Narrow Results

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to robustly and accurately quantify repeatable morphological measures from such data is aided by the ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid. The large amount of data associated with typical MRI scans makes completely manual segmentation prohibitive on a large scale. In this paper an efficient approach to the segmentation of such MR imagery is presented. The approach uses an estimation-theoretic interpretation of the segmentation problem to develop a computationally efficient, statistically-based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are extremely important both for evaluation of the estimates as well as their combination with other sources of information.

Digital papillary adenocarcinoma (DPAc) is a relatively rare neoplasm arising from the sweat glands with a predilection for the hand. A case of DPAc in the third finger at the level of the proximal phalanx in a 55-year-old male is presented. Our paper recommends specific consideration of DPAc in evaluating digital soft tissue masses, particularly those that present with an aggressive nature.

In order to conduct many non-intrusive clinical studies of the human brain, an accurate model that is capable of extracting the brain matter from magnetic resonance images (MRI) is required. We present a fully automated two-stage procedure to extract the brain matter accurately from a database of T1-weighted, high-quality MRI of healthy subjects. The procedure is initiated using a three dimensional (3D) segmentation process to separate the brain from other anatomical structures. The extracted brain is then subjected to an adaptive filter to remove cerebro-spinal fluid that fills sulcal cavities. The experiments clearly demonstrate the capability of the present technique in accurately peeling the brain. The accuracy of the results is tested using relative gray and white matter concentrations of both simulated and real MR images.