Paper Info
Title
Detection Of Lung Nodules In Thoracic Mdct Images Based On Temporal Changes From Previous And Current Images
Abstract
Temporal subtraction enhances temporal change by subtracting images captured at different times. Medical images captured currently (current images) and in previous examination (previous images) are subtracted to enhance new lesions and temporal change in existing lesion shadows. Temporal subtraction using chest MultiDetector-Row Computed Tomography (MDCT) images and currently being developed is to be applied to nodule detection in pulmonary regions. Nodule detection using conventional temporal subtraction, however, yields many false-positive results for those 20 mm or less in diameter, requiring improvement. We discuss improvements in nodule detection accuracy using temporal subtraction, first extracting rough nodules from temporal subtraction images as candidate shadows. Features are then acquired from current, previous, and temporal subtraction images. We use intensity features in previous images and shape features in the current images and in features used in conventional methods. Using acquired features, we build a neural network classifier, then extract final pulmonary candidates in unknown shadows.
Year
DOI
Venue
2011
10.20965/jaciii.2011.p0707
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Keywords
Field
DocType
temporal subtraction method, lung nodule detection, computer aided diagnosis, medical imaging, MDCT
Lung,Pattern recognition,Computer science,Medical imaging,Computer-aided diagnosis,Artificial intelligence,Radiology
Journal
Volume
Issue
ISSN
15
6
1343-0130
Citations 
PageRank 
References 
2
0.39
3
Authors
8
Name
Order
Citations
PageRank
Shinya Maeda1173.22
Yasuyuki Tomiyama220.39
Hyoungseop Kim329336.05
Noriaki Miyake440.76
Yoshinori Itai5213.86
Joo Kooi Tan610529.88
Seiji Ishikawa734249.06
Akiyoshi Yamamoto8153.46