We have developed a system to automatically adjust the display window width and level (WWL) for MR images using a neural network. There were three main points in the development of our system as follows: (1) We defined an index for the clarity of a displayed image, and we call this index 'EW'. EW is a quantitative measure of the clarity of an image displayed in a certain WWL, and can be derived from the difference between gray-level with the WWL adjusted by a human expert and with the WWL adjusted by this automatic system. (2) We extracted a group of six features from a gray-level histogram of displayed images. We designed a neural network which is able to learn the relationship between these features and the desired output (teaching signal), 'EQ', which is normalized to 0 to 1.0 from EW. Learning was performed using a back-propagation method. As a result, the neural network after learning is able to provide a quantitative measure, 'Q', of the clarity of images displayed in the designated WWL. (3) Using the 'Hill climbing' method, we have been able to determine the best possible WWL for displaying images. (a) The maximum Q is searched for and found from roughly sampled WWLs. (b) The WWL sampling intervals are gradually made finer. (c) The WWL with maximum Q searched in (b) is selected as the best possible WWL. We have tested this technique for MR brain images. The results show that this system can adjust WWL comparable to that adjusted by a human expert for the majority of test images. The neural network is effective for the automatic adjustment of the display window for MR images. We are now studying the application of this system to sagittal and coronal images.
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