A model based on the Pennes bioheat transfer equation is valid in normal brain tissue but not brain tissue suffering focal ischaemia.

Ischaemic stroke is a major public health issue in both developed and developing nations. Hypothermia is believed to be neuroprotective in cerebral ischaemia. Conversely, elevated brain temperature is associated with poor outcome after ischaemic stroke. Mechanisms of heat exchange in normally-perfused brain are relatively well understood, but these mechanisms have not been studied as extensively during focal cerebral ischaemia. A finite element model (FEM) of heat exchange during focal ischaemia in the human brain was developed, based on the Pennes bioheat equation. This model incorporated healthy (normally-perfused) brain tissue, tissue that was mildly hypoperfused but not at risk of cell death (referred to as oligaemia), tissue that was hypoperfused and at risk of death but not dead (referred to as penumbra) and tissue that had died as a result of ischaemia (referred to as infarct core). The results of simulations using this model were found to match previous in-vivo temperature data for normally-perfused brain. However, the results did not match what limited data are available for hypoperfused brain tissue, in particular the penumbra, which is the focus of acute neuroprotective treatments such as hypothermia. These results suggest that the assumptions of the Pennes bioheat equation, while valid in the brain under normal circumstances, are not valid during focal ischaemia. Further investigation into the heat exchange profiles that do occur during focal ischaemia may yield results for clinical trials of therapeutic hypothermia.

Correction of geometrically distorted underwater images using shift map analysis.

In underwater imaging, water waves cause severe geometric distortions and blurring of the acquired short-exposure images. Corrections for these distortions have been tackled reasonably well by previous efforts but still need improvement in the estimation of pixel shift maps to increase restoration accuracy. This paper presents a new algorithm that efficiently estimates the shift maps from geometrically distorted video sequences and uses those maps to restore the sequences. A nonrigid image registration method is employed to estimate the shift maps of the distorted frames against a reference frame. The sharpest frame of the sequence, determined using a sharpness metric, is chosen as the reference frame. A k-means clustering technique is employed to discard too-blurry frames that could result in inaccuracy in the shift maps' estimation. The estimated pixel shift maps are processed to generate the accurate shift map that is used to dewarp the input frames into their nondistorted forms. The proposed method is applied on several synthetic and real-world video sequences, and the obtained results exhibit significant improvements over the state-of-the-art methods.

Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images.

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the body's internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures.

Restoring atmospheric-turbulence-degraded images.

Image data experiences geometric distortions and spatial-temporal varying blur due to the strong effects of random spatial and temporal variations in the optical refractive index of the communication path. Simultaneously removing these effects from an image is a challenging task. An efficient approach is proposed in this paper to address this problem. The approach consists of four steps. First, a frame selection strategy is employed by proposing an unsupervised k-means clustering technique. Second, a B-spline-based nonrigid image registration is carried out to suppress geometric distortions. Third, a spatiotemporal kernel regression is proposed by introducing the local sharp patch concept to fuse the registered frame sequences into an image. Finally, a blind deconvolution technique is employed to deblur the fused image. Experiments are carried out with synthetic and real-world turbulence-degraded data by implementing the proposed method and two recently reported methods. The proposed method demonstrates significant improvement over the two reported methods in terms of alleviating blur and distortions, as well as improving visual quality.

Sparsity Prior Computed Tomography Reconstruction Using a Nonstandard Simultaneous X-ray Acquisition Model.

In this article we systematically evaluate the performance of several state-of-the-art, sparsity prior computed tomography (CT) reconstruction algorithms, using a nonstandard simultaneous x-ray acquisition method. Sparsity prior is an efficient strategy in CT reconstruction, relying on iterative algorithms such as the algebraic reconstruction technique to produce a crude reconstruction, based on which sparse approximation is performed. The simultaneous x-ray acquisition model ensures rapid capture of x-rays; however, it captures a significantly fewer number of attenuation measurements, and the projections are nonuniform. We propose a weighted average filter in the reconstruction framework to ensure better quality reconstruction by minimizing the effect of nonuniform projections. The performance of the state-of-the-art algorithms is analyzed with and without weighted averaging before sparse approximation, in simulated and real environments. Experiments in the simulated environment are conducted with and without the presence of noise. From the results, it is evident that sparsity prior algorithms are capable of producing cross-sectional reconstruction using the simultaneous x-ray acquisition model, and better reconstruction quality is achievable with the incorporation of weighted averaging in the reconstruction framework.

Visualization of Nerve Fiber Orientations in the Human Optic Chiasm Using Photomicrographic Image Analysis.

PURPOSE: Hemidecussation of fibers entering the optic chiasm from the optic nerves is well recognized. The reason why bitemporal hemianopia results from chiasmal compression has not been fully explained. There is still a paucity of data relating to the precise details of the routes that the nerve fibers take through the chiasm and, in particular, where and how nerve fibers cross each other. This information is important to understanding why crossing fibers are selectively damaged as a result of chiasmal compression. METHODS: An optic chiasm obtained at postmortem was fixed, stained, and sectioned to allow high-resolution photomicrographs to be taken. The photomicrographs were integrated to allow regions of interest across entire sections to be analyzed for fiber direction and crossing. RESULTS: The results confirmed that fibers from the temporal retina pass directly backward in the lateral chiasm to the optic tract, whereas fibers from the nasal retina cross to the contralateral optic tract. Crossings take place in the paracentral regions of the chiasm rather than in the center of the chiasm (where the nerve fibers are traveling mostly in parallel). The paracentral crossing regions are distributed in a largely postero-superior to antero-inferior arrangement. CONCLUSIONS: These findings clarify the precise locations and crossing angles of crossing nerve fibers in the chiasm. This information may help explain the clinical observation of junctional scotoma and will provide a much better basis for structural modeling of chiasmal compression which, in turn, will improve our understanding of how and why bitemporal hemianopia occurs.

Geometric correction of atmospheric turbulence-degraded video containing moving objects.

Long-distance surveillance is a challenging task because of atmospheric turbulence that causes time-varying image shifts and blurs in images. These distortions become more significant as the imaging distance increases. This paper presents a new method for compensating image shifting in a video sequence while keeping real moving objects in the video unharmed. In this approach, firstly, a highly accurate and fast optical flow technique is applied to estimate the motion vector maps of the input frames and a centroid algorithm is employed to generate a geometrically correct frame in which there is no moving object. The second step involves applying an algorithm for detecting real moving objects in the video sequence and then restoring it with those objects unaffected. The performance of the proposed method is verified by comparing it with that of a state-of-the-art approach. Simulation experiments using both synthetic and real-life surveillance videos demonstrate that this method significantly improves the accuracy of image restoration while preserving moving objects.

Model-free prediction of atmospheric warp based on artificial neural network.

This paper presents the application of artificial neural network for predicting the warping of images of remote objects or scenes ahead of time. The algorithm is based on estimating the pattern of warping of previously captured short-exposure frames through a generalized regression neural network (GRNN) and then predicting the warping of the upcoming frame. A high-accuracy optical flow technique is employed to estimate the dense motion fields of the captured frames, which are considered as training data for the GRNN. The proposed approach is independent of the pixel-oscillatory model unlike the state-of-the-art Kalman filter (KF) approach. Simulation experiments on synthetic and real-world turbulence degraded videos show that the proposed GRNN-based approach performs better than the KF approach in atmospheric warp prediction.

Simple and efficient approach for restoration of non-uniformly warped images.

A high accuracy image dewarping method is proposed to restore images from non-uniformly warped video sequences degraded by atmospheric turbulence. This approach contains three major steps. First, a non-rigid image registration technique is employed to register all the frames in the sequence to a reference frame and estimate the motion fields. Second, an iterative First Register Then Average And Subtract (iFRTAAS) method is applied to correct the geometric deformations of the warped frames. The third step involves applying a non-local means filter for the compensation of noise and to improve the signal-to-noise ratio (SNR) of the restored reference frame. Simulations are carried out by applying the method to synthetic and real-life turbulence degraded videos and by determining various quality metrics. A performance comparison is presented between the proposed method and two earlier methods, which verifies that the proposed method provides significant improvement on the image restoration accuracy.

Finite element modeling of optic chiasmal compression.

BACKGROUND: The precise mechanism of bitemporal hemianopia is still not clear. Our study investigated the mechanism of bitemporal hemianopia by studying the biomechanics of chiasmal compression caused by a pituitary tumor growing below the optic chiasm. METHODS: Chiasmal compression and nerve fiber interaction in the chiasm were simulated numerically using finite element modeling software. Detailed mechanical strain distributions in the chiasm were obtained to help understand the mechanical behavior of the optic chiasm. Nerve fiber models were built to determine the relative difference in strain experienced by crossed and uncrossed nerve fibers. RESULTS: The central aspect of the chiasm always experienced higher strains than the peripheral aspect when the chiasm was loaded centrally from beneath. Strains in the nasal (crossed) nerve fibers were dramatically higher than in temporal (uncrossed) nerve fibers. CONCLUSIONS: The simulation results of the macroscopic chiasmal model are in agreement with the limited experimental results available, suggesting that the finite element method is an appropriate tool for analyzing chiasmal compression. Although the microscopic nerve fiber model was unvalidated because of lack of experimental data, it provided useful insights into a possible mechanism of bitemporal hemianopia. Specifically, it showed that the strain difference between crossed and uncrossed nerve fibers may account for the selective nerve damage, which gives rise to bitemporal hemianopia.