Brain anatomical structure segmentation by adaptive bandwidth density estimation.

Determination of region in a space of multimodal features of brain MR images requires kernel estimation tecniques with bandwidths that are adapted locally. The bandwidth selection is a critical aspect at the filtering stage of image segmentation. This work presents two methods for determinate the adaptive bandwidth in the application of density estimation, in the segmentation of regions at the feature space of an MRI. Two adaptive methods: sample point and k-nearest neighbors, where applied for real and synthetic data, achieved similarity indexes of 0.68 and 0.71 for gray matter and white matter respectively.

Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images.

Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation. The quality of the class boundaries is improved by including an edge confidence map, that is the confidence of truly being in the presence of a border between adjacent regions. The adjacency graph, that is constructed with the label regions, is analyzed and pruned to merge adjacent regions. The method was applied to real images, keeping all parameters constant throughout the process for each data set. The combination of region segmentation and edge detection proved to be a robust automatic technique of segmentation from DWI images of cerebral infarction regions in acute ischemic stroke. In a comparison with the reference infarct lesions segmentation, the automatic segmentation presented a significant correlation (r=0.935), and an average Tanimoto index of 0.538.

Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information.

Brain magnetic resonance imaging segmentation is accomplished in this work by applying nonparametric density estimation, using the mean shift algorithm in the joint spatial-range domain. The quality of the class boundaries is improved by including an edge confidence map, that represents the confidence of truly being in the presence of a border between adjacent regions; an adjacency graph is then constructed with the labeled regions, and analyzed and pruned to merge adjacent regions. In order to assign image regions to a cerebral tissue type, a spatial normalization between image data and standard probability maps is carried out, so that for each structure a maximum a posteriori probability criterion is applied. The method was applied to synthetic and real images, keeping all parameters constant throughout the process for each type of data. The combination of region segmentation and edge detection proved to be a robust technique, as adequate clusters were automatically identified, regardless of the noise level and bias. In a comparison with reference segmentations, average Tanimoto indexes of 0.90-0.99 were obtained for synthetic data and of 0.59-0.99 for real data, considering gray matter, white matter, and background.

Segmenting brain MRI using adaptive mean shift.

To delineate arbitrarily shaped clusters in a complex multimodal feature space, such as the brain MRI intensity space, often requires kernel estimation techniques with locally adaptive bandwidths, such as the adaptive mean shift procedure. Proper selection of the kernel bandwidth is a critical step for a better quality in the clustering. This paper presents a solution for the bandwidth selection, which is completely nonparametric and is based on the sample point estimator to yield a spatial pattern of local bandwidths. The method was applied to synthetic brain images, showing a high performance even in the presence of varying noise level and bias.