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检索条件"主题词=Image processing algorithms and systems"

共 335 条记录，以下是161-170 订阅

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FAST PARALLEL algorithms - FROM imageS TO LEVEL SETS AND LABELS

FAST PARALLEL ALGORITHMS - FROM IMAGES TO LEVEL SETS AND LAB...

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CONF ON PARALLEL ARCHITECTURES FOR image processing

作者： NGUYEN, HT JUNG, KK RAGHAVAN, R Lockheed Palo Alto Research Lab. (United States)

ISBN: (纸本)0819402931

Decomposition into level sets refers to assigning a code with respect to intensity or elevation while labeling refers to assigning a code with respect to disconnected regions. We present a sequence of parallel algorithms for these two processes. The process of labeling includes re-assign labels into a natural sequence and compare different labeling algorithm. We discuss the difference between edge-based and region-based labeling. The speed improvements in this labeling scheme come from the collective efficiency of different techniques. We have implemented these algorithms on an in-house built Geometric Single Instruction Multiple Data (GSIMD) parallel machine with global buses and a Multiple Instruction Multiple Data (MIMD) controller. This allows real time image interpretation on live data at a rate that is much higher than video rate. The performance figures will be shown.

关键词： image processing Binary data image segmentation Detection and tracking algorithms image processing algorithms and systems Parallel computing Parallel processing Array processing Information operations Video

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Segmentation of inversion recovery MR images using neural networks - a study on aging

Segmentation of inversion recovery MR images using neural ne...

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Medical Imaging 1998 Conference on image processing

作者： Glass, JO Reddick, WE Yo, VS Steen, RG St Jude Childrens Res Hosp Memphis TN 38105 USA

ISBN: (纸本)0819427837

Clinicians have long desired early detection of neurological abnormality for treatment of brain malignancies. In attempts to address this concern, there are numerous reports publishing normative databases of age-related changes of the brain in healthy controls, many using magnetic resonance imaging (MRI). However, most of the methods used to assess tissue volumes were subject to observer variability. We developed a Kohonen self-organizing map (SOM) to automatically segment MR images for reproducible and accurate identification of tissues. The developed method was applied to quantitatively assess subtle volume differences in normal controls due to maturational and degenerative changes. The volumes calculated in the test population of 73 controls agreed with current hypothesizes concerning age-related changes of the brain as determined by linear regression analysis of segmented tissue to age. Percent gray mater and percent white matter, as well as the ratio of gray matter to white matter were all found to be significantly correlated with age. Percent gray matter and the ratio of gray matter to white matter were inversely proportional to age while percent white matter was directly proportional to age. These results suggest the utility of the developed segmentation technique, as well as the clinical application it may hold.

关键词： image segmentation Tissues Brain Magnetic resonance imaging Neurons image processing algorithms and systems image classification Neural networks Statistical analysis Control systems

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Metrics for image segmentation 7

Metrics for image segmentation

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Visual Information processing VII

作者： Rees, G Greenway, P Morray, D British Aerosp Sowerby Res Ctr Bristol Avon England

ISBN: (纸本)0819428361

An important challenge in mapping image-processing techniques onto applications is the lack of quantitative performance measures. From a systems engineering perspective these are essential if system level requirements are to be decomposed into sub-system requirements which can be understood in terms of algorithm selection and performance optimisation. Nowhere in computer vision is this more evident than in the area of image segmentation. This is a vigorous and innovative research activity, but even after nearly two decades of progress, it remains almost impossible to answer the question "what would the performance of this segmentation algorithm be under these new conditions?" To begin to address this shortcoming, we have devised a well-principled metric for assessing the relative performance of two segmentation algorithms. This allows meaningful objective comparisons to be made between their outputs. It also estimates the absolute performance of an algorithm given ground truth. Our approach is an information theoretic one. In this paper, we describe the theory and motivation of our method, and present practical results obtained from a range of state of the art segmentation methods. We demonstrate that it is possible to measure the objective performance of these algorithms, and to use the information so gained to provide clues about how their performance might be improved.

关键词： image segmentation image processing image processing algorithms and systems Cameras Machine vision Signal processing image acquisition Algorithm development Magnetorheological finishing Computer vision technology

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Multi-level feature extraction for skin lesion segmentation in dermoscopic images

Multi-level feature extraction for skin lesion segmentation ...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Khakabi, Sina Wighton, Paul Lee, Tim K. Atkins, M. Stella Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780819489647

This paper presents a novel approach in computer aided skin lesion segmentation of dermoscopic images. We apply spatial and color features in order to model the lesion growth pattern. The decomposition is done by repeatedly clustering pixels into dark and light sub-clusters. A novel tree structure based representation of the lesion growth pattern is constructed by matching every pixel sub-cluster with a node in the tree structure. This model provides a powerful framework to extract features and to train models for lesion segmentation. The model employed allows features to be extracted at multiple layers of the tree structure, enabling a more descriptive feature set. Additionally,there is no need for preprocessing such as color calibration or artifact disocclusion. Preliminary features (mean over RGB color channels) are extracted for every pixel over four layers of the growth pattern model and are used in association with radial distance as a spatial feature to segment the lesion. The resulting per pixel feature vectors of length 13 are used in a supervised learning model for estimating parameters and segmenting the lesion. A dataset containing 116 challenging images from dermoscopic atlases is used to validate the method via a 10-fold cross validation procedure. Results of segmentation are compared with six other skin lesion segmentation methods. Our method outperforms five other methods and performs competitively with another method. We achieve a per-pixel sensitivity/specificity of 0.890 and 0.901 respectivey.

关键词： image segmentation Skin Feature extraction RGB color model image processing algorithms and systems Machine learning Melanoma Calibration Cancer Microscopy

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Local area signal-to-noise ratio (LASNR) algorithm for image segmentation - art. no. 66962H

Local area signal-to-noise ratio (LASNR) algorithm for image...

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Conference on Applications of Digital image processing XXX

作者： Kegelmeyer, Laura Mascio Fong, Philip W. Glenn, Steven M. Liebman, Judy A. Lawrence Livermore Natl Lab Livermore CA 94551 USA

ISBN: (纸本)9780819468444

Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed. is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.

关键词： Signal to noise ratio image segmentation Interference (communication) Inspection National Ignition Facility image processing algorithms and systems Optical inspection Boxcar filters Gaussian filters MATLAB

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Structured region growing and recognition algorithm for nondestructive evaluation 19

Structured region growing and recognition algorithm for nond...

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Conference on Applications of Digital image processing XIX

作者： Sacha, JP Shabestari, BN Cios, KJ Edison Industrial Systems Ctr. (United States) Univ. of Toledo (United States)

ISBN: (纸本)0819422355

A novel syntactic region growing and recognition algorithm called SRG will be presented. The primary function of SRG algorithm is detection of structured regions of interest in given image. The recognition technique operates on region's selected features, it is a subject of the next paper. The SRG algorithm can be outlined as follows. Preprocessing is performed to isolate the kernels of potential regions. The first level regions are grown until the region transition are detected. Regions sharing the same transition boundary are merged. The second level regions are grown and merged if they are within the same transition boundary. Growing process is repeated as necessary until whole image or specified region of interest is covered. Features such as area, depth, and others, are computed for each level region and are used for recognition. The recognition is based on region features without regard to region level. The algorithm was designed for analysis of NDE (nondestructive evaluation) images. In particular, it was successfully tested on X-ray images and on ultrasonic contact scan images of ceramic specimens for detection of microstructural defects. The results of these tests are included in the paper.

关键词： Detection and tracking algorithms image processing Nondestructive evaluation image segmentation Ultrasonics Ceramics image processing algorithms and systems X-ray imaging X-rays image analysis

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LOSSY COMPRESSION OF MEDICAL imageS USING PREDICTION AND CLASSIFICATION

LOSSY COMPRESSION OF MEDICAL IMAGES USING PREDICTION AND CLA...

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CONF ON MEDICAL IMAGING 1993 : image CAPTURE, FORMATING, AND DISPLAY

作者： LEE, HS KIM, YM ROWBERG, AH FRANK, MS LEE, WB Univ. of Washington (United States)

ISBN: (纸本)0819411299

In this paper, a lossy image compression algorithm based on a prediction and classification scheme is discussed. The algorithm decomposes an image into four subimages by subsampling pixels at even and odd row and column locations. Since the four subimages have strong correlations to one another, one of them is used in predicting all the others and the resulting differences between the predicted subimages and the original subimages are encoded. Estimated differences tend to be large in a region where pixel values change rapidly, while the differences are small in a monotonous region. This redundancy is explored by dividing the estimated differences into subsets based on the slope of pixel changes, the basis for which is found in some human perception models used to measure the visibility of distortion. The resulting classified estimated differences having different visibilities are encoded with classified vector quantizers.

关键词： image compression image quality image segmentation Distortion Visualization Visibility image classification image processing algorithms and systems Quantization Medical imaging

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The algorithm Study for Using the Back Propagation Neural Network in CT image Segmentation

The algorithm Study for Using the Back Propagation Neural Ne...

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International Conference on Innovative Optical Health Science

作者： Zhang, Peng Liu, Jie Chen, Chen Li, YingQi Beijing Jiaotong Univ Sch Comp & Informat Technol Dept Biomed Engn Beijing 100044 Peoples R China

ISBN: (数字)9781510609921

ISBN: (纸本)9781510609914;9781510609921

Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can'tt accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.

关键词： Neural networks Biomedical optics Evolutionary algorithms image processing algorithms and systems image segmentation Optical imaging

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ADAPTIVE, MODEL-BASED RESTORATION OF TEXTURES BY GENERALIZED WIENER FILTERING

ADAPTIVE, MODEL-BASED RESTORATION OF TEXTURES BY GENERALIZED...

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Meeting on Visual Communications and image Communication 93

作者： KRISHNAMURTHY, R WOODS, JW FRANCOS, JM RENSSELAER POLYTECH INST DEPT ELECTR COMP & SYST ENGNCTR IMAGE PROC RESTROYNY 12180

ISBN: (纸本)0819413690

We consider the adaptive restoration of inhomogeneous textured images degraded by linear blur and additive white Gaussian noise. The method consists of segmenting the image into individual homogeneous textures and restoring each texture separately. The individual textures are assumed to be realizations of 2-D Wold-decomposition based regular, homogeneous random fields which may possess deterministic components. The conventional Wiener filter assumes that the spectral distribution of the signal is absolutely continuous and, therefore, cannot be directly used to restore the individual textures. A generalized Wiener filter accommodates the unified texture model and is shown to yield minimum mean-squared error estimates for fields with discontinuous spectral distributions. Texture discrimination is performed by obtaining maximum a posteriori estimates for the label field using simulated annealing. The performance of our segmentation algorithm is investigated in the presence of noise.

关键词： Autoregressive models image segmentation Filtering (signal processing) Electronic filtering image restoration image filtering Signal to noise ratio Performance modeling image processing algorithms and systems Visualization

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BOUNDARY DETECTION AND COLOR SEGMENTATION IN SKIN TUMOR imageS

BOUNDARY DETECTION AND COLOR SEGMENTATION IN SKIN TUMOR IMAG...

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CONF ON BIOMEDICAL image processing AND BIOMEDICAL VISUALIZATION

作者： ERCAL, F MOGANTI, M STOECKER, WV MOSS, RH Univ. of Missouri/Rolla (United States)

ISBN: (纸本)0819411388

Boundary detection has been recognized as one of the difficult problems in image processing and pattern analysis, in particular in medical imaging applications. There is no unified approach to this problem which has been found to be application dependent. In this paper, we present a simple and yet effective method to find the borders of tumors as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from red, green, and blue (RGB) planes that contain information to discriminate the tumor from the background. Using this suitable coordinate transformation the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results which verify the effectiveness of our approach are given.

关键词： Tumors image segmentation Skin image processing image processing algorithms and systems RGB color model Digital filtering image filtering Intelligence systems Molybdenum

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