. Random Walker segmentation is then employed to accurately delineate lung nodules, even in cases of irregular boundaries. GLCM analysis and Haralick texture features extraction capture nuanced textural information within segmented nodules, facilitating the characterization of potential cancerous regions. Experimental results determine the effectiveness of proposed approach. By integrating BM3D filtering, Random Walker segmentation, and texture analysis, the method achieves robust lung nodule detection and accurate cancer region identification. Comparative analysis against existing techniques highlights its promising performance. This research contributes to the field of lung cancer detection by presenting an integrated framework that leverages cutting-edge image processing techniques. The combination of BM3D filtering, Random Walker segmentation, and texture analysis enhances CT image lung cancer detection accuracy. The findings underscore the potential of this approach as a valuable tool for early diagnosis, ultimately contributing to improved patient outcomes.