An adaptive window width/center adjustment system with online training capabilities for MR images.
OBJECTIVE
Adaptive and automatic adjustment of the display window parameters for magnetic resonance images under different viewing conditions is a challenging problem in medical image perception. An adaptive hierarchical neural network-based system with online adaptation capabilities is presented to achieve this goal in this paper.


METHODS
The online adaptation capabilities are primarily attributed to the use of the hierarchical neural networks and the development of a new width/center mapping algorithm. The large training image set is hierarchically organized for efficient user interaction and effective re-mapping of the width/center settings. The width/center mapping functions are estimated from the new user-adjusted width/center values of some representative images by using a global spline function for the entire training images as well as a first-order polynomial function for each selected image sequence. The hierarchical neural networks are then re-trained for the new training data set after this mapping process.


RESULTS
The proposed automatic display window parameter adjustment system is implemented as a program on a personal computer for testing its adaptation performance. Experimental results show that the proposed system can successfully adapt its parameter adjustment on a variety of MR images after user re-adjustment and re-training of neural networks.


CONCLUSIONS
This demonstrates the effective adaptation capabilities of the proposed system based on the framework of training data mapping and neural network re-training.
Lai
Shang-Hong
SH
Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC. lai@cs.nthu.edu.tw
Fang
Ming
M
D000465:Algorithms; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D014584:User-Computer Interface