Showing papers by "Miguel Ángel González Ballester published in 2004"

Improved EM-Based Tissue Segmentation and Partial Volume Effect Quantification in Multi-sequence Brain MRI

Abstract: The Expectation Maximization algorithm is a powerful probabilistic tool for brain tissue segmentation. The framework is based on the Gaussian mixture model in MRI, and employs a probabilistic brain atlas as a prior to produce a segmentation of white matter, grey matter and cerebro-spinal fluid (CSF). However, several artifacts can alter the segmentation process. For example, CSF is not a well defined class because of the large quantity of voxels affected by the partial volume effect which alters segmentation results and volume computation. In this study, we show that ignoring vessel segmentation when handling partial volume effect can also lead to false results, more specifically to an over-estimation of the CSF variance in the intensity space. We also propose a more versatile method to improve tissue classification, without a requirement of any outlier class, so that brain tissues, especially the cerebro-spinal fluid, follows the Gaussian noise model in MRI correctly.

Automated Analysis of Basal Ganglia Intensity Distribution in Multisequence MRI of the Brain - Application to Creutzfeldt-Jakob Disease

Abstract: We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence MRI of the brain. One common feature of most forms of prion protein infections is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted MR images. We employ T1, T2 and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data is registered to a probabilistic atlas and normalised in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas is employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance to the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (CJD), in agreement with the histological data. The algorithm permitted to classify the intensities of abnormal signals in sporadic CJD patient FLAIR images with a more significant hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Using normalised measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sporadic CJD and new-variant CJD patients, as a first attempt towards an automatic classification tool of human spongiform encephalopathies.