CONTENT-BASED IMAGE RETRIEVAL TECHNIQUES FOR MAMMOGRAPHIC IMAGES USING SOFT COMPUTING TECHNIQUES

Article Sidebar

PDF
Published: Dec 25, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i9.5132
Keywords:
Mammograms, BIRADS (Breast Image Reporting and Data System), Breast cancer, Support Vector Machines, Pectoral Muscle

Main Article Content

Sumit Chopra
KC College of Engineering
Dr. V. K. Banga

Abstract

Content-based image Retrieval has been one of the lively research in the field of the computer science over the last decade or so. Many programs and tools have been used to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. But still a lot of work needs to be done in case of mammographic images. Breast cancer has been one of the causes of the increasing death of the European women. Early diagnosis of the breast cancer can save one’s life while incorrect diagnosis can lead a patient to unwanted stress and treatment. In this paper, a survey of the techniques used for the various steps of Content-Based Image Retrieval of Mammograms is done. Section 1 gives the general introduction to Content-Based Image Retrieval for Mammographic Images. Section 2 details about the various techniques used for pre-processing of the Mammographic images. Section 3 provided a survey of the various algorithms used for segmentation of the pectoral muscles from the mammogram. Section 4 briefs about various feature extraction and selection methods for mammographic images. Section 5 survey the various algorithms for detection and classification of abnormalities in mammograms.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 8 No. 9 (2017): NOVEMBER-DECEMBER 2017
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

K. Beaver and G. Witham, “Information needs of the informal careers of women treated for breast cancer,†Elsevier European Journal of Oncology Nursing , Vol. 11, No. 1, pp. 16-25, February 2007.

M. O. Guld , C. Thies , B. Fischer , and T. M. Lehmann , “A generic concept for the implementation of medical image retrieval system,†Elsevier International Journal of Medical Informatics , Vol. 26, No. 2-3, pp. 252- 259, Feburary-March, 2007.

J. Suckling , J. Parker , D. R. Dance , S. M. Astely, I. Hutt, C.R.M. Boggis , I. Ricketts, E. Stamatakis, N, Cerneas, S. L. Kok, P. Taylor, D. Betal, and J. Savage “The Mammographic Image Analysis Society digital mammogram database,†Proc. Workshop on Digital Mammography, Vol. 3, No. 2, pp. 211-221,1994.

M. Keath, K. Bowyer , D. Kopans , R. Moore, and P.J. Kegelmeyer, “The Digital Database for screening Mammography,†Proc. International Workshop on Digital Mammography, Vol. 5, No. 2, pp. 212-218, 2000.

H. D. Cheng, and H. Xu , “A noval fuzzy logic approach to mammogram contrast enhancement,†Elsevier Information Sciences , Vol. 148, No. 1-4, pp. 167-184, 2002.

S. Sundram , K. Ramar , N. Arumugam, and G.Prabin, “Histogram Modified Local Contrast Enhancement for mammogram images,†Applied Soft Computing , Vol. 11, No. 8, pp. 5809-5816, December 2011.

I. K. Maitra , S. Nag, and S.K. Bandyopadhay ,“Technique for preprocessing of digital mammograms,†Computer Methods and Programs in Biomedicine , Vol. 107 , No. 2, pp. 175-188 , August 2012.

S. Anand and S. Gayathri,“Mammogram image enhancement by two stage adaptive histogram equalization,†Elsevier Optik – International Journal for Light and Electronic Optics , Vol. 126, No. 21, pp. 3150- 3152 , November 2015.

J. Jiang , B. Yao, and A . M. Wason “ Integration of fuzzy logic and structure tesnor towards mammogram contrast enhancement,†Computerized Medical Imaging and Graphics , Vol. 29 , pp. 83-90 , 2005

V. Bhateja, M. Misra, and S. Urooj, “Human visual system based unsharp masking for enhancement of mammographic images,†Elsevier Journal of Computational Science , Vol. 21, pp. 387-393 , July 2017.

S. Jenifer, S. Parasuraman, and A. Kadirvelu, “ Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast limited adaptive histogram equalization algorithm,†Applied soft computing , Vol. 42, pp. 167-177 , 2016.

He W. , Hogg P. , Juette A. , Denton E. R. E. , Zwiggelaar R. “ Breast image preprocessing for mammographic tissue segmentation,†Computers in Biology and Medicine , Vol. 67, No. C, pp. 61-73, 2015.

N. A. Najdawi, M. Biltawi, and S. Tedmori, “Mammogram image visual enhancement, mass segmentation and classification,†Elsevier Applied Soft Computingâ€, Vol. 35, No. C, pp. 175-185, October 2015.

F. Ma , M. Bajger , J. P. Slavotinek , and M. J. Bottema, “Two graph theory based methods for identifying the pectoral muscles in mammograms,†Elsevier Pattern Recognition , Vol. 40 , No. 9 , pp. 2592- 2602 , September 2007.

P. Kus and I. Karagoz , “ Fully automated gradient based breast boundary detection for digitized X – ray mammograms,†Computers in Biology and Medicine , Vol. 42 , No. 1 , pp. 75-82, January 2012.

M. Mustra and M. Grgic “ Robust automatic breast and pectoral muscle segmentation from scanned mammograms, †Elsevier Signal Processing , Vol. 93 , No. 10 , pp. 2817-2827 , October 2013.

Y. Li, H. Chen, Y. Yang, and N. Yang “ Pectoral muscle segmentation in mammograms based on homogeneous texture and intensity deviation, †Vol. 46 , No. 3, pp. 681-691 , March 2013.

S. Sreedevi and E. Sherly, “ A Novel Approach for Removal of pectoral muscles in Digital Mammogram,†Proc. International Conf. on International Conference on Information and Communication Technologies , Vol. 46 , No. , pp. 1724-1731 ,2014.

P.S.Vikhe, and V. R. Thool, “Intensity based Automatic Boundary Identification of PectoralMuscles in Mammograms,†Proc. 7th International Conference on Communication, computing and Virtualization, Vol. 79, Issue , pp. 262-269 , 2016

A. Rampun , P. J. Morrow, B. W. Scotney, and J.Winder , “ Fully automated breast boundary and pectoral muscle segmentation in mammograms,†, Elsevier Artifical Intelligence in Medicine , Vol. 79, pp. 28-41, 2017.

K. Ganesan , U. R. Acharaya, K. C. Chua, L. C. Min, and K. T. Abraham “ Pectoral muscle segmentation: A review,†Computer Methods and Programs in Biomedicine , Vol. 110 , No. 1, pp. 48-57 , April 2013.

I. Buciu and A. Gascad “ Directional features for automatic tumor classification of mammogram images,†, Biomedical Signal Processing and Control, Vol. 6 , No. 4, pp. 370-378, October 2011.

F. Moayedi , Z. Azimifar, R. Boostani, and S. Katebi “ Contourlet- based mammography mass classification using the SVM family,â€Elsevier Computes in Biology and Medicine , Vol. 40 , No. 4, pp. 373-380 , 2010.

L. Nanni, S. Brahnam , and A. Lumini, “ A very high performing system to discriminate tissues in mammograms as malignant and beingn, †Elsevier Expert System With Applications, Vol. 39 , No. 2, pp. 1968-1971, 2012.

H. D. Cheng , X. J. Shi, R. Min, L. M. Hu , X. P. Cai, and H. N. Du , “Approaches for automatic detection and classification of masses in mammograms,â€Elsevier Pattern Recognition , Vol. 39 , No. 4, pp. 646-668 , April 2006.

B. Verma and P. Zhang, “ A novel – genetic algorithm to find the most significant combination of features in digital mammograms,†Applied Soft Computing , Vol. 7, No. 2, pp. 612-625, March, 2007.

K. Geetha, K. Thanushkodi , and A. K. Kumar,“New particle Swarm Optimization For Feature Selection and Classification of Microcalcifications in Mammograms,â€Proc. International Conference on Signal Processing, Communication and Networking, pp. 458-463, 2008.

M. K. Pal, B. Bhowmick, S. K. Patel , S. Pal, and J. Das, “A multistage neural network aided system for detection of microcalcifications in digitized mammogramsâ€, Neurocomputing , Vol. 71, No. 13, pp. 2625-2634, August 2008.

M. Sameti , R. K. Ward, J. K. Parks , and B. Palcic “Image Feature Extraction in the Last Screening Mammograms Prior to Detection of Breast Cancer,†IEEE Journal of Selected Topics in Signal Processing , Vol. 3, No. 1, pp. 46-52, Febuary 2009.

M. Suganthi and M. Madheswaran, “An improved medical Decision Support System to Identifiy the Breast Cancer Using Mammogram,†Journal of Medical System, Vol. 36, No. 1, pp. 79-91, Feburary 2012.

W. Han, J. Dong, Y. Guo, M. Zhang, and J. Wang, “Identification of masses in digital mammogram using optimal set of features,†Proc. IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communication , pp. 1763-1768, 2011.

A. K. Mohanty, M. R. Senapati, and S. K. Lenka, “A novel image minning technique for classification of mammograms using hybrid feature selection,†Neurocomputing and Applications , Vol. 22, No. 6, pp. 1151-1161, May 2013.

H. Liu and L. Yu, “Towards Integrating Feature Selection Algorithms for Classification and Clustering,†Proc. IEEE Transactions on Knowledge and Data Engineering , Vol. 17 , No. 4, pp. 491- 502 , April 2005.

V. V. B. Smallenburg , L. E. M. Duijm , G. J. D. Heetan, J. H. Groenewoud , F. H. Jansen, J. Fracheboud, M. L. Plaisier , H. J. V. D. Nagtegaal, and J. M. Broeders , “ Two – view versus single – view mammography at subsequent screening in a region of the Dutch breast screening programme, †European Journal of Radiology , Vol. 81 , No. 9 , pp. 2189- 2194, September 2012.

S. Joo , W. K. Moon, and H. C. Kim “Computer- aided Diagnosis of Solid Breast Nodules on Ultrasound with Digital Image Processing and Artificial Neural Network,†Proc. International Conference IEEE Engineering in Medicine and Biology Society , pp. 1397-1400 , San Francisco , CA,September 2004.

C. Varela , P. G. Tahoces , A. J. Mendez, M. Souto, and J. J. Vidal, “ Computerized detection of breast masses in digital mammograms, †Elsevier Journal of Computers in Biology and Medicine, Vol. 37 , No. 2 , pp. 214- 226 , February 2007.

A. R. Dominguez and A. K. Nandi , “ Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation and region selection,†Elsevier Journal of Computerized Medical Imaging and Graphics , Vol. 32 , No. 4, pp. 304-315 , 2008 .

K. Rajakumar and S. Muttan, “ Medical Image retrieval using modified DCT, †Elsevier Procedia Computer Science , Vol. 2 , No. , pp. 298-302, 2010.

T. Balakumaran ,I. L. A. Vennila, and C. G. Shankar , “Micro calcificationDetection in Digital Mammograms using Novel Filter bank,†Proc. International Conference and Exhibition on Biometrics Technology , Vol. 2 , Issue , pp. 272-282, 2010.

T. S. Subashini, V. Ramalingam, and S. Palanivel “Automated assessment of breast tissue density in digital mammograms,†Elsevier Computer Vision and Image Understanding , Vol. 114, No. 1, pp. 33-43 , January 2010

J. E. E. D. Oliveira, A. M. C. Machado, G. C. Chavez, A. P. B. Lopes, T. M. Deserno, and A. D. A. Araujo, “ MammoSys : A content-based image retrieval system using breast density patterns,†Elsevier Computer Methods and Programs in Biomedicine , Vol. 99 , No. 3, pp. 289-297 , March 2010.

G. Quellec, M. Lamard , L. Bekri, and G. Cazuguel, “Medical Case Retrieval From a Committee of Decision Trees†Proc. IEEE Transactions on Information Technology in Biomedicine , Vol. 14, No. 5, pp. 1227- 1235, Sept. 2010.

C. H. Wei , Y. Li, and P. J. Huang, “ Mammogram retrieval through machine learning within BI-RADS standards†Elsevier Journal of Biomedical Informatics , Vol. 44, No. 4, pp. 607-614 , August 2011.

M. A. Mazurowski ,J. Y. Lo , and B. P. Harrawood, and G. D. Tourassi, “Mutual information based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis, †Elsevier Journal of Biomedical Informatics,Vol. 44, No. 5, pp. 815-823 , San Diego , USA, October 2011.

W. B. Sampaio , E. M. Diniz, A. C. Silva ,A. C. D. Paiva, and M. Gattass,“Detection of masses in mammogram images using CNN, geostatistic functions and SVM, †Elsevier Computers in Biology and Medicine , Vol. 41, No.8 , pp. 653-664, 2011.

C. H. Wei , S. Y. Chen , and X. Liu, “ Mammogram retrieval on similar mass lesions, â€Elsevier Computer Methods and Programs in Biomedicine, Vol. 106 , No. 3, pp. 234-248, 2012 .

L. Tsochatzidis, K. Zagoris, M. Savelonas, and I. Pratikakis, “ SVM – based CBIR of Breast Masses on Mammograms, †Proc. European Conference on Artificial Intelligence (ECAI) , Vol. , No. , pp. 26-30 ,2014.

Y. Z. Hsieh, C. M. Su , and P. C. Wang “ A PSO – based rule extractor for medical diagnosis,†Elsevier Journal of Biomedical Informatics , Vol. 49 , pp. – 53- 60, June 2014.

J. Dheeba, N. A. Singh, and S. T. Selvi ,“ Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimize wavelet neural network approach,†Elsevier Journal of Biomedical Informatics, Vol. 49, pp. 45-52, February 2014

K. Seetharaman , and S. Sathiamoorthy “ A unified learning framework for content based medical image retrieval using a statistical model†, Journal of King Saud University- Computer and Information Sciences , Vol. 28, No. 1, pp. 110-124, January , 2016 .

S. R. Dubey , S. K. Singh , and R. K. Singh , “ Local Bit-plane decoded pattern: A Novel Feature Descriptor for Biomedical Image Retrieval,†IEEE Journal of Biomedical and Health Informatics , Vol. 20 , No. 4, pp. 1139-1147 , 2016

A. K. Singh, and B. Gupta, “A Novel Approach for Breast Cancer Detection and Segmentation in Mammogram,†Proc. Eleventh International Multi Conference on Information Processing , Vol. 54 , No.2 , pp. -676-682 , 2015.

P. V. Anuradha , B. R. Jose, and J. Mathew ,“ Improved Segmentation of Suspicious Regions of Masses in Mammograms by Watershed Transform,†Elsevier Procedia Computer Science , Vol. 46 , pp. 1483-1490 , 2015

M. Pratiwi, Alexander, J. Harefa, and S. Nanda , “Mammograms Classification using Gray –level Co-occurrence Matrix and Radial Basis Function Neural Network, †Proc. International Conference on Computer Science and Computational Intelligence (ICCSCI 2015) , Elsevier Procedia Computer Science , Vol. 59 , No. , pp. 83-91, 2015 .

A. S. Setiawan , Elysia , J. Wesley, and Y. Purnama, “Mammogram Classification using Law’s Texture Energy Measure and Neural Networkâ€, Proc. International Conference on Computer Science and Computational Intelligence (ICCSCI 2015) , Elsevier Procedia Computer Science , Vol. 59 , pp. 92-97, 2015.

H. Muller, W. Muller, D. M. Squire , S. M. Maillet, and T. Pun, “ Performance evaluation in content based image retrieval : overview and proposals,â€Elsevier Pattern Recognition Letters, Vol. 22, No. , pp. 593-601, 2001.