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

• Published in: 2011 18th IEEE International Conference on Image Processing pp. 1421 - 1424
• Main Authors: Masuda, Y., Tateyama, T., Wei Xiong, Jiayin Zhou, Wakamiya, M., Kanasaki, S., Furukawa, A., Yen Wei Chen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2011
• Subjects: Computed tomography; CT images; EM/MPM algorithm; Histograms; Image segmentation; Liver; Liver tumor segmentation; Measurement; Probability density function; Shape; Tumors
• ISBN: 1457713047; 9781457713040
• ISSN: 1522-4880
• DOI: 10.1109/ICIP.2011.6115708

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.