Shape features extraction from pulmonary nodules in X-ray CT images

Noriyasu Homma, Kazuhisa Saito, Tadashi Ishibashi, Madan M. Gupta, Zeng Guang Hou, Ashu M.G. Solo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a new computer aided diagnosis method of pulmonary nodules in X-ray CT images to reduce false positive (FP) rate under high true positive (TP) rate conditions. An essential core of the method is to extract and combine two novel and effective features from the raw CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a Gabor filter, while the other is variation of CT values of the ROI in the direction along body axis. By using the extracted features, a principal component analysis technic and any pattern recognition technics such as neural network approaches can then used to discriminate between nodule and non-nodule images. Simulation results show that discrimination performance using the proposed features is extremely improved compared to that of the conventional method.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages3396-3400
Number of pages5
DOIs
Publication statusPublished - 2008 Nov 24
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 2008 Jun 12008 Jun 8

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period08/6/108/6/8

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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