Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images

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Dueñas M.
Frias J.
Martínez-Villaluenga C.
Paucar-Menacho, Luz María
Peñas E.
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IEEE Xplore
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This work presents a model to support medical diagnosis through the classification of abnormality normality in medical brain images, in order to help to specialist as a previous step in the brain pathology diagnosis. Our proposal was incorporated into a content-based image retrieval system, thus we developed a useful tool for radiologists. The first step produces the features vector of MR image using Gabor Filter for the data train and test, then as second step features vector of training data are indexed into CBIR module. The third step makes the training of SVM and as four step the test dataset is classified with the SVM trained. Finally, the result of classification are presented with a set of similar images product of a KNN query. This model was implemented as a software tool with graphical interface. We obtained 94.12% of correct classification. Our medical image dataset is composed of 187 MRI images collected from a medical diagnosis company and selected by medical specialist. The result shows that the proposed model is robust and effective as a software tool to aid support to medical diagnostic.
The authors would like to thank to SEDIMED (a medical diagnosis center company in Peru) who supported with a medical images database. This work was partially funded by the Fondos para la Innovacion, Ciencia y Tecnologia (FINCyT-Peru) under contract 142-10-PITEA-FINCyT and CONCYTEC-Peru with STIC-AmSud 2013 under the FERMI project.
Palabras clave
svm, cbir, pattern recognition, computer aided diagnosis