Development of an automatic classification system for differentiation of obstructive lung disease using HRCT.

Journal of Digital Imaging
Namkug KimSuk-Ho Kang

Abstract

The motivation is to introduce new shape features and optimize the classifier to improve performance of differentiating obstructive lung diseases, based on high-resolution computerized tomography (HRCT) images. Two hundred sixty-five HRCT images from 82 subjects were selected. On each image, two experienced radiologists selected regions of interest (ROIs) representing area of severe centrilobular emphysema, mild centrilobular emphysema, bronchiolitis obliterans, or normal lung. Besides 13 textural features, additional 11 shape features were employed to evaluate the contribution of shape features. To optimize the system, various ROI size (16 x 16, 32 x 32, and 64 x 64 pixels) and other classifier parameters were tested. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess cross-validation of the system, a five-folding method was used. In the comparison of methods employing only the textural features, adding shape features yielded the significant improvement of overall sensitivity (7.3%, 6.1%, and 4.1% in the Bayesian and 9.1%, 7.5%, and 6.4% in the SVM, in the ROI size 16 x 16, 32 x 32, 64 x 64 pixels, respectively; t test, P < 0.01). After feature selection, most of ...Continue Reading

References

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Citations

Dec 5, 2012·Clinical Imaging·Qingzhu WangBin Wang
May 17, 2012·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Adrien DepeursingeHenning Müller
Mar 22, 2012·IEEE Transactions on Bio-medical Engineering·Ulas BagciDaniel J Mollura
Aug 12, 2014·European Child & Adolescent Psychiatry·Laura Pina-CamachoCelso Arango
Oct 16, 2018·PloS One·Isabel Pino PeñaMarleen de Bruijne
Sep 2, 2009·Korean Journal of Radiology : Official Journal of the Korean Radiological Society·Sang Ok ParkSuk-Ho Kang

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