Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

While accurate tumor delineation in FDG-PET is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available methods, PET voxels are described not only by intensities but also complementally by features extracted from patches. Considering there are a large amount of features without consensus regarding the most informative ones, and some of them are even unreliable due to image quality, a specific procedure is adopted to adapt distance metric for properly representing clustering distortions and neighborhood similarities. A specific spatial regularization is also included in the clustering algorithm to effectively quantify local homogeneity. The proposed method has been evaluated by real-patient images, showing good performance.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages1177-1180
Number of pages4
ISBN (Electronic)9781509011711
DOIs
StatePublished - Jun 15 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: Apr 18 2017Apr 21 2017

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/TerritoryAustralia
CityMelbourne
Period04/18/1704/21/17

Keywords

  • Adaptive Distance Metric
  • Belief Functions
  • Feature Selection
  • PET Image Segmentation
  • Spatial Evidential c-Means

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