Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images
Joint Authors
Yu, Mei
Gao, Yang
Yang, Wei
Feng, Qianjin
Chen, Wufan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-09-04
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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%.
American Psychological Association (APA)
Yu, Mei& Feng, Qianjin& Yang, Wei& Gao, Yang& Chen, Wufan. 2012. Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-512510
Modern Language Association (MLA)
Yu, Mei…[et al.]. Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-12.
https://search.emarefa.net/detail/BIM-512510
American Medical Association (AMA)
Yu, Mei& Feng, Qianjin& Yang, Wei& Gao, Yang& Chen, Wufan. Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-12.
https://search.emarefa.net/detail/BIM-512510
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-512510