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Proceedings Paper

Tumor state evaluation method using texture analysis based on the information theory for PET images
Author(s): Yuki Koike; Osamu Sakata
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Paper Abstract

Positron emission tomography (PET) images are often used clinically as they can non-invasively show the accumulation of cancer cells. The standardized uptake value (SUV) is the most common semi-quantitative measurement derived from PET images. However, SUV have some limitations, such as the difficulty of expressing temporal change quantitatively and unifying imaging conditions such as uptake time after medicine administration. Also, the textural features obtained from PET images show the presence of tumors represented by a vague shadow. Although feature analysis of tumors by using SUV have been widely studied, numerical information obtained on tumors from PET images is limited, and thus the wealth of information cannot be utilized. So, parameter to evaluate quantitatively the state of tumor within PET images called texture should be more established. Texture analysis involves various mathematical methods that are applied to quantify the relationships between the grey level intensity value of pixels or voxels and their spatial pattern within PET images, and are used for classification and discrimination. In this study, we propose texture analysis statistically, specifically by using Shannon’s information entropy and KullbackLeibler divergence. We verified our method by using a simulation, and quantified the distribution inside a tumor. We also examined clinical data in the same way; however, as no appropriate evaluation result was obtained, there is room for further improvement of this system.

Paper Details

Date Published: 17 April 2019
PDF: 5 pages
Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710R (17 April 2019); doi: 10.1117/12.2516367
Show Author Affiliations
Yuki Koike, Tokyo Univ. of Science (Japan)
Osamu Sakata, Tokyo Univ. of Science (Japan)

Published in SPIE Proceedings Vol. 11071:
Tenth International Conference on Signal Processing Systems
Kezhi Mao; Xudong Jiang, Editor(s)

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