Contrast enhancement of radiograph images based on local heterogeneity measures

Contrast enhancement of the lateral skull X-ray images is very important in orthodontic practice, cephalometric evaluation and craniofacial landmarking. In this paper we present computational techniques involving contrast enhancement of two-dimensional digitized X-ray images. The algorithm is developed based on the use of local heterogeneity measures of pixel distribution. Image enhancement is accomplished by an adaptive gray scale pixels transformation depending on results of local heterogeneity contribution measures. The proposed approach was tested on different images and the results prove that the proposed method has better performance the existing conventional methods.

[1]  Wen-Rong Wu,et al.  Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation , 1998, IEEE Trans. Medical Imaging.

[2]  Heng-Da Cheng,et al.  A novel fuzzy logic approach to contrast enhancement , 2000, Pattern Recognit..

[3]  Maher A. Sid-Ahmed,et al.  Image processing, theory, algorithms and architectures , 1995 .

[4]  Gert Cauwenberghs,et al.  Robust cephalometric landmark identification using support vector machines , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[5]  Malur K. Sundareshan,et al.  Adaptive image contrast enhancement based on human visual properties , 1994, IEEE Trans. Medical Imaging.

[6]  R. Gordon,et al.  Enhancement of Mammographic Features by Optimal Adaptive Neighborhood Image Processing , 1986, IEEE Transactions on Medical Imaging.

[7]  Sabine Dippel,et al.  Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform , 2002, IEEE Transactions on Medical Imaging.