RESEARCH PAPERS

medical image processing


 

Medical Image Processing
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ABSTRACT DIPE is a distributed environment that provides image processing services over integrated teleradiology services networks. DIPE integrates existing and new image processing software and employs sophisticated execution scheduling mechanisms for the   

Validation of medical image processing in image-guided therapy.
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INTRODUCTION linical use of image-guided therapy (IGT) systems has grown this last decade, creating the need for a common and rigorous validation methodology, as reported in recent workshops and conferences [1,2,3,4,5,6]. One key characteristic of IGT systems is   

Medical Image Processing
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ABSTRACT My work is focused in finding solutions for medical image processing and analysis in MRI images with applications for Parkinson's Disease. At the processing level I develop algorithms that are independent on the patient variability for detecting the volumes of   

Validation in medical image processing.
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Medical imaging is one of our most powerful tools for gaining insight into normal and pathological processes that affect health. The role of image processing in medicine is expanding with the increasing importance of finding ways to improve workflow in reading   

The design and implementation of a C++ toolkit for integrated medical image processingand analyzing
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ABSTRACT With the success of VTK and ITK, there are more attentions to the toolkit development issue in medical imaging community. This paper introduces MITK, an integrated medical image processing and analyzing toolkit. Its main purpose is to provide 

Model for defining and reporting reference-based validation protocols in medical image processing
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ABSTRACT Objectives Image processing tools are often embedded in larger systems. Validation of image processing methods is important because the performance of such methods can have an impact on the performance of the larger systems and consequently   

Medical image processing in MediGRID
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ABSTRACT In MediGRID a diverse spectrum of application scenarios from areas of bioinformatics, medical image processing, numerical simulations and clinical trials will be integrated into a Grid environment. In this paper we present the MediGRID infrastructure 

Wavelets in medical image processing: denoising, segmentation, and registration
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Wavelets have been widely used in signal and image processing for the past 20 years. Although a milestone paper by Grossmann et al.[3] was considered the beginning of modern wavelet analysis, similar ideas and theoretical bases can be found back in the early   

From plastic to gold: A unified classification scheme for reference standards in medical image processing
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ABSTRACT Reliable evaluation of medical image processing is of major importance for routine applications. Nonetheless, evaluation is often omitted or methodically defective when novel approaches or algorithms are introduced. Adopted from medical diagnosis, 

Applications of medical image processing in the diagnosis and treatment of spinal deformity
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Spinal deformities are a group of disorders characterized by abnormal curvature of the spine. In the healthy spine, natural curves occur in the sagittal plane, with a lordosis (concave curvature) in the lower back (lumbar) region and kyphosis (convex curvature) in   

Medical Image Processing and Analysis
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The tremendous pace of development in brain imaging technologies has revolutionized our ability to investigate brain structure and function. Techniques are now available to capture features of anatomy and function at both molecular and whole-brain scales, mapping   

Medical image processing methods for the development of a virtual endoscope
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ABSTRACT Virtual endoscopes give internal views of the human body without penetrating it, based on a set of parallel cross-sections produced with any computed tomography method. This paper presents some ideas concerning the design and implementation of a software   

Active contours in medical image processing using a networked SIMD array processor
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This paper reports work carried out within the BIOLAB consortium of the CEC Advanced Informatics in Medicine (AIM) programme. The overall purpose of the work is to assess the potential for commercially available parallel computer systems to enhance practical   

Simplified grid implementation of medical image processing algorithms using a workflow managment system
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ABSTRACT Complex analysis of large amounts of medical image data quickly exceeds storage capacity and computing power of single workstations or small local networks. When limited hardware resources impede full utilization of medical image analysis, a possible solution   

Application of neural networks in medical image processing
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ABSTRACT This paper reviews the application of artificial neural networks in medical image preprocessing, in medical image object detection and recognition. Main advantages and drawbacks of artificial neural networks were discussed. By this survey, the paper try to 

Challenges of medical image processing
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ABSTRACT In todays health care, imaging plays an important role throughout the entire clinical process from diagnostics and treatment planning to surgical procedures and follow up studies. Since most imaging modalities have gone directly digital, with continually 

Mimos: a description framework for exchanging medical image processing results
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ABSTRACT Image processing plays increasingly important role in using medical images, both for routine as for research purposes, due to the growing interest in functional studies (PET, MR, etc.). Unfortunately, there exist nearly as many formats for data and results coding as 

Digital and medical image processing
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Image processing: definition and examples

IJCARS special issue: BVM 2007 German conference on medical image processing
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Medical image processing applications have become a ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on   

Tissue segmentation in medical images based on image processing chain optimization
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Segmentation is a crucial task in medical image processing.  provided. Keywords: Lung segmentation, Breast segmentation, Differential Evolution (DE), Ultrasound, X-ray, Medical image processing 1. Introduction   

WCL-Viewer: An integrated system for medical image administration and processing
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 of Patras, Greece. Keywords: ,Picture archiving and communication systems, Hospital Information System, Diagnostic Viewing Station, Medical Image Processing, DICOM, HL7. 1 Introduction Digital   

Increasing the speed of medical image processing in MatLab
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ABSTRACT MatLab has often been considered an excellent environment for fast algorithm development but is generally perceived as slow and hence not fit for routine medical image processing, where large data sets are now available eg, high-resolution CT image sets 

Advances in medical image processing
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ABSTRACT For more than 20 years, the German Workshop on Medical Image Processing is held annually and has been established recently as a European conference. In 2010, the workshop was held in Aachen, Germany. Based   

Dynamical Systems and Bio-medical Image Processing
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ABSTRACT We present in this paper a series of operators coming from discrete (neural networks) or continuous (differential equations) dynamical systems and able to give adequate responses to low level image processing tasks like contrasting, segmenting and 

The MEDIP–Platform independent software system for medical image processing project
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ABSTRACT In this paper we present the structure and the achieved results of the R&D project IKTA-4, 6/2001 MEDIP-Platform independent software system for medical image processing supported by the Hungarian Ministry of Education. The aim of the project was   

Development and Evaluation of an ODL course on Medical Image Processing
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ABSTRACT Image processing is an attractive subject to explore the Collaborative Learning Model (CLM) paradigm in Open Distance Learning (ODL). An on-line course on Medical Image Processing following the principles of collaborative learning has been developed   

A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms
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ABSTRACT In the field of medical image processing, the evaluation of new algorithms is often a difficult task since real data sets do not allow a quantitative evaluation of the algorithms' properties and the correctness of results. Thus, a phantom design toolbox was developed   

A novel approach noise filtration for MRI image sample in medical image processing
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YS Lalitha, MV Latte ,International Journal of Computer Science and In this paper a modified spatial filtration approach is suggested for image denoising applications. The existing spatial filtration techniques were improved for the ability to reconstruct noise-affected medical images. The developed modified approach is developed to 

Distributed computation of feature-detectors for medical image processing on GPGPU and cell processors
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Automated classification of medical (computed tomography) images may ultimately lead to faster and improved diagnosis, benefiting both patients and clinicians. We describe a software system, that can be trained for classification purposes in the area of medical   

Design of a medical image processing software for clinical-PACS
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Software modules for interactive display, manipulation and retrieval of medical images have been designed for a Picture Archiving and Communications System (PACS). The target of these modules is not for a high-end diagnostic workstation for radiologists, but for a PC- 

An optimal control problem in medical image processing
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As a starting point of this paper we present a problem from mammographic image processing. We show how it can be formulated as an optimal control problem for PDEs and illustrate that it leads to penalty terms which are non-standard in the theory of optimal   

An overview of medical image processing methods
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Since human life is worthier than all things, efforts on virtual animation and visualization of human body's viscera, without surgical interference to diagnose a disease is very important. Recently, modern medical instruments are able to produce views which can be used for   

Medical image processing using a service oriented architecture
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ABSTRACT The aim of this paper is to present a services based architecture for medical image processing in assisted diagnosis. Service oriented architecture (SOA) improves the reusability and maintainability of distributed systems. In service oriented architectures, the most 

Fourier-domain based datacentric performance ranking of competing medical image processingalgorithms
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ABSTRACT To accomplish a given computational task, a number of algorithmic and heuristic approaches can be employed to act upon the ever-varying input data. Depending upon the assumptions made regarding the data, the algorithm and the task, the end result   

Medical image processing in hip arthroplasty
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ABSTRACT Computers have become indispensable in all domains, and the medical segment does not represent an exception. The need for accuracy and speed has led to a tight collaboration between machines and human beings. Maybe the future will allow the   

The increasing influence of medical image processing in clinical neuroimaging
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ABSTRACT –This paper review the evolution of clinical neuroinformatics domain in the passed and gives an outlook how this research field will evolve in clinical neurology (eg Epilepsy, Multiple Sclerosis, Dementia) and neurosurgery (eg image guided surgery, intra-operative 

neural network diagnosis in medical



Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease
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ABSTRACT The goal of this paper is to evaluate artificial neural network [ANN] in disease diagnosis. ANN's are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. ANN's are finding many uses in the medical 

Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging
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Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum 

Evolution of Modular Neural Network in Medical Diagnosis
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ABSTRACT Intelligent systems have been extensively used in the area of biomedical engineering for assisting the doctors in medical diagnosis. The inability of simple neural networks to solve the diagnosis problem, due to extensively large data size as well as 

Neural network diagnosis of malignant melanoma from color images
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 NEURAL NETWORK DIAGNOSIS OF MALIGNANT MELANOMA  In recent years, neural networks have been used as pat- tern classifiers in medical diagnosis [6  in the use of neural networks to solve a variety of problems in many areas of medicine and engineering 

Medical analysis and diagnosis by neural networks
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there are many reviews for the use of artificial neural networks in medicine, see eg [9  Therefore, in order to make a prediction or classification system acceptable and usable in the medical world, after creating a successful neural network diagnosis system or 

On using feedforward neural networks for clinical diagnostic tasks
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on Computer Applications in Medical Care, IEEE Computer Society Press (1989  Neural network diagnosis of avascular necrosis from magnetic resonance images.  MJ Gelfand, SR Thomas (Eds.), Effective Use of Computers in Nuclear Medicine, McGraw-Hill, New York (1988), pp 

Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study
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 Grey area indicates classification error. The cost of medical decision can be weighed by selecting an appropriate SP–SE pair on the curve.  10. Ercal F, Chawla A, Stoecker WV, Lee HC Moss RH. Neural network diagnosis of malignant melanoma from color images. 

Automated melanoma detection: multispectral imaging and neural network approach for classification
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Using an artificial neural network to diagnose hepatic masses
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 The subjects were patients at Veterans Administration Medical Center in Memphis, Tennessee, who had been diagnosed as having liver lesions and later had the diagnosis verified by either surgical  These results are similar to previous studies on neural network diagnosis. 

Application of an artificial neural network in radiographic diagnosis
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A neural network approach to the biopsy diagnosis of early acute renal transplant rejection
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 as useful tools in decision-making in a variety of medical applications for a number of years (reviewed in 10). A relatively simple form of inference network, the Bayesian belief network, has recently received some prominence in the Neural network diagnosis of rejection 465 

Automated melanoma recognition
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 SPIE Medical Imaging 1996: Physics of Medical Imaging, vol.  [29] F. Ercal, A. Chawla, WV Stoecker, HC Lee, and RH Moss, Neural network diagnosis of malignant  le- sions and estimation of prognosis in stage I, in Update: Dermatology in General Medicine, TB Fitzpatrick, AZ 

Application of neural networks in medical image processing
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Neural Network Diagnosis of Malignant  Based Texture Analysis of Ultrasonic Images, IEEE Engineering in Medicine and Biology.  Microcalcifications: Pattern Recognition with an Artificial Neural Network,  Medical Physics vol. 

Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis
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B Rosado, S Menzies, A Harbauer Archives of 2003 archderm.jamanetwork.com

Towards a computer-aided diagnosis system for pigmented skin lesions
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 JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Computerized Medical Imaging and Graphics.  The reader should refer to and [4] for additional information about this scheme and the medical background. 

Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions
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 For example, recently ANNs have been applied in diverse medical applications.  protocols approved by the University of Maryland/Baltimore VA Hospital and Mount Sinai School of Medicine Institutional Review  Fig. 2. Graphic representation of artificial neural network diagnosis. 

coma patients expression



Coma patients expression analysis under different lighting using k-NN and LDA
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Abstract:This paper presents an integrated system for detecting facial changes of patient in a hospital Intensive Care Unit (ICU). In this research we have considered the facial changes as represented by eyes and mouth movements. The proposed system uses color images


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