Paper Info
Title
A visual data-mining approach using 3D thoracic CT images for classification between benign and malignant pulmonary nodules
Abstract
This paper presents a visual data-mining approach to assist physicians for classification between benign and malignant pulmonary nodules. This approach retrieves and displays nodules which exhibit morphological and internal profiles consistent to the nodule in question. It uses a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. The central module in this approach makes possible analysis of the query nodule image and extraction of the features of interest: shape, surrounding structure, and internal structure of the nodules. The nodule shape is characterized by principal axes, while the surrounding and internal structure is represented by the distribution pattern of CT density and 3-D curvature indexes. The nodule representation is then applied to a similarity measure such as a correlation coefficient. For each query case, we sort all the nodules of the database from most to less similar ones. By applying the retrieval method to our database, we present its feasibility to search the similar 3-D nodule images.
Year
DOI
Venue
2003
10.1117/12.480648
Proceedings of SPIE
Keywords
Field
DocType
visual data-mining,computer-aided diagnosis,pulmonary nodule,3-D thoracic CT images
Correlation coefficient,Computer vision,Curvature,Similarity measure,Computer-aided diagnosis,sort,Principal axis theorem,Artificial intelligence,Image database,Medical diagnostics,Mathematics
Conference
Volume
ISSN
Citations 
5032
0277-786X
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Yoshiki Kawata119254.44
Noboru Niki218866.10
Hironobu Ohmatsu313845.23
masahiko kusumoto44616.28
Ryutaro Kakinuma59724.90
Kiyoshi Mori64710.84
k yamada700.34
hiroyuki nishiyama84611.62
Kenji Eguchi912942.78
Masahiro Kaneko105519.24
Noriyuki Moriyama1114850.47