No abstract

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the EM algorithm leads to a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.

No abstract

An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas.

ÐThis paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values.

Medical Image Computing researchers often face the problem of moving promising new algorithms from the proof of concept stage into a form compatible with clinical use. Algorithm developers lack the time and resources to engineer their code for robustness and compatibility, while end-users are anxious to try new techniques but require well designed and tested user interfaces to make practical use of them. The NA-MIC Kit is a collection of software and methodology specifically designed to address these problems and facilitate the rapid advancement of the field.

Magnetic resonance (MR) image-based computerized segmentation was used to measure various intracranial compartments in 49 normal volunteers ranging in age from 24 to 80 years to determine age-related changes in brain, ventricular, and extraventricular cerebrospinal fluid (CSF) volumes. The total intracranial volume (sum of brain, ventricular, and extraventricular CSF) averaged 1469 +/- 102 cm3 in men and 1289 +/- 111 cm3 in women. The difference was attributable primarily to brain volume, which accounted for 88.6% of the respective intracranial volumes in both sexes, but was significantly larger in men (1302 +/- 112 cm3) than in women (1143 +/- 105 cm3). In both, the cranial CSF volume averaged 11.4%. Total intracranial volume did not change with age, although the normalized brain volume of both men and women began to decrease after the age of 40 years. This decrease was best reflected by expansion of the extraventricular CSF volume which, after the age of 50 years, was more marked in men than in women. The volume of the cranial CSF, as determined by MR image-based computerized segmentation, is considerably larger than traditionally accepted and resides mostly extraventricularly. Expansion of CSF volume with age provides a good index of brain shrinkage although evolving changes and growth of the head with age tend to confound the results.

No abstract