IEEE transactions on medical imaging
-
IEEE Trans Med Imaging · Aug 2004
Comparative StudyObject-based morphometry of the cerebral cortex.
Most of the approaches dedicated to automatic morphometry rely on a point-by-point strategy based on warping each brain toward a reference coordinate system. In this paper, we describe an alternative object-based strategy dedicated to the cortex. ⋯ The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.
-
IEEE Trans Med Imaging · Jul 2004
A method of generalized projections (MGP) ghost correction algorithm for interleaved EPI.
Investigations into the method of generalized projections (MGP) as a ghost correction method for interleaved EPI are described. The technique is image-based and does not require additional reference scans. The algorithm was found to be more effective if a priori knowledge was incorporated to reduce the degrees of freedom, by modeling the ghosting as arising from a small number of phase offsets. ⋯ Dependence of convergence on the region of support was also investigated. A fully automatic version of the method was developed, using results from the simulations. When tested on in vivo 2-, 16-, and 32-interleaved spin-echo EPI data, the method achieved deghosting and image restoration close to that obtained by both reference scan and odd/even filter correction, although some residual artifacts remained.
-
IEEE Trans Med Imaging · Jul 2004
Quantitative evaluation of image-based distortion correction in diffusion tensor imaging.
A statistical method for the evaluation of image registration for a series of images based on the assessment of consistency properties of the registration results is proposed. Consistency is defined as the residual error of the composition of cyclic registrations. By combining the transformations of different algorithms the consistency error allows a quantitative comparison without the use of ground truth, specifically, it allows a determination as to whether the algorithms are compatible and hence provide comparable registrations. ⋯ Transformations derived from imaging physics and a three-dimensional affine transformation as well as mutual information (MI) and local correlation (LC) similarity are compared to each other by means of consistency testing. The dedicated transformations could not demonstrate a significant difference for more than half of the series considered. LC similarity is well-suited for distortion correction providing more consistent registrations which are comparable to MI.
-
IEEE Trans Med Imaging · Apr 2004
Comparative StudyImproved watershed transform for medical image segmentation using prior information.
The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. ⋯ Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.
-
IEEE Trans Med Imaging · Mar 2004
Comparative Study Clinical TrialModel-independent method for fMRI analysis.
This paper presents a fast method for delineation of activated areas of the brain from functional magnetic resonance imaging (fMRI) time series data. The steps of the work accomplished are as follows. 1) It is shown that the detection performance evaluated by the area under the receiver operating characteristic curve is directly related to the signal-to-noise ratio (SNR) of the composite image generated in the detection process. 2) Detection and segmentation of activated areas are formulated in a vector space framework. In this formulation, a linear transformation (image combination method) is shown to be desirable to maximize the SNR of the activated areas subject to the constraint of removing inactive areas. 3) An analytical solution for the problem is found. 4) Image pixel vectors and expected time series pattern (signature) for inactive pixels are used to calculate weighting vector and identify activated regions. 5) Signatures of the activated regions are used to segment different activities. 6) Segmented images by the proposed method are compared with those generated by the conventional methods (correlation, t-statistic, and z statistic). ⋯ The proposed approach outperforms the conventional methods of fMRI analysis. In addition, it is model-independent and does not require a priori knowledge of the fMRI response to the paradigm. Since the method is linear and most of the work is done analytically, numerical implementation and execution of the method are much faster than the conventional methods.