International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Survey Paper on Diagnosis of Breast Cancer Using Image Processing Techniques

    , ,

Author Affiliations

  • 1Department of Computer Science, COMSATS Institute of Information Technology, PAKISTAN

Res. J. Recent Sci., Volume 2, Issue (10), Pages 88-98, October,2 (2013)

Abstract

Breast cancer is the oldest known type of cancer in humans. The oldest identification and definition of cancer was recordedin Egypt in around 1600 BC. Since then this disease has been researched and studied to avoid outcomes caused by it butstill this disease is considered as one of the most deadliest diseases of all times, as deaths caused by breast cancer only inUS in 2012 reached 40,000. In the modern medical science there are plenty of newly devised methodologies and techniquesfor the timely detection of breast cancer. Most of these techniques make use of highly advanced technologies such asmedical image processing. This research study is an attempt to highlight the available breast cancer detection techniquesbased on image processing and provides an overview about the affordability, reliability and outcomes of each technique.

References

  1. RSN America and ACR. Radiology [Online], Available:http://www.RadiologyInfo.org (2013)
  2. Highnam R and Brady M, Mammographic ImageAnalysis”, Kluwer Academic Publishers, British Journal ofRadiology, 74(887), (2001)
  3. Indra Kanta Maitra, Sanjay Nag, Prof. Samir KumarBandyopadhyay, Identification of Abnormal Masses inDigital Mammography Images, International Journal ofComputer Graphics, 2(1), (2011)
  4. Sterns EE, Relation between clinical and mammographicdiagnosis of breast problems and the cancer/ biopsy rateCan. J. Surg., 39(2), 128-132 (1996)
  5. Kekre H.B., Sarode Tanuja K. and Gharge Saylee M.,Tumor Detection in Mammography Images using VectorQuantization Technique, International Journal ofIntelligent Information Technology Application, 2(5), 237-242 (2009)
  6. Suckling J., The Mammographic Image Analysis SocietyDigital Mammogram Database Exerpta Medica,International Congress Series, 375-378 (1995)
  7. Ball JE, Digital mammogram speculated mass detectionand spicule segmentation using level sets, Proceedings ofthe 29th Annual International Conference of the IEEEEMBS, 4979-84 (2007)
  8. Bovis K, Singh S., Detection of masses in mammogramsusing texture features, 15th International Conference onPattern Recognition, 267-70 (2000)
  9. Khuzi A Mohd, Besar R, Wan Zaki WMD, Ahmad NN,Identification of masses in digital mammogram using graylevel co-occurrence matrices, Biomed Imaging IntervJ,5(3), 1-13 (2009)
  10. T. Chrisiian Cahoon, Melanie A. Sutton, James C. Bezdek,Breast cancer detection using image processing techniques,IEEE International Conference on Fuzzy Systems, 2, 973-976 (2000)
  11. Deviiver P.A. and Kittler J., Pattern Recognition, A Statistical Approach, Prentice-Hall, International, (1982)
  12. Bezdek J.C., Hall L.O., Clark M., Goldof D. and ClarkeL.P., Segmenting medical images with fuzzy models, InFuzzy InformationEngineering, 69-92 (1997)
  13. Bezdek J. and Sutton M.A., To appear in Handbook ofFuzzy Sets, 7: Applications, Image processing in medicine Kluwer Publishing Company, (1999)
  14. Keller J.M., Gray M. and Givens J., A fuzzy k-nearestneighbor algorithm, IEEE Trans. Syst., Man and Cyberns,15(4), 580-585 (1985)
  15. Kekre H.B., Tanuja K. Sarode, Saylee M. Gharge, TumorDetection in Mammography Images using VectorQuantization Technique, International Journal ofIntelligent Information Technology Application, 2(5) (2009)
  16. Bartakke P.P., Vaidya S.A. and Sutaone M.S., Refiningstructural texture synthesis approach, Image ProcessingIET, 5(2) 184-189 (2011)
  17. Yan Zhang, Xiaoping Cheng, Medical image segmentationbased on watershed and graph theory, Image and SignalProcessing (CISP), 3, 1419-1422(2010)
  18. Mohanalin, J.,Kalra, P.K., Kumar, N., Fuzzy based microcalcification segmentation, Electrical and ComputerEngineering ICECE, 49-52 (2008)
  19. Vilovic I., Burum N. and Sipus Z., Ant colony approach inoptimization of base station position, EuCAP 2009 3rdEuropean Conference, 2882-2886 (2009)
  20. Draa A. and Meshoul S., A Quantum Inspired LearningCellular Automaton for Solving the Travelling SalesmanProblem, International Conference on Computer Modelingand Simulation (UKSim), 45-50 (2010)
  21. Ishibuchi H., Nakashima Y. and Nojima Y., Search abilityof evolutionary multi objective optimization algorithms formulti objective fuzzy genetics-based machine learning,IEEE International Conference on FUZZ, 1724-1729(2009)
  22. Renjie Liao, Tao Wan, Zengchang Qin, Classification ofBenign and Malignant Breast Tumors in Ultrasound ImagesBased on Multiple Sonographic and Textural Features,Intelligent Human-Machine Systems and Cybernetics(IHMSC), 71-74 (2011)
  23. Hassanien, A.E., and Ali, J.M.: Enhanced Rough Sets RuleReduction Algorithm for Classification DigitalMammography, Intelligent System journal, UK, Freund &Pettman, 13(2), 151-171 (2004)
  24. Irwin M.R., Downey D.B., Gardi L. and Fenster A,Registered 3-D Ultrasound and Digital StereotacticMammography for Breast Biopsy Guidance, IEEETransactions on Medical Imaging, 27(3), 391-401 (2008)
  25. Vidhya M., Sangeetha N., Vimalkumar M.N., HelenprabhaK., Early stage detection of cancer in mammogram usingstatistical feature extraction, Recent Advancements inElectrical, Electronics and Control Engineering(ICONRAEeCE), 401-404 (2011)
  26. Mustra M., Bozek J. and Grgic M., Nipple detection incranio caudal digital mammograms, ELMAR '09,International Symposium, 15-18 (2009)
  27. James F. Peters, Andrzej Skowron, Jerzy W. GrzymałaBusse,Bożena Kostek, Roman W.Świniarski, Marcin S.Szczuka, Transactions on Rough Sets, Springer-Verlag,(2004)
  28. Kexiang Wang, Hong Qin, Fisher, P.R., Wei Zhao,Automatic registration of mammograms using texturebasedanisotropic features, Nano to Macro, 3rd IEEE International Symposium on Biomedical Imaging, 864-867,(2006)
  29. Thangavel, K., Karnan, M., Siva Kumar, R., and KajMohideen, A., Automatic Detection of Micro calcificationin Mammograms-A Review, International Journal onGraphics Vision and Image Processing, 5(5), 31-61 (2005)
  30. Thangavel K., and Karnan, M., Computer Aided Diagnosisin Digital Mammograms: Detection of Micro calcificationsby Meta Heuristic Algorithms, International Journal onGraphics Vision and Image Processing, 7, 41-55 (2005)
  31. Thangavel, K., and Karnan, M. Automatic Detection ofAsymmetries in Mammograms Using Genetic Algorithm,International Journal on Artificial Intelligence andMachine Learning, 5,55-62 (2005)
  32. Thangavel, K., Karnan, M., Siva Kumar, R., andKajaMohideen, A. Segmentation and Classification of Micro calcification in Mammograms Using the Ant Colony System, International Journal on Artificial Intelligence and Machine Learning, 5, 29-40 (2005)
  33. Thangavel and K., Karnan, M., CAD system for Preprocessing and Enhancement of Digital Mammograms, International Journal on Graphics Vision and Image Processing, 9, 69-74 (2005)
  34. Alina Sultana, MihaiCiuc, RodicaStrungaru and Laura Florea., A New Approach in Breast Image Registration, International Conference on Intelligent Computer Communication and Processing, 149-154 (2010)
  35. Alina Sultana, MihaiCiuc, Laura Florea and Corneliu Florea Detection of Mammographic Micro calcifications Using a Refined Region-Growing Approach. 1-4 (2009)
  36. M. Sundarami, K. Ramar, N. Arumugami, G. Prabini.,Histogram Based Contrast Enhancement for MammogramImages, International Conference on Signal Processing, Communication, Computing and Networking Technologies,628-503 (2011)
  37. Waqas Haider, Muhammad Sharif, Mudassar Raza,Achieving accuracy in early stage tumor identificationsystems based on image segmentation and 3D structureanalysis, Computer Engineering and Intelligent Systems,2(6), (2011)
  38. John Kotre, Image processing In the fight against breastcancer”, Engineering science and Educational Journal, 41-46,(1993)
  39. Punal.M.Arabi, S. Muttan, R.Jenkin Suji, Imageenhancement for detection of early breast carcinomabyexternal irradiation, IEEE International conference onComputing, Communication and Networking Technologies,1-9 (2010).
  40. Saleh Alshehri1, Adznan Jantan1 et. al., A UWB ImagingSystem to Detect Early Breast Cancer in HeterogeneousBreast Phantom, IEEE International Conference onElectrical, Control and Computer Engineering, 238-242(2011)
  41. Magda El-Shenawee, Electromagnetic Imaging for BreastCancer Research, IEEE Bio Wireless, 55-58(2011)
  42. Kother Mohideen, Arumuga Perumal, Krishnan andMohamed Sathik, Image De noising and EnhancementUsing Multi wavelet with Hard Threshold In DigitalMammographic Images, International Arab Journal of Technology,2(1),(2011)
  43. Description of image wavelet denoising, Journal of HarbinUniversity of Science and Technology, 5, 8-12(2000)
  44. Strela V., Portilla J., Simoncelli Ep., Image Denoising ViaA Local Gaussian Scale Mixture Model in the WaveletDomain, Proceedings of the Spie 45th Annual Meeting,(2002)
  45. National Breast Cancer Foundation, U.S.A [online]Available:http://www.nationalbreastcancer.org/early_detection/index.html (2013)
  46. F.A.Cardillo, A.starita, D.Caramella, and A.Cilotti, Aneural tool for breast cancer detection and classification inMRI, Proceedings of the 23rd Annual EMBS internationalConference, 2733-36 (2001)
  47. S.Heywang-Kobrunner and R. Beck, Contrast-enhancedMRI of the Breast, springer-Verlag, (1996)
  48. Yao Yao, Segmentation of Breast Cancer Mass inMammograms and Detection using magnetic resonanceimaging, M.S. thesis, School of Electrical and ElectronicEngineering, Nanyang Technological University, Nanyang,Singapore, (2004)
  49. Rafael C. Gonzalez and Richard E.Woods, Digital ImageProcessing, (2002)
  50. H. Ghayoumizadeh, I.Abaspur Kazerouni, j.Haddadina ,Distinguish Breast Cancer Based on Thermal Features inInfrared Images, Canadian journal on image processingand computer vision,2(6), (2011)
  51. Pragati Kapoor, Dr. S.V.A.V. Prasad, Image Processing forEarly Diagnosis of Breast Cancer Using Infrared Images,International Conference on Computer and AutomationEngineering (ICCAE), 3, 564-566 (2010)
  52. CHENBao-ping, MA Zeng-qiang, Automated ImageSegmentation and Asymmetry Analysis for Breast UsingInfraredImages, International Workshop on EducationTechnology and Training & International Workshop onGeo science and Remote Sensing, (2008)
  53. N. Scales, C. Herry, M. Frize, Automated ImageSegmentation for Breast Analysis Using Infrared Images,International Conference of the IEEE EMBS SanFrancisco, 3, 1-5 (2004)
  54. Ng, E. Y. K., and Kee, E. C., Advanced integratedtechnique in breast cancer thermography, InternationalConference of Engineering in Medicine and BiologySociety, 710 – 713(2006)
  55. Koay, J., Herry, C. H., &Frize, M. Analysis of BreastThermography with Artificial Neural Network, InProceedings 26th IEEE EMBS Conf., 1159– 1162(2004)
  56. W.E. Snyder, H. Qi Machine Vision Cambridge UniversityPress, (2004)
  57. B.F. Jones., A reappraisal of the use of infrared thermalimage analysis in medicine, IEEE Transactions on MedicalImaging,17(6), 1019-1027 (1998)
  58. Ng, E.Y.K., A review of thermography as promisingnoninvasive detection modality for breast tumor, Int. J.Therm. Sci, 849–859 (2009)
  59. N. Scales, C. Herry, M. Frize, Automated ImageSegmentation for Breast Analysis Using Infrared Images,Conf Proc IEEE Eng Med BiolSoc, 3, 1737-40 (2004)
  60. D. Tsai, A machine vision approach to detecting andinspecting circular parts, Int J Adv Manuf. Technol.,15,217–221 (1999)
  61. KL Williams, BH Phillips, PA Jones, SA Beaman, PJFleming, Thermography in screening for breast cancer, JEpidemiol Community Health,44(2), 112-3 (1990)
  62. J. Koay, C. Herry, M. Frize, Analysis of BreastThermography with an Artificial Neural Network,2, 1159-6, (2004)
  63. C Lipari and J Head, Advanced infrared image processingfor breast cancer risk assessment, Proc 19th Int. Conf.IEEEEMBS, 673-676 (1997)
  64. Essafij, R. Doughrij,s. M'hiri k. Ben romdhane and f.Ghorbelm, Segmentation and classification of breast cancer cells in histological images,International Conference of theIEEE Engineering in Medicine and Biology Society, (2010)
  65. P. Phukpattaranont and P. Boonyaphiphat, Segmentation ofCancer Cells in Microscopic Images using Neural Network and Mathematical Morphology,SICE-ICASE International Joint Conference, (2006)
  66. P. Phukpattaranont, P. Boonyaphiphat, and et al.Segmentation of cancerous cell image using local adaptivethresholding and morphological operators, Conference on Artificial Life and Robotics, 68-71 (2006)
  67. M. Veta1, A. Huisman, M.A. Viergever, P.J. van Diest,J.P.W. Pluim, MARKER-Controlled WatershedSegmentation of Nuclei in H&E Stained Breast CancerBiopsy Images, International Symposium on BiomedicalImaging: From Nano to Macro, (2011)
  68. S. Naik et al., Automated gland and nuclei segmentation forgrading of prostate and breast cancer histopathology, IEEEInternational Symposium on Biomedical Imaging (ISBI),284–287 (2008)
  69. P.W. Huang and Y.H. La, Effective segmentation and classification for HCC biopsy images, Pattern Recognitionvol,43(4), 1550–1563 (2010)
  70. A.C. Ruifrok and D.A. Johnston, Quantification of histochemical staining by color de convolution, Analytical andquantitative cytology and histology, 23(4), 291– 299 (2001)
  71. Xu Liu, ZhiminHuo and Jiwu Zhang, Automatedsegmentation of breast lesions in ultrasound images, IEEEEngineering in Medicine and Biology 27th AnnualConference, 7, 7433-5 (2005)
  72. T. Pun, Entropic thresholding: A new approach”, Comput.Graphics Image Process, 16, 210-239 (1981)
  73. J. N. Kapur and P. K. Sahoo and A. K. C. Wong, A newmethod for gray-level picture thresholding using theentropy of the histogram, Comput. Graphics ImageProcess, 29, 273–285 (1985)
  74. N. R. Pal, On minimum cross entropy thresholding, PatternRecognition, 26, 575-580 (1996)
  75. P. K. Sahoo and S. Soltani and A. K. C. Wong, A survey of thresholding techniques, Comput. Vis. Graphics ImageProcess, 41, 233-260 (1988)
  76. Paulo S. Rodrigues, Gilson A. Giraldi, Ruey-Feng Chang,Jasjit S. Suri, Non-Extensive Entropy for CAD Systems ofBreast Cancer Images, Brazilian Symposium on ComputerGraphics and Image Processing, (2006)
  77. Sangyun Park, Hyoun-Joong Kong, Woo Kyoung Moon,and Hee Chan Kim, Segmentation of Solid Nodules in Ultrasonographic Breast Image Based on Wavelet Transform,IEEE Engineering in Medicine and Biology Society, (2007)
  78. Etienne von Lavante, J. Alison Noble, Segmentation ofbreast cancer masses in ultrasound using radio frequencysignal derived parameters and strain estimates,International Symposium on Biomedical Imaging: FromNano to Macro, 536 – 539, (2008)
  79. E von Lavante and J. A. Noble, Improving the contrast ofbreast cancer masses, MICCAI, 1, 153–160 (2007)
  80. Kyung-Hoon Hwang, Jun Gu Lee, Jong Hyo Kim, HyungJiLee, Kyong-Sik Om, Minki Yoon, WonsickChoe,Computer aided diagnosis (CAD) of breast mass on ultrasonography and scinti mammography, 2005 Proceedings of7th International Workshop on HEALTHCOM, 187- 189,(2005)
  81. M. Geiger, Computer-aided diagnosis of breast lesions inmedical images, Comput. Med., 39–45, (2000)
  82. Anant Madabhushi and Dimitris N. Metaxas, CombiningLow-, High-Level and Empirical Domain Knowledge forAutomated Segmentation of ultrasonic breast lesions, IEEEtransactions on medical imaging, 22(2), (2003)
  83. K. J. W. Taylor et al., Ultrasound as a complement tomammography and breast examination to characterizebreast masses, Ultrasound Med.Biol., 28(1), 19–26 (2002)
  84. Y.H. Chou, C.M. Tu G.S. Hung S.C. Wu T.Y. Chang andH. K. Chiang, Stepwise logistic regression analysis oftumor contour features for breast ultrasound diagnosis,Ultrasound in Med.& Biol., 27(11), 1493-1498 (2001)
  85. F. lefebvre, M. Meunier, F. Thibault, P. Laugier, and G.Berger, Computerized Ultrasound B-scan Characterizationof Breast Nodules, ultrasound in med. & biol., 26, 1421-1428, (2000)
  86. R. Sivaramakrishnan, K. A. Powell, M. L. Lieber, W. A.Chilcote, and R. Shekhar, Texture analysis of lesions inbreast ultrasound images, Computerized Med. Imaging &Graphics, 26, 303–307, (2002)
  87. R.O. Duda and P. E. Hart, Pattern Classification and SceneAnalysis, (1997)
  88. D. Guliato, R. Rangayyan, W. Carnielli, J. Zuffo, and J. E.L. Desautels, Segmentation of breast tumors inmammograms by fuzzy region growing, Proc. IEEEEngineering in Medicine and Biology Society, 2, 1002-1005(1998)
  89. J. K. Udupa and S. Samarsekera, Fuzzy connectedness andobject definition: Theory, algorithms, and applications 58, 246-261 (1996)
  90. S. K. Moore, Better breast cancer detection, IEEE Spectr.,38, 50–54 (2001)
  91. Katsuhiro Kida, Tsutomu Kajitani, Sachiko Goto, YokoTsuji , Toshinori Maruyama Yoshiharu Azuma, Detectionof Calcification Using High-pass Filtered Phase Image inMagnetic Resonance Imaging for Breast Cancer Screening, International Conference on Imaging Systems andTechniques (IST), 224-228 (2011)
  92. George G. Cheng, Yong Zhu, and Jan Grzesik, MicrowaveImaging for Medical Diagnosis, General Assembly andScientific Symposium, 1-4 (2011)
  93. S. Petroudi and M. Brady, Breast density segmentationusing texture, International Workshop on DigitalMammography, 616-625 (2006)
  94. H. D. Li, M. Kallergi, L. P. Clarke, V. K. Jain, and R. A.Clark, Markov random field for tumor detection in digitalmammography, IEEE Trans. Med Imaging, 14, 565-76(1995)
  95. Christina M. Shafer, Victoria L. Seewaldt, Joseph Y. Lo, Validation of a 3D hidden-Markov model for breast tissuesegmentation and density estimation from MR and tomosynthesis images, Biomedical Sciencesand EngineeringConference, 1-4 (2011)
  96. J.R.Parker, Algorithms for Image Processing and ComputerVision. New York: John Wiley & Sons, 2, (1997)
  97. R.M. Haralick and L. G. Shapiro, Computer and RobotVision, 11, (1992)
  98. Teshnehlab M., Aliyari Shoorehdeli M. and Keyvanfard F.,Feature selection and classification of breast MRI lesionsbased on Multi classifier, International Symposium onArtificial Intelligence and Signal Processing (AISP), 54-58(
  99. R. Rotman, Recent Advances using Microwaves forImaging, Hyperthermia and Interstitial Ablation of BreastCancer Tumors, Microwaves, Communications, Antennasand Electronics Systems, 1-4 (2011)