Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by a triad of signs: stereotyped behaviors, verbal and non-verbal communication problems. The scientific community has been interested on quantifying anatomical brain alterations of this disorder. Several studies have focused on measuring brain cortical and sub-cortical volumes. This article presents a fully automatic method which finds out differences among patients diagnosed with autism and control patients. After the usual pre-processing, a template (MNI152) is registered to an evaluated brain which becomes then a set of regions. Each of these regions is the represented by the normalized histogram of intensities which is approximated by mixture of Gaussian (GMM). The gray and white matter are separated to calculate the mean and standard deviation of each Gaussian. These features are then used to train, region per region, a binary SVM classifier. The method was evaluated in an adult population aged from 18 to 35 years, from the public database Autism Brain Imaging Data Exchange (ABIDE). Highest discrimination values were found for the Right Middle Temporal Gyrus, with an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) the curve of 0.72.
An early diagnosis of Parkinson’s Disease (PD) is crucial towards devising successful rehabilitation programs. Typically, the PD diagnosis is performed by characterizing typical symptoms, namely bradykinesia, rigidity, tremor, postural instability or freezing gait. However, traditional examination tests are usually incapable of detecting slight motor changes, specially for early stages of the pathology. Recently, eye movement abnormalities have correlated with early onset of some neurodegenerative disorders. This work introduces a new characterization of the Parkinson disease by describing the ocular motion during a common daily activity as the gait. This paper proposes a fully automatic eye motion analysis using a dense optical flow that tracks the ocular direction. The eye motion is then summarized using orientation histograms constructed during a whole gait cycle. The proposed approach was evaluated by measuring the χ<sup>2</sup> distance between the orientation histograms, showing substantial differences between control and PD patients.
The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.
Accurate segmentation of the prostate and organs at risk is the fundamental guide for planning any radiotherapy. Such task is currently performed using a manual delineation of the organ on the MRI, a highly time consuming responsibility which in addition introduces inter and intra expert variability. Automatic MRI segmentation is a very challenging goal because of the large organ variability and the proximity of the neighboring organs. This work presents an automatic atlas-based segmentation strategy that selects the most probable template from a database using a robust multiscale similarity analysis. Once that probable template is selected, the associated segmentation is non-rigidly registered to the new MRI. The proposed method takes advantage of both the interindividual shape variation and intra-individual salient point representation. Results show that the method produces reliable segmentations, obtaining an average Dice Coefficient of 72% when comparing with the expert manual segmentation under a leave-one-out scheme with the training database.