چکیده مقاله :
In this paper, a method for automatic stitching of radiology images based on pixel features has been presented. In this method, according to the smooth texture of radiological images and in order to increase the number of the extracted features after quality enhancement of initial radiology images, 45 degree isotropic mask is applied to each radiology image to observe the image details. After this process, we used statistical and heuristic image noise extraction method (SHINE) to acceptably reduce the noise resulting from radiation of alternating X-rays on detector. Pixel point’s features are obtained by selecting maximum or minimum value of the brightness of pixels in certain neighborhood of the resulting radiology images. This algorithm transmutes point’s features to 128 dimensional vector features. In order to identify the segments overlapping in basic radiology images, we specify equivalent vector features of each radiology image using the mathematical properties of the vectors and find the fit geometry transform between pairs features matched by the random sample consensus (RANSAC) algorithm. Finally, resulted motion model is applied to the initial radiology images and we stitch them together in a common surface
منابع و مأخذ:
[1]
W.C.Chia, L.Ang, and K.P.Seng. “Performance Evaluation of Feature Detection in using Subsampled Images for Image Stitching”. In Proceeding Of 3th IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp.60-64, 2010.
[2]
R.Szeliski, ”Image Alignment And Stitching: A Tutorial”. Foundations And Trends In Computer Graphics And Vision, Vol.2, No1, pp.1-104, 2006.
[3]
A.Gooben, T.Pralow, and R.R.Grigat. “Automatic Stitching Of Digital Radiographies Using Image Interpretation”. Germany, 2010. Unpublished.
[4]
A.Gooben, M.Schluter, and M.Hensel, T.Pralow, R.R. Grigat. “Ruler-Based Automatic Stitching Of Spatially Overlapping Radiographs”. Germany, 2010. Unpublished.
[5]
Y.Tang , H.Jiang. “Highly Efficient Image Stitching Based on Energy Map”. In Proceeding Of 2nd International congress on Image and Signal Processing (CISP’9), pp.1-5, 2009.
[6]
Z. Xiuying , W.Hongyu , and W.Yongxue . “Medical Image Seamlessly Stitching By SIFT And GIST“ . In Proceeding Of International Conference on E-Product E-Service and E-Entertainment (ICEEE), pp.1-4, 2010.
[7]
Z.Hua, Y.Li, and J.Li. “Image Stitching Algorithm Based On SIFT And MVSC”. In Proceeding Of 7th International Conference On Fuzzy Systems And Knowledge Discovery (FSKD), pp.2628-2632, 2010.
[8] Y.Lan, H.Ren, C.Li, Xuefeng Zhao, and Z.Min. “Feature Based Sequence Image Stitching Method”. In Proceeding Of International Conference on Computational Intelligence and Software Engineering (CISE), pp.1-4, 2010.
[9] E. Sajjadi, R.fadaie. “Applied Learning advanced topics of Electrical engineering in MATLAB”. 2nd ed., Naghos
International Journal of Smart Electrical Engineering, Vol.2, No.2, Spring 2013 ISSN: 2251-9246
84
Publications, Tehran, 2010.
[10]
D.G.Lowe . “Distinctive Image Features From Scale-Invariant Keypoints”. International Journal on Computer Vision, pp.91-110, 2004.
[11]
Capek M , Wegenkittl R , and Felkel P . “A Fully Automatic Stitching of 2D Medical Data Sets “.Austria, 2001. Unpublished.
[12]
Yu Wang, Mingquan Wang, “Research On Stitching Technique of Medical Infrared Images”. In Proceeding Of International Conference Computer Application and System Modeling (ICCASM), pp.490-493, 2010.
[13]
F.Estrada, A.Jepson, and D.Fleet. “Local Features Tutorial”. Private Communication, Nova, 2004.
[14]
R.C.Gonzalez and R.E.Woods. “digital image processing” , in bibliographical, 2nded. New Jersey: Prentice Hall, pp.66-80, 88-112, 568-585, 2002.
[15]
A.Levin, A.Zomet , S.Peleg , and Y.Weiss. “Seamless Image Stitching In The Gradient Domain”. In Proceedings of 9th the European Conference on Computer Vision, 2006.
[16]
A.Levin, A.Zomet , S.Peleg , Y.Weiss. “Seamless Image Stitching by Minimizing False Edges”. IEEE Transactions On Image Processing, Vol.15, pp.969-977, April 2006.
[17]
McAndrew, Alasdair. “An introduction to digital image processing with MATLAB”. 1sted. Cengage learning India, pp.1-41, 49-107,127-148, 2013.
[18]
P .Hannequin, J.Mas. “Statistical And Heuristic Image Noise Extraction (SHINE): A New method For Processing Poisson Noise In scintigraphic images”. Institute Of Physics Publishing, pp.4329-4334, 2002.
[19]
T.Linderberg. “Scale-Space”. In Encyclopedia of Computer Science and Engineering 1nd ed., B.Wah, ed., Hoboken, New Jersey: John Wiley and Sons, Vol.4, pp.2495-2504, 2009. .
[20]
G.Vaca-Castano. “Matlab Tutrial ,SIFT”. Private Communication, Nova, 2011.
[21]
RANSAC. (Online) in Wikipedia Foundation Inc. Available: http://en.wikipedia.org/wiki/Ransac. last modified , April , 2012.
