Work

Segmenting CT prostate images using population and patient-specific statistics for radiotherapy

Year: 2009

Type: conference-paper

Abstract: This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi.2009.5193039

Authors: Qianjin Feng, Mark Foskey, Songyuan Tang, Wufan Chen, Dinggang Shen

Institutions: Southern Medical University, University of North Carolina at Chapel Hill, North Carolina State University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of Mental Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering