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Proceedings Paper

A learning-based automatic clinical organ segmentation in medical images
Author(s): Xiaoqing Liu; Jagath Samarabandu; Shuo Li; Ian Ross; Greg Garvin
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Paper Abstract

Image segmentation plays an important role in medical image analysis and visualization since it greatly enhances the clinical diagnosis. Although many algorithms have been proposed, it is challenging to achieve an automatic clinical organ segmentation which requires speed and robustness. Automatically segmenting cardiac Magnetic Resonance Imaging (MRI) image is extremely challenging due to the artifacts of cardiac motion and characteristics of MRI. Moreover many of the existing algorithms are specific to a particular view of cardiac MRI images. We proposed a generic view-independent, learning-based method to automatically segment cardiac MRI images, which uses machine learning techniques and the geometric shape information. A main feature of our contribution is the fact that the proposed algorithm can use a training set containing a mix of various views and is able to successfully segment any given views. The proposed method consists of four stages. First, we partition the input image into a number of image regions based on their intensity characteristics. Then, we calculate the pre-selected feature descriptions for each generated region and use a trained to learn the conditional probabilities for every pixel based on the calculated features. In this paper, we use the Support Vector Machine (SVM) to train our classifier. The learned conditional probabilities of every pixel are then fed into an energy function to segment the input image. We optimize our energy function with graph cuts. Finally, domain knowledge is applied to verify the segmentation. Experimental results show that this method is very efficient and robust with respect to image views, slices and motion phases. The method also has the potential to be imaging modality independent as the proposed algorithm is not specific to a particular imaging modality.

Paper Details

Date Published: 26 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120Y (26 March 2007); doi: 10.1117/12.709433
Show Author Affiliations
Xiaoqing Liu, Univ. of Western Ontario (Canada)
Jagath Samarabandu, Univ. of Western Ontario (Canada)
Shuo Li, GE Healthcare (Canada)
Ian Ross, London Health Sciences Ctr. (Canada)
Greg Garvin, St. Joseph's Health Care (Canada)

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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