DocumentCode
320156
Title
Lung nodule detection in curvature space with multilayer perceptron network
Author
Penedo, M.G. ; Mosquera, A. ; Carreira, M.J. ; Cabello, D.
Author_Institution
Dept. Computacion, Univ. de la Coruna, Spain
Volume
3
fYear
1996
fDate
31 Oct-3 Nov 1996
Firstpage
1130
Abstract
Automatic methods developed for detection of lung nodules in chest radiographs usually present an excessive number of false positive detections. In this work the authors show how the local curvature image of suspected nodule pixels provides a new description that permits to distinguish true nodules from those false positives. A multilayer perceptron network with supervised learning is able to recognize the images of nodule local curvature peaks. The results obtained with a set of 23 chest images each one with at least one nodule show a sensibility in the global detection process of 93% with a mean number of 2 false positives per image
Keywords
diagnostic radiography; lung; medical image processing; multilayer perceptrons; chest images; chest radiographs; curvature space; false positive detections; global detection process sensibility; lung nodule detection; medical diagnostic imaging; multilayer perceptron network; suspected nodule pixels; Image recognition; Intelligent networks; Lungs; Multilayer perceptrons; Nonhomogeneous media; Pixel; Proposals; Radiography; Supervised learning; Surface topography;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
Conference_Location
Amsterdam
Print_ISBN
0-7803-3811-1
Type
conf
DOI
10.1109/IEMBS.1996.652741
Filename
652741
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