A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN
 |
International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2014 by IJETT Journal | ||
| Volume-17 Number-9 |
||
| Year of Publication : 2014 | ||
| Authors : NehaTripathi, Supriya P Panda |
||
| DOI : 10.14445/22315381/IJETT-V17P290 |
Citation
NehaTripathi, Supriya P Panda "A Review on Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification Using GLCM & RBFNN", International Journal of Engineering Trends and Technology (IJETT), V17(9),462-464 Nov 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Computer Aided Diagnosis (CAD) is used as second opinion forthe Radiologist and used as a solution in detection of breast cancer.It already proved its success in the reduction of human error in reading the mammogram images, and their better and reliable classification into benign and malignant masses. This paper proposed an algorithm for the detection of breast cancer in early stage, textural feature analysis proves to be one of the competent step for the detection of abnormalities. This paper estimate Gray-Level-Co-Occurrence Matrix (GLCM) method for extraction of textural feature from the segmented mammograms,and Radial Basis Function Neural Network (RBFNN) is taken as classifier and its performance evaluation is done with different textural features. The objective of this paper is to find a clear classification of breast cancer in early stage.
References
[1] Sheshadri HS, Kandaswamy A, “Detection of breast cancer by mammogram Image segmentation”J Cancer Res Ther - December 2005 - Volume 1 – Issue, 4,232-234.
[2] Jinshan Tang, Rangaraj. M. Rangayyan, Issam El Naqa, Yongyi Yang. “Computer-aided detection and diagnosis of breast cancer with mammography: Recent Advancements,” IEEE Trans. Biomed. Eng, vol 13.No 2,March 2009.
[3] KunioDoi, “Computer-Aided Diagnosis in Medical Imaging: Historical Review,Current Status and Future Potential”,Comput Med Imaging Graph. 2007 ; 31(4-5): 198–211.
[4] Khalid bashir, Anuj Sharma.” Review Paper on Classification onMammography”,International Journal of Engineering Trends and Technology (IJETT) ,Volume 14 Number 4 – Aug 2014.
[5] Atam P. Dhawan, YateenChitre, Christine Bonasso and Kevin Wheeler"; Radial-Basis-Function Based Classification of Mammographic Microcalcifications Using Texture Features; Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference (Volume:1 ) 535 - 536 vol.1
[6] D.NarainPonraj, M.Evangelin Jenifer, P. Poongodi, J.SamuelManoharan. “A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer” Journal of Emerging Trends in Computing and Information Sciences, VOL. 2, NO. 12, December 2011 .
[7] NorliaMdYusof, NorAshidi Mat Isa and HarsaAmylia Mat Sakim. Computer-Aided Detection and Diagnosis for Microcalcifications in Mammogram: A Review,’’ IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.6, June 2007
[8] P. Mohanaiah, P. Sathyanarayana, L. GuruKumar, “Image texture feature extraction using GLCM approach” International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013.
[9] Aswini Kumar Mohanty, SwapnasiktaBeberta, Saroj Kumar Lenka, “Classifying Benign and Malignant Mass using GLCM and GLRLM based Texture Features from Mammogram” IJERA, Vol. 1, Issue 3, pp.687-693.
[10] Robert M. Harlick,K.Shannugam,Its’hakDinsten, “Textural features for image classification”IEEE,Transaction on Systems Man &Cybernetics,Vol.SMC-3,No-6,Nov-1973,pp 610-621.
[11] J. Jiang , P. Trundle, J. Ren ; Medical image analysis with artificial neural networks; Computerized Medical Imaging and Graphics;Volume 34, Issue 8, December 2010, Pages 617–631
[12] I Claristoyintzni, E. Derimatas ,G. Kokkinakis; Neural Classification Of Abnormal Tissue In Digital Mammography Using Statistical Features Of The Texture; Electronics, Circuits and Systems, 1999. Proceedings of ICECS `99. The 6th IEEE International Conference on (Volume:1 )
[13] SatishSaini, Ritu Vijay “Performance Analysis of Artificial Neural Network Based Breast Cancer Detection System”; International Journal of Soft Computing and Engineering (IJSCE), Volume-4 Issue-4, September 2014.
Keywords
Mammogram, Breast cancer, Textural Feature, GLCM, RBFNN,CAD, ANN.