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Role of Image Processing in Cancer Detection and Treatments

R. Aktharunisa Begum

Abstract


Image process techniques square determine extensive employed in many medical areas for image improvement in exposure and cure stages. Processing image may be a technique to convert a picture into digital form to make operations, increased image to associate with nursing or to extract some helpful data from it. Cancer Diseases characteristic by out of control growth.  There square measure over 100 different kinds of cancer and each cell first affected by class. Carcinoma is the uncontrolled growth kick off one or each lungs of abnormal cells. Blood cancer is Associate in nursing umbrella term for cancers that have an effect on the blood, bone marrow and system a lymphaticum. Carcinoma may be a cancer that forms in tissues of the breast. Carcinoma is ductal cancer is foremost variety which begins within the lining of the milk ducts. Bone cancer is either primary or secondary bone cancer. Primary bone cancer started within the bone at first fashioned, whereas secondary cancer started within the body and spread out to the bone. Abnormal cells in tissues in the brain causes brain tumor. Brain tumors is benign (not cancer) or malignant (cancer).


Keywords


Cancer, Cells, Detection, Diagnosis, Disease, Enhancement Watershed, Exploitation, Feature Extraction, Fusion, Masking, Threshold, Tissue, Tumors, Segmentation.

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References


Mokhled S. AL-TARAWNEH ,” Lung Cancer Detection Using Image Processing Techniques”, Leonardo Electronic Journal of Practices and Technologies, ISSN 1583-1078,issue 20, January-June 2012 p. 147-158

Tobias Christian cahoon, James c. Bezdek,Melanie A.sutton “ Breast cancer detection using image processing” IEEE, ISSN- 07803-5877,May-2000

Devijver,P.A and Kittler,J (1982) pattern Recongnition, A statistical Approach ,Prentice all International, Inc

Non-Small Cell Lung Cancer, Available at: http://www.katemacintyrefoundation.org/ pdf/non-small-cell.pdf, Adapted from National Cancer Institute (NCI) and Patients Living with Cancer (PLWC), 2007, (accessed July 2011).

Tarawneh M., Nimri O., Arqoub K., Zaghal M., Cancer Incidence in Jordan 2008, Available at: http://www.moh.gov.jo/MOH/Files/Publication/Jordan%20Cancer%2 0Registry_2008%20Report_1.pdf, 2008, (accessed July 2011).

Levener I., Zhang H, Classification- Driven Watershed Segmentation, IEEE Transactions on Image Processing, 2007,16(5),1437-45

Bezdek, J.C., Hsll, L.O., clark, Goldof,D. and Clarke, l.P(1997). Segmenting medical images with fuzzy models: An update. In Fuzzy Information Engineering, ed. Dubois, D., Prade, H. and Yagar, R., Wiley, NY, 69-92

Nur Alom Talunkdar, Daizy Deb, Sudipta Roy ,”Automated Blood Caner Detection using Image processing based on fuzzy system”, IJARSC, ISSN-2277 -128X ,vol 4, issue 8, August 2014

Nilkamal S. Ramteke and Shweta V. Jain, “Detection & Analysis of Skin Cancer using Wavelet Techniques,” International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 6- June 2013.

Skin Cancer Recognition by Using a Neuro-Fuzzy System, http://www.la-press.com

Nisthula P, Mr.Yadhu .R.B ,” A Novel Method to detect bone cancer using image fusion and edge detection”,IJECS ,ISSN 2319-7242, issue 6 june 2013 page -2012-2018

MA,Jing, BI,Qiang, “Processing practice of remote sensing image based on spatial modeler” The national Natural Science Foundation of china(No . 41071237)IEEE .2012

Changtao He, Guiqun cao,, Fangnian Lang, “An Efficient Fusion Approach for Multispectral and panchromatic medical imaging”, Biomedical Eningeering Research , March 2013, vol.2 iss 1.,

Basser L.W Manjikian V.III,Gold R.H(1990),mammography and breast cancer screening(Review) surg clinics of north America ,775-800

Miss. Roopali R. Laddha1, Dr. Siddharth A. Ladhake, “ Brain Tumor Detection Using Morphological and Watershed Operators” IJAIEM, ISSN 2319-4847 ,vol 3, issue 3, march 2014

Oelze, M.L,Zachary, J.F. , O'Brien, W.D., Jr., “Differentiation of tumor types in vivo by scatterer property estimates and parametric images using ultrasound backscatter “ , on page(s) :1014 - 1017 Vol.1, 5-8 Oct. 2003

Jichuan Shi, “Adaptive local threshold with shape information and its application to object segmentation‖”, Page(s)1123 - 1128, Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference,19-23 Dec. 2009.


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