Paper Info
Title
Methods for detecting multiple small nodules from 3D chest X-ray CT images
Abstract
This paper describes a method for detecting small nodules from 3D X-ray CT images, considering small nodules with CT values of at least -600 H.U. and diameters of at least 2 mm. The proposed method is largely composed of two stages. In the first stage, for the region obtained by the threshold processing, the shape feature parameters of the figure calculated from the distance values in the connected component are applied, and the initial nodule candidate regions are extracted by discriminating the nodule region and the vessel/bronchus region. In the second stage, a minimum directional difference filter in which the filter radius is adaptively adjusted according to the size of the initial nodule candidate region is applied in order to reduce the overextracted regions. The proposed method is applied to seven actual chest CT images (six pathological cases, including multiple nodules, and one normal case), and the result for 361 nodules with a CT value not less than -600 H.U. and diameter not less than 2 mm is that the number of overextracted regions per case is 7.4 on the average, when the detection rate is set as 71%. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(9): 55–64, 2005; Published online in Wiley InterScience (). DOI 10.1002/scj.20175
Year
DOI
Venue
2005
10.1002/scj.v36:9
Systems and Computers in Japan
Field
DocType
Volume
Computer vision,X-ray,Artificial intelligence,Connected component,Mathematics
Journal
36
Issue
Citations 
PageRank 
9
0
0.34
References 
Authors
1
7
Name
Order
Citations
PageRank
Yosuke Hayase100.34
Yoshito Mekada211516.08
Kensaku Mori31125160.28
Jun-ichi Hasegawa422161.17
Jun-ichiro Toriwaki5578136.04
Masaki Mori614417.48
Hiroshi Natori722028.49