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Proceedings Paper

Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network
Author(s): Osamu Tsujii; Matthew T. Freedman M.D.; Seong Ki Mun
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Paper Abstract

The purposes of this research are to investigate the effectiveness of our novel image features for segmentation of anatomic regions such as the lungs and the mediastinum in chest radiographs and to develop an automatic computerized method for image processing. A total of 85 screening chest radiographs from Johns Hopkins University Hospital were digitized to 2K by 2.5K pixels with 12-bit gray scale. To reduce the amount of information, the images were smoothed and subsampled to 256 by 310 pixels with 8-bit. The determination approach consists of classifying each pixel into two anatomic classes (lung and others) on the basis of several image features: (1) relative pixel address (Rx, Ry) based on lung edges extracted through image processing using profile, (2) density nonnalized from lungs and mediastinum density, and (3) histogram equalized entropy. The combinations of image features were evaluated using an adaptive-sized hybrid neural network (ASH-NN) consisting of an input, a hidden and an output layer. Fourteen images were used for the training of the neural network and the remaining 71 images for testing. Using four features of relative address (Rx,Ry), normalized density, and histogram equalized entropy, the neural networks classified lungs at 92% accuracy against test images following the same rules as for the training images. Key Words: Chest Radiograph, Anatomic Region, Segmentation, Image Feature, Neural Network, Self-Organization

Paper Details

Date Published: 25 April 1997
PDF: 10 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274167
Show Author Affiliations
Osamu Tsujii, Georgetown Univ. Medical Ctr. (United States)
Matthew T. Freedman M.D., Georgetown Univ. Medical Ctr. (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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