Wavelet based Image Registration Technique for Matching Dental x-rays
Image registration plays an important role in the
diagnosis of dental pathologies such as dental caries, alveolar bone
loss and periapical lesions etc. This paper presents a new wavelet
based algorithm for registering noisy and poor contrast dental x-rays.
Proposed algorithm has two stages. First stage is a preprocessing
stage, removes the noise from the x-ray images. Gaussian filter has
been used. Second stage is a geometric transformation stage.
Proposed work uses two levels of affine transformation. Wavelet
coefficients are correlated instead of gray values. Algorithm has been
applied on number of pre and post RCT (Root canal treatment)
periapical radiographs. Root Mean Square Error (RMSE) and
Correlation coefficients (CC) are used for quantitative evaluation.
Proposed technique outperforms conventional Multiresolution
strategy based image registration technique and manual registration
technique.
[1] Barbara Zitova, Jan Flusser, "Image Registration Methods: a survey"
Image and Vision computing 2003; 21:977-1000.
[2] Anil K Jain, Hong Chen, "Matching of dental x-ray images for human
identification" Pattern Recognition 2004; 37:1519-1532.
[3] George Lazaridis and Maria Petrou, "Image Registration using Walsh
Transform" IEEE Trans. on Image Processing, Vol 15, No.8, Aug 2006.
[4] EI Zacharaki, GK Matsopoulos, PA Asvestas, KS Nikita, K Grondahl
and H G Grondahl, "A digital subtraction radiography scheme based on
automatic multiresolution registration" Dentomaxillofacial radiology
2004; 33:379-390.
[5] F Haiter and a Wenzel, "Noise in subtraction images made from pairs of
bitewing radiographs; A comparison between two subtraction programs"
Dentomaxillofacial Radiology 2005; 34:357-361.
[6] A Gegler, CEW Mahl and V Fontanells, "Reproducibility and file format
effect on Digital subtraction radiography of simulated external root
resorptions" Dentomaxillofacial Radiology 2006; 35:10-13.
[7] Pierre Gravel, Gilles Beaudoin and Jacques A De Guise, " A method for
modeling noise in medical images", IEEE Trans. on Medical Imaging,
Vol. 23, No. 10, October 2004.
[8] Deng. G, Cahill, L.W, "An Adaptive Gaussian filter for noise reduction
and edge detection", Nuclear Science Symposium and Medical Imaging
Conference, Vol. 3, Pages: 1615-1619, 1993.
[9] M.McGuire, "An image registration technique for recovering rotation,
scale and translation parameters" NEC Tech. Report, Feb 1998.
[10] Thenevez P, Unser M, Optimization of mutual information for
Multiresolution image registration" IEEE Trans. on Image Processing
2000:9:2083-2099.
[11] Yoon D.C, "A new method for the automated alignment of dental
radiographs for digital subtraction radiography" Dentomaxillofacial
radiology 2000; 29: 11-19.
[1] Barbara Zitova, Jan Flusser, "Image Registration Methods: a survey"
Image and Vision computing 2003; 21:977-1000.
[2] Anil K Jain, Hong Chen, "Matching of dental x-ray images for human
identification" Pattern Recognition 2004; 37:1519-1532.
[3] George Lazaridis and Maria Petrou, "Image Registration using Walsh
Transform" IEEE Trans. on Image Processing, Vol 15, No.8, Aug 2006.
[4] EI Zacharaki, GK Matsopoulos, PA Asvestas, KS Nikita, K Grondahl
and H G Grondahl, "A digital subtraction radiography scheme based on
automatic multiresolution registration" Dentomaxillofacial radiology
2004; 33:379-390.
[5] F Haiter and a Wenzel, "Noise in subtraction images made from pairs of
bitewing radiographs; A comparison between two subtraction programs"
Dentomaxillofacial Radiology 2005; 34:357-361.
[6] A Gegler, CEW Mahl and V Fontanells, "Reproducibility and file format
effect on Digital subtraction radiography of simulated external root
resorptions" Dentomaxillofacial Radiology 2006; 35:10-13.
[7] Pierre Gravel, Gilles Beaudoin and Jacques A De Guise, " A method for
modeling noise in medical images", IEEE Trans. on Medical Imaging,
Vol. 23, No. 10, October 2004.
[8] Deng. G, Cahill, L.W, "An Adaptive Gaussian filter for noise reduction
and edge detection", Nuclear Science Symposium and Medical Imaging
Conference, Vol. 3, Pages: 1615-1619, 1993.
[9] M.McGuire, "An image registration technique for recovering rotation,
scale and translation parameters" NEC Tech. Report, Feb 1998.
[10] Thenevez P, Unser M, Optimization of mutual information for
Multiresolution image registration" IEEE Trans. on Image Processing
2000:9:2083-2099.
[11] Yoon D.C, "A new method for the automated alignment of dental
radiographs for digital subtraction radiography" Dentomaxillofacial
radiology 2000; 29: 11-19.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61560", author = "P. Ramprasad and H. C. Nagaraj and M. K. Parasuram", title = "Wavelet based Image Registration Technique for Matching Dental x-rays", abstract = "Image registration plays an important role in the
diagnosis of dental pathologies such as dental caries, alveolar bone
loss and periapical lesions etc. This paper presents a new wavelet
based algorithm for registering noisy and poor contrast dental x-rays.
Proposed algorithm has two stages. First stage is a preprocessing
stage, removes the noise from the x-ray images. Gaussian filter has
been used. Second stage is a geometric transformation stage.
Proposed work uses two levels of affine transformation. Wavelet
coefficients are correlated instead of gray values. Algorithm has been
applied on number of pre and post RCT (Root canal treatment)
periapical radiographs. Root Mean Square Error (RMSE) and
Correlation coefficients (CC) are used for quantitative evaluation.
Proposed technique outperforms conventional Multiresolution
strategy based image registration technique and manual registration
technique.", keywords = "Diagnostic imaging, geometric transformation,image registration, multiresolution.", volume = "2", number = "8", pages = "1703-4", }