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Proceedings Paper

Multi-view learning based robust collimation detection in digital radiographs
Author(s): Hongda Mao; Zhigang Peng; Frank Dennerlein; Yoshihisa Shinagawa; Yiqiang Zhan; Xiang Sean Zhou
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Paper Abstract

In X-ray examinations, it is essential that radiographers carefully use collimation to the appropriate anatomy of interest to minimize the overall integral dose to the patient. The shadow regions are not diagnostically meaningful and could impair the overall image quality. Thus, it is desirable to detect the collimation and exclude the shadow regions to optimize image display. However, due to the large variability of collimated images, collimation detection remains a challenging task. In this paper, we consider a region of interest (ROI) in an image, such as the collimation, can be described by two distinct views, a cluster of pixels within the ROI and the corners of the ROI. Based on this observation, we propose a robust multi-view learning based strategy for collimation detection in digital radiography. Specifically, one view is from random forests learning based region detector, which provides pixel-wise image classification and each pixel is labeled as either in-collimation or out-of-collimation. The other view is from a discriminative, learning-based landmark detector, which detects the corners and localizes the collimation within the image. Nevertheless, given the huge variability of the collimated images, the detection from either view alone may not be perfect. Therefore, we adopt an adaptive view fusing step to obtain the final detection by combining region and corner detection. We evaluate our algorithm in a database with 665 X-ray images in a wide variety of types and dosages and obtain a high detection accuracy (95%), compared with using region detector alone (87%) and landmark detector alone (83%).

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903421 (21 March 2014); doi: 10.1117/12.2043818
Show Author Affiliations
Hongda Mao, Siemens Medical Solutions USA, Inc. (United States)
Rochester Institute of Technology (United States)
Zhigang Peng, Siemens Medical Solutions USA, Inc. (United States)
Frank Dennerlein, Siemens AG (Germany)
Yoshihisa Shinagawa, Siemens Medical Solutions USA, Inc. (United States)
Yiqiang Zhan, Siemens Medical Solutions USA, Inc. (United States)
Xiang Sean Zhou, Siemens Medical Solutions USA, Inc. (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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