Liver tumor detection in CT images by adaptive contrast enhancement and the EM/MPM algorithm

Yu Masuda, Tomoko Tateyama, Wei Xiong, Jiayin Zhou, Makoto Wakamiya, Syuzo Kanasaki, Akira Furukawa, Yen Wei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

Automatic tumor detection and segmentation is essential for the computer-aided diagnosis of live tumors in CT images. However, it is a challenging task in low-contrast images as the low-level images are too weak to detect. In this paper, we propose a new method for the automatic detection of liver tumors. We first adaptively enhance the intensity contrast of CT images by probability density function estimation. Then, to detect tumorous regions, we use the expectation maximization/maximization of the posterior marginal (EM/MPM) algorithm, which utilizes both the intensity and label information of the adjacent regions. Finally, a shape constraint is applied to reduce noise and identify focal tumors. Quantitative evaluation experiments show that our method can accurately and effectively detect tumors even in poor-contrast CT images.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages1421-1424
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11-09-201114-09-2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period11-09-1114-09-11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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