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Kido Syoji


Classification of pulmonary nodules using support vector machine

大島商船高等専門学校紀要 Volume 42 Page 73-80
published_at 2009-12
OS10042000011.pdf
[fulltext] 1.32 MB
Title
サポートベクターマシンを用いた肺腫瘍の鑑別
Classification of pulmonary nodules using support vector machine
Creators Yamada Nami
Creators Tachibana Rie
Creators Kido Syoji
Source Identifiers
Creator Keywords
small pulmonary nodule segmentation repeated CT doubling time
Accurate segmentation of small pulmonary nodules (SPNs) is an important technique for volumetric doubling time estimation and for feature characterization of the diagnosis of SPNs. Therefore, we have developed an automated volumetric segmentation algorithm of SPNs on thoracic CT images. This paper presents quantitative evaluation of the shape of the segmented SPNs on thoracic CT images, and also presents quantitative estimation of the growth rate of SPNs by use of repeated thoracic CT images. To quantitatively evaluate the performance of our algorithm, we employed the coincident rate. This coincident rate was calculated with both the computerized segmented region of an SPN and the region traced by a chest radiologist on axial, coronal MPR, sagittal MPR images. As the result of 90 selected cases, the mean value of the total coincident rate was 0.75 +/- 0.081. We also estimated the performance of our algorithm by use of the doubling time of SPNs on repeated CT images. The doubling time of SPNs obtained from computerized segmented regions closely correlated with the image findings reported by radiologists. From these results, we consider that our algorithm is useful for quantitative estimation for the shape and the growth rate of SPNs on repeated CT in daily clinical practice.
Languages jpn
Resource Type departmental bulletin paper
Publishers 大島商船高等専門学校
Date Issued 2009-12
File Version Version of Record
Access Rights open access
Relations
[ISSN]0387-9232
[NCID]AN00031668