Pipeline Architecture and Parallel Computation-Based Real-Time Stereovision Tracking System for Surgical Navigation

This paper studies the development of a real-time stereovision system to track multiple infrared markers attached to a surgical instrument. Multiple stages of pipeline in field-programmable gate array (FPGA) are developed to recognize the targets in both left and right image planes and to give each target a unique label. The pipeline architecture includes a smoothing filter, an adaptive threshold module, a connected component labeling operation, and a centroid extraction process. A parallel distortion correction method is proposed and implemented in a dual-core DSP. A suitable kinematic model is established for the moving targets, and a novel set of parallel and interactive computation mechanisms is proposed to position and track the targets, which are carried out by a cross-computation method in a dual-core DSP. The proposed tracking system can track the 3-D coordinate, velocity, and acceleration of four infrared markers with a delay of 9.18 ms. Furthermore, it is capable of tracking a maximum of 110 infrared markers without frame dropping at a frame rate of 60 f/s. The accuracy of the proposed system can reach the scale of 0.37 mm RMS along the x- and y-directions and 0.45 mm RMS along the depth direction (the depth is from 0.8 to 0.45 m). The performance of the proposed system can meet the requirements of applications such as surgical navigation, which needs high real time and accuracy capability.

Pipeline Architecture and Parallel Computation-Based Real-Time Stereovision Tracking System for Surgical Navigation

This paper studies the development of a real-time stereovision system to track multiple infrared markers attached to a surgical instrument. Multiple stages of pipeline in field-programmable gate array (FPGA) are developed to recognize the targets in both left and right image planes and to give each target a unique label. The pipeline architecture includes a smoothing filter, an adaptive threshold module, a connected component labeling operation, and a centroid extraction process. A parallel distortion correction method is proposed and implemented in a dual-core DSP. A suitable kinematic model is established for the moving targets, and a novel set of parallel and interactive computation mechanisms is proposed to position and track the targets, which are carried out by a cross-computation method in a dual-core DSP. The proposed tracking system can track the 3-D coordinate, velocity, and acceleration of four infrared markers with a delay of 9.18 ms. Furthermore, it is capable of tracking a maximum of 110 infrared markers without frame dropping at a frame rate of 60 f/s. The accuracy of the proposed system can reach the scale of 0.37 mm RMS along the x- and y-directions and 0.45 mm RMS along the depth direction (the depth is from 0.8 to 0.45 m). The performance of the proposed system can meet the requirements of applications such as surgical navigation, which needs high real time and accuracy capability.

Optical image contrast reversal using bacteriorhodopsin films

The implementation of image contrast reversal by using a photochromic material of Bacteriorhodopsin (BR) films is demonstrated with two methods based on the optical properties of BR. One is based on the absorption difference between the B and M states. Images recorded by green light can be contrast reversed readout by violet light. The other is based on the photoinduced anisotropy of BR when it is excited by linear polarization light. By placing the BR film between two crossed polarizers (i.e. a polarizer and an analyser), the difference of polarization states of the recorded area and the unrecorded area can be detected, and thus different contrast images can be obtained by rotating the polarization axis of the analyser.

A multi-frame image super-resolution method

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction. (C) 2009 Elsevier B.V. All rights reserved.

A static polarization imaging spectrometer based on a Savart polariscope

This paper describes the design of an interference imaging spectrometer. A static Polarization Imaging Spectrometer (PIS) based on a single Savart polariscope has been developed. It produces the interferogram and target's image in the spatial domain which are recorded by using a two-dimensional (2D) CCD detector. Imaging lens localizes the interference fringes and target's image coincident with the plane of detector, thereby facilitating an extremely compact design. The spectrum of the input light is reconstructed through the Fourier-transform of the interferogram. The total optics is as small as 20 x 6 cm phi in size and the spectral resolution of the prototype system is 97.66 cm(-1) between 25,000 and 10, 000 cm(-1). The polarization interference imaging device has advantages of ultra-compact size, wide field of view, high throughput and without any moving parts. (C) 2002 Published by Elsevier Science B.V.

Ultrahigh-resolution fiber-optic image guides derived from microstructured polymer optical fiber preforms

Ultrahigh-resolution fiber-optic image guides-fused image fiber, faceplate, and taper-were fabricated by using microstructured polymer optical fiber (MPOF) preforms composed of two polymers: polymethylmethacrylate and polystyrene. The pixel diameter in the resultant MPOF-based image guides was as small as 3 mu m. The imaging capabilities of these types of fiber-optic elements were demonstrated. (C) 2009 Optical Society of America

Droplet Image Feedback Control System in Evaporation Experiment

In order to realize the steady-state droplet evaporation, image feedback control system is designed based on DSP. The system has three main functions: to capture and store droplet images during the experiment; to calculate droplet geometrical and physical parameters such as volume, surface area, surface tension and evaporation velocity at a high-precision level; to keep the droplet volume constant. The DSP can drive an injection controller with the PID control to inject liquid so as to keep the droplet volume constant. The evaporation velocity of droplet can be calculated by measuring the injected volume during the evaporation. The structure of hardware and software of the control system, key processing methods such as contour fitting and experimental results are described.

Detection of avian influenza virus subtype H5 using a biosensor based on imaging ellipsometry

A novel method is reported for the detection of avian influenza virus subtype H5 using a biosensor based on high spatial resolution imaging ellipsometry (IE). Monoclonal antibodies specific to H5 hemagglutinin protein were immobilized on silicon wafers and used to capture virus particles. Resultant changes on the surface of the wafers were visualized directly in gray-scale on an imaging ellipsometry image. This preliminary study has shown that the assay is rapid and specific for the identification of avian influenza virus subtype H5. Compared with lateral-flow immunoassays, this biosensor not only has better sensitivity, but can also simultaneously perform multiplexed tests. These results suggest that this biosensor might be a valuable diagnostic toot for avian influenza virus detection. (c) 2009 Elsevier B.V. All rights reserved.

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.