Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

Effect of brain edema on the recurrence pattern of malignant gliomas

PURPOSE: To assess the influence of initial preoperative brain edema in malignant gliomas on regrowth patterns. SUBJECTS AND METHODS: 79 patients with histologically verified supratentorial malignant glioma were prospectively studied by magnetic resonance imaging (MRI) before and every 2-3 months after surgery. The median follow-up time was 11 months. We correlated the configuration of the initial vasogenic edema on T2-weighted images with tumor regrowth patterns on contrast-enhanced T1-weighted images. RESULTS: 35/47 tumor regrowths (75%) imitated the initial edema configuration, while 11/47 occurred within the initial tumor bed; in one case tumor recurrence was multilocal. CONCLUSION: In glioblastoma, tumor regrowth patterns correlate positively with the configuration of the initial vasogenic brain edema. The initial, "presurgical" peritumoral edema should thus be considered when planning further treatment.     

Design rules for an integratable low-power amplifier/filter combination

In this paper design rules for a circuit topology in which there is an inseparable combination of an amplifier and a filter characteristic, are presented. By intentionally using the capacitance of an already present input sensor for the filtering, the total required integrated capacitance is much less than that in circuits, which have a separately designed amplifier and filter function. Consequently, it is possible to have the advantage of a better integratability. Moreover, less complexity in the design is achieved. The presented circuit shows a current-to-voltage conversion and an inherently controllable second-order low-pass filter characteristic. A discrete realization has been designed to test the circuit. This circuit operates down to a 1 V supply voltage and the transfer shows a 1.8 M currentto-voltage conversion with a bandwidth of 6 kHz. Measurement results of this circuit show that a 63 dB dynamic range can be achieved with a total required integrated capacitance of only 31 pF.     

Capillary HPLC-NMR Coupling: High-Resolution (1)H NMR Spectroscopy in the Nanoliter Scale

Coupling HPLC and NMR is one of the most powerful techniques for simultaneous separation and structural elucidation of unknown compounds in mixtures. To date, however, minimizing the detection volume, as is required when coupling NMR with miniaturized separation techniques, has been accompanied by a dramatic loss in resolution of the NMR spectra. Here, we report on the coupling of gradient capillary HPLC with on-column, high-resolution NMR detection. On-line stopped-flow and static (1)H NMR spectra were acquired with capillary columns of 75-315 μm i.d. With detection over a length of 1.2 cm, cell volumes cover a range of 50-900 nL. An on-line-detected NMR separation of dansylated amino acids was carried out in a 315 μm i.d. fused silica capillary packed to a length of 12 cm with C(18) stationary phase. The low solvent consumption makes the use of fully deuterated solvents economically feasible. NMR spectra with resolution on the order of 3 Hz were obtained using a 50 nL detection cell to measure 1.1 nmol of dansylated γ-aminobutyric acid under static conditions in a 75 μm i.d. capillary.     

A SIMD-efficient 14 instruction shader program for high-throughput microtriangle rasterization

This paper shows that breaking the barrier of 1 triangle/clock rasterization rate for microtriangles in modern GPU architectures in an efficient way is possible. The fixed throughput of the special purpose culling and triangle setup stages of the classic pipeline limits the GPU scalability to rasterize many triangles in parallel when these cover very few pixels. In contrast, the shader core counts and increasing GFLOPs in modern GPUs clearly suggests parallelizing this computation entirely across multiple shader threads, making use of the powerful wide-ALU instructions. In this paper, we present a very efficient SIMD-like rasterization code targeted at very small triangles that scales very well with the number of shader cores and has higher performance than traditional edge equation based algorithms. We have extended the ATTILA GPU shader ISA (del Barrioet al. in IEEE International Symposium on Performance Analysis of Systems and Software, pp. 231–241, 2006) with two fixed point instructions to meet the rasterization precision requirement. This paper also introduces a novel subpixel Bounding Box size optimization that adjusts the bounds much more finely, which is critical for small triangles, and doubles the 2×2-pixel stamp test efficiency. The proposed shader rasterization program can run on top of the original pixel shader program in such a way that selected fragments are rasterized, attribute interpolated and pixel shaded in the same pass. Our results show that our technique yields better performance than a classic rasterizer at 8 or more shader cores, with speedups as high as 4× for 16 shader cores.     

Mediating Media Studies – Stimulating critical awareness in a virtual environment

Second Life has become increasingly popular with educators and educational institutions, because of the educational possibilities it seems to offer. While there has been a lot of hype and academic publications about educational design or the theoretical framing of teaching in virtual environments, there have been few publications focussing on actual teaching and learning experiences of lecturers and students in Higher Education institutions. This article uses practical examples from an introductory course on Media studies taught at the Hong Kong Polytechnic University to demonstrate that even simple tasks in Second Life can be used to great effect if properly contextualised into the course being taught. High levels of technical skills, or extraordinary educational designs are less important to employing Second Life successfully in a Higher Education environment, than a proper evaluation of student learning outcomes, and teaching goals.     

3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD): Its Application to Edge Detection and Vascular Segmentation in Magnetic Resonance Angiography

Extracting reliable image edge information is crucial for active contour models as well as vascular segmentation in magnetic resonance angiography (MRA). However, conventional edge detection techniques, such as gradient-based methods and wavelet-based methods, are incapable of returning reliable detection responses from low contrast edges in the images. In this paper, we propose a novel edge detection method by combining B-spline wavelet magnitude with standard deviation inside local region. It is proved theoretically and demonstrated experimentally in this paper that the new edge detection method, namely BWLSD, is able to give consistent and reliable strengths for edges with different image contrasts. Moreover, the relationship between the size of local region with non-zero wavelet magnitudes and the scale of wavelet function is established. This relationship indicates that if the scale of the adopted wavelet function is s, then the size of a local region, from which the standard deviation is estimated, should be 2s−1. The proposed edge detection technique is embedded in FLUX, namely, BWLSD-FLUX, for vascular segmentation in MRA image volumes. Experimental results on clinical images show that, as compared with the conventional FLUX, BWLSD-FLUX can achieve better segmentations of vasculatures in MRA images under same initial conditions.     

Anisotropic Smoothness Classes: From Finite Element Approximation to Image Models

We propose and study quantitative measures of smoothness f ? A(f) which are adapted to anisotropic features such as edges in images or shocks in PDE’s. These quantities govern the rate of approximation by adaptive finite elements, when no constraint is imposed on the aspect ratio of the triangles, the simplest example being \(A_{p}(f)=\|\sqrt{|\mathrm{det}(d^{2}f)|}\|_{L^{\tau}}\) which appears when approximating in the L ^p norm by piecewise linear elements when \(\frac{1}{\tau}=\frac{1}{p}+1\). The quantities A(f) are not semi-norms, and therefore cannot be used to define linear function spaces. We show that these quantities can be well defined by mollification when f has jump discontinuities along piecewise smooth curves. This motivates for using them in image processing as an alternative to the frequently used total variation semi-norm which does not account for the smoothness of the edges.