Triagem robusta de melanoma : em defesa dos descritores aprimorados de nível médio

Orientadores: Eduardo Alves do Valle Junior, Sandra Eliza Fontes de AvilaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Melanoma é o tipo de câncer de pele que mais leva à morte, mesmo sendo o mais curável, se detectado precocemente. Considerando que a presença de um dermatologista em tempo integral não é economicamente viável para muitas cidades e especialmente em comunidades carentes, ferramentas de auxílio ao diagnóstico para a triagem do melanoma têm sido um tópico de pesquisa ativo. Muitos trabalhos existentes são baseados no modelo Bag-of-Visual-Words (BoVW), combinando descritores de cor e textura. No entanto, o modelo BoVW vem se aprimorando e hoje existem várias extensões que levam a melhores taxas de acerto em tarefas gerais de classificação de imagens. Estes modelos avançados ainda não foram explorados para rastreio de melanoma, motivando assim este trabalho. Aqui nós apresentamos uma nova abordagem para rastreio de melanoma baseado nos descritores BossaNova, que são estado-da-arte, mostrando resultados muito promissores, com uma AUC de 93,7%. Este trabalho também propõe uma nova estratégia de pooling espacial especialmente desenhada para rastreio de melanoma. Outra contribuição dessa pesquisa é o uso inédito do BossaNova na classificação de melanoma. Isso abre oportunidades de exploração deste descritor em outros contextos médicosAbstract: Melanoma is the type of skin cancer that most leads to death, even being the most curable, if detected early. Since the presence of a full time dermatologist is not economical feasible for many small cities and specially in underserved communities, computer-aided diagnosis for melanoma screening has been a topic of active research. Much of the existing art is based on the Bag-of-Visual-Words (BoVW) model, combining color and texture descriptors. However, the BoVW model has been improving and nowadays there are several extensions that perform better classification rates in general image classification tasks. These enhanced models were not explored yet for melanoma screening, thus motivating our work. Here we present a new approach for melanoma screening, based upon the state-of-the-art BossaNova descriptors, showing very promising results for screening, reaching an AUC of up to 93.7%. This work also proposes a new spatial pooling strategy specially designed for melanoma screening. Other contribution of this research is the unprecedented use of BossaNova in melanoma classification. This opens the opportunity to explore this enhanced mid-level descriptors in other medical contextsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Analysis Of Brain White Matter Hyperintensities Using Pattern Recognition Techniques

The brain white matter is responsible for the transmission of electrical signals through the central nervous system. Lesions in the brain white matter, called white matter hyperintensity (WMH), can cause a significant functional deficit. WMH are commonly seen in normal aging, but also in a number of neurological and psychiatric disorders. We propose here an automatic method for WHM analysis in order to distinguish regions of interest between normal and non-normal white matter (identification task) and also to distinguish different types of lesions based on their etiology: demyelinating or ischemic (classification task). The method combines texture analysis with the use of classifiers, such as Support Vector Machine (SVM), Nearst Neighboor (1NN), Linear Discriminant Analysis (LDA) and Optimum Path Forest (OPF). Experiments with real brain MRI data showed that the proposed method is suitable to identify and classify the brain lesions. © 2013 SPIE.8669The Society of Photo-Optical Instrumentation Engineers (SPIE),Aeroflex Incorporated,CREOL - Univ. Central Florida, Coll. Opt. Photonics,DQE Instruments, Inc.,Medtronic, Inc.,PIXELTEQ, Multispectral Sensing and ImagingAppenzeller, S., Faria, A.V., Li, L., Costallat, L.T., Cendes, F., Quantitative magnetic resonance imaging analyses and clinical significance of hyperintense white matter lesions in systemic lupus erythematosus patients (2008) Annals of Neurology, 64 (6), pp. 635-643Klöppel, S., Abdulkadir, A., Hadjidemetriou, S., Issleib, S., Frings, L., Thanh, T., Mader, I., Ronneberger, O., A comparison of different automated methods for the detection of white matter lesions in mri data (2011) NeuroImage, 57 (2), pp. 416-422Anbeek, P., Vincken, K.L., Osch, M.J.P., Bisschops, R.H.C., Grond, J., Probabilistic segmentation of whitematter lesions in mr imaging (2004) NeuroImage, 21 (3), pp. 1037-1044Wu, M., Rosano, C., Butters, M., Whyte, E., Nable, M., Crooks, R., Meltzer, C.C., Aizenstein, H.J., A fully automated method for quantifying and localizing white matter hyperintensities on mr images (2006) Psychiatry Research, 148 (2-3), pp. 133-142Zimring, D.G., Achiron, A., Miron, S., Faibel, M., Azhari, H., Automatic detection and characterization of multiple sclerosis lesions in brain mr images (1998) Magnetic Resonance Imaging, 16 (3), pp. 311-318Haralick, R.M., Shanmugam, K., Dinstein, I., Textural features for image classification (1973) , IEEE Transactions on Systems, Man and Cybernetics, 3 (6), pp. 610-621Castellano, G., Bonilha, L., Cendes, F., Texture analysis of medical images (2004) Clinical Radiology, 59 (12), pp. 1061-1069Lerski, R.A., Schad, L., Boyce, D., Blül, S., Zuna, I., Mr image texture analysis: An approach to tissue characterization (1993) Magnetic Resonance Imaging, 11 (6), pp. 873-887Kruggel, F., Paul, J., Gertz, H., Texture-based segmentation of diffuse lesions of the brain's white matter (2008) Neuroimage, 39 (3), pp. 987-996Byun, H., Lee, S.W., Applications of support vector machines for pattern recognition: A survey (2002) Proc. First International Workshop on Pattern Recognition with Support Vector Machines, pp. 213-236Bhatia, N., Survey of nearest neighbor techniques (2010) International Journal of Computer Science and Information Security, 8 (2), pp. 302-305Webb, A.R., (2002) Statistical Pattern Recognition, pp. 123-163. , John Wiley & Sons, MalvernCappabianco, F., Falcão, A., Rocha, L., Clustering by optimum path forest and its application to automatic gm/wm classification in mr-t1 images of the brain (2008) Proc. 5th IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp. 428-431Lotufo, R., Machado, R., Körbes, A., Ramos, R., Adessowiki: On-line collaborative scientific programming platform (2009) Proc 5th International Symposium on Wikis and Open Collaboration, 10, pp. 1-10. , 6Schwartz, W.R., Siqueira, F.R., Pedrini, H., Evaluation of feature descriptors for texture classification (2012) Journal of Electronic Imaging, 21 (2), pp. 1-17Han, J., Kamber, M., (2006) Data Mining: Concepts and Techniques, pp. 291-310. , Elsevier, San Diego & London & San FransciscoTaylor, J.S., Cristianini, N., (2000) Support Vector Machines and other Kernel-based Learning Methods, pp. 93-122. , Cambridge University Press, New KingdomPapa, J., Falcão, A.X., Suzuki, C.T.N., Supervised pattern classification based on optimum-path forest (2009) International Journal of Imaging Systems and Technology, 19 (2), pp. 120-131Duda, R.O., Hart, P.E., Stork, D.G., (2001) Pattern Classification, , Wiley, Guelph OntarioSouza, R., Rittner, L., Lotufo, R., A comparison between optimum-path forest and k-nearest neighbors classifier (2012) Proc. XXV SIBGRAPI - Conference on Graphics, Patterns and Images, pp. 260-267Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in python (2011) Journal of Machine Learning Research, 12 (10), pp. 2825-283