Unsupervised detection of density changes through principal component analysis for lung lesion classification

Unsupervised detection of density changes through principal component analysis for lung lesion... Lung cancer remains one of the most common cancers worldwide. Temporal evaluation is a useful tool for analyzing the malignant behavior of a lesion during treatment or that of indeterminate lesions which may be benign. Thereby, this work proposes a methodology for analysis, quantification and visualization of unsupervised changes in lung lesions, through principal component analysis. From change regions, we extracted texture features for lesion classification as benign or malignant. To reach this purpose, two databases with distinct behavior were used, one of which concerning malign under treatment and another indeterminate, but likely benign, lesions. The results have shown that the lesion’s density changes in a public database of malignant lesions under treatment were greater than the private database of benign lung nodules. From the texture analysis of the regions where the density changes occurred, we were able to discriminate lung lesions with an accuracy of 98.41 %, showing that these changes could point out the nature of the lesion. Other contribution was visualization of changes occurring in the lesions over time. Besides, we quantified these changes and analyzed the entire set through volumetry, the most commonly used technique to evaluate progression of lung lesions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Unsupervised detection of density changes through principal component analysis for lung lesion classification

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Springer US
Copyright © 2017 by Springer Science+Business Media New York
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
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