{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:02:17Z","timestamp":1772766137834,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T00:00:00Z","timestamp":1515369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["690238"],"award-info":[{"award-number":["690238"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region (    F G  D  r o i      ) instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi-orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, the Support Vector Machine classifier is used to perform the classification, and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to     86.13 %     and     82.02 %     accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state-of-the-art in the literature.<\/jats:p>","DOI":"10.3390\/jimaging4010014","type":"journal-article","created":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T12:26:02Z","timestamp":1515414362000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8340-2408","authenticated-orcid":false,"given":"Andrik","family":"Rampun","sequence":"first","affiliation":[{"name":"School of Computing, Ulster University, Coleraine BT52 1SA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan","family":"Scotney","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Coleraine BT52 1SA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Morrow","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Coleraine BT52 1SA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing, Ulster University, Jordanstown, Newtownabbey BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Winder","sequence":"additional","affiliation":[{"name":"School of Health Sciences, Ulster University, Newtownabbey BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,8]]},"reference":[{"key":"ref_1","unstructured":"Cancer Research UK (2017, January 06). Breast cancer statistics. Available online: http:\/\/www.cancerresearchuk.org\/health-professional\/cancer-statistics\/statistics-by-cancer-type\/breast-cancer."},{"key":"ref_2","unstructured":"Breast Cancer (2017, January 06). U.S. Breast Cancer Statistics. Available online: http:\/\/www.breastcancer.org\/symptoms\/understand_bc\/statistics."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TITB.2007.903514","article-title":"A Novel Breast Tissue Density Classification Methodology","volume":"12","author":"Oliver","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_4","unstructured":"Bovis, K., and Singh, S. (2002, January 7\u201310). Classification of Mammographic Breast Density Using a Combined Classifier Paradigm. Proceedings of the 4th International Workshop on Digital Mammography, Nijmegen, Netherlands."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1007\/s10278-015-9777-5","article-title":"Breast Density Analysis Using an Automatic Density Segmentation Algorithm","volume":"28","author":"Oliver","year":"2015","journal-title":"J. Digit. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"225","DOI":"10.3233\/IDA-2010-0418","article-title":"Fuzzy-rough approaches for mammographic risk analysis","volume":"14","author":"Jensen","year":"2010","journal-title":"Intell. Data Anal."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Denton, E., and Zwiggelaar, R. (2011, January 15\u201317). Local feature based mamographic tissue pattern modelling and breast density classification. Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China.","DOI":"10.1109\/BMEI.2011.6098279"},{"key":"ref_8","unstructured":"Bosch, A., Munoz, X., Oliver, A., and Mart\u00ed, J. (2006, January 17\u201322). Modeling and Classifying Breast Tissue Density in Mammograms. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Oliver, A., Denton, E., and Zwiggelaar, R. (2013). Automated Mammographic Risk Classification Based on Breast Density Estimation. Pattern Recognition and Image Analysis; Volume 7887 of the series Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-642-38628-2_28"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1002\/1097-0142(197605)37:5<2486::AID-CNCR2820370542>3.0.CO;2-8","article-title":"Risk for breast cancer development determined by mammographic parenchymal pattern","volume":"37","author":"Wolfe","year":"1976","journal-title":"Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.bspc.2011.03.008","article-title":"Mammographic Image Segmentation and Risk Classification Based on Mammographic Parenchymal Patterns and Geometric Moments","volume":"6","author":"He","year":"2011","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_12","unstructured":"Petroudi, S., Kadir, T., and Brady, M. (2003, January 17\u201321). Automatic Classification of Mammographic Parenchymal Patterns: A Statistical Approach. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), Cancun, Mexico."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13058-015-0626-8","article-title":"Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: Comparison of fully automated area and volumetric density measures in a case-control study with digital mammography","volume":"17","author":"Keller","year":"2015","journal-title":"Breast Cancer Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rampun, A., Morrow, P.J., Scotney, B.W., and Winder, R.J. (2017, January 11\u201313). Breast density classification in mammograms using local quinary patterns. Proceedings of the Annual Conference on Medical Image Understanding and Analysis MIUA 2017: Medical Image Understanding and Analysis, Edinburgh, UK.","DOI":"10.1007\/978-3-319-60964-5_32"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","article-title":"INbreast: Toward a full-field digital mammographic database","volume":"19","author":"Moreira","year":"2011","journal-title":"Acad. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1088\/0031-9155\/41\/5\/007","article-title":"Automated analysis of mammographic densities","volume":"41","author":"Byng","year":"1996","journal-title":"Phys. Med. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"362","DOI":"10.7305\/automatika.53-4.281","article-title":"A Novel Breast Tissue Density Classification Methodology. Breast Density Classification Using Multiple Feature Selection","volume":"53","year":"2012","journal-title":"Automatika"},{"key":"ref_18","unstructured":"Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., and Ricketts, I. (1994). The mammographic image analysis society digital mammogram database. Proc. Excerpta Med. Int. Congr. Ser., 375\u2013378."},{"key":"ref_19","unstructured":"Tamrakar, D., and Ahuja, K. (arXiv, 2017). Density-Wise Two Stage Mammogram Classification Using Texture Exploiting Descriptors, arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.compbiomed.2014.05.008","article-title":"A new feature extraction framework based on wavelets for breast cancer diagnosis","volume":"51","author":"Ergin","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.asoc.2016.04.004","article-title":"A new feature extraction method based on multiresolution representations of mammograms","volume":"44","author":"Gedik","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1109\/TMI.2016.2532122","article-title":"Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring","volume":"35","author":"Kallenberg","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahn, C.K., Heo, C., Jin, H., and Kim, J.H. (2017, January 3). A Novel Deep Learning-based Approach to High Accuracy Breast Density Estimation in Digital Mammography. Proceedings of the SPIE Medical Imaging 2017: Computer-Aided Diagnosis, Orlando, FL, USA.","DOI":"10.1117\/12.2254264"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.cmpb.2015.12.014","article-title":"Representation learning for mammography mass lesion classification with convolutional neural networks","volume":"127","author":"Arevalo","year":"2016","journal-title":"Comput. Methods Program. Biomed."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Wang, Y., Yan, S., Tan, M., Cheng, S., Liu, H., and Zheng, B. (2016, January 24). An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. Proceedings of the SPIE Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, CA, USA.","DOI":"10.1117\/12.2216275"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"24454","DOI":"10.1038\/srep24454","article-title":"Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans","volume":"15","author":"Cheng","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neucom.2016.02.060","article-title":"A deep feature based framework for breast masses classification","volume":"197","author":"Jiao","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.artmed.2017.06.001","article-title":"Fully Automated Breast Boundary and Pectoral Muscle Segmentation in Mammograms","volume":"79","author":"Rampun","year":"2017","journal-title":"Artif. Intell. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hadid, A., Pietikainen, M.K., Zhao, G., and Ahonen, T. (2011). Computer Vision Using Local Binary Patterns, Springer.","DOI":"10.1007\/978-0-85729-748-8"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, X., and Triggs, B. (2007). Enhanced local texture feature sets for face recognition under difficult lighting conditions. Analysis and Modelling of Faces and Gestures, Springer.","DOI":"10.1007\/978-3-540-75690-3_13"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rampun, A., Morrow, P.J., Scotney, B.W., and Winder, J. (2017, January 21\u201323). A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography. Proceedings of the International Conference on Innovation in Medicine and Healthcare, Vilamoura, Portugal.","DOI":"10.1007\/978-3-319-59397-5_31"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.artmed.2010.02.006","article-title":"Local binary patterns variants as texture descriptors for medical image analysis","volume":"49","author":"Nanni","year":"2010","journal-title":"Artif. Intell. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3834","DOI":"10.1016\/j.patcog.2012.04.003","article-title":"Discriminative features for feature description","volume":"45","author":"Gio","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.procs.2016.07.026","article-title":"A quantitative study of texture features across different window sizes in prostate t2-weighted mri","volume":"90","author":"Rampun","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5412","DOI":"10.1118\/1.4962031","article-title":"Computer aided diagnosis of prostate cancer: A texton based approach","volume":"43","author":"Rampun","year":"2016","journal-title":"Med. Phys."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4796","DOI":"10.1088\/0031-9155\/61\/13\/4796","article-title":"Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone","volume":"61","author":"Rampun","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rampun, A., Morrow, P.J., Scotney, B.W., and Winder, R.J. (2017, January 5\u20137). Breast density classification in mammograms using local ternary patterns. Proceedings of the International Conference Image Analysis and Recognition ICIAR 2017: Image Analysis and Recognition, Montreal, QC, Canada.","DOI":"10.1007\/978-3-319-59876-5_51"},{"key":"ref_41","unstructured":"Aly, M. (2017, December 04). Survey on Multiclass Classification Methods. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.175.107."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.patrec.2007.05.001","article-title":"Approximating the multiclass ROC by pairwise analysis","volume":"28","author":"Landgrebe","year":"2007","journal-title":"Pattern Recognit. Lett."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:50:29Z","timestamp":1760194229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,8]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["jimaging4010014"],"URL":"https:\/\/doi.org\/10.3390\/jimaging4010014","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,8]]}}}