A cad system for improving classification performance in breast cancer detection

  • Authors

    • Bincy Babu
    • A Josephin Arockia Dhivya
    • R Chandrasekaran
    • T R. Thamizhvani
    • R J.Hemalatha
    2018-05-03
    https://doi.org/10.14419/ijet.v7i2.25.16566
  • Micro Calcification, Marker Controlled Watershed Segmentation, GLCM, GLRLM.

  • Early detection is a key factor in reducing breast cancer mortality rate. Research works in the area of mammography plays an important role in identification of calcification clusters and detection of breast cancer.The purpose of proposed research is to find the best combination of feature extraction algorithm to classify mammogram into benign and malignant. It includes Marker Controlled Watershed Segmentation Technique (MCWS), feature set extraction methods and SVM classifier algorithm. The GLCM, GLRLM and first order texture descriptors are used to describe the calcification clusters.The standard inputs such as normal and abnormal breast images for the proposed system are taken from Digital Database for Screening Mammography (DDSM). The computational study showed that combination of all the three features descriptors provide better classification result with 97% accuracy and it ensures improved the CAD system performance for small training data sets compared to existing techniques.

     

     

  • References

    1. [1] X. Zhou, R. Gordon; “Detection of early breast cancer: an overview and future prospectsâ€, Crit. Rev. Biomed. Eng, Vol. 17, (1989), pp. 203–255, available online: https://europepmc.org/abstract/med/2673660.

      [2] S.L. Olson, B.W. Fam, P.F. Winter, F.J. Scholz, A.K. Lee, S.E. Gordon; “Breast calcifications: analysis of imaging propertiesâ€, Radiology, Vol. 169, No.2, (1988), pp. 329–332, available online: https://pubs.rsna.org/doi/abs/10.1148/radiology.169.2.3174980.

      [3] Mohapatra P J, Nanda P K, Panda S; “Color Image Segmentation Using MRF Model and Simulated Annealingâ€, Proceedings of Softcomputing technique for Engineering Applications, (2006), pp. 1-10, http:// http://dspace.nitrkl.ac.in/dspace/handle/2080/357.

      [4] Dana Cobzas, Neil Birkbeck, Mark Schmidt, Martin Jagersand; “3DVariational Brain Tumour Segmentation Using a High Dimensional Feature Setâ€, IEEE 11th International Conference on Computer Vision, (2007), pp.1-8, http://doi.ieeecomputersociety.org/10.1109/ICCV.2007.4409130.

      [5] Albert Law KW, Law FK, Francis Chan HY; “A Fast Deformable Region Model for Brain Tumor Boundary Extraction’, Engineering in Medicine and Biologyâ€, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society (EMBS/BMES) Conference, Vol.2, (2002), pp.1055-1056, http://dx.doi.org/ 10.1109/IEMBS.2002.1106273.

      [6] Azadeh Yazdan Shahmorad, Hamid Soltanianzadeh, Reza Zoroofi A; “MRSI– Braintumor characterization using Wavelet and Wavelet packets Feature spaces and Artificial Neural Networksâ€, Engineering in Medicine and Biology Society, 26th Annual International Conference of the IEEE, Vol.1, No. 1-5, (2004), pp.1810-1813, http:// dx.doi.org/ 10.1109/IEMBS.2004.1403540.

      [7] K.S. Woods, C.C. Doss, K.W. Bowyer, J.L. Solka, C.E. Priebe, W.P. Kegelmeyer; “Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammographyâ€, Int. J. Pattern Recognition Artif. Intell, Vol. 7, (1993), pp.1417–1436, available online: https:// www.worldscientific.com /doi /abs /10.1142 /S0218001493000698

      [8] B. Zheng, W. Qian, L.P. Clarke; “Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcificationsâ€, IEEE Transactions of Medical Imaging, Vol. 15, No.5, (1996), pp. 589–597, available online: https://ieeexplore.ieee.org /abstract /document /538936 / 10.1109/42.538936.

      [9] W.J.H. Veldkamp, N. Karssemeijer; “An improved method for detection of microcalcification clusters in digital mammogramsâ€, The SPIE Conference on Image Processing, Vol. 3661, (1999), pp. 512–522, https://doi.org/10.1117/12.348607.

      [10] S. Bothorel, B.B. Meunier, S. Muller; “A fuzzy logic based approach for semiological analysis of microcalcifications in mammographic imagesâ€, Int. J. Intelligent Systems, Vol. 12, (1997), pp. 819–848, available online: https:// onlinelibrary.wiley.com /doi /abs /10.1002/(SICI)1098-111X(199711/12)12:11 /12%3C819 :: AID-INT3%3E3.0.CO;2-%23.

      [11] Ball J.E, Bruce L.M; “Digital Mammogram Spiculated Mass Detection and Spicule Segmentation using Level Setsâ€, In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, France, Vol.12, (2007), pp.4979–4984, http:// dx.doi.org/ 10.1109/IEMBS.2007.4353458.

      [12] Timp S, Karssemeijer N; “Interval Change Analysis to Improve Computer Aided Detection in Mammographyâ€, Medical Image Analysis 10, Vol.17, (2006), pp.82–95, available online: https://www.sciencedirect.com/science/article/pii/S1361841505000484.

      [13] Fauci F, Bagnasco S, Bellotti, R., Cascio D, Cheran S.C, De Carlo, F., De Nunzio, Fantacci, M.E., Forni, G., Lauria, A; “Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Networkâ€, In Proceedings: IEEE Nuclear Science Symposium Conference Record, Rome, Italy, Vol. 5, No.16, (2004), pp. 2695–2699, http:// dx.doi.org/ 10.1109/NSSMIC.2004.1462823.

      [14] Rangayyan R.M, Guliato D, Carvalho, Santiago J.D; “Feature Extraction from the Turning Angle Function for the Classification of Contours of Breast Tumorsâ€, In: IEEE Special Topic Symposium on Information Technology in Biomedicine, Iaonnina, Greece, Vol.8, No.23, (2006), pp. 56-59, available online: https://pdfs.semanticscholar.org/1db7/bb353c675d17a63792ff5e610826b60e1a2f.pdf.

      [15] Joseph Peter V, Karnan M; “Medical Image Analysis Using Unsupervised and Supervised Classification Techniquesâ€, International Journal of Innovative Technology and Exploring Engineering, Vol. 3, No.5, (2013), pp. 40-45., avaiable online: https://pdfs.semanticscholar.org/1ba9/d67c80b6a762c11b9d519367e9e13a9c5c4f.pdf.

      [16] Komal Sharma, AK winder Kaur, Shruti Gujral; “Brain Tumor Detection based on Machine Learning Algorithmsâ€, International Journal of Computer Applications,Vol. 103, No.1, (2014), pp.8875 – 8887, available online: https:// pdfs.semanticscholar.org /c102 /9db73a242801430602c108786cbe6796b7f2.pdf.

      [17] Blagojce Jankulovski, Ivan Kitanovski, Katarina Trojacanec, Ivica Dimitrovski, Suzana Loskovska; “Mammography Image Classification Using Texture Featuresâ€, Proceedings of 9th Conference for Informatics and Information Technology, Vol. 86, No.112, (2012), pp.129-132, available online: https:// pdfs.semanticscholar.org /51bc/ 6966dfc03bf18f5979acaba30adaaa61e6c2.pdf.

      [18] Wu X, Kumar, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GA, Liu P; “Top 10 algorithms in data miningâ€, Knowledge and Information Systems, Vol. 14, No. 1, (2008), pp. 1–37, available online: www.cs.uvm.edu /~icdm/algorithms /10Algorithms-08.pdf

      [19] Kavithal R.K, DoraiRangasamy D; “Predicting Breast Cancer Survivability Using Naïve Baysein Classifier and C4.5 Algorithmâ€, Elysium journal Engineering Research and management,Vol. 25, No.42, (2014), pp.115-120, available online: http://ejerm.com/.../Predicting_Breast_Cancer_Survivability_Using_Naïve_Baysein_Classifier.

      [20] Krishnaveni, R.Bhanumathi, T.Pugazharasan; “Study of Mammogram Microcalcification to aid tumour detection using Naive Bayes Classifierâ€, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering ,Vol. 3, No. 3, (2014), pp.8274-8282, available online: http:// www.siteadvisor.com /restricted.html?domain= http:%2F%2Fwww.rroij.com%2Fopen-access%2 Fstudy-of-mammogram-microcalcification-toaid-tumour-detection-using-naïve.

      [21] Wei L, Yang Y, Nishikawa R.M, Jiang Y; “A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcificationsâ€, IEEE Transactions on Medical Imaging, Vol. 24, No. 10, (2005), pp. 1278-1285, available online: https://ieeexplore.ieee.org/document/1397824/ 10.1109/TMI.2004.842457.

      [22] Vijayarani, Muthulakshmi M; “Evaluating The Efficiency Of Rule Techniques For File Classificationâ€, International Journal of Research in Engineering and Technology, Vol. 20, No.18, (2013), pp: 2321- 2329, available online: https://pdfs.semanticscholar.org /1f33/1854e02204786f882a0492693c9b368ec9e7.pdf

      [23] Dheeba J and Wiselin Jiji.G; “Detection of Microcalcification Clusters in Mammograms using Neural Networkâ€, International Journal of Advanced Science and Technology, Vol. 19, No.34, (2010), pp.13-22, available online: www.sersc.org/journals/IJAST/vol19/2.pdf.

      [24] “USF digital mammography home pageâ€, available online: http: //marathon.csee.usf.ecu/Mammography/Database.html,(accessed May 18, 2009).

      [25] Liu CC, Tsai CY, Liu J, Yu CY, Yu SS; “A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysisâ€, Computer and Mathematics with Applications, Vol. 64, No. 5, (2012), pp.100-1107, available online: https:// www.sciencedirect.com /science /article/pii/S0898122112002337.

      [26] Ferrari RJ, Rangayyan RM, Desautels J E, Borges RA, Frere AF; “Automatic identification of the pectoral muscle in mammogramsâ€, IEEE Transactions on Medical Imaging, 2004; Vol. 23, No. 2, (2004), pp.232-245, available online: https://ieeexplore.ieee.org/document/1263612.

      [27] R. C. Gonzalez, R. E. Woods, S. L. Eddins; “Digital Image Processing Using MATLAB,†Pearson Prentice Hall, Vol. 11, (2004).

      [28] J.K. Kim, H.W. Park; “Statistical textural features for detection of microcalci"cations in digitized mammogramsâ€, IEEE Trans. Med. Imag, Vol. 18, No. 3, (1999), pp. 231–238, available online: https://ieeexplore.ieee.org/document/764896.

      [29] Neeta Jog, Arvind Pandey; “Implementation of Segmentation and Classification Techniques for Mammogram Imagesâ€, IOSR Journal of Engineering (IOSRJEN), Vol. 5, No. 2, (2015), available online: http://www.rroij.com /peer-reviewed/ implementation-of-segmentation-and-classification-techniques-for-mammogram-images-50187.html.

      [30] S. Naresh, S. Vani Kumari; “Breast Cancer Detection using Local Binary Patternsâ€, International Journal Of Computer Applications, Vol. 123, No.16, (2015), pp. 6-9, available online: https://pdfs.semanticscholar.org/137b/70428ab323da303d8d0b107c7be7571a08e3.pdf.

  • Downloads

  • How to Cite

    Babu, B., Josephin Arockia Dhivya, A., Chandrasekaran, R., R. Thamizhvani, T., & J.Hemalatha, R. (2018). A cad system for improving classification performance in breast cancer detection. International Journal of Engineering & Technology, 7(2.25), 89-94. https://doi.org/10.14419/ijet.v7i2.25.16566