A content-based image retrieval framework for multi-modality lung images

This paper presents a framework for effective and fast content-based image retrieval for multi-modality PET-CT lung scans. PET-CT scans present significant advantages in tumor staging, but also place new challenges in computerized image analysis and retrieval. Our framework comprises 5 major components: lung field estimation, texture feature extraction, feature categorization, refinement using SVM, and similarity measure. Clinical data from lung cancer patients are used as case studies, and effective retrieval performance is demonstrated.