References
- Ahonen T, Hadid A, Pietikainen M. 2006. Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell. 28:2037–2041.
- Andonie R. 2010. Extreme data mining: inference from small datasets. Int J Comput Commun Control. 5:280–291.
- Baxevanis AD, Ouellette BFF. 2004. Bioinformatics: a practical guide to the analysis of genes and proteins. Methods of biochemical analysis. Wiley. Available from: http://books.google.com.br/books?id=ghUZaEAdHUC
- Berbar MA, Reyad YA, Hussain M. 2012. Breast mass classification using statistical and local binary pattern features. In: Banissi E, Bertschi S, Forsell C, Johansson J, Kenderdine S, Marchese FT, Sarfraz M, Stuart LJ, Ursyn A, Wyeld TG, et al., editors. Vol. IV. IEEE Computer Society; p. 486–490.
- Chang C-C, Lin C-J. 2011. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2:27:1–27:27. Software Available from: http://www.csie.ntu.edu.tw/cjlin/libsvm
- Cimpoi M, Maji S, Kokkinos I, Vedaldi A. 2016. Deep filter banks for texture recognition, description, and segmentation. Int J Comput Vision. 118:65–94.
- Cimpoi M, Maji S, Vedaldi A. 2015. Deep filter banks for texture recognition and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; p. 3828–3836.
- Coello Coello C, Pulido G. 2001. A micro-genetic algorithm for multiobjective optimization. In: Zitzler E, Thiele L, Deb K, Coello Coello C, Corne D, editors. Evolutionary multi-criterion optimization. Vol. 1993 of Lecture Notes in Computer Science. Berlin, Heidelberg; p. 126–140. Available from: http://dx.doi.org/10.1007/3-540-44719-9-9
- Costa D, Campos L, Barros A. 2011. Classification of breast tissue in mammograms using efficient coding. Biomed Eng Online. 10:55. Available from: http://www.biomedical-engineering-online.com/content/10/1/55
- Da Silva IA, Batalha MA. 2006. Taxonomic distinctness and diversity of a hyperseasonal savanna in central Brazil. Divers Distrib. 12:725–730. Available from: http://dx.doi.org/10.1111/j.1472-4642.2006.00264.x
- Dagli CH, editor. 1990. Micro-genetic algorithms for stationary and non-stationary function optimization. Vol. 1196. Available from: http://dx.doi.org/10.1117/12.969927
- de Nazaré Silva J, de Carvalho Filho AO, Silva AC, De Paiva AC, Gattass M. 2015. Automatic detection of masses in mammograms using quality threshold clustering, correlogram function, and SVM. J Digital Imag. 28:323–337.
- de Oliveira Martins L, Silva AC, de Paiva AC, Gattass M. 2009. Detection of breast masses in mammogram images using growing neural gas algorithm and ripley’s k function. Signal Process Syst. 55:77–90. Available from: http://dblp.uni-trier.de/db/journals/vlsisp/vlsisp55.htmlMartinsSPG09
- Goldman N, Anderson JP, Rodrigo AG. 2000. Likelihood-based tests of topologies in phylogenetics. Syst Biol. 49:652–670. Available from: http://sysbio.oxfordjournals.org/cgi/content/abstract/49/4/652
- Gonzalez RC, Woods RE. 1992. Digital image processing. 2nd ed. Boston, MA: Addison-Wesley Longman Publishing.
- Gu X, Fu YX, Li WH. 1995. Maximum likelihood estimation of the heterogeneity of substitution rate among nucleotide sites. Mol Biol Evol. 12:546–557. Available from: http://www.biomedsearch.com/nih/Maximum-likelihood-estimation-heterogeneit
- Hadid A, Zhao G. 2011. Computer vision using local binary patterns. Vol. 40. Springer.
- Heath M, Bowyer K, Kopans D. 1998. Current status of the digital database for screening mammography. Digital mammography. Kluwer Academic Publishers; p. 457–460.
- Hussain M. 2013. False positive reduction using gabor feature subset selection. In International Conference on Information Science and Applications (ICISA); p. 1–5.
- Jain A, Duin RPW, Mao J. 2000 Jan. Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell. 22:4–37.
- Junior GB, da Rocha SV, Gattass M, Silva AC, de Paiva AC. 2013. A mass classification using spatial diversity approaches in mammography images for false positive reduction. Expert Syst Appl. 40:7534–7543. Available from: http://www.sciencedirect.com/science/article/pii/S0957417413005137
- Junior GB, de Paiva AC, Silva AC, de Oliveira ACM. 2009. Classification of breast tissues using moran’s index and geary’s coefficient as texture signatures and SVM. Comput Biol Med. 39:1063–1072. Available from: http://www.sciencedirect.com/science/article/pii/S0010482509001620
- Kohavi R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence -- Vol. 2. IJCAI’95. San Francisco, CA: Morgan Kaufmann; p. 1137–1143. Available from: http://dl.acm.org/citation.cfm?id=1643031.1643047
- Koller D, Sahami M. 1996. Toward optimal feature selection. In: Saitta L, editor. ICML, Morgan Kaufmann; p. 284–292. Available from http://dblp.uni-trier.de/db/conf/icml/icml1996.htmlKollerS96
- Muralidhar G, Markey M, Bovik A. 2010. Snakules for automatic classification of candidate spiculated mass locations on mammography. In IEEE Southwest Symposiumon. Image Analysis Interpretation (SSIAI); p. 197–200.
- Nguyen M, Truong Q, Nguyen D, Nguyen T, Nguyen V. 2013. An alternative approach to reduce massive false positives in mammograms using block variance of local coefficients features and support vector machine. Procedia Comput Sci. 20:399–405. Complex Adaptive Systems. Available from: http://www.sciencedirect.com/science/article/pii/S1877050913010934
- Ojala T, Pietikinen M, Harwood D. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29:51–59. Available from: http://www.sciencedirect.com/science/article/pii/0031320395000674
- Parkin DM. Bray F. Ferlay J 2005. Global cancer statistics, 2002. CA: Cancer J Clin.. 55: 74–108. Available from: http://dx.doi.org/10.3322/canjclin.55.2.74
- Pereira DC, Ramos RP, do Nascimento MZ. 2014. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed. 114:88–101. Available from: http://www.sciencedirect.com/science/article/pii/S0169260714000261
- Perner P, editor. 2012. Machine learning and data mining in pattern recognition -- 8th international conference. In Proceedings. Vol. 7376 of Lecture Notes in Computer Science; 2012 Jul 13--20; Berlin, Germany: Springer.
- Qi X, Xiao R, Li C-G, Qiao Y, Guo J, Tang X. 2014. Pairwise rotation invariant co-occurrence local binary pattern. IEEE Trans Pattern Anal Mach Intell. 36:2199–2213.
- Ramos RP, do Nascimento MZ, Pereira DC. 2012. Texture extraction: an evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms. Expert Syst Appl. 39:11036–11047. Available from: http://www.sciencedirect.com/science/article/pii/S0957417412004903
- Rocha SVD, Braz Junior G, Silva AC, Paiva ACD. 2014. Texture analysis of masses in digitized mammograms using gleason and menhinick diversity indexes. Rev Bras Eng Bioméd. 30:27–34.
- Rogers SI, Clarke KR, Reynolds JD. 1999. The taxonomic distinctness of coastal bottom-dwelling fish communities of the north-east Atlantic. J Anim Ecol. 68:769–782. Available from: http://dx.doi.org/10.1046/j.1365-2656.1999.00327.x
- Sampaio WB, Diniz EM, Silva AC, de Paiva AC, Gattass M. 2011. Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Comput Biol Med. 41:653–664. Available from: http://dblp.uni-trier.de/db/journals/cbm/cbm41.htmlSampaioDSPG11
- Sousa Carvalho PM, Paiva AC, Silva AC. 2012. Classification of breast tissues in mammographic images in mass and non-mass using mcintoshs diversity index and SVM. In: Perner P, editor. Machine learning and data mining in pattern recognition. Vol. 7376., Berlin, Heidelberg: Springer; p. 482–494. Available from: http://dx.doi.org/10.1007/978-3-642-31537-4-38
- Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S, et al. 1994. The mammographic images analysis society digital mammogram database. Experta Med Int Congr Ser. 1069:375–378.
- Surendiran B, Vadivel A. 2011. Feature selection using stepwise anova discriminant analysis for mammogram mass classification. Int J Signal Image Process. 2:4. Available from: http://doi.searchdl.org/01.IJSIP.2.1.195
- WHO. 2006. World Health Organization statistical information system. Available from: http://www.who.int/whosis/mort/en/index.html
- Xing E. 2003. Feature selection in microarray analysis. In: Berrar D, Dubitzky W, Granzow M, editors. A practical approach to microarray data analysis. Springer; p. 110–131. Available from: http://dx.doi.org/10.1007/0-306-47815-3-6
- Zuiderveld K. 1994. Contrast limited adaptive histogram equalization. In: Heckbert PS, editor. Graphics gems IV. San Diego, CA: Academic Press Professional; p. 474–485. Available from: http://dl.acm.org/citation.cfm?id=180895.180940