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A kidney segmentation approach from DCE-MRI using level sets

A kidney segmentation approach from DCE-MRI using level sets

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： H. Abdelmunim Aly A. Farag W. Miller Mohamed AboelGhar Department of Computer Science Computational Biomedicine Laboratory University of Houston Houston TX USA Computer Vision and Image Processing LabDepartment of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical School Mansoura University Mansoura Egypt

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. Automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCEMRI), is of great importance. Kidney segmentation is the first step for such classification. The image intensity inside the kidney is used as an indication of failure/success. Differentiating between different cases cases is implemented by comparing subsequential kidney scans signals. So, this process is mainly dependent on segmentation. This paper introduces a new shape-based segmentation approach based on level sets. Training shapes are collected from different real data sets to represent the shape variations. Signed distance functions are used to represent these shapes. The methodology incorporates image and shape prior information in a variational framework. The shape registration is considered the backbone of the approach where more general transformations can be used to handle the process. We introduce a novel shape dissimilarity measure that enables the use of different (inhomogeneous) scales. The approach gives successful results compared with other techniques restricted to transformations with homogeneous scales. Results for segmenting kidney images will be illustrated and compared with other approaches to show the efficiency of the proposed technique.

关键词： Level set image segmentation computer vision Magnetic resonance imaging Shape measurement Biopsy Biomedical computing computer science image processing Biomedical imaging

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Constrained Markov Random Field Model for Color and Texture image Segmentation

Constrained Markov Random Field Model for Color and Texture ...

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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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A NEW GLOBAL REGISTRATION APPROACH OF MEDICAL IMAGING USING VECTOR MAPS

A NEW GLOBAL REGISTRATION APPROACH OF MEDICAL IMAGING USING ...

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IEEE International Symposium on Biomedical Imaging

作者： Hossam E. Abd El Munim Aly A. Farag Computer Vision and Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA

ISBN: (纸本)1424406714

In this paper, we introduce a novel global image registration approach using vector distance functions (VDF's). Edges of the source and target objects are used to represent their images. The VDF's of these edges are calculated as an implicit representation of these boundaries. An energy is formulated to measure the differences of these vectors. A variational formulation is considered to estimate the transformation parameters. Promising results in 2D and 3D are demonstrated to show the efficiency of the proposed algorithm.

关键词： Biomedical imaging image edge detection image registration Parameter estimation computer vision Mutual information Data mining Shape image processing Energy measurement

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High Resolution image Reconstruction in Shape from Focus

High Resolution Image Reconstruction in Shape from Focus

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IEEE International Conference on image processing

作者： R. R. Sahay A. N. Rajagopalan Image Processing and Computer Vision Lab Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

In the Shape from Focus (SFF) method, a sequence of images of a 3D object is captured for computing its depth profile. However, it is useful in several applications to also derive a high resolution focused image of the 3D object. Given the space-variantly blurred frames and the depth map, we propose a method to optimally estimate a high resolution image of the object within the SFF framework.

关键词： image resolution image reconstruction Shape Focusing Cameras Apertures Pixel computer vision image processing Application software

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A comparision of Model-based methods for knee cartilage segmentation

A comparision of Model-based methods for knee cartilage segm...

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2nd International Conference on computer vision Theory and Applications, VISAPP 2007

作者： Cheong, J. Faggian, N. Langs, G. Suter, D. Cicuttini, F. Dept. of Electrical Dept. of Computer Systems Engineering Monash University Australia Clayton School of Information Technology Monash University Australia Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria Dept. of Epidemiology Dept. of Preventive Medicine Monash University Australia

Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.

关键词： Medical imaging

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Super-resolution in the presence of space-variant blur

Super-resolution in the presence of space-variant blur

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International Conference on Pattern Recognition

作者： K.V. Suresh A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering IIT Madras Chennai India

An efficient algorithm using maximum a posteriori-Markov random field (MAP-MRF) based approach for recovering a high-resolution image from multiple sub-pixel shifted low-resolution images is proposed. The algorithm can be used for super-resolution of both space-invariant and space-variant blurred images. We prove an important theorem that the posterior is also Markov and derive the exact posterior neighborhood structure in the presence of warping, blurring and down-sampling operations. The posterior being Markov enables us to perform all matrix operations as local image domain operations thereby resulting in a considerable speedup. Experimental results are given to demonstrate the effectiveness of our method

关键词： Spatial resolution image resolution Cameras image processing computer vision Laboratories Layout Frequency domain analysis Markov random fields Reconstruction algorithms

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Improving Shape from Focus Using Defocus Information

Improving Shape from Focus Using Defocus Information

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International Conference on Pattern Recognition

作者： K.S. Pradeep A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras India

ISBN: (纸本)0769525210

Shape from focus (SFF) method determines the degree of focus in a sequence of observations to estimate the shape of a 3-D object. Existing SFF algorithms use an ad hoc interpolation strategy to account for the error due to the finite step-size by which the translational table is moved while capturing the images. We propose an improved SFF method that uses relative defocus blur derived from actual image data to arrive at the final estimates of the shape of the object. A space-variant image restoration scheme is also proposed to obtain a focused image of the 3-D object. The shape estimates as well as the quality of the restored image using the proposed method are superior to that of traditional SFF

关键词： Shape Focusing Cameras Lenses image restoration Interpolation Pixel Polynomials image sensors image processing

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AN EFFICIENT REPRESENTATION OF DIGITAL CURVES WITH LINE SEGMENTS AND ELLIPTICAL ARCS

AN EFFICIENT REPRESENTATION OF DIGITAL CURVES WITH LINE SEGM...

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2006 IET International Conference on Visual Information engineering

作者： C. Paramanand A.N. Rajagopalan Department of Electrical Engineering Image Processing and Computer Vision Laboratory Indian Institute of Technology Chennai India

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ROBUST SPACE-VARIANT SUPER-RESOLUTION

ROBUST SPACE-VARIANT SUPER-RESOLUTION

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2006 IET International Conference on Visual Information engineering

作者： K.V. Suresh A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

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Brain MR image segmentation using tabu search and hidden markov random field model

Brain MR image segmentation using tabu search and hidden mar...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Nanda, P.K. Patra, D. Pradhan, A. Department of Electrical Engineering Image Processing and Computer Vision Laboratory National Institute of Technology Rourkela Orissa 769008 India

ISBN: (纸本)0972741216

In this paper, we propose a hybrid Tabu Expectation Maximization (TEM) Algorithm for segmentation of Brain Magnetic Resonance (MR) images in both supervised and unsupervised framewrok. Gaussian Hidden Markov Random Field (GHMRF) is used to model the available degraded image. In supervised framework, the apriori image MRF model parameters as well as the GHMRF model parameters are assumed to be known. The class labels are estimated using the Maximum a Posteriori (MAP) estimation criterion. In unsupervised framework, the problem of model parameter estimation and label estimation is formulated in Expectation Maximization (EM) framework. The labels are estimated using the proposed Tabu Search algorithm while the model parameters are the maximum likelihood estimates. Our proposed algorithm yields results with arbitrary initial paramater set and thus overcomes the problem of proper choice of initial parameters. The results obtained are comparable with the results obtained by using the algorithm proposed by Zhang ***. [15] , where the Iterated Conditional Mode (ICJM) algorithm is used for computing the MAP estimates. Copyright © IICAI 2005.

关键词： Tabu search

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