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Proceedings Paper

Robust and automatic adjustment of display-window width and center for MR images
Author(s): Shang-Hong Lai; Ming Fang
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Paper Abstract

The display of a 12-bit MR image on a common 8-bit computer monitor is usually achieved by linearly mapping the image values through a display window, which is determined by the width and center values. The adjustment of the display window for a variety of MR images involves considerable user interaction. In this paper, we present an advanced algorithm with the hierarchical neural network structure for robust and automatic adjustment of display window width and center for a wide range of MR images. This algorithm consists of a feature generator utilizing both histogram and spatial information computed from a MR image, a wavelet transform for compressing the feature vector, a competitive layer neural network for clustering MR images into different subclasses, a bi-modal linear estimator and an RBF (radial basis function) network based estimator for each subclass, as well as a data fusion process to integrate estimates from both estimators of different subclasses to compute the final display parameters. Both estimators can adapt a new types of MR images simply by training them with those images, thus making the algorithm adaptive and extendable. This trainability makes also possible for advanced future developments such as adaptation of the display parameters to user's personal preference. While the RBF neural network based estimators perform very well for images similar to those in the training data set, the bi-modal linear estimators provide reasonable estimation for a wide range of images that may not be included in the training data set. The data fusion step makes the final estimation of the display parameters accurate for trained images and robust for the unknown images. The algorithm has been tested on a wide range of MR images and shown satisfactory results. Although the proposed algorithm is very comprehensive, its execution time is kept within a reasonable range.

Paper Details

Date Published: 21 April 1998
PDF: 12 pages
Proc. SPIE 3340, Medical Imaging 1998: Image Perception, (21 April 1998); doi: 10.1117/12.306175
Show Author Affiliations
Shang-Hong Lai, Siemens Corporate Research, Inc. (Taiwan)
Ming Fang, Siemens Corporate Research, Inc. (United States)


Published in SPIE Proceedings Vol. 3340:
Medical Imaging 1998: Image Perception
Harold L. Kundel, Editor(s)

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