Early efforts in fMRI classification were limited in that individual voxels were used as features (e.g. [1]), yet voxels divide images into regions that do not directly correspond to underlying neural activity. A growing trend is to perform spatial smoothing that captures the correlation between nearby voxels. Unfortunately, the optimal spatial resolution for this smoothing is unknown and likel...

Machine Learning techniques have been used quite widely for the task of predicting cognitive processes from fMRI data. However, these models do not describe well the fMRI signal when it is generated by multiple cognitive processes that are simultaneously active. In this paper we consider the problem of accurately modeling the fMRI signal of a human subject who is performing a task involving mul...

The effects of new treatments need to be assessed: in the case of multiple sclerosis it is possible to measure those effects by studying temporal lesions’ evolutions in time series of MRI. But it is a laborious task to manually analyze such sets of images. This article proposes a new method to statistically analyze a series of T2-weighted MRI of a patient with multiple sclerosis lesions taking ...

Partial volume averaging (PVA) is present in nearly all practical imaging situations, medical imaging in particular. One method that has been used to account for the effects of PVA is the fuzzy c-means algorithm (FCM). We propose a new method for estimating the partial volume coefficient of each class at each voxel in a given image using a Bayesian statistical model. A prior probability on the ...

Registration between voxel images of human anatomy enables cross-patient diagnosis and post-treatment analysis. However, innate variations in the shape, size, and density of non-pathological anatomical structures between individuals make accurate registration difficult. Characterization of such normal but inherent variations provides guidance for registration.We extracted the pattern of normal ...

In this paper, we propose a progressive encoding algorithm for the geometric information of a 3D object, which is represented by binary voxels. Using the morphological pyramidal decomposition, the proposed algorithm first generates the multi-resolution models of a 3D object. Then, each resolution model is predicted from its lower resolution model, and the prediction errors are encoded using an ...

Gliomas are malignant brain tumors that grow by invading adjacent tissue. We propose and evaluate a 3D classification-based growth model, CDM, that predicts how a glioma will grow at a voxel-level, on the basis of features specific to the patient, properties of the tumor, and attributes of that voxel. We use Supervised Learning algorithms to learn this general model, by observing the growth pat...