Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 972037, 12 pages
Research Article

Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
2Shandong Medical College, Linyi 276000, China

Received 22 March 2012; Revised 24 June 2012; Accepted 16 July 2012

Academic Editor: Guilherme de Alencar Barreto

Copyright © 2012 Mei Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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%.