PI161-5-5Salpat : patrones basados en saliencia para imágenes de resonancia magnética cerebral

Los modelos de atención visual buscan emular el desempeño Sistemas Visual Humano en la selección de características relevantes para procesamiento visual eficiente sobre una escena. Como resultado los mapas de saliencia visual resaltan patrones visuales sobre una imagen, posiblemente asociado con objetos o conceptos específicos. En el análisis de imágenes médicas esto permite a un radiólogo o un experto clínico enfocar su atención en anomalías o patrones específicos que podrían sugerir la presencia de una patología. Nuestro estudio presenta una exploración inicial del efecto de los modelos de saliencia visual en la extracción de patrones relevantes asociados a patologías, adecuados para la clasificación de imágenes de resonancia magnética MRI de pacientes de control y pacientes con probable Alzheimer. Ajustando los modelos de saliencia para su operación en imágenes médicas, combinando este proceso con una clasificación usando Support Vector Machine SVM.Computational visual attention models aim to emulate the Human Visual System performance in selecting relevant features for efficient visual scene processing. As a result, visual saliency maps highlights relevant visual patterns in an image, possibly associated with objects or specific concepts. In the analysis of medical images, this allows the radiologist or clinical expert to focus the attention on image anormalities or specific patterns that could suggest the presence of a pathology. This work presents the exploration of the effect of visual saliency models in the extraction of pathology-related relevant patterns, suitable for classification of Magnetic Resonance images of normal controls and probable Alzheimer s disease patients. By adjusting the saliency models to work on medical images, and combining this process with a Support Vector Machine for classification process.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí

Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images

Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer’s disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, 1) the discriminative landmarks are first automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; 2) high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine (SVM) is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the ADNI database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD vs. HC and 79.02% for MCI vs. HC, respectively

Detecting Anatomical Landmarks for Fast Alzheimer’s Disease Diagnosis

Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer’s disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this limitation, we propose a landmark-based feature extraction method that does not require nonlinear registration and tissue segmentation. In the training stage, in order to distinguish AD subjects from healthy controls (HCs), group comparisons, based on local morphological features, are first performed to identify brain regions that have significant group differences. In general, the centers of the identified regions become landmark locations (or AD landmarks for short) capable of differentiating AD subjects from HCs. In the testing stage, using the learned AD landmarks, the corresponding landmarks are detected in a testing image using an efficient technique based on a shape-constrained regression-forest algorithm. To improve detection accuracy, an additional set of salient and consistent landmarks are also identified to guide the AD landmark detection. Based on the identified AD landmarks, morphological features are extracted to train a support vector machine (SVM) classifier that is capable of predicting the AD condition. In the experiments, our method is evaluated on landmark detection and AD classification sequentially. Specifically, the landmark detection error (manually annotated versus automatically detected) of the proposed landmark detector is 2.41mm, and our landmark-based AD classification accuracy is 83.7%. Lastly, the AD classification performance of our method is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the proposed method is approximately 50 times faster

Morphometric data fusion for early detection of alzheimer’s disease

Abstract. We present a morphometry method which uses brain models generated using Nonnegative Matrix Factorization (NMF) characterized by signatures calculated from perceptual features such as intensities, edges and orientations, of some regions obtained by comparing the models. Two different measures are used to calculate volume-models distances in the regions of interest. The discerning power of these distances is tested by using them as features for a Support Vector Machine classifier. This work shows the usefulness of both measures as metrics in medical image applications when they are used in binary classification tasks. Our methodology was tested with two experimental groups extracted from a public brain MR dataset (OASIS), the classification between healthy subjects and patients with mild AD reveals an equal error rate (EER) measure which is better than previous approaches tested on the same dataset (0.1 in the former and 0.2 in the latter). When detecting very mild AD, our results (near to 75% of sensitivity and specificity) are comparable to the results with those approaches.Presentamos un m´etodo de morfometr´ı que usa modelos de cerebro que se generan usando factorizaci´on de matrices no-negativas (NMF por su nombre en ingl´es) y se caracterizan por firmas calculadas de rasgos perceptules como las intensidades, bordes y orientaciones de algunas regiones del cerebro obtenidas de la comparaci´on entre modelos. Dos medidas, la divergencia de Kullback-Leibler y la “Earth Mover’s Distance”, son usadas para calcular la distancia entre vol´umenes y modelos en las regiones de inter´es. Probamos el poder discriminante de estas distancias us´andolas para construir los vectores de caracter´ısticas para una m´aquina de soporte vectorial. Este trabajo muestra la utilidad de ambas medidas en tareas de clasificaci´on binaria. Nuestra metodolog´ıa fue probada con dos grupos experimentales extra´ıdos de la base de datos OASIS, la clasificaci´on entre sujetos sanos y pacientes con Alzheimer leve revela un EER que mejora los resultados obtenidos por trabajos publicados previamente con los mismos grupos experimentales. Cuando se trata de detectar Alzheimer muy leve, los resultados (cercanos a 75% de sensibilidad y especificidad) son comparables con los resultados obtenidos en dichas publicaciones.Maestrí