Medical image retrieval using texture, locality and colour

We describe our experiments for the Image CLEF medical retrieval task. Our efforts were focused on the initial visual search. A content-based approach was followed. We used texture, localisation and colour features that have been proven by previous experiments. The images in the collection had specific characteristics. Medical images have a formulaic composition for each modality and anatomic region. We were able to choose features that would perform well in this domain. Tiling a Gabor texture feature to add localisation information proved to be particularly effective. The distances from each feature were combined with equal weighting. This smoothed the performance across the queries. The retrieval results showed that this simple approach was successful, with our system coming third in the automatic retrieval task

Comparative Study Of Computacional Models Generated From Representations Of Colonoscopic Images: Normal Mucosal Tissues Vs Mucosal Tissues Of Colic Polyp [estudo Comparativo De Modelos Computacionais Gerados Sobre Representações De Imagens De Coloscopia: Tecido De Mucosa Normal Vs Tecido De Mucosa De Pólipo Cólico]

Purpose: to evaluate the predictive quality of computational models to differentiate colic tissues, based on Co-occorrurence Matrices (MC) representation of Coloscopic Images (IC). Materials and Methods: Image analysis and artificial intelligence methods were employed to construct computational models. Sixty seven IC images, containing polyp, were considered in this work, from which a part containing a polypus and another without it were collected given origin to 134 images. For each one of these, different MC were constructed considering five distance parameters (D = 1 to 5) and the extraction of 11 texture characteristics. With this representation, five computational models were generated based on decision trees. These models were evaluated using two techniques: (a) cross-validation and (b) contingency tables. Results: for the (a) analysis, the model with D = 3 presented the smaller average error (22.25% ± 11.85%). For the (b) analysis, models with D = 1 and 3 presented the best precision values. Conclusion: parameters D = 1 and 3 presented models with the best predictive qualities. Results showed that the constructed models were promising to be applied within decision making computational systems.2912329Karkanis, S., Galoussi, K., Maroulis, D., Classification of Endoscopic Images Based on Texture Spectrum. Advance Course in Artificial Intelligence (1999) Proceedings of Workshop on Machine Learning in Medical Applications, pp. 63-7. , Chania, GreciaFelipe, J.C., Traina, A.J.M., Traina, C., Retrieval by Content of Medical Images Using Texture for Tissue Identification (2003) Proceeding of the 16th IEEE Symposium on Computer-based Medical Systems, pp. 175-6. , New York, USARezende, S.O., (2003) Sistemas Inteligentes: Fundamentos e Aplicações, , Barueri (SP), Brasil: Editora Manole(2007) Estimativa 2008: Incidência de Câncer no Brasil, , Instituto Nacional de Câncer (INCA)., Rio de Janeiro (RJ), BrasilFerrero, C.A., Lee, H.D., Cherman, E.A., Coy, C.S.R., Fagundes, J.J., Góes, J.R.N., Et al., Utilização de Atributos Baseados em Textura para a Caracterização de Tecidos Cólicos em Imagens de Colonoscopia. (2007) Anais do VII Workshop de Informática Médica, pp. 204-213. , Porto de Galinhas (PE), BrasilFerrero, C.A., Spolaôr, N., Lee, H.D., Coy, C.S.R., Fagundes, J.J., Wu, F.C., Estudo Comparativo de Matrizes de Co-ocorrência em Análise de Imagens Médicas: Diferenciação de Tecidos Cólicos (2008) Anales del 11° Simposio Argentino de Informática y Salud, 37° Jornadas Argentinas de Informática, pp. 1-12. , Santa Fe (SF), Argentina;Haralick, R., Shanmugam, K., Dinstein, I., Texture Features for Image Classification (1973) Proceeding of IEEE Transaction on Systems, Man, and Cybernetis, 3 (6), pp. 610-621Quinlan, J.R., (1993) C4.5: Programs for Machine Learning, , San Mateo (CA), USA: Editora Morgan KaufmannDoria, U., (1999) Introdução à Bioestatística: Para simples mortais, , São Paulo (SP), Brasil: Editora ElseiverHowarth, P., Yavlinsky, A., Heesch, D., Rüger, S., (2005) Medical Image Retrieval Using Texture, Locality and Colour, pp. 740-749. , Lecture Notes from the Cross Language Evaluation ForumKarkanis, S., Detecting Abnormalities in Colonoscopic Images by Textural Description and Neural Networks (1999) Proceedings of Workshop on Machine Learning in Medical Applications, Advance Course in Artificial Intelligence, 5, pp. 59-62. , Chania, GreeceFerrero, C.A., Lee, H.D., Cherman, E.A., Coy, C.S.R., Góes, J.R.N., Fagundes, J.J., Wu, F.C., Utilização de Técnicas Computacionais para a Diferenciação Cólicos em Imagens Colonoscópicas (2007) 56° Congresso Brasileiro de Coloproctologia: Rev Bras de Coloproct, 27, pp. 45-46. , Curitiba (PR), Brasi