Segmentação e parametrização de imagens de ressonância magnética do cérebro : método semi-automático de extração de características para apoio a diagnóstico de pacientes com esquizofrenia

Schizophrenia is one of several mental disorders that affect humans, and is also a mental illness that causes more stigma to the patient. It affects not only the patient but also his family and close people. The diagnosis of this disorder is done by an expert based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is based on observation of symptoms made by the specialist. Recent studies indicate that there are structural abnormalities in the brain which are correlated with the presence of schizophrenia. Based on these studies a study was conducted at the University of Brasilia where an expert manually extracted certain characteristics which exhibited these abnormalities, who were 14 lengths between specific points in the brain. These characteristics were used to train a classifier for aid in the diagnosis of schizophrenia. No similar studies were found using these abnormalities to aid in diagnosis. The method for the extraction of these features, however, is performed manually and thus it is slow, and obtain a small number characteristics. Given these issues this paper proposes a semi-automatic segmentation and parametrization method of these structures, which is faster and produces a greater number of data. The proposed method consists in the parametrization of the contour of each structure of interest by the Fourier series coefficients that describes the coordinates of the contour points. To this end, structures were manually segmented in an image used as template, and then the points on the contour of each structure were automatically traced by the Moore-Neighbor algorithm and the coordinates expanded in Fourier series where the series coefficients were used as initial parameters of an optimization algorithm. For each magnetic resonance image in the database (which were used 140, where 69 were from schizophrenic patients and 71 from the control group) was obtained by Nelder-Mead numerical optimization algorithm the Fourier coefficients that minimized a cost function that returned the error between the image analyzed and the structure whose contour was described by the Fourier coefficients, having as the initial parameter the Fourier coefficients which was obtained from the base images. The error was calculated using the Frobenius norm of the difference between the analyzed image binarized by the Otsu thresholding method and the image formed by the contour filled described by the Fourier coefficients. The binary image formed by the filled contour which is described by the Fourier coefficients that minimize this cost function is then dilated and an intersection operation is performed between this and the binarized vesrion of the analyzed image. The result is a segmentation of the structure of interest the in the analyzed, and its contour is parameterized as was the structures in the base image. For comparison with the results from the expert, were extracted the same characteristics using heuristics based on the parameterized structures and then the relative errors were calculated. Most of the relative errors for each 14 distances in each image analyzed was below 10%. Another test performed to compare the results was train a classifier in the same manner as made by the expert, using 80% of the data, chosen at random, to train the classifier, and then use the 20% others for validation. n the best result the classifier missed only 10.71% of data from validation.