Presentation 2015-03-02
Image Feature Extraction and Construction of a Classifier for Discriminating Pulmonary Nodules in X-CT Images
Ryuta MORI, Takumi NAITO, Hidekata HONTANI, Shingo IWANO,
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Abstract(in English) In this article, the authors report about a method for classifying pulmonary nodules in three-dimensional X-CT images and about image features used for the classification. The classification method firstly segments the nodules in given three-dimensional X-CT images and then extracts nine kinds of image features that are represented by a fifty-one vector, which is input to the classifier. Given 309 training images of pulmonary nodules with five-grade evaluations, the authors constructed the classifier using a kernel SVM. Its classification ratio was about 75%. The F-score of each of the image features was evaluated and was used for selecting ons that contributed the classification well. In this article, it is reported that which images features contributed to the classification and that how the classification performance changed with respect to the change of the number of the input image features.
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Keyword(in English) Computed Aided Diagnosis / CT images / lung nodules / classification / SVM
Paper # MI2014-61
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Committee MI
Conference Date 2015/2/23(1days)
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Paper Information
Registration To Medical Imaging (MI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Image Feature Extraction and Construction of a Classifier for Discriminating Pulmonary Nodules in X-CT Images
Sub Title (in English)
Keyword(1) Computed Aided Diagnosis
Keyword(2) CT images
Keyword(3) lung nodules
Keyword(4) classification
Keyword(5) SVM
1st Author's Name Ryuta MORI
1st Author's Affiliation Nagoya Institute of Technology()
2nd Author's Name Takumi NAITO
2nd Author's Affiliation Nagoya Institute of Technology
3rd Author's Name Hidekata HONTANI
3rd Author's Affiliation Nagoya Institute of Technology
4th Author's Name Shingo IWANO
4th Author's Affiliation Graduate School of Medicine, Nagoya University
Date 2015-03-02
Paper # MI2014-61
Volume (vol) vol.114
Number (no) 482
Page pp.pp.-
#Pages 4
Date of Issue