3D Reconstruction and Pseudo Coloring of Images in Digital Mammography
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Mammography is radiological technique alone, which is used to image breast tissues as it was known for the past two decades till 1966, meaningful solutions to reduce the pain making diagnosis and also suggested effective methods to detect breast carcinoma at the earliest possible time. Mammography is the leading method for breast imaging today. Here we will be arriving a methodology by taking two plain film (x-ray) mammography at an angle 45º / 90º between two and collect the details in digital form in a computer for segmentation and reconstruction. The application of Pseudocolouring with the help of Photoshop colour policies will use to separate the carcinoma and to apply various colours for the calcifications in gray scale for better perception and understanding, and improve the diagnostic quality. Because any human eye can easily differentiate the colours, the shape size and location of the occult carcinoma must easily detect. Also the 3D mammographic breast imaging techniques with the help of Pseudocolouring have potentials for both early cancer detection and diagnosis.
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Boag A. H., L. A. Kennedy, M. J. Miller. Three Dimensional Microscopic Image Reconstruction of Prostatic Adenocarcinoma. Archives of Pathology and Laboratory Medicine: 125(4):562–566. April 2001.
Kopans D.B. (1998) Breast Imaging. 2nd Edition. Lippincott- Raven. Philadelphia.
Li, Jun; Hou, Edwin S. (1996) Pseudocoloring schemes for thermal imaging Proc. SPIE Visual Data Exploration and Analysis III, Georges G. Grinstein; Robert F. Erbacher; Eds.Vol. 2656-77
Liu S., C. F. Babbs, E. J. Delp (1998). Normal Mammogram Analysis and Recognition. Proceedings of the IEEE International Conference on Image Processing, pp.4-7.
Mudigonda N.R., R.M. Rangayyan, J. Desautels (2001). Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imaging. 20(12):1215–1227
Pratt.W.K. (2003) Digital image processing. John Wiley & Sons. Inc. New York, NY, USA. Second edition.
Rafael C. Gonzalez, Richard E.Woods (2001) Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA second edition.
Sheng Liu, C.F. Babbs; Delp, E.J. (2001) Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans. Image Processing. 10(6):874 –884.
Yam M., M. Brady, R. Highnam, C. Behrenbruch (2001) Three–dimensional Reconstruction of micro- calcifications clusters from two mammographic view's. IEEE Trans. Medical Imaging. 20(6): 479 – 489.
Zheng B., Y.H.Chang, D.Gur (1995) Computerized Detection of masses in Digitized Mammograms using single image segmentation and a multilayer topograophic feature analysis. Acad. Radiology. 2(5): 959-966. US Patent Issued on March 6, 2001
Geetha, V., Chandrakala, D., Nadarajan, R., Dass, C.K., A bayesian classification approach for handling uncertainty in adaptive E-assessment, (2013) International Review on Computers and Software (IRECOS), 8 (4), pp. 1045-1052.
Lekha, A., Srikrishna, C.V., Vinod, V., Prediction algorithms for mining biological databases, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 650-658.
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