title: Extraction of lesion-partitioned features and retrieval of contrast-enhanced liver images.
abstract: The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT) scans. The algorithm includes mainly two processes: (1) distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2) representation using bag of visual words (BoW) based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Yu
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.forename: Yang
authors.initials: Y
authors.lastname: Gao
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D003287:Contrast Media / Q000494:pharmacology*; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000473:pathology; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D003287:Contrast Media