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SU‐E‐I‐100: Heterogeneity Studying for Primary and Lymphoma Tumors by Using Multi‐Scale Image Texture Analysis with PET‐CT Images
Author(s) -
Li Dengwang,
Wang Qinfen,
Li H,
Chen J
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4888050
Subject(s) - homogeneity (statistics) , region of interest , artificial intelligence , gray level , nuclear medicine , lymphoma , pattern recognition (psychology) , mathematics , medicine , computer science , pathology , statistics , image (mathematics)
Purpose: The purpose of this research is studying tumor heterogeneity of the primary and lymphoma by using multi‐scale texture analysis with PET‐CT images, where the tumor heterogeneity is expressed by texture features. Methods: Datasets were collected from 12 lung cancer patients, and both of primary and lymphoma tumors were detected with all these patients. All patients underwent whole‐body 18F‐FDG PET/CT scan before treatment. The regions of interest (ROI) of primary and lymphoma tumor were contoured by experienced clinical doctors. Then the ROI of primary and lymphoma tumor is extracted automatically by using Matlab software. According to the geometry size of contour structure, the images of tumor are decomposed by multi‐scale method.Wavelet transform was performed on ROI structures within images by L layers sampling, and then wavelet sub‐bands which have the same size of the original image are obtained. The number of sub‐bands is 3L+1. The gray level co‐occurrence matrix (GLCM) is calculated within different sub‐bands, thenenergy, inertia, correlation and gray in‐homogeneity were extracted from GLCM.Finally, heterogeneity statistical analysis was studied for primary and lymphoma tumor using the texture features. Results: Energy, inertia, correlation and gray in‐homogeneity are calculated with our experiments for heterogeneity statistical analysis.Energy for primary and lymphomatumor is equal with the same patient, while gray in‐homogeneity and inertia of primaryare 2.59595±0.00855, 0.6439±0.0007 respectively. Gray in‐homogeneity and inertia of lymphoma are 2.60115±0.00635, 0.64435±0.00055 respectively. The experiments showed that the volume of lymphoma is smaller than primary tumor, but thegray in‐homogeneity and inertia were higher than primary tumor with the same patient, and the correlation with lymphoma tumors is zero, while the correlation with primary tumor isslightly strong. Conclusion: This studying showed that there were effective heterogeneity differences between primary and lymphoma tumor by multi‐scale image texture analysis. This work is supported by National Natural Science Foundation of China (No. 61201441), Research Fund for Excellent Young and Middle‐aged Scientists of Shandong Province (No. BS2012DX038), Project of Shandong Province Higher Educational Science and Technology Program (No. J12LN23), Jinan youth science and technology star (No.20120109)