Multi-stage Thresholded Region Classification for Whole-Body PET-CT Lymphoma Studies
Positron emission tomography computed tomography (PET-CT) is the preferred imaging modality for the evaluation of the lymphomas. Disease involvement in the lymphomas usually appear as foci of increased Fluorodeoxyglucose (FDG) uptake. Thresholding methods are applied to separate different regions of involvement. However, the main limitation of thresholding is that it also includes regions where there is normal FDG excretion and FDG uptake (NEUR) in structures such as the brain, bladder, heart and kidneys. We refer to these regions as NEURs (the normal excretion and uptake (of FDG) regions). NEURs can make image interpretation problematic. The ability to identify and label NEURs and separate them from abnormal regions is an important process that could improve the sensitivity of lesion detection and image interpretation. In this study, we propose a new method to automatically separate NEURs in thresholded PET images. We propose to group thresholded regions of the same structure with spatial and texture based clustering; we then classified NEURs on PET-CT contextual features. Our findings were that our approach had better accuracy when compared to conventional methods.
Unable to display preview. Download preview PDF.
- 1.Bi, L., Kim, J., Wen, L., Feng, D.D.: Automated and robust percist-based thresholding framework for whole body pet-ct studies. In: EMBC 2012, pp. 5335–5338. IEEE (2012)Google Scholar
- 2.Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM TIST 2(3), 27 (2011)Google Scholar
- 3.Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in ct volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis (2009)Google Scholar
- 4.Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
- 9.Kim, J., Hu, Y., Eberl, S., Feng, D., Fulham, M.: A fully automatic bed/linen segmentation for fused pet/ct mip rendering. In: Society of Nuclear Medicine Annual Meeting Abstracts, vol. 49, p. 387. Soc. Nuclear Med (2008)Google Scholar
- 11.Pescia, D., Paragios, N., Chemouny, S.: Automatic detection of liver tumors. In: ISBI 2008, pp. 672–675. IEEE (2008)Google Scholar
- 13.Wahl, R.L., Jacene, H., Kasamon, Y., Lodge, M.A.: From recist to percist: evolving considerations for pet response criteria in solid tumors. J. Nucl. Med. 50(Suppl. 1), 122S–150S (2009)Google Scholar
- 14.Wu, B., Khong, P.-L., Chan, T.: Automatic detection and classification of nasopharyngeal carcinoma on pet/ct with support vector machine. IJCARS 7(4), 635–646 (2012)Google Scholar