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Proceedings Paper

A stereo matching model observer for stereoscopic viewing of 3D medical images
Author(s): Gezheng Wen; Mia K. Markey; Gautam S. Muralidlhar
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Paper Abstract

Stereoscopic viewing of 3D medical imaging data has the potential to increase the detection of abnormalities. We present a new stereo model observer inspired by the characteristics of stereopsis in human vision. Given a stereo pair of images of an object (i.e., left and right images separated by a small displacement), the model observer rst nds the corresponding points between the two views, and then fuses them together to create a 2D cyclopean view. Assuming that the cyclopean view has extracted most of the 3D information presented in the stereo pair, a channelized Hotelling observer (CHO) can be utilized to make decisions. We conduct a simulation study that attempts to mimic the detection of breast lesions on stereoscopic viewing of breast tomosynthesis projection images. We render voxel datasets that contain random 3D power-law noise to model normal breast tissues with various breast densities. 3D Gaussian signal is added to some of the datasets to model the presence of a breast lesion. By changing the separation angle between the two views, multiple stereo pairs of projection images are generated for each voxel dataset. The performance of the model is evaluated in terms of the accuracy of binary decisions on the presence of the simulated lesions.

Paper Details

Date Published: 11 March 2014
PDF: 8 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370Z (11 March 2014); doi: 10.1117/12.2043175
Show Author Affiliations
Gezheng Wen, The Univ. of Texas at Austin (United States)
The Univ. of Texas MD Anderson Cancer Ctr. (United States)
Mia K. Markey, The Univ. of Texas at Austin (United States)
The Univ. of Texas MD Anderson Cancer Ctr. (United States)
Gautam S. Muralidlhar, VuCOMP (United States)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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