3D Tortuosity computation as a shape descriptor and its application to brain structure analysis.

In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity ( τ 3 D ) as a shape descriptor was investigated by characterizing brain structures. The results of the τ 3 D computation on the central sulcus and the main lobes revealed significant differences between Alzheimer's disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a p < 0.05 for the left central sulcus and the four brain lobes.

A novel voxel-based method to estimate cortical sulci width and its application to compare patients with Alzheimer's disease to controls.

A voxel-based method for measuring sulcal width was developed, validated and applied to a database. This method (EDT-based LM) employs the 3D Euclidean Distance Transform (EDT) of the pial surface and a Local Maxima labeling algorithm. A computational phantom was designed to test method performance; results revealed the method's inaccuracy δ, to range between 0.1 and 0.5 voxels, for a width that varied between 1 and 7 voxels. Two morphological descriptors were computed to characterize each defined sulcus: mean sulcal width (MSW) and mean absolute deviation (MAD). The former is the average width for all available width measurements within the sulcus, and the latter is the deviation of these measurements. The EDT-based LM method was applied to the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution Magnetic Resonance (MR) images of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. AD causes significant gray matter loss; hence, some sulci were expected to broaden. Methodological results concurred with this hypothesis. After a Wilcoxon test, MSW was grater in the case of all sulci pertaining to AD patients, (p < 0.05, FDR corrected), whereas MAD showed significant differences in 8 sulci (p < 0.05, FDR corrected). This work presents a novel voxel-based method for measuring sulcal width and extracting descriptors to characterize and compare the sulci within and across subjects.

P300 Detection Based on EEG Shape Features.

We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject's P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA's performance using our shape-feature vector was 93%, that is, 10% higher than the one obtained with BCI2000-feature's vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification.

Neuromorphometry of primary brain tumors by magnetic resonance imaging.

Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of [Formula: see text], whereas oligodendrogliomas exhibit a mean of [Formula: see text]. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of [Formula: see text], and the necrotic region presented a mean of [Formula: see text]. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.

Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures.

We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.