DocumentCode :
777149
Title :
Enhancing digital cephalic radiography with mixture models and local gamma correction
Author :
Frosio, I. ; Ferrigno, G. ; Borghese, N.A.
Author_Institution :
Comput. Sci. Dept., Univ. of Milan, Italy
Volume :
25
Issue :
1
fYear :
2006
Firstpage :
113
Lastpage :
121
Abstract :
We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under- and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.
Keywords :
Gaussian distribution; bone; brain; diagnostic radiography; image enhancement; medical image processing; 1 s; Gaussian distributions; bone tissue; digital cephalic radiography; image enhancement; inverted lognormal distribution; local gamma correction; mixture models; soft-tissue filter; Biological tissues; Bones; Clustering algorithms; Computer science; Diagnostic radiography; Filters; Image coding; Intelligent systems; Pixel; Visualization; Digital radiography; histogram-based clustering; image enhancement; local gamma correction; mixture models; soft-tissue filter (STF); Algorithms; Cephalometry; Computer Simulation; Connective Tissue; Head; Humans; Models, Biological; Models, Statistical; Numerical Analysis, Computer-Assisted; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Skull;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2005.861017
Filename :
1564331
Link To Document :
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