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Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques

Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques

Xiangrong Zhou, Hiroshi Fujita
Copyright: © 2012 |Pages: 16
ISBN13: 9781466600591|ISBN10: 1466600594|EISBN13: 9781466600607
DOI: 10.4018/978-1-4666-0059-1.ch019
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MLA

Zhou, Xiangrong, and Hiroshi Fujita. "Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques." Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, IGI Global, 2012, pp. 403-418. https://doi.org/10.4018/978-1-4666-0059-1.ch019

APA

Zhou, X. & Fujita, H. (2012). Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques. In K. Suzuki (Ed.), Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis (pp. 403-418). IGI Global. https://doi.org/10.4018/978-1-4666-0059-1.ch019

Chicago

Zhou, Xiangrong, and Hiroshi Fujita. "Automatic Organ Localization on X-Ray CT Images by Using Ensemble-Learning Techniques." In Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, 403-418. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0059-1.ch019

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Abstract

Location of an inner organ in a CT image is the basic information that is required for medical image analysis such as image segmentation, lesion detection, content-based image retrieval, and anatomical annotation. A general approach/scheme for the localization of different inner organs that can be adapted to suit various types of medical image formats is required. However, this is a very challenging problem and can hardly be solved by using traditional image processing techniques. This chapter introduces an ensemble-learning-based approach that can be used to solve organ localization problems. This approach can be used to generate a fast and efficient organ-localization scheme from a limited number of training samples that include both original images and target locations. This approach has been used for localizing five different human organs in CT images, and the accuracy, robustness, and computational efficiency of the designed scheme were validated by experiments.

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