乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:28:45Z","timestamp":1740122925019,"version":"3.37.3"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2017,9,5]],"date-time":"2017-09-05T00:00:00Z","timestamp":1504569600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61403287","61472293","31201121","61572381","61273303"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2014M552039"],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2014CFB288"],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s11042-017-5150-7","type":"journal-article","created":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T18:46:45Z","timestamp":1504550805000},"page":"15773-15802","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multiple TBSVM-RFE for the detection of architectural distortion in mammographic images"],"prefix":"10.1007","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3467-5607","authenticated-orcid":false,"given":"Xiaoming","family":"Liu","sequence":"first","affiliation":[]},{"given":"Leilei","family":"Zhai","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,9,5]]},"reference":[{"key":"5150_CR1","unstructured":"A. C. O. R. (ACR) (1998) Illustrated breast imaging reporting and data system (BI-RADS). ed. Reston"},{"key":"5150_CR2","doi-asserted-by":"crossref","unstructured":"Anand S, Rathna RAV (2013) Architectural Distortion Detection in Mammogram using Contourlet Transform and Texture Features. Int J Comput Appl 74(5):12\u201319","DOI":"10.5120\/12880-9752"},{"key":"5150_CR3","doi-asserted-by":"crossref","unstructured":"Ayres FJ, Rangayyan RM (2004) Detection of architectural distortion in mammograms using phase portraits. In: Medical Imaging 2004. San Diego, California, United States, International Society for Optics and Photonics, p 587\u2013597","DOI":"10.1117\/12.530966"},{"issue":"1","key":"5150_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00301ED1V01Y201010BME038","volume":"5","author":"FJ Ayres","year":"2010","unstructured":"Ayres FJ, Rangayyan RM, Desautels JL (2010) Analysis of oriented texture with applications to the detection of architectural distortion in mammograms. Synth Lect Biomed Eng 5(1):1\u2013162","journal-title":"Synth Lect Biomed Eng"},{"key":"5150_CR5","doi-asserted-by":"crossref","unstructured":"Banik S, Rangayyan RM, Desautels JL (2011) R\u00e9nyi entropy of angular spread for detection of architectural distortion in prior mammograms. In: Medical Measurements and Applications Proceedings (MeMeA), 2011 I.E. International Workshop on. IEEE, pp. 609\u2013612","DOI":"10.1109\/MeMeA.2011.5966645"},{"key":"5150_CR6","doi-asserted-by":"crossref","unstructured":"Banik S, Rangayyan RM, Desautels JL (2012) Digital Image Processing and Machine Learning Techniques for the Detection of Architectural Distortion in Prior Mammograms. Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis: Medical Imaging Intelligence and Analysis, p 23","DOI":"10.4018\/978-1-4666-0059-1.ch002"},{"issue":"1","key":"5150_CR7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s11548-012-0681-x","volume":"8","author":"S Banik","year":"2013","unstructured":"Banik S, Rangayyan RM, Desautels JL (2013) Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms. Int J Comput Assist Radiol Surg 8(1):121\u2013134","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"5150_CR8","unstructured":"BCDR: Breast Cancer Digital Repository. Available: http:\/\/bcdr.inegi.up.pt\/ . Accessed 8\/21\/2017."},{"key":"5150_CR9","doi-asserted-by":"crossref","unstructured":"Ben-Ari R, Akselrod-Ballin A, Karlinsky L, Hashoul S (2017) Domain specific convolutional neural nets for detection of architectural distortion in mammograms. In: Biomedical Imaging (ISBI 2017), 2017 I.E. 14th International Symposium on, pp 552\u2013556. IEEE","DOI":"10.1109\/ISBI.2017.7950581"},{"key":"5150_CR10","unstructured":"Bowyer K et al (1996) The digital database for screening mammography. In: Third international workshop on digital mammography, vol 58, p 27"},{"issue":"6","key":"5150_CR11","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TPAMI.2016.2587647","volume":"39","author":"H Cevikalp","year":"2017","unstructured":"Cevikalp H (2017) Best fitting hyperplanes for classification. IEEE Trans Pattern Anal Mach Intell 39(6):1076\u20131088","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"5150_CR12","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1007\/s11042-013-1511-z","volume":"72","author":"DA Chandy","year":"2014","unstructured":"Chandy DA, Johnson JS, Selvan SE (2014) Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval. Multimed Tools Appl 72(2):2011\u20132024","journal-title":"Multimed Tools Appl"},{"key":"5150_CR13","doi-asserted-by":"crossref","unstructured":"DeSantis C, Fedewa S, Goding A, Kramer J, Smith R, Jemal A (2016). Breast cancer statistics, 2015: Convergence of incidence rates between black and white women. CA: Cancer J Clin 66(1):31-42","DOI":"10.3322\/caac.21320"},{"issue":"3","key":"5150_CR14","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TNB.2005.853657","volume":"4","author":"K-B Duan","year":"2005","unstructured":"Duan K-B, Rajapakse JC, Wang H, Azuaje F (2005) Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans NanoBiosci 4(3):228\u2013234","journal-title":"IEEE Trans NanoBiosci"},{"key":"5150_CR15","volume-title":"Pattern classification","author":"RO Duda","year":"2012","unstructured":"Duda RO, Hart PE, Stork DG (2012) Pattern classification. John Wiley & Sons, Hoboken"},{"issue":"2","key":"5150_CR16","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TMI.2003.823062","volume":"23","author":"RJ Ferrari","year":"2004","unstructured":"Ferrari RJ, Rangayyan RM, Desautels JL, Borges R, Frere AF (2004) Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23(2):232\u2013245","journal-title":"IEEE Trans Med Imaging"},{"key":"5150_CR17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/RBME.2012.2232289","volume":"6","author":"K Ganesan","year":"2013","unstructured":"Ganesan K, Acharya UR, Chua CK, Min LC, Abraham KT, Ng K-H (2013) Computer-aided breast cancer detection using mammograms: a review. IEEE Rev Biomed Eng 6:77\u201398","journal-title":"IEEE Rev Biomed Eng"},{"key":"5150_CR18","volume-title":"The nature of mathematical modeling","author":"NA Gershenfeld","year":"1999","unstructured":"Gershenfeld NA (1999) The nature of mathematical modeling. Cambridge university press, Cambridge"},{"key":"5150_CR19","doi-asserted-by":"crossref","unstructured":"Gidaris S, Komodakis N (2015) Object detection via a multi-region and semantic segmentation-aware cnn model. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1134\u20131142","DOI":"10.1109\/ICCV.2015.135"},{"issue":"5","key":"5150_CR20","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","volume":"35","author":"H Greenspan","year":"2016","unstructured":"Greenspan H, van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35(5):1153\u20131159","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"5150_CR21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11548-008-0276-8","volume":"4","author":"Q Guo","year":"2009","unstructured":"Guo Q, Shao J, Ruiz VF (2009) Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms. Int J Comput Assist Radiol Surg 4(1):11\u201325","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"5150_CR22","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"1\u20133","key":"5150_CR23","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1\u20133):389\u2013422","journal-title":"Mach Learn"},{"key":"5150_CR24","doi-asserted-by":"crossref","unstructured":"Hara T, Makita T, Matsubara T, Fujita H, Inenaga Y, Endo T, Iwase T (2006) Automated detection method for architectural distortion with spiculation based on distribution assessment of mammary gland on mammogram. In: International Workshop on Digital Mammography. Springer, Manchester, UK, pp 370\u2013375","DOI":"10.1007\/11783237_50"},{"issue":"1","key":"5150_CR25","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1148\/radiol.14131502","volume":"272","author":"S Hofvind","year":"2014","unstructured":"Hofvind S, Skaane P, Elmore JG, Sebu\u00f8deg\u00e5rd S, Hoff SR, Lee CI (2014) Mammographic performance in a population-based screening program: before, during, and after the transition from screen-film to full-field digital mammography. Radiology 272(1):52\u201362","journal-title":"Radiology"},{"key":"5150_CR26","doi-asserted-by":"crossref","unstructured":"Ichikawa T, Matsubara T, Hara T, Fujita H, Endo T, Iwase T (2004) Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis. In: Medical Imaging 2004 (12 May 2004). International Society for Optics and Photonics, p 920\u2013925","DOI":"10.1117\/12.535116"},{"key":"5150_CR27","doi-asserted-by":"crossref","unstructured":"Kamra A, Jain V, Singh S, Mittal S (2016) Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation. J Digit Imaging 29(1):104\u2013114","DOI":"10.1007\/s10278-015-9807-3"},{"issue":"1","key":"5150_CR28","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11042-015-3017-3","volume":"76","author":"S Khan","year":"2017","unstructured":"Khan S, Hussain M, Aboalsamh H, Bebis G (2017) A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimed Tools Appl 76(1):33\u201357","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"5150_CR29","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"R Khemchandani","year":"2007","unstructured":"Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"5150_CR30","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/S0025-6196(12)60194-3","volume":"68","author":"AM Knutzen","year":"1993","unstructured":"Knutzen AM, Gisvold JJ (1993) Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clin Proc 68(5):454\u2013460 Elsevier","journal-title":"Mayo Clin Proc"},{"key":"5150_CR31","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi T et al (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303\u2013312","journal-title":"Med Image Anal"},{"issue":"1","key":"5150_CR32","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/10798587.2017.1257544","volume":"23","author":"R Lakshmanan","year":"2017","unstructured":"Lakshmanan R, Jacob SM, Pratab T, Thomas C, Thomas V (2017) Detection of architectural distortion in mammograms using geometrical properties of thinned edge structures. Intell Autom Soft Comput 23(1):183\u2013197","journal-title":"Intell Autom Soft Comput"},{"issue":"7553","key":"5150_CR33","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"1","key":"5150_CR34","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Level Otsu","year":"1979","unstructured":"Level Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 9(1):62\u201366","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"3","key":"5150_CR35","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1118\/1.1644514","volume":"31","author":"H Li","year":"2004","unstructured":"Li H et al (2004) Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. Med Phys 31(3):549\u2013555","journal-title":"Med Phys"},{"issue":"3","key":"5150_CR36","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/JSYST.2013.2286539","volume":"8","author":"X Liu","year":"2014","unstructured":"Liu X, Tang J (2014) Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. Systems Journal, IEEE 8(3):910\u2013920","journal-title":"Systems Journal, IEEE"},{"key":"5150_CR37","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.neucom.2014.10.040","volume":"152","author":"X Liu","year":"2015","unstructured":"Liu X, Zeng Z (2015) A new automatic mass detection method for breast cancer with false positive reduction. Neurocomputing 152:388\u2013402","journal-title":"Neurocomputing"},{"issue":"1","key":"5150_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1687-6180-2015-1","volume":"2015","author":"X Liu","year":"2015","unstructured":"Liu X, Mei M, Liu J, Hu W (2015) Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method. EURASIP J Adv Signal Process 2015(1):1\u201313","journal-title":"EURASIP J Adv Signal Process"},{"key":"5150_CR39","unstructured":"Liu X, Zhai L, Zhu T, Zhang K (2016) A new feature selection method for the detection of architectural distortion in mammographic images. In: Eighth International Conference on Digital Image Processing (ICDIP 2016). International Society for Optics and Photonics, pp 1003341\u20131003341-5"},{"key":"5150_CR40","doi-asserted-by":"crossref","unstructured":"Liu X, Zhai L, Zhu T, (2016) Recognition of architectural distortion in mammographic images with transfer learning. In: Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), International Congress on. IEEE, pp 494\u2013498","DOI":"10.1109\/CISP-BMEI.2016.7852761"},{"key":"5150_CR41","doi-asserted-by":"publisher","unstructured":"Liu X, Zhu T, Zhai L, Liu J (2017) Mass classification of benign and malignant with a new twin support vector machine joint l 2, 1-norm. Int J Mach Learn Cybern. doi: 10.1007\/s13042-017-0706-4","DOI":"10.1007\/s13042-017-0706-4"},{"issue":"8","key":"5150_CR42","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/34.531803","volume":"18","author":"BS Manjunath","year":"1996","unstructured":"Manjunath BS, Ma W-Y (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837\u2013842","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5150_CR43","doi-asserted-by":"crossref","unstructured":"Matsubara T, Ichikawa T, Hara T, Fujita H, Kasai S, Endo T, Iwase T (2003) Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms. In: International Congress Series, vol 1256. Elsevier, pp 950\u2013955","DOI":"10.1016\/S0531-5131(03)00496-5"},{"key":"5150_CR44","doi-asserted-by":"crossref","unstructured":"T. Matsubara et al., Detection method for architectural distortion based on analysis of structure of mammary gland on mammograms. Int Congr Ser, 2005, vol. 1281, pp. 1036-1040: Elsevier","DOI":"10.1016\/j.ics.2005.03.324"},{"issue":"11","key":"5150_CR45","first-page":"3534","volume":"3","author":"Minavathi","year":"2011","unstructured":"Minavathi, Murali S, Dinesh M (2011) Model based approach for detection of architectural distortions and spiculated masses in mammograms. Int J Comput Sci Eng 3(11):3534","journal-title":"Int J Comput Sci Eng"},{"issue":"2","key":"5150_CR46","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) Inbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236\u2013248","journal-title":"Acad Radiol"},{"issue":"2","key":"5150_CR47","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s10916-016-0672-5","volume":"41","author":"F Narv\u00e1ez","year":"2017","unstructured":"Narv\u00e1ez F, Alvarez J, Garcia-Arteaga JD, Tarquino J, Romero E (2017) Characterizing Architectural Distortion in Mammograms by Linear Saliency. J Med Syst 41(2):26","journal-title":"J Med Syst"},{"issue":"1","key":"5150_CR48","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11548-008-0267-9","volume":"4","author":"M Nemoto","year":"2009","unstructured":"Nemoto M, Honmura S, Shimizu A, Furukawa D, Kobatake H, Nawano S (2009) A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int J Comput Assist Radiol Surg 4(1):27\u201336","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"5150_CR49","doi-asserted-by":"crossref","unstructured":"Prajna S, Rangayyan RM, Ayres FJ, Desautels JL (2008) Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using texture and fractal analysis. In: Medical Imaging. International Society for Optics and Photonics, pp 691529\u2013691529-8","DOI":"10.1117\/12.767669"},{"issue":"10","key":"5150_CR50","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s11517-006-0088-3","volume":"44","author":"RM Rangayyan","year":"2006","unstructured":"Rangayyan RM, Ayres FJ (2006) Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput 44(10):883\u2013894","journal-title":"Med Biol Eng Comput"},{"issue":"5","key":"5150_CR51","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s10278-009-9257-x","volume":"23","author":"RM Rangayyan","year":"2010","unstructured":"Rangayyan RM, Banik S, Desautels JL (2010) Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J Digit Imaging 23(5):611\u2013631","journal-title":"J Digit Imaging"},{"key":"5150_CR52","doi-asserted-by":"crossref","unstructured":"Rangayyan RM, Banik S, Desautels JL (2012) Detection of architectural distortion in prior mammograms using measures of angular dispersion. In: Medical Measurements and Applications Proceedings (MeMeA), 2012 I.E. International Symposium on. IEEE, pp 1\u20134","DOI":"10.1109\/MeMeA.2012.6226626"},{"issue":"4","key":"5150_CR53","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s11548-012-0793-3","volume":"8","author":"RM Rangayyan","year":"2013","unstructured":"Rangayyan RM, Banik S, Chakraborty J, Mukhopadhyay S, Desautels JL (2013) Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms. Int J Comput Assist Radiol Surg 8(4):527\u2013545","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"5150_CR54","volume-title":"A taxonomy for texture description and identification","author":"AR Rao","year":"2012","unstructured":"Rao AR (2012) A taxonomy for texture description and identification. Springer Science & Business Media, Berlin"},{"issue":"7","key":"5150_CR55","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/34.142908","volume":"14","author":"AR Rao","year":"1992","unstructured":"Rao AR, Jain RC (1992) Computerized flow field analysis: Oriented texture fields. IEEE Trans Pattern Anal Mach Intell 14(7):693\u2013709","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"5150_CR56","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1\u20132","key":"5150_CR57","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1\u20132):23\u201369","journal-title":"Mach Learn"},{"key":"5150_CR58","first-page":"27","volume":"5747","author":"MP Sampat","year":"2005","unstructured":"Sampat MP, Whitman GJ, Markey MK, Bovik AC (2005) Evidence based detection of spiculated masses and architectural distortions. Proc of SPIE Vol 5747:27","journal-title":"Proc of SPIE Vol"},{"issue":"6","key":"5150_CR59","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/TNN.2011.2130540","volume":"22","author":"Y-H Shao","year":"2011","unstructured":"Shao Y-H, Zhang C-H, Wang X-B, Deng N-Y (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962\u2013968","journal-title":"IEEE Trans Neural Netw"},{"key":"5150_CR60","first-page":"29952","volume":"1","author":"B Singh","year":"2015","unstructured":"Singh B, Jain V (2015) Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features. Computer 1:29952","journal-title":"Computer"},{"key":"5150_CR61","first-page":"375","volume":"1069","author":"J Suckling","year":"1994","unstructured":"Suckling J et al (1994) \"The mammographic image analysis society digital mammogram database,\" in Exerpta Medica. Int Congr Ser 1069:375\u2013378","journal-title":"Int Congr Ser"},{"issue":"2","key":"5150_CR62","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TITB.2008.2009441","volume":"13","author":"J Tang","year":"2009","unstructured":"Tang J, Rangayyan RM, Xu J, El Naqa I, Yang Y (2009) Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13(2):236\u2013251","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"5","key":"5150_CR63","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1088\/0031-9155\/51\/5\/018","volume":"51","author":"GD Tourassi","year":"2006","unstructured":"Tourassi GD, Delong DM, Floyd CE Jr (2006) A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 51(5):1299","journal-title":"Phys Med Biol"},{"key":"5150_CR64","doi-asserted-by":"crossref","unstructured":"Yamazaki M, Teramoto A, Fujita H (2016) A Hybrid Detection Scheme of Architectural Distortion in Mammograms Using Iris Filter and Gabor Filter. In: International Workshop on Digital Mammography. Springer, pp 174\u2013182","DOI":"10.1007\/978-3-319-41546-8_23"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-017-5150-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5150-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5150-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,2]],"date-time":"2019-10-02T23:57:24Z","timestamp":1570060644000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-017-5150-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,5]]},"references-count":64,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["5150"],"URL":"https:\/\/doi.org\/10.1007\/s11042-017-5150-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2017,9,5]]}}}