We have proposed a new method for illumination suppression in hyperspectral image data. This involves transforming
the data into a hyperspherical coordinate system, segmenting the data cloud into a large number of classes according to
the radius dimension, and then demeaning each class, thereby eliminating the distortion introduced by differential
absorption in shaded regions. This method was evaluated against two other illumination-suppression methods using two
metrics: visual assessment and spectral similarity of similar materials in shaded and fully illuminated regions. The
proposed method shows markedly superior performance by each of these metrics.
Rapid, accurate and reproducible delineation and measurement of arbitrary anatomical structures in medical images is a widely held goal, with important applications in both clinical diagnostics and, perhaps more significantly, pharmaceutical trial evaluation. This process requires the ability first to localize a structure within the body, and then to find a best approximation of the structure’s boundaries within a given scan. Structures that are tortuous and small in cross section, such as the hippocampus in the brain or the abdominal aorta, present a particular challenge. Their apparent shape and position can change significantly from slice to slice, and accurate prior shape models for such structures are often difficult to form. In this work, we have developed a system that makes use of both a user-defined shape model and a statistical maximum likelihood classifier to identify and measure structures of this sort in MRI and CT images. Experiments show that this system can reduce analysis time by 75% or more with respect to manual tracing with no loss of precision or accuracy.
A method for fully automating the measurement of various neurological structures in MRI is presented. This technique uses an atlas-based trained maximum likelihood classifier. The classifier requires a map of prior probabilities, which is obtained by registering a large number of previously classified data sets to the atlas and calculating the resulting probability that each represented tissue type or structure will appear at each voxel in the data set. Classification is then carried out using the standard maximum likelihood discriminant function, assuming normal statistics. The results of this classification process can be used in three ways, depending on the type of structure that is being detected or measured. In the most straightforward case, measurement of a normal neural sub-structure such as the hippocampus, the results of the classifier provide a localization point for the initiation of a deformable template model, which is then optimized with respect to the original data. The detection and measurement of abnormal structures, such as white matter lesions in multiple sclerosis patients, requires a slightly different approach. Lesions are detected through the application of a spectral matched filter to areas identified by the classifier as white matter. Finally, detection of unknown abnormalities can be accomplished through anomaly detection.
Two novel methods for automated quantification of total lesion burden in multiple sclerosis patients using multi-spectral magnetic resonance (MR) imaging are examined. The first method, geometrically constrained region growth, requires user specification of lesion location. The second, directed multi-spectral segmentation, requires only the location of a single exemplar lesion. The performances of these methods are compared to manual tracing using three parameters: speed, precision, and accuracy. Both methods are shown to provide significant improvement over manual tracing in terms of processing time, inter- and intra-operator coefficients of variation, and global accuracy using both phantoms and clinical data.
In this paper, a novel technique is presented for the extraction of features from 3D medical image sequences. This technique involves grayscale segmentation, followed by application of a 3D deformable model algorithm which smooths the data and compensates for drop-out regions in the segmentation. These properties are particularly desirable in the application studied here, which is the extraction of the left ventricle from a 3D ultrasound scan. THe algorithm is shown to produce a good reconstruction of the LV, as well as an accurate measurement of its volume.
We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation. Results appear to provide improvements over previous techniques, in terms of both low-resolution detail and accuracy.