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Cancer Detection Using Hyperspectral Imaging and M-FISH Technique

S. Ranjitha Kumari, Dr. A. Kavitha

Abstract


Medical imaging plays a crucial role in the diagnosis of various diseases. Modern technology has made many innovations for diagnosing the disease so that human race can lead a healthy life. One of such innovation is medical imaging. Along with MRI (Magnetic Resonance Imaging), Computed Tomography (CT), Ultrasonopgraphy a new technology called Hyperspetral imaging is gaining popularity in medical field. This paper provides an overview about the Hyperspectral technology and the algorithms used. We have discussed the hyperspectral image along M-FISH technique act as an efficient way to detect the cancer in its early stage. The main challenge in this method is the overlapping of the spectrum of the fluorescent colors in the hyperspectral image. We have presented a review of spectral unmixing algorithms which are used in classification of overlapping of spectrum of fluorescent colors and suggested a combination of algorithms to effectively reduce the overlapping of spectral.

Keywords


Autofluoroscence, Flourochromes, Hyperspectral, M-FISH, VARIMAX

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References


Ankita Roy, “Quantitative methods and Detection techniques in Hyperspectral imaging involving medical and other Applications” A Dissertation presented to Graduate School of Clempson University., Aug 2007

Bijay Singh & Jimly Dowerah, “Hyperspectral Imaging: New Generation Remote Sensing” Open access e-Journal Earth Science India, Vol. 3 (III), July, 2010 Popular Issue, http://www.earthscienceindia.info/; ISSN: 0974 – 8350

Hamed Akbari, Yukio kosugi,” Hyperspectral Imaging: A new Modality in Surgery” Recent Advances in Biomedical Engineering Hyperspectral Imaging

Karmon Marie Vongsy,” Change detection methods for hyperspectral imagery” A thesis sumitted to Wright State University, 2007.

Futian Yao, Yuntao Qian, “Band selection based gaussian processes for hyperspectral remote Sensing images classification” 978-1-4244-5654-3/09/ 2009 IEEE 2845 ICIP 2009

Gustavo Camps-Valls Lorenzo Bruzzone,” Kernel-Based Methods for Hyperspectral Image Classification”IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 6, June 2005 1351

Abel Paz, Antonio Plaza and Soraya Bl´azquez, ”Parallel Implementation Of Target And Anomaly Detection Algorithms For Hyperspectral Imagery”Department of Technology of Computers and Communications, University of Extremadura Avda. de la Universidad s/n, E-10071 C´aceres, Spain

Franco Woolfe, Mauro Maggioni, Gustave Davis, Frederick Warner, Ronald Coifman, and Steven Zucker,” Hyper-spectral microscopic discrimination between normal and cancerous colon biopsies” IEEE Transactions on Medical Imaging, vol. 99, no. 99, November 9999 1

Qian Du, Ivica Kopriva, Harold Szu,” Independent-component analysis for Hyperspectral Remote Sensing Imagery Classification”.

Meiching Fong,”Dimension Reduction on Hyperspectral Images”August 31, 2007 UCLA Department of Mathematics

Lalita Khaodhiar, Thanh Dinh, Kevin T Schomacker, Svetlana V Panasyuk, “The Use of Medical Hyperspectral Technology to Evaluate Microcirculatory Changes in Diabetic Foot Ulcers and Predict Clinical Outcomes” Joslin-Beth Israel Deaconess Foot Center and Microcirculation

Oscar Carrascoa, Richard Gomezb, Arun Chainani, William Ropera,”Hyperspectral Imaging Applied to Medical Diagnoses and Food Safety”

Antonio Plaza, Qian Du, Yang-Lang Chang, “High performance computing for Hyperspectral Remote sensing”, IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing” Feb 2011

Gr´egoire MERCIER, Marc LENNON,” Support Vector Machines for Hyperspectral Image Classification with Spectral-based kernels” 0-7803-7930-6/$17.00 (C) 2003 IEEE

Seong G. Kong, Matthew E. Martin, and Tuan Vo-Dinh,” Hyperspectral Fluorescence Imaging for Mouse Skin Tumor Detection” ETRI Journal, Volume 28, Number 6, December 2006

Chein-i chang, Mingkai Hsueh, Weimin liu, Chao-cheng wu, Farzeen Chaudhry, Gregory Solyar,” A Pyramid-based block of skewers for pixel purity index for Endmember extraction in Hyperspectral imagery” International Journal of High Speed Electronics and SystemsVol. 18, No. 2 (2008) 469–482

Gary Shaw and Dimitris Manolakis,” Signal Processing for Hyperspectral Image Exploitation “ Massachusettes Institute of Technology, Lincoln Laboratory

Nirmal Keshava, John Kerekes, Dimitris Manolakis, Gary Shaw,” Algorithm Taxonomy for Hyperspectral Unmixing” Proceedings of SPIE Vol. 4049 (2000) • 0277-786X/OO/$1

Nadhi Thekkek, BS and Rebecca Richards-Kortum,”Optical Imaging for Cervical Cancer Detection: Solutions for a Continuing Global Problem” Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77005

Nadina Erill, Anna Colomer, Míriam Gorriz, Naim Hannaoui, Ruth Román, Maria Conangla, Josep M Banús, Carlos Cordón-Cardo, Xavier Puig, “ Comparison of Fluorescence In Situ Hybridization –FISH– and Conventional Cytology for Early Detection of Urothelial Carcinoma”

Pengo, A. Muñoz-Barrutía, and C. Ortiz-de-Solórzano,” Spectral unmixing of multiply stained fluorescence samples”, Microscopy: Science, Technology, Applications and Education

Martin De Biasio, Raimund Leitner, Franz G. Wuertz, Sergey Verzakov and Pierre J. Elbischger, “Enhancement of m-FISH Images using Spectral Unmixing”, International Journal of Biological and Medical Sciences 3:2 2008

Thomas Arnold, Raimund Leitner, Franz G. Wuertz, and Pierre J. Elbischger “Spot Counting for Automated Analysis of Unmixed Hyper-Spectral M-FISH Images” http://www.waset.org/journals/waset/v44/v44-42.pdf


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