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Conference on image processing - algorithms and systems vi

作者： Yildiz, Yesna O. Abraham, Douglas Q. Agaian, Sos Panetta, Karen Anal Sci Corp 8 Centennial Dr Peabody MA 01960 USA UTSA Dept Elect & Comp Engn San Antonio TX 78249 USA Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA

ISBN: (纸本)9780819469847

Automated Explosive Detection systems (EDS) utilizing Computed Tomography (CT) generate a series of 2-D projections from a series of X-ray scans OF luggage under inspection. 3-D volumetric images can also be generated from the collected data set. Extensive data manipulation of the 2-D and 3-D image sets for detecting the presence of explosives is done automatically by EDS. The results are then forwarded to human screeners for final review. The final determination as to whether the luggage contains an explosive and needs to be searched manually is performed by trained TSA (Transportation Security Administration) screeners following an approved TSA protocol. The TSA protocol has the screeners visually inspect the resulting images and the renderings from the EDS to determine if the luggage is suspicious and consequently should be searched manually. Enhancing those projection images delivers a higher quality screening, reduces screening time and also reduces the amount of luggage that needs to be manually searched otherwise. This paper presents a novel edge detection algorithm that is geared towards, though not exclusive to, automated explosive detection systems. The goal of these enhancements is to provide a higher quality screening process while reducing the overall screening time and luggage search rates. Accurately determining the location of edge pixels within 2-D signals, often the first step in segmentation and recognition systems indicates the boundary between overlapping objects in a luggage. Most of the edge detection algorithms such as Canny, Prewitt, Roberts, Sobel, and Laplacian methods are based on the first and second derivatives/difference operators. These operators detect the discontinuities in the differences of pixels. These approaches are sensitive to the presence of noise and could produce false edges in noisy images. Including large scale filters, may avoid errors generated by noise, but often simultaneously eliminating the finer edge details as we

关键词： edge detection computed tomography explosive detection systems

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High-quality image interpolation via nonlinear image decomposition

High-quality image interpolation via nonlinear image decompo...

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Conference on image processing - algorithms and systems vi

作者： Saito, Takahiro Ishii, Yuki Aizawa, Haruya Komatsu, Takashi Kanagawa Univ High Tech Res Ctr Dept E&I Frontiers Kanagawa Ku Yokohama Kanagawa 2218686 Japan

ISBN: (纸本)9780819469847

This paper presents a new image-interpolation approach where one can adjust edge sharpness and texture intensity according to one's taste. This approach is composed of the three stages. At the first stage, with the BV-G image-decomposition variational model, an image is represented as a product of its two components so that its separated structural component may correspond to a cartoon image-approximation and its separated texture components may collect almost all oscillatory variations representing textures, and the texture component can be amplified or attenuated according to user's taste. At the second stage, each separated component is interpolated with an interpolation method suitable to it. Since the structural component keeps sharp edges, its proper interpolation method is a TV-regularization super-resolution interpolation method that can restore frequency components higher than the Nyquist frequency and remove sample-hold blurs without producing ringing artifacts near edges. The texture component is an oscillatory function, and its proper interpolation method is a smoothness-regularization super-resolution interpolation method that can restore continuous variations and remove the blurs. At the final stage, the two interpolated components are combined. The approach enlarges images without not only blurring edges but also destroying textures, and removes blurs caused by the sample-hold and/or the optical low-pass filter without producing ringing artifacts.

关键词： image decomposition variational problem interpolation deblurring super-resolution

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Deblurring noisy radial-blurred images: spatially adaptive filtering approach

Deblurring noisy radial-blurred images: spatially adaptive f...

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Conference on image processing - algorithms and systems vi

作者： Boracchi, Giacomo Foi, Alessandro Katkovnik, Vladimir Egiazarian, Karen Politecn Milan Dipartimento Elettron & Informaz Via Ponzio34-5 I-20133 Milan Italy Tampere Univ Technol Tampere Int Ctr Signal Proc FIN-33101 Tampere Finland Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9780819469847

The deblurring of images corrupted by radial blur is studied. This type of blur appears in images acquired during any camera translation having a substantial component orthogonal to the image plane. The point spread functions (PSF) describing this blur are spatially varying. However, this blurring process does not mix together pixels lying on different radial lines, i.e. lines stemming from a unique point in the image, the so called "blur center". Thus, in suitable polar coordinates, the blurring process is essentially a 1-D linear operator, described by the multiplication with the blurring matrix. We consider images corrupted simultaneously by radial blur and noise. The proposed deblurring algorithm is based on two separate forms of regularization of the blur inverse. First, in the polar domain, we invert the blurring matrix using the Tikhonov regularization. We then derive a particular modeling of the noise spectrum after both the regularized inversion and the forward and backward coordinate transformations. Thanks to this model, we successfully use a denoising algorithm in the Cartesian domain. We use a non-linear spatially adaptive filter, the Pointwise Shape-Adaptive DCT, in order to exploit the image structures and attenuate noise and artifacts. Experimental results demonstrate that the proposed algorithm can effectively restore radial blurred images corrupted by additive white Gaussian noise.

关键词： Cameras Point spread functions Denoising image processing image filtering Digital filtering Nonlinear filtering Transform theory image restoration Stereolithography

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Effect of hierarchical deformable motion compensation on image enhancement for DSA acquired via C-ARM

Effect of hierarchical deformable motion compensation on ima...

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Conference on image processing - algorithms and systems vi

作者： Wei, Liyang Shen, Dinggang Kumar, Dinesh Turlapati, Ram Suri, Jasjit S. Eigen LLC 13366 Grass Valley Ave Grass Valley CA 95945 USA Univ Penn Sch Med Dept Radiol Philadelphia PA 19104 USA Univ Wisconsin Theda Clark Hosp Appleton Madison WI 54911 USA

ISBN: (纸本)9780819469847

DSA images suffer from challenges like system X-ray noise and artifacts due to patient movement. In this paper, we present a two-step strategy to improve DSA image quality. First, a hierarchical deformable registration algorithm is used to register the mask frame and the bolus frame before subtraction. Second, the resulted DSA image is further enhanced by background diffusion and nonlinear normalization for better visualization. Two major changes are made in the hierarchical deformable registration algorithm for DSA images: 1) B-Spline is used to represent the deformation field in order to produce the smooth deformation field;2) two features are defined as the attribute vector for each point in the image, i.e., original image intensity and gradient. Also, for speeding up the 2D image registration, the hierarchical motion compensation algorithm is implemented by a multi-resolution framework. The proposed method has been evaluated on a database of 73 subjects by quantitatively measuring signal-to-noise (SNR) ratio. DSA embedded with proposed strategies demonstrates an improvement of 74.1% over conventional DSA in terms of SNR. Our system runs on Eigen's DSA workstation using C++ in Windows environment.

关键词： DSA hierarchical deformable registration diffusion nonlinear normalization

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Statistical edge detection of concealed weapons using artificial neural networks

Statistical edge detection of concealed weapons using artifi...

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Conference on image processing - algorithms and systems vi

作者： Williams, Ian Svoboda, David Bowring, Nicholas Guest, Elizabeth Manchester Metropolitan Univ Dept Engn & Technol Manchester M15 6BH Lancs England Masaryk Univ Fac Informat Ctr Biomed Image Anal Brno Czech Republic Leeds Metropolitan Univ Sch Comp Leeds W Yorkshire England

ISBN: (纸本)9780819469847

A novel edge detector has been developed that utilises statistical masks and neural networks for the optimal detection of edges over a wide range of image types. The failure of many common edge detection techniques has been observed when analysing concealed weapons X-ray images, biomedical images or images with significant levels of noise, clutter or texture. This novel technique is based on a statistical edge detection filter that uses a range of two-sample statistical tests to evaluate any local image texture differences and by applying a pixel region mask (or kernel) to the image analyse the statistical properties of that region. The range and type of tests has been greatly expanded from the previous work of Bowring et al.(1) This process is further enhanced by applying combined multiple scale pixel masks and multiple statistical tests, to Artificial Neural Networks (ANN) trained to classify different edge types. Through the use of Artificial Neural Networks (ANN) we can combine the output results of several statistical mask scales into one detector. Furthermore we can allow the combination of several two sample statistical tests of varying properties (for example;mean based, variance based and distribution based). This combination of both scales and tests allows the optimal response from a variety of statistical masks. From this we can produce the optimum edge detection output for a wide variety of images, and the results of this are presented.

关键词： edge detection segmentation neural networks statistics cluttered images X-ray Baggage screening

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Application of statistical cancer atlas for 3D biopsy

Application of statistical cancer atlas for 3D biopsy

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Conference on image processing - algorithms and systems vi

作者： Narayanan, Ramkrishnan Shen, Dinggang Davatzikos, Christos Crawford, E. David Barqawi, Albaha Werahera, Priya Kumar, Dinesh Suri, Jasjit S. Eigen LLC 13366 Grass Valley Ave Grass Valley CA 95945 USA Univ Penn Dept Radiol Sect Biomed Image Anal Philadelphia PA 19104 USA Univ Colorado Hosp Denver CO 80262 USA

ISBN: (纸本)9780819469847

Prostate cancer is the most commonly diagnosed cancer in males in the United States and the second leading cause of cancer death. While the exact cause is still under investigation, researchers agree on certain risk factors like age, family history, dietary habits, lifestyle and race. It is also widely accepted that cancer distribution within the prostate is inhomogeneous, i.e. certain regions have a higher likelihood of developing cancer. In this regard extensive work has been done to study the distribution of cancer in order to perform biopsy more effectively. Recently a statistical cancer atlas of the prostate was demonstrated along with an optimal biopsy scheme achieving a high detection rate. In this paper we discuss the complete construction and application of such an atlas that can be used in a clinical setting to effectively target high cancer zones during biopsy. The method consists of integrating intensity statistics in the form of cancer probabilities at every voxel in the image with shape statistics of the prostate in order to quickly warp the atlas onto a subject ultrasound image. While the atlas surface can be registered to a pre-segmented subject prostate surface or instead used to perform segmentation of the capsule via optimization of shape parameters to segment the subject image, the strength of our approach lies in the fast mapping of cancer statistics onto the subject using shape statistics. The shape model was trained from over 38 expert segmented prostate surfaces and the atlas registration accuracy was found to be high suggesting the use of this method to perform biopsy in near real time situations with some optimization.

关键词： prostate registration atlas shape cancer

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Microcalcification detection system in digital mammogram using two-layer SVM

Microcalcification detection system in digital mammogram usi...

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Conference on image processing - algorithms and systems vi

作者： Cho, Sunil Jin, Sung Ho Kwon, Ju Won Ro, Yong Man Kim, Sung Min ICU Image & Video Syst Lab Munji 103-6 Taejon 305732 South Korea KonKuk Univ Sch Med Dept Biomed Engn Seoul South Korea

ISBN: (纸本)9780819469847

Microcalcification detection in a mammogram is an effective method to find the early stage of breast tumor. Especially, computer aided diagnosis (CAD) improves the working performance of radiologists and doctors as it offers an efficient microcalcification detection. In this paper, we propose a microcalcification detection system which consists of three modules;coarse detection, clustering, and fine detection module. The coarse detection module finds candidate pixels from an entire mammogram which are suspected as a part of a microcalcification. The module not only extracts two median contrast features and two contrast-to-noise ratio features, but also categorizes the candidate pixels with a linear kernel-based SVM classifier. Then, the clustering module forms the candidate pixels into regions of interest (ROI) using a region growing algorithm. The objective of the fine detection module is to decide whether the corresponding region classifies as a microcalcification or not. Eleven features including distribution, variance, gradient, and various edge components are extracted from the clustered ROIs and are fed into a radial basis function-based SVM classifier to determine the microcalcification. In order to verify the effectiveness of the proposed microcalcification detection system, the experiments are performed with full-field digital mammogram (FFDM). We also compare its detection performance with an ANN-based detection system.

关键词： microcalcification CAD (computer aided diagnosis) SVM (support vector machine)

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Effect of 3-D automated prostate segmentation for ultrasound image guided repeat biopsy application

Effect of 3-D automated prostate segmentation for ultrasound...

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Conference on image processing - algorithms and systems vi

作者： Guo, Yujun Li, Lu Narayanan, Ramkrishnan Kumar, Dinesh Barqawi, Albaha Crawford, E. David Suri, Jasjit S. Eigen LLC 13366 Grass Valley Ave Grass Valley CA 95945 USA Univ Colorado Hlth Sci Ctr Aurora CO USA

ISBN: (纸本)9780819469847

Prostate repeat biopsy has become one of the key requirements in today's prostate cancer detection. Urologists are interested in knowing previous 3-D biopsy locations during the current visit of the patient. Eigen has developed a system for performing 3-D Ultrasound image guided prostate biopsy. The repeat biopsy tool consists of three stages: (1) segmentation of the prostate capsules from previous and current ultrasound volumes;(2) registration of segmented surfaces using adaptive focus deformable model;(3) mapping of old biopsy sites onto new volume via thin-plate splines (TPS). The system critically depends on accurate 3-D segmentation of capsule volumes. In this paper, we study the effect of automated segmentation technique on the accuracy of 3-D ultrasound guided repeat biopsy. Our database consists of 38 prostate volumes of different patients which are acquired using Philips sidefire transrectal ultrasound (TRUS) probe. The prostate volumes were segmented in three ways: expert segmentation, semi-automated segmentation, and fully automated segmentation. New biopsy sites were identified in the new volumes from different segmentation methods, and we compared the mean squared distance between biopsy sites. It is demonstrated that the performance of our fully automated segmentation tool is comparable to that of semi-automated segmentation method.

关键词： repeat biopsy semi-automated segmentation automated segmentation adaptive focus deformable model (AFDM) thin-plate splines (TPS)

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Multi-source image reconstruction: exploitation of EO-1/ALI in Landsat-7/ETM+ SLC-off gap filling

Multi-source image reconstruction: exploitation of EO-1/ALI ...

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Conference on image processing - algorithms and systems vi

作者： Boloorani, Ali Darvishi Erasmi, Stefan Kappas, Martin Univ Gottingen Inst Geog GIS & RS Dept Cartog D-37077 Gottingen Germany

ISBN: (纸本)9780819469847

The Landsat-7 Enhanced Thematic Mapper Plus (ETM+) is the sensor payload on the Landsat-7 satellite imager (launched on April 15th, 1999) that is a derivative of the Landsat-4 and 5 Thematic Mapper (TM) land imager sensors. Scan Line Corrector (SLC) malfunctioning appeared onboard on May 31, 2003. The SLC-Off problem was caused by failure of the SLC which compensates for the forward motion of the satellite [1]. As ETM+ is still capable of acquiring images with the SLC-Off mode, the need of applying new techniques and using other data sources to reconstruct the missed data is a challenging for scientists and final users of remotely sensed images. One of the predicted future roles of the Advanced Land imager (ALI) onboard the Earth Observer One (EO-1) is its ability to offer a potential technological direction for Landsat data continuity missions [2]. In this regard more than the purposes of the work as fabricating the gapped area in the ETM+ the attempt to evaluate the ALI imagery ability is another noticeable point in this work. In the literature there are several techniques and algorithms for gap filling. For instance local linear histogram matching [3], ordinary kriging, and standardized ordinary cokriging [4]. Here we used the Regression Based Data Combination (RBDC) in which it is generally supposed that two data sets (i.e. Landsat/ETM+ and EO-1/ALI) in the same spectral ranges (for instance band 3 ETM+ and band 4 ALI in 0.63 - 0.69 mu m) will have meaningful and useable statistical characteristics. Using this relationship the gap area in ETM+ can be filled using EO-1/ALI data. Therefore the process is based on the knowledge of statistical structures of the images which is used to reconstruct the gapped areas. This paper presents and compares four regression based techniques. First two ordinary methods with no improvement in the statistical parameters were undertaken as Scene Based (SB) and Cluster Based (CB) followed by two statistically developed algorithms in

关键词： Landsat/ETM plus SLC-off EO-1/ALI Regression Based Data Combination (RBDC) gap filling

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Perfect vision

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Manufacturing Engineer 2006年第5期85卷 42-45页

作者： Lu, Kevin VI Engineering Inc. VI Engineering Inc. Farmington Hills MI United States

The award winning paper at the NI Week Manufacturing Awards proposed to develop a system that measures time spacing and XY position of fast falling objects. The system is slated to distinguish objects in a clump with object counting accuracy that is higher than 99% at the rate of up to 200 per second. The system is a machine vision system that is based on a double line scan cameras/backlighting units with special image processing algorithms to identify, match, count and measure objects. the system specifications can be improved by using components with higher performance, such as cameras with higher line rate, more pixels, and frame grabbers with PCI Express technology. The machine vision system based on FireWire line scan camera delivered good performance at a lower cost. One special advantage of the system is that it can distinguish clump objects that were mis-counted by previous systems.

关键词： Production engineering

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