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International Conference on Signal processing, Communication and Networking (ICSCN)

作者： Rahul Dey P. K. Nanda Sucheta Panda Image Processing and Computer Vision Lab Department of Electrical Engineering National Institute of Technology Rourkela Orissa India Department of Electronics & Telecommunication Engineering C.V. Raman College of Engineering Bhubaneswar Orissa India

In this paper, the problem of color image segmentation is addressed as a pixel labeling problem. The observed color image is assumed to be the degraded version of the image labels. We have proposed a new Markov random field (MRF) model known as constrained MRF (CMRF) model to model the unknown image labels and Ohta (I 1 I 2 I 3 ) model is used as the color model. The unique feature of the proposed CMRF model is found to posses a unifying feature of modeling scene and texture images as well. The labels are estimated using maximum a posteriori (MAP) estimation criterion. A hybrid algorithm is proposed to obtain the MAP estimate and the performance of the algorithm is found to be better than that of using simulated annealing (SA) algorithm. The performance of the proposed model is compared with JSEG method and the proposed model is found to be better than JSEG method.

关键词： Markov random fields image segmentation Color Hidden Markov models Pixel Degradation Layout Signal processing algorithms image converters Labeling

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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DeWAFF: A novel image abstraction approach to improve the performance of a cell tracking system

DeWAFF: A novel image abstraction approach to improve the pe...

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International Work Conference on Bio-inspired Intelligence (IWOBI)

作者： S. Calderón A. Sáenz R. Mora F. Siles I. Orozco M.E. Buemi Department of Electrical Engineering School of Engineering Cancer-Lab Universidad de Costa Rica San José Costa Rica Image Processing and Computer Vision Group Universidad de Buenos Aires Buenos Aires Argentina

This paper presents a new image abstraction approach, aiming to improve typical image related pattern recognition tasks such as segmentation, tracking, and classification. The proposed image abstraction framework performs image denoising and homogeneous region simplification, along with border and region enhancement. The proposed framework consists in a novel generalized approach of common weighted averaging denoising algorithms mixed with Unsharp Masking (USM) border enhancement techniques, to avoid typical USM artifacts as ringing. Results of the different configurations within the image abstraction framework for a cell tracking application are presented.

关键词： image edge detection Noise reduction Histograms AWGN Pattern recognition image segmentation

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Binary morphological coding of grayscale images

Binary morphological coding of grayscale images

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S.A. Rajala H.A. Peterson E.J. Delph School of Electrical Engineering Computer Vision and Image Processing Laboratory Purdue University West Lafayette IN USA Sch. of Electr. Eng. Purdue Univ. West Lafayette IN USA

One approach to image coding is to segment the original gray-scale image and then encode the boundaries and the interiors of the segments to represent the image. The authors propose a novel image coding method based on this approach, with an important difference. With the proposed coding technique, an alternative representation of the image segments is used; instead of segment boundaries, morphological skeletons are used to represent the segments. The skeleton is a thin-lined caricature of the segment that summarizes its shape and conveys information about its size, orientation, and connectivity. A binary image morphological skeletonization procedure is used to create skeletons of the image segments. In this way, the gray-scale image is represented for coding purposes by the skeletons and the intensities of the segments. Preliminary estimates show a data rate of 0.12 bits per pixel can be obtained with this coding technique.< >

关键词： image coding Gray-scale image segmentation Skeleton Morphology ISDN computer vision image processing Laboratories Shape

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Morphological based target enhancement algorithms to counter the hostile nuclear environment

Morphological based target enhancement algorithms to counter...

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IEEE International Conference on Systems, Man and Cybernetics

作者： C.H.G. Wright E.J. Delp N.C. Gallagher HQ Space Systems Division Los Angeles Air Force Base Los Angeles CA USA Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

It is noted that a necessary requirement of a strategic defense system is the detection of incoming nuclear warheads in an environment that may include nuclear detonations of undetected or missed target warheads. A computer model is described which simulates incoming warheads as distant endoatmospheric targets. A model of the expected electromagnetic noise present in the nuclear environment is developed; predicted atmospheric effects are included. Various morphological-based image-enhancement algorithms are examined with regard to their ability to suppress the noise and atmospheric effects of the nuclear environment. These algorithms are then tested, using the combined target and noise models, and evaluated in terms of noise removal and the ability to resolve closely spaced targets.< >

关键词： Counting circuits Atmospheric modeling Working environment noise Nuclear weapons Computational modeling computer simulation Predictive models Electromagnetic modeling Electromagnetic interference Testing

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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Features extraction based on fuzzy clustering and segmentation onto the motion region for medium field surveillance application

Features extraction based on fuzzy clustering and segmentati...

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International Conference on computer Graphics, Imaging and Visualization, CGIV

作者： M.N. Maliki S.A.R.A.B. Al-Attas Computer Vision Video and Image Processing (CVVIP) Research Group Department of Microelectronic and Computer Engineering (MiCE) Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Computer Vision Universiti Teknologi Malaysia Skudai Johor Malaysia Universiti Teknologi Malaysia Skudai Johor MY Dept. of Microelectron. & Comput. Eng. Univ. Teknologi Malaysia Johor Malaysia

Fuzzy-based clustering algorithm is becoming popular in image processing, especially in image segmentation. Based on the degree of the membership function, each pixel can be grouped into "natural group" of pixels according to the spatial coordinate using their values. In this work we present features extraction based on fuzzy clustering and segmentation onto the motion region for medium field surveillance application. Instead of using blob analysis, we believe that fuzzy based clustering algorithm, can also be used to generate different clusters. Incorporating the motion-based segmentation the complexity of the fuzzy clustering is also reduced because only the motion region is processed to the clustering algorithm.

关键词： Feature extraction Surveillance Clustering algorithms image segmentation Equations Prototypes Application software computer vision image processing Data mining

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Accurate and fast 3D colon segmentation in CT colonography

Accurate and fast 3D colon segmentation in CT colonography

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IEEE International Symposium on Biomedical Imaging

作者： Dongqing Chen Rachid Fahmi Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Medical Imaging Jewish Hospital and Saint Marys Healthcare Louisville KY USA Division of Gastroenterology/Hepatology Department of Medicine University of Louisville Louisville KY USA

ISBN: (纸本)9781424439317

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

关键词： Colonography Colon Virtual colonoscopy Cancer image segmentation Colonic polyps Level set Deformable models Computed tomography Visualization

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Performance Comparison Between SURF and SIFT for Content-Based image Retrieval

Performance Comparison Between SURF and SIFT for Content-Bas...

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IEEE International Conference on Signal and image processing Applications (ICSIPA)

作者： Najeeb Ur Rehman Malik Awais Gul Airij Sheeraz Ahmed Memon Yasmeen Naz Panhwar Syed A.R. Abu-Bakar Mohamed A. El-Khoreby University of Wollongong New South Wales Australia Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Bahru Campus Johor Malaysia Mehran University of Engineering & Technology Jamshoro Sindh Pakistan

Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) have been two well-known methods used in extracting features. This paper presents and analyzes performance comparison between the SURF approach and the SIFT technique for content-based image retrieval (CBIR) application. In particular, we are interested in comparing the accuracy and the response time between these two methods. For the testing purposes, we make use sample images obtained for the Pennsylvania State College of Information Science and Technology database. As it turns out, in this paper, we will demonstrate that in terms of accuracy and speed, SURF shows superior performance compared to SIFT.

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Fully automated region growing segmentation of brain lesion in diffusion-weighted MRI

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2012年第2期39卷 155-164页

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Faculty of Electrical Engineering Computer Vision Video and Image Processing Research Lab Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

This paper presents a fully automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI). The lesions are infarction, hemorrhage, tumor and abscess. Pre-processing stage is performed for intensity normalization, background removal and intensity enhancement. Then, split and merge algorithm is designed. Several statistical features are discussed and evaluated to select the best feature as homogeneity criteria. Lesions are segmented by merging the homogenous regions according to the selected criteria. This process produces blocky lesion region. Then, histogram thresholding is acquired to automate the seeds selection for region growing process. The region is iteratively grown by comparing all unallocated neighboring pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. The proposed segmentation technique has been validated by using misclassified area (MA), false positive rate (FPR), false negative rate (FNR), mean absolute percentage error (MAPE) and pixel absolute error ratio (rerr), and compared with previous methods. The result shows that automatic region growing method can successfully segment the lesions and is suitable for analysis and classification of DWI.

关键词： Magnetic resonance imaging

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