WorldWideScience

Sample records for medical imaging based

  1. Medical image retrieval based on plaque appearance and image registration.

    Science.gov (United States)

    Amores, Jaume; Radeva, Petia

    2005-01-01

    The increasing amount of medical images produced and stored daily in hospitals needs a datrabase management system that organizes them in a meaningful way, without the necessity of time-consuming textual annotations for each image. One of the basic ways to organize medical images in taxonomies consists of clustering them depending of plaque appearance (for example, intravascular ultrasound images). Although lately, there has been a lot of research in the field of Content-Based Image Retrieval systems, mostly these systems are designed for dealing a wide range of images but not medical images. Medical image retrieval by content is still an emerging field, and few works are presented in spite of the obvious applications and the complexity of the images demanding research studies. In this chapter, we overview the work on medical image retrieval and present a general framework of medical image retrieval based on plaque appearance. We stress on two basic features of medical image retrieval based on plaque appearance: plaque medical images contain complex information requiring not only local and global descriptors but also context determined by image features and their spatial relations. Additionally, given that most objects in medical images usually have high intra- and inter-patient shape variance, retrieval based on plaque should be invariant to a family of transformations predetermined by the application domain. To illustrate the medical image retrieval based on plaque appearance, we consider a specific image modality: intravascular ultrasound images and present extensive results on the retrieval performance.

  2. PERFORMANCE EVALUATION OF CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES

    Directory of Open Access Journals (Sweden)

    SASI KUMAR. M

    2013-04-01

    Full Text Available Content-based image retrieval (CBIR technology benefits not only large image collections management, but also helps clinical care, biomedical research, and education. Digital images are found in X-Rays, MRI, CT which are used for diagnosing and planning treatment schedules. Thus, visual information management is challenging as the data quantity available is huge. Currently, available medical databases utilization is limited image retrieval issues. Archived digital medical images retrieval is always challenging and this is being researched more as images are of great importance in patient diagnosis, therapy, medical reference, and medical training. In this paper, an image matching scheme using Discrete Sine Transform for relevant feature extraction is presented. The efficiency of different algorithm for classifying the features to retrieve medical images is investigated.

  3. Massive Medical Images Retrieval System Based on Hadoop

    Directory of Open Access Journals (Sweden)

    Qing-An YAO

    2014-02-01

    Full Text Available In order to improve the efficiency of massive medical images retrieval, against the defects of the single-node medical image retrieval system, a massive medical images retrieval system based on Hadoop is put forward. Brushlet transform and Local binary patterns algorithm are introduced firstly to extract characteristics of the medical example image, and store the image feature library in the HDFS. Then using the Map to match the example image features with the features in the feature library, while the Reduce to receive the calculation results of each Map task and ranking the results according to the size of the similarity. At the end, find the optimal retrieval results of the medical images according to the ranking results. The experimental results show that compared with other medical image retrieval systems, the Hadoop based medical image retrieval system can reduce the time of image storage and retrieval, and improve the image retrieval speed.

  4. Medical Image Retrieval Based on Multi-Layer Resampling Template

    Institute of Scientific and Technical Information of China (English)

    WANG Xin-rui; YANG Yun-feng

    2014-01-01

    Medical image application in clinical diagnosis and treatment is becoming more and more widely, How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose. This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition, the image retrieval method consists of two retrieval process which is coarse and fine retrieval. Coarse retrieval process is the medical image retrieval process based on the image contour features. Fine retrieval process is the medical image retrieval process based on multi-layer resampling template, a multi-layer sampling operator is employed to extract image resampling images each layer, then these resampling images are retrieved step by step to finish the process from coarse to fine retrieval.

  5. [Consistent presentation of medical images based on CPI integration profile].

    Science.gov (United States)

    Jiang, Tao; An, Ji-ye; Chen, Zhong-yong; Lu, Xu-dong; Duan, Hui-long

    2007-11-01

    Because of different display parameters and other factors, digital medical images present different display states in different section offices of a hospital. Based on CPI integration profile of IHE, this paper implements the consistent presentation of medical images, and it is helpful for doctors to carry out medical treatments of teamwork.

  6. Comparison of Two Distance Based Alignment Method in Medical Imaging

    Science.gov (United States)

    2001-10-25

    very helpful to register large datasets of contours or surfaces, commonly encountered in medical imaging . They do not require special ordering or...COMPARISON OF TWO DISTANCE BASED ALIGNMENT METHOD IN MEDICAL IMAGING G. Bulan, C. Ozturk Institute of Biomedical Engineering, Bogazici University...Two Distance Based Alignment Method in Medical Imaging Contract Number Grant Number Program Element Number Author(s) Project Number Task Number

  7. An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

    Directory of Open Access Journals (Sweden)

    Ningning Zhou

    2014-01-01

    Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

  8. A survey of GPU-based medical image computing techniques.

    Science.gov (United States)

    Shi, Lin; Liu, Wen; Zhang, Heye; Xie, Yongming; Wang, Defeng

    2012-09-01

    Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine.

  9. [A medical image semantic modeling based on hierarchical Bayesian networks].

    Science.gov (United States)

    Lin, Chunyi; Ma, Lihong; Yin, Junxun; Chen, Jianyu

    2009-04-01

    A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in allusion to characters of medical images. It used GMM (Gaussian mixture models) to map low-level image features into object semantics with probabilities, then it captured high-level semantics through fusing these object semantics using a Bayesian network, so that it built a multi-layer medical image semantic model, aiming to enable automatic image annotation and semantic retrieval by using various keywords at different semantic levels. As for the validity of this method, we have built a multi-level semantic model from a small set of astrocytoma MRI (magnetic resonance imaging) samples, in order to extract semantics of astrocytoma in malignant degree. Experiment results show that this is a superior approach.

  10. Tele-medical imaging conference system based on the Web.

    Science.gov (United States)

    Choi, Heung-Kook; Park, Se-Myung; Kang, Jae-Hyo; Kim, Sang-Kyoon; Choi, Hang-Mook

    2002-06-01

    In this paper, a medical imaging conference system is presented, which is carried out in the Web environment using the distributed object technique, CORBA. Independent of platforms and different developing languages, the CORBA-based medical imaging conference system is very powerful for system development, extension and maintenance. With this Web client/server, one could easily execute a medical imaging conference using Applets on the Web. The Java language, which is object-oriented and independent of platforms, has the advantage of free usage wherever the Web browser is. By using the proposed system, we envisage being able to open a tele-conference using medical images, e.g. CT, MRI, X-ray etc., easily and effectively among remote hospitals.

  11. Active index for content-based medical image retrieval.

    Science.gov (United States)

    Chang, S K

    1996-01-01

    This paper introduces the active index for content-based medical image retrieval. The dynamic nature of the active index is its most important characteristic. With an active index, we can effectively and efficiently handle smart images that respond to accessing, probing and other actions. The main applications of the active index are to prefetch image and multimedia data, and to facilitate similarity retrieval. The experimental active index system is described.

  12. A similarity-based data warehousing environment for medical images.

    Science.gov (United States)

    Teixeira, Jefferson William; Annibal, Luana Peixoto; Felipe, Joaquim Cezar; Ciferri, Ricardo Rodrigues; Ciferri, Cristina Dutra de Aguiar

    2015-11-01

    A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing.

  13. Medical Images Watermarking Algorithm Based on Improved DCT

    Directory of Open Access Journals (Sweden)

    Yv-fan SHANG

    2013-12-01

    Full Text Available Targeting at the incessant securities problems of digital information management system in modern medical system, this paper presents the robust watermarking algorithm for medical images based on Arnold transformation and DCT. The algorithm first deploys the scrambling technology to encrypt the watermark information and then combines it with the visual feature vector of the image to generate a binary logic series through the hash function. The sequence as taken as keys and stored in the third party to obtain ownership of the original image. Having no need for artificial selection of a region of interest, no capacity constraint, no participation of the original medical image, such kind of watermark extracting solves security and speed problems in the watermark embedding and extracting. The simulation results also show that the algorithm is simple in operation and excellent in robustness and invisibility. In a word, it is more practical compared with other algorithms

  14. Medical image segmentation based on SLIC superpixels model

    Science.gov (United States)

    Chen, Xiang-ting; Zhang, Fan; Zhang, Ruo-ya

    2017-01-01

    Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

  15. Nonlocal Means-Based Denoising for Medical Images

    Directory of Open Access Journals (Sweden)

    Ke Lu

    2012-01-01

    Full Text Available Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.

  16. Spatial Information Based Medical Image Registration using Mutual Information

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    Benzheng Wei

    2011-06-01

    Full Text Available Image registration is a valuable technique for medical diagnosis and treatment. Due to the inferiority of image registration using maximum mutual information, a new hybrid method of multimodality medical image registration based on mutual information of spatial information is proposed. The new measure that combines mutual information, spatial information and feature characteristics, is proposed. Edge points are used as features, obtained from a morphology gradient detector. Feature characteristics like location, edge strength and orientation are taken into account to compute a joint probability distribution of corresponding edge points in two images. Mutual information based on this function is minimized to find the best alignment parameters. Finally, the translation parameters are calculated by using a modified Particle Swarm Optimization (MPSO algorithm. The experimental results demonstrate the effectiveness of the proposed registration scheme.

  17. 3D Medical Image Segmentation Based on Rough Set Theory

    Institute of Scientific and Technical Information of China (English)

    CHEN Shi-hao; TIAN Yun; WANG Yi; HAO Chong-yang

    2007-01-01

    This paper presents a method which uses multiple types of expert knowledge together in 3D medical image segmentation based on rough set theory. The focus of this paper is how to approximate a ROI (region of interest) when there are multiple types of expert knowledge. Based on rough set theory, the image can be split into three regions:positive regions; negative regions; boundary regions. With multiple knowledge we refine ROI as an intersection of all of the expected shapes with single knowledge. At last we show the results of implementing a rough 3D image segmentation and visualization system.

  18. Medical Imaging.

    Science.gov (United States)

    Barker, M. C. J.

    1996-01-01

    Discusses four main types of medical imaging (x-ray, radionuclide, ultrasound, and magnetic resonance) and considers their relative merits. Describes important recent and possible future developments in image processing. (Author/MKR)

  19. Physics-based deformable organisms for medical image analysis

    Science.gov (United States)

    Hamarneh, Ghassan; McIntosh, Chris

    2005-04-01

    Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

  20. Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

    Science.gov (United States)

    Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

    2012-08-01

    This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

  1. elastix: a toolbox for intensity-based medical image registration.

    Science.gov (United States)

    Klein, Stefan; Staring, Marius; Murphy, Keelin; Viergever, Max A; Pluim, Josien P W

    2010-01-01

    Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

  2. 3D Medical Image Interpolation Based on Parametric Cubic Convolution

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter, which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures: mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.

  3. A New Medical Image Enhancement Based on Human Visual Characteristics

    Institute of Scientific and Technical Information of China (English)

    DONG Ai-bin; HE Jun

    2013-01-01

    Study of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact effectively to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest (ROI) is segmented; then the Un-sharp Masking (USM) is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.

  4. Medical imaging

    CERN Document Server

    Townsend, David W

    1996-01-01

    Since the introduction of the X-ray scanner into radiology almost 25 years ago, non-invasive imaging has become firmly established as an essential tool in the diagnosis of disease. Fully three-dimensional imaging of internal organs is now possible, b and for studies which explore the functional status of the body. Powerful techniques to correlate anatomy and function are available, and scanners which combine anatomical and functional imaging in a single device are under development. Such techniques have been made possible through r ecent technological and mathematical advances. This series of lectures will review both the physical basis of medical imaging techniques using X-rays, gamma and positron emitting radiosiotopes, and nuclear magnetic resonance, and the mathematical methods used to reconstruct three-dimentional distributions from projection data. The lectures will trace the development of medical imaging from simple radiographs to the present-day non-invasive measurement of in vivo biochemistry. They ...

  5. A Speckle Reduction Filter Using Wavelet-Based Methods for Medical Imaging Application

    Science.gov (United States)

    2001-10-25

    A Speckle Reduction Filter using Wavelet-Based Methods for Medical Imaging Application Su...Wavelet-Based Methods for Medical Imaging Application Contract Number Grant Number Program Element Number Author(s) Project Number Task Number Work

  6. Content-based retrieval based on binary vectors for 2-D medical images

    Institute of Scientific and Technical Information of China (English)

    龚鹏; 邹亚东; 洪海

    2003-01-01

    In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...

  7. Content Based Medical Image Retrieval for Histopathological, CT and MRI Images

    Directory of Open Access Journals (Sweden)

    Swarnambiga AYYACHAMY

    2013-09-01

    Full Text Available A content based approach is followed for medical images. The purpose of this study is to access the stability of these methods for medical image retrieval. The methods used in color based retrieval for histopathological images are color co-occurrence matrix (CCM and histogram with meta features. For texture based retrieval GLCM (gray level co-occurrence matrix and local binary pattern (LBP were used. For shape based retrieval canny edge detection and otsu‘s method with multivariable threshold were used. Texture and shape based retrieval were implemented using MRI (magnetic resonance images. The most remarkable characteristics of the article are its content based approach for each medical imaging modality. Our efforts were focused on the initial visual search. From our experiment, histogram with meta features in color based retrieval for histopathological images shows a precision of 60 % and recall of 30 %. Whereas GLCM in texture based retrieval for MRI images shows a precision of 70 % and recall of 20 %. Shape based retrieval for MRI images shows a precision of 50% and recall of 25 %. The retrieval results shows that this simple approach is successful.

  8. Medical Image Fusion Based on Feature Extraction and Sparse Representation.

    Science.gov (United States)

    Fei, Yin; Wei, Gao; Zongxi, Song

    2017-01-01

    As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

  9. Detectors based on silicon photomultiplier arrays for medical imaging applications

    Energy Technology Data Exchange (ETDEWEB)

    Llosa, G.; Barrio, J.; Cabello, J.; Lacasta, C.; Oliver, J. F. [Instituto de Fisica Corpuscular - IFIC-CSIC/UVEG, Valencia (Spain); Rafecas, M. [Instituto de Fisica Corpuscular - IFIC-CSIC/UVEG, Valencia (Spain); Departamento de Fisica Atomica, Molecular Y Nuclear, Universitat de Valencia, Valencia (Spain); Stankova, V.; Solaz, C. [Instituto de Fisica Corpuscular - IFIC-CSIC/UVEG, Valencia (Spain); Bisogni, M. G.; Del Guerra, A. [Universite di Pisa, INFN Pisa, Pisa (Italy)

    2011-07-01

    Silicon photomultipliers (SiPMs) have experienced a fast development and are now employed in different research fields. The availability of 2D arrays that provide information of the interaction position in the detector has had a high interest for medical imaging. Continuous crystals combined with segmented photodetectors can provide higher efficiency than pixellated crystals and very high spatial resolution. The IRIS group at IFIC is working on the development of detector heads based on continuous crystals coupled to SiPM arrays for different applications, including a small animal PET scanner in collaboration with the Univ. of Pisa and INFN Pisa, and a Compton telescope for dose monitoring in hadron therapy. (authors)

  10. Nonrigid Medical Image Registration Based on Mesh Deformation Constraints

    Directory of Open Access Journals (Sweden)

    XiangBo Lin

    2013-01-01

    Full Text Available Regularizing the deformation field is an important aspect in nonrigid medical image registration. By covering the template image with a triangular mesh, this paper proposes a new regularization constraint in terms of connections between mesh vertices. The connection relationship is preserved by the spring analogy method. The method is evaluated by registering cerebral magnetic resonance imaging (MRI image data obtained from different individuals. Experimental results show that the proposed method has good deformation ability and topology-preserving ability, providing a new way to the nonrigid medical image registration.

  11. MEDICAL IMAGE SEGMENTATION BASED ON A MODIFIED LEVEL SET ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    Yang Yong; Lin Pan; Zheng Chongxun; Gu Jianwen

    2005-01-01

    Objective To present a novel modified level set algorithm for medical image segmentation. Methods The algorithm is developed by substituting the speed function of level set algorithm with the region and gradient information of the image instead of the conventional gradient information. This new algorithm has been tested by a series of different modality medical images. Results We present various examples and also evaluate and compare the performance of our method with the classical level set method on weak boundaries and noisy images. Conclusion Experimental results show the proposed algorithm is effective and robust.

  12. BIRAM: a content-based image retrieval framework for medical images

    Science.gov (United States)

    Moreno, Ramon A.; Furuie, Sergio S.

    2006-03-01

    In the medical field, digital images are becoming more and more important for diagnostics and therapy of the patients. At the same time, the development of new technologies has increased the amount of image data produced in a hospital. This creates a demand for access methods that offer more than text-based queries for retrieval of the information. In this paper is proposed a framework for the retrieval of medical images that allows the use of different algorithms for the search of medical images by similarity. The framework also enables the search for textual information from an associated medical report and DICOM header information. The proposed system can be used for support of clinical decision making and is intended to be integrated with an open source picture, archiving and communication systems (PACS). The BIRAM has the following advantages: (i) Can receive several types of algorithms for image similarity search; (ii) Allows the codification of the report according to a medical dictionary, improving the indexing of the information and retrieval; (iii) The algorithms can be selectively applied to images with the appropriated characteristics, for instance, only in magnetic resonance images. The framework was implemented in Java language using a MS Access 97 database. The proposed framework can still be improved, by the use of regions of interest (ROI), indexing with slim-trees and integration with a PACS Server.

  13. Adapting content-based image retrieval techniques for the semantic annotation of medical images.

    Science.gov (United States)

    Kumar, Ashnil; Dyer, Shane; Kim, Jinman; Li, Changyang; Leong, Philip H W; Fulham, Michael; Feng, Dagan

    2016-04-01

    The automatic annotation of medical images is a prerequisite for building comprehensive semantic archives that can be used to enhance evidence-based diagnosis, physician education, and biomedical research. Annotation also has important applications in the automatic generation of structured radiology reports. Much of the prior research work has focused on annotating images with properties such as the modality of the image, or the biological system or body region being imaged. However, many challenges remain for the annotation of high-level semantic content in medical images (e.g., presence of calcification, vessel obstruction, etc.) due to the difficulty in discovering relationships and associations between low-level image features and high-level semantic concepts. This difficulty is further compounded by the lack of labelled training data. In this paper, we present a method for the automatic semantic annotation of medical images that leverages techniques from content-based image retrieval (CBIR). CBIR is a well-established image search technology that uses quantifiable low-level image features to represent the high-level semantic content depicted in those images. Our method extends CBIR techniques to identify or retrieve a collection of labelled images that have similar low-level features and then uses this collection to determine the best high-level semantic annotations. We demonstrate our annotation method using retrieval via weighted nearest-neighbour retrieval and multi-class classification to show that our approach is viable regardless of the underlying retrieval strategy. We experimentally compared our method with several well-established baseline techniques (classification and regression) and showed that our method achieved the highest accuracy in the annotation of liver computed tomography (CT) images.

  14. Texture based feature extraction methods for content based medical image retrieval systems.

    Science.gov (United States)

    Ergen, Burhan; Baykara, Muhammet

    2014-01-01

    The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

  15. Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

    OpenAIRE

    Huda Ahmed; Emad N. Hassan; Amr A. Badr

    2016-01-01

    Medical Image Fusion (MIF) can improve the performance of medical diagnosis, treatment planning and image-guided surgery significantly through providing high-quality and rich-information medical images. Traditional MIF techniques suffer from common drawbacks such as: contrast reduction, edge blurring and image degradation. Pulse-coupled Neural Network (PCNN) based MIF techniques outperform the traditional methods in providing high-quality fused images due to its global coupling and pulse sync...

  16. Log-Gabor Energy Based Multimodal Medical Image Fusion in NSCT Domain

    OpenAIRE

    Yong Yang; Song Tong; Shuying Huang; Pan Lin

    2014-01-01

    Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. ...

  17. Medical image segmentation based on cellular neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The application of cellular neural network (CNN) has made great progress in image processing. When the selected objects extraction (SOE) CNN is applied to gray scale images, its effects depend on the choice of initial points. In this paper, we take medical images as an example to analyze this limitation. Then an improved algorithm is proposed in which we can segment any gray level objects regardless of the limitation stated above. We also use the gradient information and contour detection CNN to determine the contour and ensure the veracity of segmentation effectively. Finally, we apply the improved algorithm to tumor segmentation of the human brain MR image. The experimental results show that the algorithm is practical and effective.

  18. Segmentation of Pre-processed Medical Images: An Approach Based on Range Filter

    Directory of Open Access Journals (Sweden)

    Amir Rajaei

    2012-09-01

    Full Text Available Medical image segmentation is a frequent processing step. Medical images are suffering from unrelated article and strong speckle noise. In this paper, we propose an approach to remove special markings such as arrow symbols and printed text along with medical image segmentation using range filter. The special markings are extracted using Sobel edge detection technique and then the intensity values of the detected markings are substituted by the intensity values of their corresponding neighborhood pixels. Next, three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. Finally range filter is applied to segment the texture content of different modalities of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed approach which lead to have precise content based medical image classification and retrieval systems.

  19. An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising

    OpenAIRE

    2014-01-01

    Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient s...

  20. Medical Image Fusion

    Directory of Open Access Journals (Sweden)

    Mitra Rafizadeh

    2007-08-01

    Full Text Available Technological advances in medical imaging in the past two decades have enable radiologists to create images of the human body with unprecedented resolution. MRI, PET,... imaging devices can quickly acquire 3D images. Image fusion establishes an anatomical correlation between corresponding images derived from different examination. This fusion is applied either to combine images of different modalities (CT, MRI or single modality (PET-PET."nImage fusion is performed in two steps:"n1 Registration: spatial modification (eg. translation of model image relative to reference image in order to arrive at an ideal matching of both images. Registration methods are feature-based and intensity-based approaches."n2 Visualization: the goal of it is to depict the spatial relationship between the model image and refer-ence image. We can point out its clinical application in nuclear medicine (PET/CT.

  1. Non-rigid registration of medical images based on ordinal feature and manifold learning

    Science.gov (United States)

    Li, Qi; Liu, Jin; Zang, Bo

    2015-12-01

    With the rapid development of medical imaging technology, medical image research and application has become a research hotspot. This paper offers a solution to non-rigid registration of medical images based on ordinal feature (OF) and manifold learning. The structural features of medical images are extracted by combining ordinal features with local linear embedding (LLE) to improve the precision and speed of the registration algorithm. A physical model based on manifold learning and optimization search is constructed according to the complicated characteristics of non-rigid registration. The experimental results demonstrate the robustness and applicability of the proposed registration scheme.

  2. A Novel Encryption Frame for Medical Image with Watermark Based on Hyperchaotic System

    Directory of Open Access Journals (Sweden)

    Shun Zhang

    2014-01-01

    Full Text Available An encryption frame of medical image with watermark based on hyperchaotic system is proposed in this paper. Medical information, such as the patients’ private information, data needed for diagnosis, and information for authentication or protection of medical files, is embedded into the regions of interest (ROI in medical images with a high capacity difference-histogram-based reversible data-hiding scheme. After that, the watermarked medical images are encrypted with hyperchaotic systems. In the receiving end, the receiver with encryption key can decrypt the image to get similar images for diagnosis. If the receiver has the key for data hiding at the same time, he/she can extract the embedded private information and reversibly recover the original medical image. Experiments and analyses demonstrate that high embedding capacity and low distortion have been achieved in the process of data hiding, and, at the same time, high security has been acquired in the encryption phase.

  3. RayPlus: a Web-Based Platform for Medical Image Processing.

    Science.gov (United States)

    Yuan, Rong; Luo, Ming; Sun, Zhi; Shi, Shuyue; Xiao, Peng; Xie, Qingguo

    2017-04-01

    Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

  4. SOFT COMPUTING BASED MEDICAL IMAGE RETRIEVAL USING SHAPE AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    M. Mary Helta Daisy

    2014-01-01

    Full Text Available Image retrieval is a challenging and important research applications like digital libraries and medical image databases. Content-based image retrieval is useful in retrieving images from database based on the feature vector generated with the help of the image features. In this study, we present image retrieval based on the genetic algorithm. The shape feature and morphological based texture features are extracted images in the database and query image. Then generating chromosome based on the distance value obtained by the difference feature vector of images in the data base and the query image. In the selected chromosome the genetic operators like cross over and mutation are applied. After that the best chromosome selected and displays the most similar images to the query image. The retrieval performance of the method shows better retrieval result.

  5. DICOM image communication in globus-based medical grids.

    Science.gov (United States)

    Vossberg, Michal; Tolxdorff, Thomas; Krefting, Dagmar

    2008-03-01

    Grid computing, the collaboration of distributed resources across institutional borders, is an emerging technology to meet the rising demand on computing power and storage capacity in fields such as high-energy physics, climate modeling, or more recently, life sciences. A secure, reliable, and highly efficient data transport plays an integral role in such grid environments and even more so in medical grids. Unfortunately, many grid middleware distributions, such as the well-known Globus Toolkit, lack the integration of the world-wide medical image communication standard Digital Imaging and Communication in Medicine (DICOM). Currently, the DICOM protocol first needs to be converted to the file transfer protocol (FTP) that is offered by the grid middleware. This effectively reduces most of the advantages and security an integrated network of DICOM devices offers. In this paper, a solution is proposed that adapts the DICOM protocol to the Globus grid security infrastructure and utilizes routers to transparently route traffic to and from DICOM systems. Thus, all legacy DICOM devices can be seamlessly integrated into the grid without modifications. A prototype of the grid routers with the most important DICOM functionality has been developed and successfully tested in the MediGRID test bed, the German grid project for life sciences.

  6. Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

    Directory of Open Access Journals (Sweden)

    YiNan Zhang

    2017-01-01

    Full Text Available Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.

  7. Amorphous selenium based detectors for medical imaging applications

    Science.gov (United States)

    Mandal, Krishna C.; Kang, Sung H.; Choi, Michael; Jellison, Gerald E., Jr.

    2006-08-01

    We have developed and characterized large volume amorphous (a-) selenium (Se) stabilized alloys for room temperature medical imaging devices and high-energy physics detectors. The synthesis and preparation of well-defined and high quality a-Se (B, As, Cl) alloy materials have been conducted using a specially designed alloying reactor at EIC and installed in an argon atmosphere glove box. The alloy composition has been precisely controlled and optimized to ensure good device performance. The synthesis of large volume boron (B) doped (natural and isotopic 10B) a-Se (As, Cl) alloys has been carried out by thoroughly mixing vacuum distilled and zone-refined (ZR) Se with previously synthesized Se-As master alloys, Se-Cl master alloys and B. The synthesized a-Se (B, As, Cl) alloys have been characterized by x-ray diffraction (XRD), differential scanning calorimetry (DSC), Fourier transform infra-red spectroscopy (FTIR), x-ray photoelectron spectroscopy (XPS), inductively coupled plasma mass spectroscopy (ICP-MS), and detector testing. The a- Se alloys have shown high promise for x-ray detectors with its high dark resistivity (10 10-10 13 Ωcm), good charge transport properties, and cost-effective large area scalability. Details of various steps about detector fabrication and testing of these imaging devices are also presented.

  8. Medical image retrieval based on texture and shape feature co-occurrence

    Science.gov (United States)

    Zhou, Yixiao; Huang, Yan; Ling, Haibin; Peng, Jingliang

    2012-03-01

    With the rapid development and wide application of medical imaging technology, explosive volumes of medical image data are produced every day all over the world. As such, it becomes increasingly challenging to manage and utilize such data effectively and efficiently. In particular, content-based medical image retrieval has been intensively researched in the past decade or so. In this work, we propose a novel approach to content-based medical image retrieval utilizing the co-occurrence of both the texture and the shape features in contrast to most previous algorithms that use purely the texture or the shape feature. Specifically, we propose a novel form of representation for the co-occurrence of the texture and the shape features in an image, i.e., the gray level and edge direction co-occurrence matrix (GLEDCOM). Based on GLEDCOM, we define eleven features forming a feature vector that is used to measure the similarity between images. As a result, it consistently yields outstanding performance on both images rich in texture (e.g., image of brain) and images with dominant smooth regions and sharp edges (e.g., image of bladder). As demonstrated by experiments, the mean precision of retrieval with GLEDCOM algorithm outperforms a set of representative algorithms including the gray level co-occurrence matrix (GLCM) based, the Hu's seven moment invariants (HSMI) based, the uniformity estimation method (UEM) based and the the modified Zernike moments (MZM) based algorithms by 10%-20%.

  9. CBMIR: SHAPE-BASED IMAGE RETRIEVAL USING CANNY EDGE DETECTION AND K-MEANS CLUSTERING ALGORITHMS FOR MEDICAL IMAGES

    Directory of Open Access Journals (Sweden)

    B.Ramamurthy,

    2011-03-01

    Full Text Available The accumulation of large collections of digital images has created the need for efficient and intelligent schemes for classifying and retrieval of images. In this work, “CBMIR: Shape-Based Image Retrieval Using Canny Edge Detection and K-Means Clustering Algorithms for Medical Images”, has been developed to retrieve the medical images from huge volume of medical databases. This requires the preprocessing, feature extraction, classification, retrieval and indexing steps in order to develop an efficient medical image retrieval system. In this work, for preprocessing step, the image segmentation method has been carried out, for feature extraction, basic shape feature has been extracted using canny edge detection algorithm, and for classification, K-means classification algorithm has been used. For retrieval of images, Euclidian distance method values are calculated between query image and database images. The goal of this work is to provide a medical image retrieval system for further use of medical diagnosis purpose in the field of medical domain.

  10. A new method of medical image fusion based on nonsubsampled contourlet transform

    Science.gov (United States)

    Xu, Xuebin; Zhang, Xinman; Zhang, Deyun

    2008-12-01

    To improve the normal medical image fusion algorithm in order to avoid the loss of the detailed information in the processes of medical image fusion, a multiscale medical image fusion method based on nonsubsampled contourlet transform(NSCT) is proposed in this paper. First, the source images(MRI and CT images) are decomposed by using nonsubsampled contourlet transform. Then, the details of contourlet coefficients are fused on each corresponding levels with a vision feature fusion operator. Finally, the fused image will be obtained by taking the inverse nonsubsampled contourlet transformation. The experimental results show that the effect of the nonsubsampled contourlet-based method is obviously improved, and the proposed method can effectively preserve the detailed information of the source images.

  11. An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising.

    Science.gov (United States)

    Khanian, Maryam; Feizi, Awat; Davari, Ali

    2014-01-01

    Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

  12. An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising

    Science.gov (United States)

    Khanian, Maryam; Feizi, Awat; Davari, Ali

    2014-01-01

    Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim. PMID:24696809

  13. Medical X-Ray Image Enhancement Based on Kramer's PDE Model

    Institute of Scientific and Technical Information of China (English)

    Yan-Fei Zhao; Qing-Wei Gao; De-Xiang Zhang; Yi-Xiang Lu

    2007-01-01

    The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation (PDE) model, Kramer's PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer's PDE model. The study suggests that the Kramer's PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.

  14. Novel lattice Boltzmann method based on integrated edge and region information for medical image segmentation.

    Science.gov (United States)

    Wen, Junling; Yan, Zhuangzhi; Jiang, Jiehui

    2014-01-01

    The lattice Boltzmann (LB) method is a mesoscopic method based on kinetic theory and statistical mechanics. The main advantage of the LB method is parallel computation, which increases the speed of calculation. In the past decade, LB methods have gradually been introduced for image processing, e.g., image segmentation. However, a major shortcoming of existing LB methods is that they can only be applied to the processing of medical images with intensity homogeneity. In practice, however, many medical images possess intensity inhomogeneity. In this study, we developed a novel LB method to integrate edge and region information for medical image segmentation. In contrast to other segmentation methods, we added edge information as a relaxing factor and used region information as a source term. The proposed method facilitates the segmentation of medical images with intensity inhomogeneity and it still allows parallel computation. Preliminary tests of the proposed method are presented in this paper.

  15. Towards knowledge-based retrieval of medical images. The role of semantic indexing, image content representation and knowledge-based retrieval.

    Science.gov (United States)

    Lowe, H J; Antipov, I; Hersh, W; Smith, C A

    1998-01-01

    Medicine is increasingly image-intensive. The central importance of imaging technologies such as computerized tomography and magnetic resonance imaging in clinical decision making, combined with the trend to store many "traditional" clinical images such as conventional radiographs, microscopic pathology and dermatology images in digital format present both challenges and an opportunities for the designers of clinical information systems. The emergence of Multimedia Electronic Medical Record Systems (MEMRS), architectures that integrate medical images with text-based clinical data, will further hasten this trend. The development of these systems, storing a large and diverse set of medical images, suggests that in the future MEMRS will become important digital libraries supporting patient care, research and education. The representation and retrieval of clinical images within these systems is problematic as conventional database architectures and information retrieval models have, until recently, focused largely on text-based data. Medical imaging data differs in many ways from text-based medical data but perhaps the most important difference is that the information contained within imaging data is fundamentally knowledge-based. New representational and retrieval models for clinical images will be required to address this issue. Within the Image Engine multimedia medical record system project at the University of Pittsburgh we are evolving an approach to representation and retrieval of medical images which combines semantic indexing using the UMLS Metathesuarus, image content-based representation and knowledge-based image analysis.

  16. Region quad-tree decomposition based edge detection for medical images.

    Science.gov (United States)

    Dua, Sumeet; Kandiraju, Naveen; Chowriappa, Pradeep

    2010-05-28

    Edge detection in medical images has generated significant interest in the medical informatics community, especially in recent years. With the advent of imaging technology in biomedical and clinical domains, the growth in medical digital images has exceeded our capacity to analyze and store them for efficient representation and retrieval, especially for data mining applications. Medical decision support applications frequently demand the ability to identify and locate sharp discontinuities in an image for feature extraction and interpretation of image content, which can then be exploited for decision support analysis. However, due to the inherent high dimensional nature of the image content and the presence of ill-defined edges, edge detection using classical procedures is difficult, if not impossible, for sensitive and specific medical informatics-based discovery. In this paper, we propose a new edge detection technique based on the regional recursive hierarchical decomposition using quadtree and post-filtration of edges using a finite difference operator. We show that in medical images of common origin, focal and/or penumbral blurred edges can be characterized by an estimable intensity gradient. This gradient can further be used for dismissing false alarms. A detailed validation and comparison with related works on diabetic retinopathy images and CT scan images show that the proposed approach is efficient and accurate.

  17. Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

    Directory of Open Access Journals (Sweden)

    Huda Ahmed

    2016-10-01

    Full Text Available Medical Image Fusion (MIF can improve the performance of medical diagnosis, treatment planning and image-guided surgery significantly through providing high-quality and rich-information medical images. Traditional MIF techniques suffer from common drawbacks such as: contrast reduction, edge blurring and image degradation. Pulse-coupled Neural Network (PCNN based MIF techniques outperform the traditional methods in providing high-quality fused images due to its global coupling and pulse synchronization property; however, the selection of significant features that motivate the PCNN is still an open problem and plays a major role in measuring the contribution of each source image into the fused image. In this paper, a medical image fusion algorithm is proposed based on the Non-subsampled Contourlet Transform (NSCT and the Pulse-Coupled Neural Network (PCNN to fuse images from different modalities. Local Average Energy is used to motivate the PCNN due to its ability to capture salient features of the image such as edges, contours and textures. The proposed approach produces a high quality fused image with high contrast and improved content in comparison with other image fusion techniques without loss of significant details on both levels: the visual and the quantitative.

  18. Methods and applications of positron-based medical imaging

    Energy Technology Data Exchange (ETDEWEB)

    Herzog, H. [Institute of Medicine, Forschungszentrum Juelich, D-52425 Juelich (Germany)]. E-mail: h.herzog@fz-juelich.de

    2007-02-15

    Positron emission tomography (PET) is a diagnostic imaging method to examine metabolic functions and their disorders. Dedicated ring systems of scintillation detectors measure the 511 keV {gamma}-radiation produced in the course of the positron emission from radiolabelled metabolically active molecules. A great number of radiopharmaceuticals labelled with {sup 11}C, {sup 13}N, {sup 15}O, or {sup 18}F positron emitters have been applied both for research and clinical purposes in neurology, cardiology and oncology. The recent success of PET with rapidly increasing installations is mainly based on the use of [{sup 18}F]fluorodeoxyglucose (FDG) in oncology where it is most useful to localize primary tumours and their metastases.

  19. A web-based 3D medical image collaborative processing system with videoconference

    Science.gov (United States)

    Luo, Sanbi; Han, Jun; Huang, Yonggang

    2013-07-01

    Three dimension medical images have been playing an irreplaceable role in realms of medical treatment, teaching, and research. However, collaborative processing and visualization of 3D medical images on Internet is still one of the biggest challenges to support these activities. Consequently, we present a new application approach for web-based synchronized collaborative processing and visualization of 3D medical Images. Meanwhile, a web-based videoconference function is provided to enhance the performance of the whole system. All the functions of the system can be available with common Web-browsers conveniently, without any extra requirement of client installation. In the end, this paper evaluates the prototype system using 3D medical data sets, which demonstrates the good performance of our system.

  20. An efficient and secure medical image protection scheme based on chaotic maps.

    Science.gov (United States)

    Fu, Chong; Meng, Wei-hong; Zhan, Yong-feng; Zhu, Zhi-liang; Lau, Francis C M; Tse, Chi K; Ma, Hong-feng

    2013-09-01

    Recently, the increasing demand for telemedicine services has raised interest in the use of medical image protection technology. Conventional block ciphers are poorly suited to image protection due to the size of image data and increasing demand for real-time teleradiology and other online telehealth applications. To meet this challenge, this paper presents a novel chaos-based medical image encryption scheme. To address the efficiency problem encountered by many existing permutation-substitution type image ciphers, the proposed scheme introduces a substitution mechanism in the permutation process through a bit-level shuffling algorithm. As the pixel value mixing effect is contributed by both the improved permutation process and the original substitution process, the same level of security can be achieved in a fewer number of overall rounds. The results indicate that the proposed approach provides an efficient method for real-time secure medical image transmission over public networks.

  1. Prospective Study for Semantic Inter-Media Fusion in Content-Based Medical Image Retrieval

    CERN Document Server

    Teodorescu, Roxana; Leow, Wee-Kheng; Cretu, Vladimir

    2008-01-01

    One important challenge in modern Content-Based Medical Image Retrieval (CBMIR) approaches is represented by the semantic gap, related to the complexity of the medical knowledge. Among the methods that are able to close this gap in CBMIR, the use of medical thesauri/ontologies has interesting perspectives due to the possibility of accessing on-line updated relevant webservices and to extract real-time medical semantic structured information. The CBMIR approach proposed in this paper uses the Unified Medical Language System's (UMLS) Metathesaurus to perform a semantic indexing and fusion of medical media. This fusion operates before the query processing (retrieval) and works at an UMLS-compliant conceptual indexing level. Our purpose is to study various techniques related to semantic data alignment, preprocessing, fusion, clustering and retrieval, by evaluating the various techniques and highlighting future research directions. The alignment and the preprocessing are based on partial text/image retrieval feedb...

  2. A web-based solution for 3D medical image visualization

    Science.gov (United States)

    Hou, Xiaoshuai; Sun, Jianyong; Zhang, Jianguo

    2015-03-01

    In this presentation, we present a web-based 3D medical image visualization solution which enables interactive large medical image data processing and visualization over the web platform. To improve the efficiency of our solution, we adopt GPU accelerated techniques to process images on the server side while rapidly transferring images to the HTML5 supported web browser on the client side. Compared to traditional local visualization solution, our solution doesn't require the users to install extra software or download the whole volume dataset from PACS server. By designing this web-based solution, it is feasible for users to access the 3D medical image visualization service wherever the internet is available.

  3. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition...... to segment breast tissue and pectoral muscle area from the background in mammogram. The second focus is the choices of metric and its influence to the feasibility of a classifier, especially on k-nearest neighbors (k-NN) algorithm, with medical applications on breast cancer prediction and calcification...

  4. Natural Language Processing Versus Content-Based Image Analysis for Medical Document Retrieval.

    Science.gov (United States)

    Névéol, Aurélie; Deserno, Thomas M; Darmoni, Stéfan J; Güld, Mark Oliver; Aronson, Alan R

    2008-09-18

    One of the most significant recent advances in health information systems has been the shift from paper to electronic documents. While research on automatic text and image processing has taken separate paths, there is a growing need for joint efforts, particularly for electronic health records and biomedical literature databases. This work aims at comparing text-based versus image-based access to multimodal medical documents using state-of-the-art methods of processing text and image components. A collection of 180 medical documents containing an image accompanied by a short text describing it was divided into training and test sets. Content-based image analysis and natural language processing techniques are applied individually and combined for multimodal document analysis. The evaluation consists of an indexing task and a retrieval task based on the "gold standard" codes manually assigned to corpus documents. The performance of text-based and image-based access, as well as combined document features, is compared. Image analysis proves more adequate for both the indexing and retrieval of the images. In the indexing task, multimodal analysis outperforms both independent image and text analysis. This experiment shows that text describing images can be usefully analyzed in the framework of a hybrid text/image retrieval system.

  5. Texture-based medical image retrieval in compressed domain using compressive sensing.

    Science.gov (United States)

    Yadav, Kuldeep; Srivastava, Avi; Mittal, Ankush; Ansari, M A

    2014-01-01

    Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.

  6. Research and Realization of Medical Image Fusion Based on Three-Dimensional Reconstruction

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A new medical image fusion technique is presented. The method is based on three-dimensional reconstruction. After reconstruction, the three-dimensional volume data is normalized by three-dimensional coordinate conversion in the same way and intercepted through setting up cutting plane including anatomical structure, as a result two images in entire registration on space and geometry are obtained and the images are fused at last.Compared with traditional two-dimensional fusion technique, three-dimensional fusion technique can not only resolve the different problems existed in the two kinds of images, but also avoid the registration error of the two kinds of images when they have different scan and imaging parameter. The research proves this fusion technique is more exact and has no registration, so it is more adapt to arbitrary medical image fusion with different equipments.

  7. Unsupervised segmentation of medical image based on difference of mutual information

    Institute of Scientific and Technical Information of China (English)

    L(U) Qingwen; CHEN Wufan

    2006-01-01

    In the scope of medical image processing, segmentation is important and difficult. There are still two problems which trouble us in this field. One is how to determine the number of clusters in an image and the other is how to segment medical images containing lesions. A new segmentation method called DDC, based on difference of mutual information (dMI) and pixon, is proposed in this paper. Experiments demonstrate that dMI shows one kind of intrinsic relationship between the segmented image and the original one and so it can be used to well determine the number of clusters. Furthermore, multi-modality medical images with lesions can be automatically and successfully segmented by DDC method.

  8. Log-Gabor energy based multimodal medical image fusion in NSCT domain.

    Science.gov (United States)

    Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

    2014-01-01

    Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. The phase congruency that can provide a contrast and brightness-invariant representation is applied to fuse low-frequency coefficients, whereas the Log-Gabor energy that can efficiently determine the frequency coefficients from the clear and detail parts is employed to fuse the high-frequency coefficients. The proposed fusion method has been compared with the discrete wavelet transform (DWT), the fast discrete curvelet transform (FDCT), and the dual tree complex wavelet transform (DTCWT) based image fusion methods and other NSCT-based methods. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of multimodal medical images than other algorithms. Further, the applicability of the proposed method has been testified by carrying out a clinical example on a woman affected with recurrent tumor images.

  9. Log-Gabor Energy Based Multimodal Medical Image Fusion in NSCT Domain

    Directory of Open Access Journals (Sweden)

    Yong Yang

    2014-01-01

    Full Text Available Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. The phase congruency that can provide a contrast and brightness-invariant representation is applied to fuse low-frequency coefficients, whereas the Log-Gabor energy that can efficiently determine the frequency coefficients from the clear and detail parts is employed to fuse the high-frequency coefficients. The proposed fusion method has been compared with the discrete wavelet transform (DWT, the fast discrete curvelet transform (FDCT, and the dual tree complex wavelet transform (DTCWT based image fusion methods and other NSCT-based methods. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of multimodal medical images than other algorithms. Further, the applicability of the proposed method has been testified by carrying out a clinical example on a woman affected with recurrent tumor images.

  10. A QR code based zero-watermarking scheme for authentication of medical images in teleradiology cloud.

    Science.gov (United States)

    Seenivasagam, V; Velumani, R

    2013-01-01

    Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT)-Singular Value Decomposition (SVD) domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR) code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu's invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks.

  11. A QR Code Based Zero-Watermarking Scheme for Authentication of Medical Images in Teleradiology Cloud

    Directory of Open Access Journals (Sweden)

    V. Seenivasagam

    2013-01-01

    Full Text Available Healthcare institutions adapt cloud based archiving of medical images and patient records to share them efficiently. Controlled access to these records and authentication of images must be enforced to mitigate fraudulent activities and medical errors. This paper presents a zero-watermarking scheme implemented in the composite Contourlet Transform (CT—Singular Value Decomposition (SVD domain for unambiguous authentication of medical images. Further, a framework is proposed for accessing patient records based on the watermarking scheme. The patient identification details and a link to patient data encoded into a Quick Response (QR code serves as the watermark. In the proposed scheme, the medical image is not subjected to degradations due to watermarking. Patient authentication and authorized access to patient data are realized on combining a Secret Share with the Master Share constructed from invariant features of the medical image. The Hu’s invariant image moments are exploited in creating the Master Share. The proposed system is evaluated with Checkmark software and is found to be robust to both geometric and non geometric attacks.

  12. Modeling multiple visual words assignment for bag-of-features based medical image retrieval

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-01-01

    In this paper, we investigate the bag-of-features based medical image retrieval methods, which represent an image as a collection of local features, such as image patch and key points with SIFT descriptor. To improve the bag-of-features method, we first model the assignment of local descriptor as contribution functions, and then propose a new multiple assignment strategy. By assuming the local feature can be reconstructed by its neighboring visual words in vocabulary, we solve the reconstruction weights as a QP problem and then use the solved weights as contribution functions, which results in a new assignment method called the QP assignment. We carry our experiments on ImageCLEFmed datasets. Experiments\\' results show that our proposed method exceeds the performances of traditional solutions and works well for the bag-of-features based medical image retrieval tasks.

  13. The method for detecting small lesions in medical image based on sliding window

    Science.gov (United States)

    Han, Guilai; Jiao, Yuan

    2016-10-01

    At present, the research on computer-aided diagnosis includes the sample image segmentation, extracting visual features, generating the classification model by learning, and according to the model generated to classify and judge the inspected images. However, this method has a large scale of calculation and speed is slow. And because medical images are usually low contrast, when the traditional image segmentation method is applied to the medical image, there is a complete failure. As soon as possible to find the region of interest, improve detection speed, this topic attempts to introduce the current popular visual attention model into small lesions detection. However, Itti model is mainly for natural images. But the effect is not ideal when it is used to medical images which usually are gray images. Especially in the early stages of some cancers, the focus of a disease in the whole image is not the most significant region and sometimes is very difficult to be found. But these lesions are prominent in the local areas. This paper proposes a visual attention mechanism based on sliding window, and use sliding window to calculate the significance of a local area. Combined with the characteristics of the lesion, select the features of gray, entropy, corner and edge to generate a saliency map. Then the significant region is segmented and distinguished. This method reduces the difficulty of image segmentation, and improves the detection accuracy of small lesions, and it has great significance to early discovery, early diagnosis and treatment of cancers.

  14. Medical Imaging System

    Science.gov (United States)

    1991-01-01

    The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

  15. Quantitative evaluation of convolution-based methods for medical image interpolation.

    Science.gov (United States)

    Meijering, E H; Niessen, W J; Viergever, M A

    2001-06-01

    Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sinc-approximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

  16. On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.

    Science.gov (United States)

    Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F; Rubin, Daniel L

    2014-10-01

    Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of

  17. Color Medical Image Analysis

    CERN Document Server

    Schaefer, Gerald

    2013-01-01

    Since the early 20th century, medical imaging has been dominated by monochrome imaging modalities such as x-ray, computed tomography, ultrasound, and magnetic resonance imaging. As a result, color information has been overlooked in medical image analysis applications. Recently, various medical imaging modalities that involve color information have been introduced. These include cervicography, dermoscopy, fundus photography, gastrointestinal endoscopy, microscopy, and wound photography. However, in comparison to monochrome images, the analysis of color images is a relatively unexplored area. The multivariate nature of color image data presents new challenges for researchers and practitioners as the numerous methods developed for monochrome images are often not directly applicable to multichannel images. The goal of this volume is to summarize the state-of-the-art in the utilization of color information in medical image analysis.

  18. Artificial intelligence (AI)-based relational matching and multimodal medical image fusion: generalized 3D approaches

    Science.gov (United States)

    Vajdic, Stevan M.; Katz, Henry E.; Downing, Andrew R.; Brooks, Michael J.

    1994-09-01

    A 3D relational image matching/fusion algorithm is introduced. It is implemented in the domain of medical imaging and is based on Artificial Intelligence paradigms--in particular, knowledge base representation and tree search. The 2D reference and target images are selected from 3D sets and segmented into non-touching and non-overlapping regions, using iterative thresholding and/or knowledge about the anatomical shapes of human organs. Selected image region attributes are calculated. Region matches are obtained using a tree search, and the error is minimized by evaluating a `goodness' of matching function based on similarities of region attributes. Once the matched regions are found and the spline geometric transform is applied to regional centers of gravity, images are ready for fusion and visualization into a single 3D image of higher clarity.

  19. Lossloss encoding of medical images: hybrid modification of statistical modelling-based conception

    Science.gov (United States)

    Przelaskowski, Artur

    2001-10-01

    Methods of lossless compression of medical image data are considered. Selected class of efficient algorithms have been constructed, examined, and optimized to conclude the most useful tools for medical image archiving and transmission. Image data scanning, 2D context-based prediction and interpolation, and statistical models of entropy coder have been optimized to compress effectively ultrasound (US), magnetic resonance (MR), and computed tomography (CT) images. The SSM technique of suitable data decomposing scanning method followed by probabilistic modeling of the context in arithmetic encoding have occurred the most useful in our experiments. Context order, shape, and alphabet have been fitted to local data characteristics to decrease image data correlation and dilution of statistical model. Average bit rate value over test images is equal to 2.53 bpp for SSM coder and significantly overcomes 2.92 bpp of CALIC bit rate. Moreover, optimization of lossless wavelet coder by thinking of efficient subband decomposition schemes, and integer-to-integer transforms is reported. Efficient hybrid coding method (SHEC) as a complete tool for medical image archiving and transmission is proposed. SHEC develops SSM by including CALIC-like coder to compress the highest quality images and JPEG2000 wavelet coder for progressive delivering of high and middle quality images in telemedicine systems.

  20. Towards case-based medical learning in radiological decision making using content-based image retrieval

    Directory of Open Access Journals (Sweden)

    Günther Rolf W

    2011-10-01

    Full Text Available Abstract Background Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. Methods We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii Case-based reasoning (CBR parallels the human problem-solving process; (iii Content-based image retrieval (CBIR can be useful for computer-aided diagnosis (CAD. To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE. The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL. In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. Results We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i the IRMA core, i.e., the IRMA CBIR engine; and (ii the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM and Health Level Seven (HL7. Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. Conclusions The IBCR-RE paradigm

  1. Towards case-based medical learning in radiological decision making using content-based image retrieval.

    Science.gov (United States)

    Welter, Petra; Deserno, Thomas M; Fischer, Benedikt; Günther, Rolf W; Spreckelsen, Cord

    2011-10-27

    Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. The IBCR-RE paradigm incorporates a novel combination of essential aspects

  2. Intelligent distributed medical image management

    Science.gov (United States)

    Garcia, Hong-Mei C.; Yun, David Y.

    1995-05-01

    The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

  3. Deployment of a Grid-based Medical Imaging Application

    CERN Document Server

    Amendolia, S R; Frate, C; Gálvez, J; Hassan, W; Hauer, T; Manset, D; McClatchey, R; Odeh, M; Rogulin, D; Solomonides, T; Warren, R

    2005-01-01

    The MammoGrid project has deployed its Service-Oriented Architecture (SOA)-based Grid application in a real environment comprising actual participating hospitals. The resultant setup is currently being exploited to conduct rigorous in-house tests in the first phase before handing over the setup to the actual clinicians to get their feedback. This paper elaborates the deployment details and the experiences acquired during this phase of the project. Finally the strategy regarding migration to an upcoming middleware from EGEE project will be described. This paper concludes by highlighting some of the potential areas of future work.

  4. [Research on non-rigid medical image registration algorithm based on SIFT feature extraction].

    Science.gov (United States)

    Wang, Anna; Lu, Dan; Wang, Zhe; Fang, Zhizhen

    2010-08-01

    In allusion to non-rigid registration of medical images, the paper gives a practical feature points matching algorithm--the image registration algorithm based on the scale-invariant features transform (Scale Invariant Feature Transform, SIFT). The algorithm makes use of the image features of translation, rotation and affine transformation invariance in scale space to extract the image feature points. Bidirectional matching algorithm is chosen to establish the matching relations between the images, so the accuracy of image registrations is improved. On this basis, affine transform is chosen to complement the non-rigid registration, and normalized mutual information measure and PSO optimization algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.

  5. The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

    OpenAIRE

    Guocheng Yang; Meiling Li; Leiting Chen; Jie Yu

    2015-01-01

    We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are devel...

  6. Medical ultrasound imaging

    DEFF Research Database (Denmark)

    Jensen, Jørgen Arendt

    2007-01-01

    The paper gives an introduction to current medical ultrasound imaging systems. The basics of anatomic and blood flow imaging are described. The properties of medical ultrasound and its focusing are described, and the various methods for two- and three-dimensional imaging of the human anatomy...

  7. Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor.

    Science.gov (United States)

    Zhang, Xuming; Ren, Jinxia; Huang, Zhiwen; Zhu, Fei

    2016-09-15

    Multimodal medical image fusion (MIF) plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM) based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation.

  8. Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor

    Directory of Open Access Journals (Sweden)

    Xuming Zhang

    2016-09-01

    Full Text Available Multimodal medical image fusion (MIF plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation.

  9. Medical imaging in clinical applications algorithmic and computer-based approaches

    CERN Document Server

    Bhateja, Vikrant; Hassanien, Aboul

    2016-01-01

    This volume comprises of 21 selected chapters, including two overview chapters devoted to abdominal imaging in clinical applications supported computer aided diagnosis approaches as well as different techniques for solving the pectoral muscle extraction problem in the preprocessing part of the CAD systems for detecting breast cancer in its early stage using digital mammograms. The aim of this book is to stimulate further research in medical imaging applications based algorithmic and computer based approaches and utilize them in real-world clinical applications. The book is divided into four parts, Part-I: Clinical Applications of Medical Imaging, Part-II: Classification and clustering, Part-III: Computer Aided Diagnosis (CAD) Tools and Case Studies and Part-IV: Bio-inspiring based Computer Aided diagnosis techniques. .

  10. The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density.

    Science.gov (United States)

    Yang, Guocheng; Li, Meiling; Chen, Leiting; Yu, Jie

    2015-01-01

    We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.

  11. The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

    Directory of Open Access Journals (Sweden)

    Guocheng Yang

    2015-01-01

    Full Text Available We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD, as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.

  12. Classification in Medical Imaging

    DEFF Research Database (Denmark)

    Chen, Chen

    Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition......, a good metric is required to measure distance or similarity between feature points so that the classification becomes feasible. Furthermore, in order to build a successful classifier, one needs to deeply understand how classifiers work. This thesis focuses on these three aspects of classification...... to segment breast tissue and pectoral muscle area from the background in mammogram. The second focus is the choices of metric and its influence to the feasibility of a classifier, especially on k-nearest neighbors (k-NN) algorithm, with medical applications on breast cancer prediction and calcification...

  13. A framework for optimal kernel-based manifold embedding of medical image data.

    Science.gov (United States)

    Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

    2015-04-01

    Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images.

  14. A FAST MORPHING-BASED INTERPOLATION FOR MEDICAL IMAGES: APPLICATION TO CONFORMAL RADIOTHERAPY

    Directory of Open Access Journals (Sweden)

    Hussein Atoui

    2011-05-01

    Full Text Available A method is presented for fast interpolation between medical images. The method is intended for both slice and projective interpolation. It allows offline interpolation between neighboring slices in tomographic data. Spatial correspondence between adjacent images is established using a block matching algorithm. Interpolation of image intensities is then carried out by morphing between the images. The morphing-based method is compared to standard linear interpolation, block-matching-based interpolation and registrationbased interpolation in 3D tomographic data sets. Results show that the proposed method scored similar performance in comparison to registration-based interpolation, and significantly outperforms both linear and block-matching-based interpolation. This method is applied in the context of conformal radiotherapy for online projective interpolation between Digitally Reconstructed Radiographs (DRRs.

  15. Edge Detection of Medical Images Using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics

    Directory of Open Access Journals (Sweden)

    Puneet Rai

    2014-02-01

    Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.

  16. Non-rigid registration of medical images based on estimation of deformation states

    Science.gov (United States)

    Marami, Bahram; Sirouspour, Shahin; Capson, David W.

    2014-11-01

    A unified framework for automatic non-rigid 3D-3D and 3D-2D registration of medical images with static and dynamic deformations is proposed in this paper. The problem of non-rigid image registration is approached as a classical state estimation problem using a generic deformation model for the soft tissue. The registration technique employs a dynamic linear elastic continuum mechanics model of the tissue deformation, which is discretized using the finite element method. In the proposed method, the registration is achieved through a Kalman-like filtering process, which incorporates information from the deformation model and a vector of observation prediction errors computed from an intensity-based similarity/distance metric between images. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework. The performance of the proposed registration technique was evaluated in a number of different registration scenarios. First, 3D magnetic resonance (MR) images of uncompressed and compressed breast tissue were co-registered. 3D MR images of the uncompressed breast tissue were also registered to a sequence of simulated 2D interventional MR images of the compressed breast. Finally, the registration algorithm was employed to dynamically track a target sub-volume inside the breast tissue during the process of the biopsy needle insertion based on registering pre-insertion 3D MR images to a sequence of real-time simulated 2D interventional MR images. Registration results indicate that the proposed method can be effectively employed for the registration of medical images in image-guided procedures, such as breast biopsy in which the tissue undergoes static and dynamic deformations.

  17. Wavelets in medical imaging

    Energy Technology Data Exchange (ETDEWEB)

    Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H. [Sharda University, SET, Department of Electronics and Communication, Knowledge Park 3rd, Gr. Noida (India); University of Kocaeli, Department of Mathematics, 41380 Kocaeli (Turkey); Istanbul Aydin University, Department of Computer Engineering, 34295 Istanbul (Turkey); Sharda University, SET, Department of Mathematics, 32-34 Knowledge Park 3rd, Greater Noida (India)

    2012-07-17

    The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

  18. Medical imaging technology

    CERN Document Server

    Haidekker, Mark A

    2013-01-01

    Biomedical imaging is a relatively young discipline that started with Conrad Wilhelm Roentgen’s discovery of the x-ray in 1885. X-ray imaging was rapidly adopted in hospitals around the world. However, it was the advent of computerized data and image processing that made revolutionary new imaging modalities possible. Today, cross-sections and three-dimensional reconstructions of the organs inside the human body is possible with unprecedented speed, detail and quality. This book provides an introduction into the principles of image formation of key medical imaging modalities: X-ray projection imaging, x-ray computed tomography, magnetic resonance imaging, ultrasound imaging, and radionuclide imaging. Recent developments in optical imaging are also covered. For each imaging modality, the introduction into the physical principles and sources of contrast is provided, followed by the methods of image formation, engineering aspects of the imaging devices, and a discussion of strengths and limitations of the modal...

  19. Fast computation of Hessian-based enhancement filters for medical images.

    Science.gov (United States)

    Yang, Shih-Feng; Cheng, Ching-Hsue

    2014-10-01

    This paper presents a method for fast computation of Hessian-based enhancement filters, whose conditions for identifying particular structures in medical images are associated only with the signs of Hessian eigenvalues. The computational costs of Hessian-based enhancement filters come mainly from the computation of Hessian eigenvalues corresponding to image elements to obtain filter responses, because computing eigenvalues of a matrix requires substantial computational effort. High computational cost has become a challenge in the application of Hessian-based enhancement filters. Using a property of the characteristic polynomial coefficients of a matrix and the well-known Routh-Hurwitz criterion in control engineering, it is shown that under certain conditions, the response of a Hessian-based enhancement filter to an image element can be obtained without having to compute Hessian eigenvalues. The computational cost can thus be reduced. Experimental results on several medical images show that the method proposed in this paper can reduce significantly the number of computations of Hessian eigenvalues and the processing times of images. The percentage reductions of the number of computations of Hessian eigenvalues for enhancing blob- and tubular-like structures in two-dimensional images are approximately 90% and 65%, respectively. For enhancing blob-, tubular-, and plane-like structures in three-dimensional images, the reductions are approximately 97%, 75%, and 12%, respectively. For the processing times, the percentage reductions for enhancing blob- and tubular-like structures in two-dimensional images are approximately 31% and 7.5%, respectively. The reductions for enhancing blob-, tubular-, and plane-like structures in three-dimensional images are approximately 68%, 55%, and 3%, respectively.

  20. Non-Rigid Medical Image Registration with Joint Histogram Estimation Based on Mutual Information

    Institute of Scientific and Technical Information of China (English)

    LU Xuesong; ZHANG Su; SU He; CHEN Yazhu

    2007-01-01

    A mutual information-based non-rigid medical image registration algorithm is presented. An approximate function of Harming windowed sinc is used as kernel function of partial volume (PV)interpolation to estimate the joint histogram, which is the key to calculating the mutual information. And a new method is proposed to compute the gradient of mutual information with respect to themodel parameters. The transformation of object is modeled by a free-form deformation (FFD) based on B-splines. The experiments on 3D synthetic and real image data show that the algorithm can con-verge at the global optimum and restrain the emergency of local extreme.

  1. A HYBRID APPROACH BASED MEDICAL IMAGE RETRIEVAL SYSTEM USING FEATURE OPTIMIZED CLASSIFICATION SIMILARITY FRAMEWORK

    Directory of Open Access Journals (Sweden)

    Yogapriya Jaganathan

    2013-01-01

    Full Text Available For the past few years, massive upgradation is obtained in the pasture of Content Based Medical Image Retrieval (CBMIR for effective utilization of medical images based on visual feature analysis for the purpose of diagnosis and educational research. The existing medical image retrieval systems are still not optimal to solve the feature dimensionality reduction problem which increases the computational complexity and decreases the speed of a retrieval process. The proposed CBMIR is used a hybrid approach based on Feature Extraction, Optimization of Feature Vectors, Classification of Features and Similarity Measurements. This type of CBMIR is called Feature Optimized Classification Similarity (FOCS framework. The selected features are Textures using Gray level Co-occurrence Matrix Features (GLCM and Tamura Features (TF in which extracted features are formed as feature vector database. The Fuzzy based Particle Swarm Optimization (FPSO technique is used to reduce the feature vector dimensionality and classification is performed using Fuzzy based Relevance Vector Machine (FRVM to form groups of relevant image features that provide a natural way to classify dimensionally reduced feature vectors of images. The Euclidean Distance (ED is used as similarity measurement to measure the significance between the query image and the target images. This FOCS approach can get the query from the user and has retrieved the needed images from the databases. The retrieval algorithm performances are estimated in terms of precision and recall. This FOCS framework comprises several benefits when compared to existing CBMIR. GLCM and TF are used to extract texture features and form a feature vector database. Fuzzy-PSO is used to reduce the feature vector dimensionality issues while selecting the important features in the feature vector database in which computational complexity is decreased. Fuzzy based RVM is used for feature classification in which it increases the

  2. Implementation of Biography Based Neural Clustering (BBNC with Genetic Processing for tumor detection from medical images

    Directory of Open Access Journals (Sweden)

    Kaur Chandanpreet

    2016-01-01

    Full Text Available Segmentation is a best method to divide the required region from the medical images. This research is based on segmentation of medical images (MRI, CT scans based on the previous method known as pre-operative and post-recurrence tumor registration (PORTR and proposed method biography based neural clustering (BBNC with genetic processing for tumor segmentation. By using the new technique the extracted part can be view in 3D model and also can get the actual segmented tumor region. This new method will be helpful for diagnostics to find the tumor area as well as pixel difference in segmented part to define the tumor area accurately. While in the previous approach all the parameters have been used likewise, in which the registration method is used to transform the different sets of data into one coordinate system for segmentation of medical images. Registration basically is used to improve the signals to reduce the noise from the images. These techniques are better to find the tumor area from the MRI and CT scans, but after comparing them better results have been obtained in proposed technique. The proposed technique (BBNC reduces the extracted region again into required and actual region of tumor with accuracy of area, time and pixel difference.

  3. Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model.

    Science.gov (United States)

    Shuaiqi, Liu; Jie, Zhao; Mingzhu, Shi

    2015-01-01

    Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

  4. Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model

    Directory of Open Access Journals (Sweden)

    Liu Shuaiqi

    2015-01-01

    Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF and spiking cortical model (SCM. Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

  5. Distributed Object Medical Imaging Model

    CERN Document Server

    Noor, Ahmad Shukri Mohd

    2009-01-01

    Digital medical informatics and images are commonly used in hospitals today,. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM) to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients' data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common...

  6. FEATURE RANKING BASED NESTED SUPPORT VECTOR MACHINE ENSEMBLE FOR MEDICAL IMAGE CLASSIFICATION.

    Science.gov (United States)

    Varol, Erdem; Gaonkar, Bilwaj; Erus, Guray; Schultz, Robert; Davatzikos, Christos

    2012-01-01

    This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer's disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.

  7. A service based approach for medical image distribution in healthcare Intranets.

    Science.gov (United States)

    Kaldoudi, Eleni; Karaiskakis, Dimosthenis

    2006-02-01

    The Digital Imaging and Communications in Medicine (DICOM) protocol is currently the ubiquitous standard for the communication of medical images and related data within the radiology department. However, seamless image distribution within the healthcare enterprise and especially with research and educational information systems is still hard to achieve, as software developers of such third-party applications have to go through the rather cumbersome task of adapting the DICOM communication model and implementing the DICOM protocol. This paper gives a brief outline of current trends in medical image distribution in the healthcare enterprise, and proposes a new technological approach for distributing DICOM images and related data through commonplace Internet technologies, based on the emerging web services software paradigm. In particular, the paper describes the DICOM Image Management (DIM) web service which acts as a façade for conventional DICOM sources allowing DICOM image data and related information, to be transformed into XML documents encapsulated in SOAP messages, enabling integration at the application level through general purpose standardized web technologies. Implementation issues are discussed and a demonstration of engaging the DIM web service is included.

  8. Vascular Tree Segmentation in Medical Images Using Hessian-Based Multiscale Filtering and Level Set Method

    Directory of Open Access Journals (Sweden)

    Jiaoying Jin

    2013-01-01

    extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images.

  9. The high resolution gamma imager (HRGI): a CCD based camera for medical imaging

    Science.gov (United States)

    Lees, John. E.; Fraser, George. W.; Keay, Adam; Bassford, David; Ott, Robert; Ryder, William

    2003-11-01

    We describe the High Resolution Gamma Imager (HRGI): a Charge Coupled Device (CCD) based camera for imaging small volumes of radionuclide uptake in tissues. The HRGI is a collimated, scintillator-coated, low cost, high performance imager using low noise CCDs that will complement whole-body imaging Gamma Cameras in nuclear medicine. Using 59.5 keV radiation from a 241Am source we have measured the spatial resolution and relative efficiency of test CCDs from E2V Technologies (formerly EEV Ltd.) coated with Gadox (Gd 2O 2S(Tb)) layers of varying thicknesses. The spatial resolution degrades from 0.44 to 0.6 mm and the detection efficiency increases (×3) as the scintillator thickness increases from 100 to 500 μm. We also describe our first image using the clinically important isotope 99mTc. The final HRGI will have intrinsic sub-mm spatial resolution (˜0.7 mm) and good energy resolution over the energy range 30-160 keV.

  10. Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

    Directory of Open Access Journals (Sweden)

    Hui Huang

    2017-01-01

    Full Text Available According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

  11. Medical imaging systems

    Science.gov (United States)

    Frangioni, John V

    2013-06-25

    A medical imaging system provides simultaneous rendering of visible light and diagnostic or functional images. The system may be portable, and may include adapters for connecting various light sources and cameras in open surgical environments or laparascopic or endoscopic environments. A user interface provides control over the functionality of the integrated imaging system. In one embodiment, the system provides a tool for surgical pathology.

  12. A watermarking-based medical image integrity control system and an image moment signature for tampering characterization.

    Science.gov (United States)

    Coatrieux, Gouenou; Huang, Hui; Shu, Huazhong; Luo, Limin; Roux, Christian

    2013-11-01

    In this paper, we present a medical image integrity verification system to detect and approximate local malevolent image alterations (e.g., removal or addition of lesions) as well as identifying the nature of a global processing an image may have undergone (e.g., lossy compression, filtering, etc.). The proposed integrity analysis process is based on nonsignificant region watermarking with signatures extracted from different pixel blocks of interest, which are compared with the recomputed ones at the verification stage. A set of three signatures is proposed. The first two devoted to detection and modification location are cryptographic hashes and checksums, while the last one is issued from the image moment theory. In this paper, we first show how geometric moments can be used to approximate any local modification by its nearest generalized 2-D Gaussian. We then demonstrate how ratios between original and recomputed geometric moments can be used as image features in a classifier-based strategy in order to determine the nature of a global image processing. Experimental results considering both local and global modifications in MRI and retina images illustrate the overall performances of our approach. With a pixel block signature of about 200 bit long, it is possible to detect, to roughly localize, and to get an idea about the image tamper.

  13. A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images.

    Science.gov (United States)

    Rastgarpour, Maryam; Shanbehzadeh, Jamshid; Soltanian-Zadeh, Hamid

    2014-08-01

    medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

  14. Medical image collection indexing: shape-based retrieval using KD-trees.

    Science.gov (United States)

    Robinson, G P; Tagare, H D; Duncan, J S; Jaffe, C C

    1996-01-01

    The capacity to retrieve images containing objects with shapes similar to a query shape is desirable in medical image databases. We propose a similarity measure and an indexing mechanism for non-rigid comparison of shape which adds this capability to image databases. The (dis-)similarity measure is based on the observations that: (1) the geometry of the same organ in different subjects is not related by a strictly rigid transformation; and (2) the orientation of the organ plays a key role in comparing shape. We propose a similarity measure that computes a non-rigid mapping between curves and uses this mapping to compare oriented shape. We also show how KD-trees can index curves so that retrieval with our similarity measure is efficient. Experiments with real-world data from a database of magnetic resonance images are provided.

  15. OpenID Connect as a security service in cloud-based medical imaging systems.

    Science.gov (United States)

    Ma, Weina; Sartipi, Kamran; Sharghigoorabi, Hassan; Koff, David; Bak, Peter

    2016-04-01

    The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as "Kerberos of cloud." We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model.

  16. A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

    Directory of Open Access Journals (Sweden)

    Xiaogang Du

    2016-01-01

    Full Text Available The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU.

  17. A Fresnelet-Based Encryption of Medical Images using Arnold Transform

    Directory of Open Access Journals (Sweden)

    Muhammad Nazeer

    2013-04-01

    Full Text Available Medical images are commonly stored in digital media and transmitted via Internet for certain uses. If a medical information image alters, this can lead to a wrong diagnosis which may create a serious health problem. Moreover, medical images in digital form can easily be modified by wiping off or adding small pieces of information intentionally for certain illegal purposes. Hence, the reliability of medical images is an important criterion in a hospital information system. In this paper, the Fresnelet transform is employed along with appropriate handling of the Arnold transform and the discrete cosine transform to provide secure distribution of medical images. This method presents a new data hiding system in which steganography and cryptography are used to prevent unauthorized data access. The experimental results exhibit high imperceptibility for embedded images and significant encryption of information images.

  18. Image-based teleconsultation using smartphones or tablets: qualitative assessment of medical experts.

    Science.gov (United States)

    Boissin, Constance; Blom, Lisa; Wallis, Lee; Laflamme, Lucie

    2017-02-01

    Mobile health has promising potential in improving healthcare delivery by facilitating access to expert advice. Enabling experts to review images on their smartphone or tablet may save valuable time. This study aims at assessing whether images viewed by medical specialists on handheld devices such as smartphones and tablets are perceived to be of comparable quality as when viewed on a computer screen. This was a prospective study comparing the perceived quality of 18 images on three different display devices (smartphone, tablet and computer) by 27 participants (4 burn surgeons and 23 emergency medicine specialists). The images, presented in random order, covered clinical (dermatological conditions, burns, ECGs and X-rays) and non-clinical subjects and their perceived quality was assessed using a 7-point Likert scale. Differences in devices' quality ratings were analysed using linear regression models for clustered data adjusting for image type and participants' characteristics (age, gender and medical specialty). Overall, the images were rated good or very good in most instances and more so for the smartphone (83.1%, mean score 5.7) and tablet (78.2%, mean 5.5) than for a standard computer (70.6%, mean 5.2). Both handheld devices had significantly higher ratings than the computer screen, even after controlling for image type and participants' characteristics. Nearly all experts expressed that they would be comfortable using smartphones (n=25) or tablets (n=26) for image-based teleconsultation. This study suggests that handheld devices could be a substitute for computer screens for teleconsultation by physicians working in emergency settings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  19. A novel fuzzy logic-based image steganography method to ensure medical data security.

    Science.gov (United States)

    Karakış, R; Güler, I; Çapraz, I; Bilir, E

    2015-12-01

    This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used as the cover image. In addition to the EEG, the message is composed of the doctor׳s comments and patient information in the file header of images. Two new image steganography methods that are based on fuzzy-logic and similarity are proposed to select the non-sequential least significant bits (LSB) of image pixels. The similarity values of the gray levels in the pixels are used to hide the message. The message is secured to prevent attacks by using lossless compression and symmetric encryption algorithms. The performance of stego image quality is measured by mean square of error (MSE), peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), universal quality index (UQI), and correlation coefficient (R). According to the obtained result, the proposed method ensures the confidentiality of the patient information, and increases data repository and transmission capacity of both MR images and EEG signals. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

    Science.gov (United States)

    Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

    2014-01-01

    Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

  1. [A method for the medical image registration based on the statistics samples averaging distribution theory].

    Science.gov (United States)

    Xu, Peng; Yao, Dezhong; Luo, Fen

    2005-08-01

    The registration method based on mutual information is currently a popular technique for the medical image registration, but the computation for the mutual information is complex and the registration speed is slow. In engineering process, a subsampling technique is taken to accelerate the registration speed at the cost of registration accuracy. In this paper a new method based on statistics sample theory is developed, which has both a higher speed and a higher accuracy as compared with the normal subsampling method, and the simulation results confirm the validity of the new method.

  2. WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies

    Science.gov (United States)

    Vega, Francisco; Pérez, Wilson; Tello, Andrés.; Saquicela, Victor; Espinoza, Mauricio; Solano-Quinde, Lizandro; Vidal, Maria-Esther; La Cruz, Alexandra

    2015-12-01

    Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.

  3. [Rapid 2D-3D medical image registration based on CUDA].

    Science.gov (United States)

    Li, Lingzhi; Zou, Beiji

    2014-08-01

    The medical image registration between preoperative three-dimensional (3D) scan data and intraoperative two-dimensional (2D) image is a key technology in the surgical navigation. Most previous methods need to generate 2D digitally reconstructed radiographs (DRR) images from the 3D scan volume data, then use conventional image similarity function for comparison. This procedure includes a large amount of calculation and is difficult to archive real-time processing. In this paper, with using geometric feature and image density mixed characteristics, we proposed a new similarity measure function for fast 2D-3D registration of preoperative CT and intraoperative X-ray images. This algorithm is easy to implement, and the calculation process is very short, while the resulting registration accuracy can meet the clinical use. In addition, the entire calculation process is very suitable for highly parallel numerical calculation by using the algorithm based on CUDA hardware acceleration to satisfy the requirement of real-time application in surgery.

  4. Model-based 3D segmentation of the bones of joints in medical images

    Science.gov (United States)

    Liu, Jiamin; Udupa, Jayaram K.; Saha, Punam K.; Odhner, Dewey; Hirsch, Bruce E.; Siegler, Sorin; Simon, Scott; Winkelstein, Beth A.

    2005-04-01

    There are several medical application areas that require the segmentation and separation of the component bones of joints in a sequence of acquired images of the joint under various loading conditions, our own target area being joint motion analysis. This is a challenging problem due to the proximity of bones at the joint, partial volume effects, and other imaging modality-specific factors that confound boundary contrast. A model-based strategy is proposed in this paper wherein a rigid model of the bone is generated from a segmentation of the bone in the image corresponding to one position of the joint by using the live wire method. In other images of the joint, this model is used to search for the same bone by minimizing an energy functional that utilizes both boundary- and region-based information. An evaluation of the method by utilizing a total of 60 data sets on MR and CT images of the ankle complex and cervical spine indicates that the segmentations agree very closely with the live wire segmentations yielding true positive and false positive volume fractions in the range 89-97% and 0.2-0.7%. The method requires 1-2 minutes of operator time and 6-7 minutes of computer time, which makes it significantly more efficient than live wire - the only method currently available for the task.

  5. Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

    Science.gov (United States)

    Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

    2016-12-01

    To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

  6. Lossless Medical Image Compression

    Directory of Open Access Journals (Sweden)

    Nagashree G

    2014-06-01

    Full Text Available Image compression has become an important process in today‟s world of information exchange. Image compression helps in effective utilization of high speed network resources. Medical Image Compression is very important in the present world for efficient archiving and transmission of images. In this paper two different approaches for lossless image compression is proposed. One uses the combination of 2D-DWT & FELICS algorithm for lossy to lossless Image Compression and another uses combination of prediction algorithm and Integer wavelet Transform (IWT. To show the effectiveness of the methodology used, different image quality parameters are measured and shown the comparison of both the approaches. We observed the increased compression ratio and higher PSNR values.

  7. Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

    Science.gov (United States)

    Diamant, Idit; Klang, Eyal; Amitai, Michal; Konen, Eli; Goldberger, Jacob; Greenspan, Hayit

    2017-06-01

    We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

  8. Bag-of-features based medical image retrieval via multiple assignment and visual words weighting

    KAUST Repository

    Wang, Jingyan

    2011-11-01

    Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. © 2011 IEEE.

  9. Distributed Object Medical Imaging Model

    Directory of Open Access Journals (Sweden)

    Ahmad Shukri Mohd Noor

    2009-09-01

    Full Text Available Digital medical informatics and images are commonly used in hospitals today. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients' data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common Object Request Broker Architecture (CORBA, Java Database Connectivity (JDBC, and Java language provide the capability to combine the DOMIM resources into an integrated, interoperable, and scalable system. The underneath technology, including IDL ORB, Event Service, IIOP JDBC/ODBC, legacy system wrapping and Java implementation are explored. This paper explores a distributed collaborative CORBA/JDBC based framework that will enhance medical information management requirements and development. It encompasses a new paradigm for the delivery of health services that requires process reengineering, cultural changes, as well as organizational changes.

  10. Archimedes, an archive of medical images.

    Science.gov (United States)

    Tahmoush, Dave; Samet, Hanan

    2006-01-01

    We present a medical image and medical record database for the storage, research, transmission, and evaluation of medical images. Medical images from any source that supports the DICOM standard can be stored and accessed, as well as associated analysis and annotations. Retrieval is based on patient info, date, doctor's annotations, features in the images, or a spatial combination. This database supports the secure transmission of sensitive data for tele-medicine and follows all HIPPA regulations.

  11. Medical Imaging and Infertility.

    Science.gov (United States)

    Peterson, Rebecca

    2016-11-01

    Infertility affects many couples, and medical imaging plays a vital role in its diagnosis and treatment. Radiologic technologists benefit from having a broad understanding of infertility risk factors and causes. This article describes the typical structure and function of the male and female reproductive systems, as well as congenital and acquired conditions that could lead to a couple's inability to conceive. Medical imaging procedures performed for infertility diagnosis are discussed, as well as common interventional options available to patients. © 2016 American Society of Radiologic Technologists.

  12. Volumetric Medical Image Coding: An Object-based, Lossy-to-lossless and Fully Scalable Approach.

    Science.gov (United States)

    Danyali, Habibiollah; Mertins, Alfred

    2011-01-01

    In this article, an object-based, highly scalable, lossy-to-lossless 3D wavelet coding approach for volumetric medical image data (e.g., magnetic resonance (MR) and computed tomography (CT)) is proposed. The new method, called 3DOBHS-SPIHT, is based on the well-known set partitioning in the hierarchical trees (SPIHT) algorithm and supports both quality and resolution scalability. The 3D input data is grouped into groups of slices (GOS) and each GOS is encoded and decoded as a separate unit. The symmetric tree definition of the original 3DSPIHT is improved by introducing a new asymmetric tree structure. While preserving the compression efficiency, the new tree structure allows for a small size of each GOS, which not only reduces memory consumption during the encoding and decoding processes, but also facilitates more efficient random access to certain segments of slices. To achieve more compression efficiency, the algorithm only encodes the main object of interest in each 3D data set, which can have any arbitrary shape, and ignores the unnecessary background. The experimental results on some MR data sets show the good performance of the 3DOBHS-SPIHT algorithm for multi-resolution lossy-to-lossless coding. The compression efficiency, full scalability, and object-based features of the proposed approach, beside its lossy-to-lossless coding support, make it a very attractive candidate for volumetric medical image information archiving and transmission applications.

  13. Education of hand rubbing technique to prospective medical staff, employing UV-based digital imaging technology.

    Science.gov (United States)

    Lehotsky, Ákos; Szilágyi, László; Demeter-Iclănzan, Annamária; Haidegger, Tamás; Wéber, György

    2016-06-01

    The aim of this study was to objectively assess the hand hygiene performance of medical students. Hand rubbing technique was evaluated by employing innovative UV-light-based imaging technology, identifying patterns and trends in missed areas after applying WHO's six-step protocol. This specially designed hand hygiene education and assessment program targeted 1,344 medical students at two distant sites in Central Europe. Students were introduced to a short video, presenting the basics of hand hygiene, and then received further demonstration from professional trainers, focusing on the correct execution of WHO's six-step technique. To verify the acquired skill, participants rubbed their hands with UV-marked alcohol-based solution. Digital images of the hands were recorded under UV light, followed by computer evaluation and assessment. Immediate objective visual feedback was given to the participants showing missed areas on their hands. The statistical analysis of missed spots was based on retrospective expert-driven manual evaluation. Significant difference in rubbing quality was found between female and male participants [35.3% (CI 95%: 33-38%) versus 29.0% (CI 95%: 27-31%), p hands [43.4% (CI 95%: 39-48%) versus 34.9% (CI 95%: 32-38%), p = 0.002], and various zones of the hands' dorsal side. Based on the participants' feedback and the evaluation of the infection control specialists, it can be stated that the identification of typically missed patterns and the instant visual feedback have a vital role in improving the hand hygiene technique of prospective medical staff.

  14. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    Science.gov (United States)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  15. Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm.

    Science.gov (United States)

    Daniel, Ebenezer; Anitha, J

    2016-04-01

    Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature.

  16. Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

    Science.gov (United States)

    Hu, Jinyan; Li, Li; Yang, Yunfeng

    2017-06-01

    The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

  17. Fast Fractal Compression of Satellite and Medical Images Based on Domain-Range Entropy

    Directory of Open Access Journals (Sweden)

    Ramesh Babu Inampudi

    2010-01-01

    Full Text Available Fractal image Compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transformT, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. The other advantage is its multi resolution property, i.e. an image can be decoded at higher or lower resolutions than the original without much degradation in quality. However, the encoding time is computationally intensive. In this paper, a fast fractal image compression method based on the domain-range entropy is proposed to reduce the encoding time, while maintaining the fidelity and compression ratio of the decoded image. The method is a two-step process. First, domains that are similar i.e. domains having nearly equal variances are eliminated from the domain pool. Second, during the encoding phase, only domains and ranges having equal entropies (with an adaptive error threshold, λdepth for each quadtree depth are compared for a match within the rms error tolerance. As a result, many unqualified domains are removed from comparison and a significant reduction in encoding time is expected. The method is applied for compression of satellite and medical images (512x512, 8-bit gray scale. Experimental results show that the proposed method yields superior performance over Fisher’s classified search and other methods.

  18. Compton scattered imaging based on the V-line radon transform and its medical imaging applications.

    Science.gov (United States)

    Nguyen, M K; Regniery, R; Truong, T T; Zaidi, H

    2010-01-01

    The Radon transform (RT) on straight lines deals as mathematical foundation for many tomographic modalities (e.g. Xray scanner, Positron Emission Tomography), using only primary radiation. In this paper, we consider a new RT defined on a pair of half-lines forming a letter V, arising from the modeling a two-dimensional emission imaging process by Compton scattered gamma rays. We establish its analytic inverse, which is shown to support the feasibility of the reconstruction of a two-dimensional image from scattered radiation collected on a one-dimensional collimated camera. Moreover, a filtered back-projection inversion method is also constructed. Its main advantages are algorithmic efficiency and computational rapidity. We present numerical simulations to illustrate the working. To sum up, the V-line RT leads not only to a new imaging principle, but also to a new concept of detector with high energetic resolution capable to collect the scattered radiation.

  19. Scalar Parameters Optimization in PDE Based Medical Image Denoising by using Cellular Wave Computing

    Directory of Open Access Journals (Sweden)

    GACSÁDI Alexandru

    2016-10-01

    Full Text Available In order to help with biomedical images, a set of complex and effective mathematical models are available, based on the PDE (PDE - partial differential equation. On one hand, effective implementation of these methods is difficult, due to the difficulty of determining the scalar parameter values, on which the image processing efficiency depends, while on the other hand, due to the considerable computing power needed in order to perform in real time. Currently there are no analytical and / or experimental methods in the literature for the exact values determination of the scaled parameters to provide the best results for a specific image processing. This paper proposes a method for optimizing the values of a scaling parameter set, which ensure effective noise reduction of medical images by using cellular wave computing. To assess the overall performance of noise extraction, the error function (quantitative component and direct visualization (qualitative component are used at the same time. Moreover, by using this analysis, the degree to which the CNN templates are robust against the range of values of the scalar parameter, is obtainable.

  20. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering

    Directory of Open Access Journals (Sweden)

    Yao-Tien Chen

    2017-01-01

    Full Text Available Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.

  1. A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2014-01-01

    Full Text Available This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences.

  2. Medical image integrity control and forensics based on watermarking--approximating local modifications and identifying global image alterations.

    Science.gov (United States)

    Huang, H; Coatrieux, G; Shu, H Z; Luo, L M; Roux, Ch

    2011-01-01

    In this paper we present a medical image integrity verification system that not only allows detecting and approximating malevolent local image alterations (e.g. removal or addition of findings) but is also capable to identify the nature of global image processing applied to the image (e.g. lossy compression, filtering …). For that purpose, we propose an image signature derived from the geometric moments of pixel blocks. Such a signature is computed over regions of interest of the image and then watermarked in regions of non interest. Image integrity analysis is conducted by comparing embedded and recomputed signatures. If any, local modifications are approximated through the determination of the parameters of the nearest generalized 2D Gaussian. Image moments are taken as image features and serve as inputs to one classifier we learned to discriminate the type of global image processing. Experimental results with both local and global modifications illustrate the overall performances of our approach.

  3. A Medical Image Backup Architecture Based on a NoSQL Database and Cloud Computing Services.

    Science.gov (United States)

    Santos Simões de Almeida, Luan Henrique; Costa Oliveira, Marcelo

    2015-01-01

    The use of digital systems for storing medical images generates a huge volume of data. Digital images are commonly stored and managed on a Picture Archiving and Communication System (PACS), under the DICOM standard. However, PACS is limited because it is strongly dependent on the server's physical space. Alternatively, Cloud Computing arises as an extensive, low cost, and reconfigurable resource. However, medical images contain patient information that can not be made available in a public cloud. Therefore, a mechanism to anonymize these images is needed. This poster presents a solution for this issue by taking digital images from PACS, converting the information contained in each image file to a NoSQL database, and using cloud computing to store digital images.

  4. Event-based versus process-based informed consent to address scientific evidence and uncertainties in ionising medical imaging.

    Science.gov (United States)

    Recchia, Virginia; Dodaro, Antonio; Braga, Larissa

    2013-10-01

    Inappropriate ionising medical imaging has been escalating in the last decades. This trend leads to potential damage to health and has been associated to bioethical and legal issues of patient autonomy. While the doctrine underlines the importance of using informed consent to improve patient autonomy and physician-patient communication, some researchers have argued that it often falls short of this aim. There are basically two different informed consent practices. The first - the so-called "event-based model" - regards informed consent as a passive signature of a standard unreadable template, performed only once in each medical pathway. The second - the so-called "process-based model" - integrates information into the continuing dialogue between physician and patient, vital for diagnosis and treatment. Current medical behaviour often embraces the event-based model, which is considered ineffective and contributes to inappropriateness. We sought, in this review, to analyse from juridical and communication standpoints whether process-based informed consent can deal with scientific uncertainties in radiological decision-making. The informed consent is still a distinctive process in defence of both patients' and physicians' health and dignity in rule-of-law states and consequently in curtailing the abuse of ionising medical radiation. • Inappropriate ionising medical imaging is widespread and increasing worldwide. • This trend leads to noteworthy damage to health and is linked to the issue of patient autonomy. • Some authors have argued that informed consent often falls short of improving patient autonomy. • Process-based informed consent can deal with scientific uncertainties to contrast inappropriateness. • Informed consent is still a distinctive process in defence of both patients and physicians.

  5. A novel segmentation approach for noisy medical images using intuitionistic fuzzy divergence with neighbourhood-based membership function.

    Science.gov (United States)

    Jati, A; Singh, G; Koley, S; Konar, A; Ray, A K; Chakraborty, C

    2015-03-01

    Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.

  6. Ground truth delineation for medical image segmentation based on Local Consistency and Distribution Map analysis.

    Science.gov (United States)

    Cheng, Irene; Sun, Xinyao; Alsufyani, Noura; Xiong, Zhihui; Major, Paul; Basu, Anup

    2015-01-01

    Computer-aided detection (CAD) systems are being increasingly deployed for medical applications in recent years with the goal to speed up tedious tasks and improve precision. Among others, segmentation is an important component in CAD systems as a preprocessing step to help recognize patterns in medical images. In order to assess the accuracy of a CAD segmentation algorithm, comparison with ground truth data is necessary. To-date, ground truth delineation relies mainly on contours that are either manually defined by clinical experts or automatically generated by software. In this paper, we propose a systematic ground truth delineation method based on a Local Consistency Set Analysis approach, which can be used to establish an accurate ground truth representation, or if ground truth is available, to assess the accuracy of a CAD generated segmentation algorithm. We validate our computational model using medical data. Experimental results demonstrate the robustness of our approach. In contrast to current methods, our model also provides consistency information at distributed boundary pixel level, and thus is invariant to global compensation error.

  7. PSO-based methods for medical image registration and change assessment of pigmented skin

    Science.gov (United States)

    Kacenjar, Steve; Zook, Matthew; Balint, Michael

    2011-03-01

    There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient

  8. FILTERING OF MEDICAL ULTRASONIC IMAGES BASED ON A MODIFIED ANISTROPIC DIFFUSION EQUATION

    Institute of Scientific and Technical Information of China (English)

    Wang Ling; Li Deyu; Wang Tianfu; Lin Jiangli; Peng Yun; Rao Li; Zheng Yi

    2007-01-01

    Speckle noise reduction is a key problem of the image analysis of medical UltraSound images. In this paper, two important improvements have been developed to a fast anisotropic diffusion algorithm for speckle noise reduction. The Gaussian filter is firstly used before gradient calculation, and then the adaptive algorithm of the factor k is proposed. Numerous experimental results show that the proposed model is superior to other methods in noise removal, fidelity and edge preservation. It is suitable for the preprocessing of a great number of medical UltraSound images, such as three dimensional reconstruction.

  9. Medical Image Retrieval: A Multimodal Approach.

    Science.gov (United States)

    Cao, Yu; Steffey, Shawn; He, Jianbiao; Xiao, Degui; Tao, Cui; Chen, Ping; Müller, Henning

    2014-01-01

    Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

  10. Speckle reduction in ultrasound medical images using adaptive filter based on second order statistics.

    Science.gov (United States)

    Thakur, A; Anand, R S

    2007-01-01

    This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.

  11. MIIP: a web-based platform for medical image interpretation training and evaluation focusing on ultrasound

    Science.gov (United States)

    Lindseth, Frank; Nordrik Hallan, Marte; Schiller Tønnessen, Martin; Smistad, Erik; Vâpenstad, Cecilie

    2017-03-01

    Introduction: Medical imaging technology has revolutionized health care over the past 30 years. This is especially true for ultrasound, a modality that an increasing amount of medical personal is starting to use. Purpose: The purpose of this study was to develop and evaluate a platform for improving medical image interpretation skills regardless of time and space and without the need for expensive imaging equipment or a patient to scan. Methods, results and conclusions: A stable web application with the needed functionality for image interpretation training and evaluation has been implemented. The system has been extensively tested internally and used during an international course in ultrasound-guided neurosurgery. The web application was well received and got very good System Usability Scale (SUS) scores.

  12. NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency.

    Science.gov (United States)

    Das, Sudeb; Kundu, Malay Kumar

    2012-10-01

    In this article, a novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both the NSCT and the PCNN to obtain better fusion results. The source medical images are first decomposed by NSCT. The low-frequency subbands (LFSs) are fused using the 'max selection' rule. For fusing the high-frequency subbands (HFSs), a PCNN model is utilized. Modified spatial frequency in NSCT domain is input to motivate the PCNN, and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Finally, inverse NSCT (INSCT) is applied to get the fused image. Subjective as well as objective analysis of the results and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodal medical images.

  13. Level set segmentation of medical images based on local region statistics and maximum a posteriori probability.

    Science.gov (United States)

    Cui, Wenchao; Wang, Yi; Lei, Tao; Fan, Yangyu; Feng, Yan

    2013-01-01

    This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

  14. Improving quality of medical image compression using biorthogonal CDF wavelet based on lifting scheme and SPIHT coding

    Directory of Open Access Journals (Sweden)

    Beladgham Mohammed

    2011-01-01

    Full Text Available As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested images. Our algorithm provides very important PSNR and MSSIM values for MRI images.

  15. Multimodal Medical Image Fusion Framework Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

    Directory of Open Access Journals (Sweden)

    Nianyi Wang

    2013-06-01

    Full Text Available In this paper, we present a new medical image fusion algorithm based on nonsubsampled contourlet transform (NSCT and spiking cortical model (SCM. The flexible multi-resolution, anisotropy, and directional expansion characteristics of NSCT are associated with global coupling and pulse synchronization features of SCM. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Firstly, maximum selection rule (MSR is used to fuse low frequency coefficients. Secondly, spatial frequency (SF is applied to motivate SCM network rather than using coefficients value directly, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. The effectiveness of the proposed algorithm is achieved by the comparison with existing fusion methods.

  16. Medical imaging document sharing solutions for various kinds of healthcare services based on IHE XDS/XDS-I profiles

    Science.gov (United States)

    Zhang, Jianguo; Yang, Yuanyuan; Zhang, Kai; Sun, Jianyong; Ling, Tonghui; Wang, Tusheng; Wang, Mingqing; Bak, Peter

    2014-03-01

    One key problem for continuity of patient care is identification of a proper method to share and exchange patient medical records among multiple hospitals and healthcare providers. This paper focuses in the imaging document component of medical record. The XDS-I (Cross- Enterprise Document Sharing - Image) Profile based on the IHE IT-Infrastructure extends and specializes XDS to support imaging "document" sharing in an affinity domain. We present three studies about image sharing solutions based on IHE XDS-I Profile. The first one is to adopt the IHE XDS-I profile as a technical guide to design image and report sharing mechanisms between hospitals for regional healthcare service in Shanghai. The second study is for collaborating image diagnosis in regional healthcare services. The latter study is to investigate the XDS-I based clearinghouse for patient controlled image sharing in the RSNA Image Sharing Network Project. We conclude that the IHE XDS/XDS-I profiles can be used as the foundation to design medical image document sharing for Various Healthcare Services.

  17. Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

    Science.gov (United States)

    Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

    2016-03-01

    In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

  18. Mesh Processing in Medical Image Analysis

    DEFF Research Database (Denmark)

    The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation....

  19. Medical Image Analysis Facility

    Science.gov (United States)

    1978-01-01

    To improve the quality of photos sent to Earth by unmanned spacecraft. NASA's Jet Propulsion Laboratory (JPL) developed a computerized image enhancement process that brings out detail not visible in the basic photo. JPL is now applying this technology to biomedical research in its Medical lrnage Analysis Facility, which employs computer enhancement techniques to analyze x-ray films of internal organs, such as the heart and lung. A major objective is study of the effects of I stress on persons with heart disease. In animal tests, computerized image processing is being used to study coronary artery lesions and the degree to which they reduce arterial blood flow when stress is applied. The photos illustrate the enhancement process. The upper picture is an x-ray photo in which the artery (dotted line) is barely discernible; in the post-enhancement photo at right, the whole artery and the lesions along its wall are clearly visible. The Medical lrnage Analysis Facility offers a faster means of studying the effects of complex coronary lesions in humans, and the research now being conducted on animals is expected to have important application to diagnosis and treatment of human coronary disease. Other uses of the facility's image processing capability include analysis of muscle biopsy and pap smear specimens, and study of the microscopic structure of fibroprotein in the human lung. Working with JPL on experiments are NASA's Ames Research Center, the University of Southern California School of Medicine, and Rancho Los Amigos Hospital, Downey, California.

  20. Mesh Processing in Medical Image Analysis

    DEFF Research Database (Denmark)

    The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation.......The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation....

  1. A remote real-time PACS-based platform for medical imaging telemedicine

    Science.gov (United States)

    Maani, Rouzbeh; Camorlinga, Sergio; Eskicioglu, Rasit

    2009-02-01

    This paper describes a remote real-time PACS-based telemedicine platform for clinical and diagnostic services delivered at different care settings where the physicians, specialists and scientists may attend. In fact, the platform aims to provide a PACS-based telemedicine framework for different medical image services such as segmentation, registration and specifically high-quality 3D visualization. The proposed approach offers services which are not only widely accessible and real-time, but are also secure and cost-effective. In addition, the proposed platform has the ability to bring in a realtime, ubiquitous, collaborative, interactive meeting environment supporting 3D visualization for consultations, which has not been well addressed with the current PACS-based applications. Using this ability, physicians and specialists can consult with each other at separate places and it is especially helpful for settings, where there is no specialist or the number of specialists is not enough to handle all the available cases. Furthermore, the proposed platform can be used as a rich resource for clinical research studies as well as for academic purposes.

  2. Medical Image Encryption: An Application for Improved Padding Based GGH Encryption Algorithm.

    Science.gov (United States)

    Sokouti, Massoud; Zakerolhosseini, Ali; Sokouti, Babak

    2016-01-01

    Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images over an insecure network, developing a secure encryption algorithm is necessary. Among the three main properties of security services ( i.e. , confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging medical images among physicians. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good choice for encrypting medical images as both the algorithm and sensitive data are represented by numeric matrices. Additionally, the GGH algorithm does not increase the size of the image and hence, its complexity will remain as simple as O(n(2) ). However, one of the disadvantages of using the GGH algorithm is the Chosen Cipher Text attack. In our strategy, this shortcoming of GGH algorithm has been taken in to consideration and has been improved by applying the padding (i.e., snail tour XORing), before the GGH encryption process. For evaluating their performances, three measurement criteria are considered including (i) Number of Pixels Change Rate (NPCR), (ii) Unified Average Changing Intensity (UACI), and (iii) Avalanche effect. The results on three different sizes of images showed that padding GGH approach has improved UACI, NPCR, and Avalanche by almost 100%, 35%, and 45%, respectively, in comparison to the standard GGH algorithm. Also, the outcomes will make the padding GGH resist against the cipher text, the chosen cipher text, and the statistical attacks. Furthermore, increasing the avalanche effect of more than 50% is a promising achievement in comparison to the increased complexities of the proposed method in terms of encryption and decryption processes.

  3. Augmented reality intravenous injection simulator based 3D medical imaging for veterinary medicine.

    Science.gov (United States)

    Lee, S; Lee, J; Lee, A; Park, N; Lee, S; Song, S; Seo, A; Lee, H; Kim, J-I; Eom, K

    2013-05-01

    Augmented reality (AR) is a technology which enables users to see the real world, with virtual objects superimposed upon or composited with it. AR simulators have been developed and used in human medicine, but not in veterinary medicine. The aim of this study was to develop an AR intravenous (IV) injection simulator to train veterinary and pre-veterinary students to perform canine venipuncture. Computed tomographic (CT) images of a beagle dog were scanned using a 64-channel multidetector. The CT images were transformed into volumetric data sets using an image segmentation method and were converted into a stereolithography format for creating 3D models. An AR-based interface was developed for an AR simulator for IV injection. Veterinary and pre-veterinary student volunteers were randomly assigned to an AR-trained group or a control group trained using more traditional methods (n = 20/group; n = 8 pre-veterinary students and n = 12 veterinary students in each group) and their proficiency at IV injection technique in live dogs was assessed after training was completed. Students were also asked to complete a questionnaire which was administered after using the simulator. The group that was trained using an AR simulator were more proficient at IV injection technique using real dogs than the control group (P ≤ 0.01). The students agreed that they learned the IV injection technique through the AR simulator. Although the system used in this study needs to be modified before it can be adopted for veterinary educational use, AR simulation has been shown to be a very effective tool for training medical personnel. Using the technology reported here, veterinary AR simulators could be developed for future use in veterinary education.

  4. Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz.

    Science.gov (United States)

    Wang, Qingzhu; Chen, Xiaoming; Wei, Mengying; Miao, Zhuang

    2016-11-04

    The existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors. We propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz. The proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder. Numerical simulation results verify the validity and the reliability of this scheme.

  5. Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain.

    Science.gov (United States)

    Kaur, L; Chauhan, R C; Saxena, S C

    2005-01-01

    This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.

  6. HIGH RESOLUTION REAL-TIME MEDICAL IMAGING BASED ON PARALLEL BEAMFORMING TECHNIQUE

    Institute of Scientific and Technical Information of China (English)

    Wang Lutao; Jin Gang; Xu Hongbing

    2011-01-01

    Improvement of frame-rate is very important for high quality ultrasound imaging of fast-moving structures.It is also one of the key technologies of Three-Dimension (3-D) real-time medical imaging.In this paper,we have demonstrated a beamforming method which gives imaging frame-rate increment without sacrificing the quality of medical images.By using wider and fewer transmit beams in combination with four narrower parallel receive beams,potentially increasing the imaging frame-rate by a factor four.Through employing full transmit aperture,controlling the mainlobe width,and suppressing sidelobes of angular responses,the inherent gain loss of normal parallel beamfomer can be compensated in the maximal degree.The noise and interference signals also can be suppressed effectively.Finally,we show comparable lateral resolution and contrast of ultrasound images to normal single widow weighting beamformer on simulated phantoms of point targets,cyst and fetus of 12th week.As the computational cost is linear with the number of array elements and the same with Delay And Sum (DAS) beamformers,this method has great advantages of possibility for high frame-rate real-time applications.

  7. Photons-based medical imaging - Radiology, X-ray tomography, gamma and positrons tomography, optical imaging; Imagerie medicale a base de photons - Radiologie, tomographie X, tomographie gamma et positons, imagerie optique

    Energy Technology Data Exchange (ETDEWEB)

    Fanet, H.; Dinten, J.M.; Moy, J.P.; Rinkel, J. [CEA Leti, Grenoble (France); Buvat, I. [IMNC - CNRS, Orsay (France); Da Silva, A. [Institut Fresnel, Marseille (France); Douek, P.; Peyrin, F. [INSA Lyon, Lyon Univ. (France); Frija, G. [Hopital Europeen George Pompidou, Paris (France); Trebossen, R. [CEA-Service hospitalier Frederic Joliot, Orsay (France)

    2010-07-01

    This book describes the different principles used in medical imaging. The detection aspects, the processing electronics and algorithms are detailed for the different techniques. This first tome analyses the photons-based techniques (X-rays, gamma rays and visible light). Content: 1 - physical background: radiation-matter interaction, consequences on detection and medical imaging; 2 - detectors for medical imaging; 3 - processing of numerical radiography images for quantization; 4 - X-ray tomography; 5 - positrons emission tomography: principles and applications; 6 - mono-photonic imaging; 7 - optical imaging; Index. (J.S.)

  8. [Research on Three-dimensional Medical Image Reconstruction and Interaction Based on HTML5 and Visualization Toolkit].

    Science.gov (United States)

    Gao, Peng; Liu, Peng; Su, Hongsen; Qiao, Liang

    2015-04-01

    Integrating visualization toolkit and the capability of interaction, bidirectional communication and graphics rendering which provided by HTML5, we explored and experimented on the feasibility of remote medical image reconstruction and interaction in pure Web. We prompted server-centric method which did not need to download the big medical data to local connections and avoided considering network transmission pressure and the three-dimensional (3D) rendering capability of client hardware. The method integrated remote medical image reconstruction and interaction into Web seamlessly, which was applicable to lower-end computers and mobile devices. Finally, we tested this method in the Internet and achieved real-time effects. This Web-based 3D reconstruction and interaction method, which crosses over internet terminals and performance limited devices, may be useful for remote medical assistant.

  9. Level Set Segmentation of Medical Images Based on Local Region Statistics and Maximum a Posteriori Probability

    Directory of Open Access Journals (Sweden)

    Wenchao Cui

    2013-01-01

    Full Text Available This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP and Bayes’ rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

  10. Task-Based Modeling of a 5k Ultra-High-Resolution Medical Imaging System for Digital Breast Tomosynthesis.

    Science.gov (United States)

    Zhao, Chumin; Kanicki, Jerzy

    2017-09-01

    High-resolution, low-noise X-ray detectors based on CMOS active pixel sensor (APS) technology have demonstrated superior imaging performance for digital breast tomosynthesis (DBT). This paper presents a task-based model for a high-resolution medical imaging system to evaluate its ability to detect simulated microcalcifications and masses as lesions for breast cancer. A 3-D cascaded system analysis for a 50- [Formula: see text] pixel pitch CMOS APS X-ray detector was integrated with an object task function, a medical imaging display model, and the human eye contrast sensitivity function to calculate the detectability index and area under the ROC curve (AUC). It was demonstrated that the display pixel pitch and zoom factor should be optimized to improve the AUC for detecting small microcalcifications. In addition, detector electronic noise of smaller than 300 e(-) and a high display maximum luminance (>1000 cd/cm (2)) are desirable to distinguish microcalcifications of [Formula: see text] in size. For low contrast mass detection, a medical imaging display with a minimum of 12-bit gray levels is recommended to realize accurate luminance levels. A wide projection angle range of greater than ±30° in combination with the image gray level magnification could improve the mass detectability especially when the anatomical background noise is high. On the other hand, a narrower projection angle range below ±20° can improve the small, high contrast object detection. Due to the low mass contrast and luminance, the ambient luminance should be controlled below 5 cd/ [Formula: see text]. Task-based modeling provides important firsthand imaging performance of the high-resolution CMOS-based medical imaging system that is still at early stage development for DBT. The modeling results could guide the prototype design and clinical studies in the future.

  11. Coding technique with progressive reconstruction based on VQ and entropy coding applied to medical images

    Science.gov (United States)

    Martin-Fernandez, Marcos; Alberola-Lopez, Carlos; Guerrero-Rodriguez, David; Ruiz-Alzola, Juan

    2000-12-01

    In this paper we propose a novel lossless coding scheme for medical images that allows the final user to switch between a lossy and a lossless mode. This is done by means of a progressive reconstruction philosophy (which can be interrupted at will) so we believe that our scheme gives a way to trade off between the accuracy needed for medical diagnosis and the information reduction needed for storage and transmission. We combine vector quantization, run-length bit plane and entropy coding. Specifically, the first step is a vector quantization procedure; the centroid codes are Huffman- coded making use of a set of probabilities that are calculated in the learning phase. The image is reconstructed at the coder in order to obtain the error image; this second image is divided in bit planes, which are then run-length and Huffman coded. A second statistical analysis is performed during the learning phase to obtain the parameters needed in this final stage. Our coder is currently trained for hand-radiographs and fetal echographies. We compare our results for this two types of images to classical results on bit plane coding and the JPEG standard. Our coder turns out to outperform both of them.

  12. A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.

    Science.gov (United States)

    Kurtz, Camille; Beaulieu, Christopher F; Napel, Sandy; Rubin, Daniel L

    2014-06-01

    Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification

  13. Medical alert bracelet (image)

    Science.gov (United States)

    People with diabetes should always wear a medical alert bracelet or necklace that emergency medical workers will be able to find. Medical identification products can help ensure proper treatment in an ...

  14. BrainIACS: a system for web-based medical image processing

    Science.gov (United States)

    Kishore, Bhaskar; Bazin, Pierre-Louis; Pham, Dzung L.

    2009-02-01

    We describe BrainIACS, a web-based medical image processing system that permits and facilitates algorithm developers to quickly create extensible user interfaces for their algorithms. Designed to address the challenges faced by algorithm developers in providing user-friendly graphical interfaces, BrainIACS is completely implemented using freely available, open-source software. The system, which is based on a client-server architecture, utilizes an AJAX front-end written using the Google Web Toolkit (GWT) and Java Servlets running on Apache Tomcat as its back-end. To enable developers to quickly and simply create user interfaces for configuring their algorithms, the interfaces are described using XML and are parsed by our system to create the corresponding user interface elements. Most of the commonly found elements such as check boxes, drop down lists, input boxes, radio buttons, tab panels and group boxes are supported. Some elements such as the input box support input validation. Changes to the user interface such as addition and deletion of elements are performed by editing the XML file or by using the system's user interface creator. In addition to user interface generation, the system also provides its own interfaces for data transfer, previewing of input and output files, and algorithm queuing. As the system is programmed using Java (and finally Java-script after compilation of the front-end code), it is platform independent with the only requirements being that a Servlet implementation be available and that the processing algorithms can execute on the server platform.

  15. Geometry-based vs. intensity-based medical image registration: A comparative study on 3D CT data.

    Science.gov (United States)

    Savva, Antonis D; Economopoulos, Theodore L; Matsopoulos, George K

    2016-02-01

    Spatial alignment of Computed Tomography (CT) data sets is often required in numerous medical applications and it is usually achieved by applying conventional exhaustive registration techniques, which are mainly based on the intensity of the subject data sets. Those techniques consider the full range of data points composing the data, thus negatively affecting the required processing time. Alternatively, alignment can be performed using the correspondence of extracted data points from both sets. Moreover, various geometrical characteristics of those data points can be used, instead of their chromatic properties, for uniquely characterizing each point, by forming a specific geometrical descriptor. This paper presents a comparative study reviewing variations of geometry-based, descriptor-oriented registration techniques, as well as conventional, exhaustive, intensity-based methods for aligning three-dimensional (3D) CT data pairs. In this context, three general image registration frameworks were examined: a geometry-based methodology featuring three distinct geometrical descriptors, an intensity-based methodology using three different similarity metrics, as well as the commonly used Iterative Closest Point algorithm. All techniques were applied on a total of thirty 3D CT data pairs with both known and unknown initial spatial differences. After an extensive qualitative and quantitative assessment, it was concluded that the proposed geometry-based registration framework performed similarly to the examined exhaustive registration techniques. In addition, geometry-based methods dramatically improved processing time over conventional exhaustive registration.

  16. High performance 3D adaptive filtering for DSP based portable medical imaging systems

    Science.gov (United States)

    Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark

    2015-03-01

    Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.

  17. Localized Energy-Based Normalization of Medical Images: Application to Chest Radiography.

    Science.gov (United States)

    Philipsen, R H H M; Maduskar, P; Hogeweg, L; Melendez, J; Sánchez, C I; van Ginneken, B

    2015-09-01

    Automated quantitative analysis systems for medical images often lack the capability to successfully process images from multiple sources. Normalization of such images prior to further analysis is a possible solution to this limitation. This work presents a general method to normalize medical images and thoroughly investigates its effectiveness for chest radiography (CXR). The method starts with an energy decomposition of the image in different bands. Next, each band's localized energy is scaled to a reference value and the image is reconstructed. We investigate iterative and local application of this technique. The normalization is applied iteratively to the lung fields on six datasets from different sources, each comprising 50 normal CXRs and 50 abnormal CXRs. The method is evaluated in three supervised computer-aided detection tasks related to CXR analysis and compared to two reference normalization methods. In the first task, automatic lung segmentation, the average Jaccard overlap significantly increased from 0.72±0.30 and 0.87±0.11 for both reference methods to with normalization. The second experiment was aimed at segmentation of the clavicles. The reference methods had an average Jaccard index of 0.57±0.26 and 0.53±0.26; with normalization this significantly increased to . The third experiment was detection of tuberculosis related abnormalities in the lung fields. The average area under the Receiver Operating Curve increased significantly from 0.72±0.14 and 0.79±0.06 using the reference methods to with normalization. We conclude that the normalization can be successfully applied in chest radiography and makes supervised systems more generally applicable to data from different sources.

  18. Secure annotation for medical images based on reversible watermarking in the Integer Fibonacci-Haar transform domain

    Science.gov (United States)

    Battisti, F.; Carli, M.; Neri, A.

    2011-03-01

    The increasing use of digital image-based applications is resulting in huge databases that are often difficult to use and prone to misuse and privacy concerns. These issues are especially crucial in medical applications. The most commonly adopted solution is the encryption of both the image and the patient data in separate files that are then linked. This practice results to be inefficient since, in order to retrieve patient data or analysis details, it is necessary to decrypt both files. In this contribution, an alternative solution for secure medical image annotation is presented. The proposed framework is based on the joint use of a key-dependent wavelet transform, the Integer Fibonacci-Haar transform, of a secure cryptographic scheme, and of a reversible watermarking scheme. The system allows: i) the insertion of the patient data into the encrypted image without requiring the knowledge of the original image, ii) the encryption of annotated images without causing loss in the embedded information, and iii) due to the complete reversibility of the process, it allows recovering the original image after the mark removal. Experimental results show the effectiveness of the proposed scheme.

  19. Region of Interest-Based Tamper Detection and Lossless Recovery Watermarking Scheme (ROI-DR) on Ultrasound Medical Images.

    Science.gov (United States)

    Khor, Hui Liang; Liew, Siau-Chuin; Zain, Jasni Mohd

    2017-06-01

    Tampering on medical image will lead to wrong diagnosis and treatment, which is life-threatening; therefore, digital watermarking on medical image was introduced to protect medical image from tampering. Medical images are divided into region of interest (ROI) and region of non-interest (RONI). ROI is an area that has a significant impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper has proposed ROI-based tamper detection and recovery watermarking scheme (ROI-DR) that embeds ROI bit information into RONI least significant bits, which will be extracted later for authentication and recovery process. The experiment result has shown that the ROI-DR has achieved a good result in imperceptibility with peak signal-to-noise ratio (PSNR) values approximately 48 dB, it is robust against various kinds of tampering, and the tampered ROI was able to recover to its original form. Lastly, a comparative table with the previous research (TALLOR and TALLOR-RS watermarking schemes) has been derived, where these three watermarking schemes were tested under the same testing conditions and environment. The experiment result has shown that ROI-DR has achieved speed-up factors of 22.55 and 26.65 in relative to TALLOR and TALLOR-RS watermarking schemes, respectively.

  20. Desktop supercomputers. Advance medical imaging.

    Science.gov (United States)

    Frisiello, R S

    1991-02-01

    Medical imaging tools that radiologists as well as a wide range of clinicians and healthcare professionals have come to depend upon are emerging into the next phase of functionality. The strides being made in supercomputing technologies--including reduction of size and price--are pushing medical imaging to a new level of accuracy and functionality.

  1. Machine learning approaches in medical image analysis

    DEFF Research Database (Denmark)

    de Bruijne, Marleen

    2016-01-01

    Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols......, learning from weak labels, and interpretation and evaluation of results....

  2. Image workstation in a medical intensive care unit changes viewing patterns and timing of image-based clinical actions in routine portable chest radiographs

    Science.gov (United States)

    Redfern, Regina O.; Kundel, Harold L.; Polansky, Marcia; Langlotz, Curtis P.; Lanken, Paul N.; Brikman, Inna; Horii, Steven C.; Bozzo, Mary T.; Feingold, Eric R.; Nodine, Calvin F.

    1996-05-01

    In order to determine the effect of an image workstation, viewing patterns and related clinical actions were evaluated in a randomized prospective study. During 16 weeks of Computed Radiography data collection, an image workstation was conveniently available to the Medical Intensive Care Unit clinicians. The workstation was not available for clinical use during 16 weeks of Analog Film data collection. Viewing patterns were evaluated by comparing viewing times. Patient care was evaluated by comparing the time of performing image based clinical actions. The percentage of routine exams viewed before AM Radiology Conference increased from 0% during the Analog Periods to 27% during the CR PACS Periods. Clinicians selected images taken during the first few days of the patient's admission for viewing before conference. Images taken later in admission were viewed during or after conference. On days when radiology conference was not held, images were viewed significantly earlier when the workstation was available. Clinical actions based on images viewed on the workstation were performed significantly earlier. When an image workstation was available routine images were viewed sooner and image based actions occurred earlier.

  3. [Present status and trend of heart fluid mechanics research based on medical image analysis].

    Science.gov (United States)

    Gan, Jianhong; Yin, Lixue; Xie, Shenghua; Li, Wenhua; Lu, Jing; Luo, Anguo

    2014-06-01

    With introduction of current main methods for heart fluid mechanics researches, we studied the characteristics and weakness for three primary analysis methods based on magnetic resonance imaging, color Doppler ultrasound and grayscale ultrasound image, respectively. It is pointed out that particle image velocity (PIV), speckle tracking and block match have the same nature, and three algorithms all adopt block correlation. The further analysis shows that, with the development of information technology and sensor, the research for cardiac function and fluid mechanics will focus on energy transfer process of heart fluid, characteristics of Chamber wall related to blood fluid and Fluid-structure interaction in the future heart fluid mechanics fields.

  4. A cloud solution for medical image processing

    Directory of Open Access Journals (Sweden)

    Ali Mirarab,

    2014-07-01

    Full Text Available The rapid growth in the use of Electronic Health Records across the globe along with the rich mix of multimedia held within an EHR combined with the increasing level of detail due to advances in diagnostic medical imaging means increasing amounts of data can be stored for each patient. Also lack of image processing and analysis tools for handling the large image datasets has compromised researchers and practitioner‟s outcome. Migrating medical imaging applications and data to the Cloud can allow healthcare organizations to realize significant cost savings relating to hardware, software, buildings, power and staff, in addition to greater scalability, higher performance and resilience. This paper reviews medical image processing and its challenges, states cloud computing and cloud computing benefits due to medical image processing. Also, this paper introduces tools and methods for medical images processing using the cloud. Finally a method is provided for medical images processing based on Eucalyptus cloud infrastructure with image processing software “ImageJ” and using improved genetic algorithm for the allocation and distribution of resources. Based on conducted simulations and experimental results, the proposed method brings high scalability, simplicity, flexibility and fully customizability in addition to 40% cost reduction and twice increase in speed.

  5. Interdisciplinary Approach to Tool-Handle Design Based on Medical Imaging

    Directory of Open Access Journals (Sweden)

    G. Harih

    2013-01-01

    Full Text Available Products are becoming increasingly complex; therefore, designers are faced with a challenging task to incorporate new functionality, higher performance, and optimal shape design. Traditional user-centered design techniques such as designing with anthropometric data do not incorporate enough subject data to design products with optimal shape for best fit to the target population. To overcome these limitations, we present an interdisciplinary approach with medical imaging. The use of this approach is being presented on the development of an optimal sized and shaped tool handle where the hand is imaged using magnetic resonance imaging machine. The obtained images of the hand are reconstructed and imported into computer-aided design software, where optimal shape of the handle is obtained with Boolean operations. Methods can be used to develop fully customized products with optimal shape to provide best fit to the target population. This increases subjective comfort rating, performance and can prevent acute and cumulative trauma disorders. Provided methods are especially suited for products where high stresses and exceptional performance is expected (high performance tools, professional sports, and military equipment, etc.. With the use of these interdisciplinary methods, the value of the product is increased, which also increases the competitiveness of the product on the market.

  6. Human-machine interface for a VR-based medical imaging environment

    Science.gov (United States)

    Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

    1997-05-01

    Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

  7. A comparative study on medical image segmentation methods

    OpenAIRE

    Praylin Selva Blessy SELVARAJ ASSLEY; Helen Sulochana CHELLAKKON

    2014-01-01

    Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disa...

  8. A survey of medical diagnostic imaging technologies

    Energy Technology Data Exchange (ETDEWEB)

    Heese, V.; Gmuer, N.; Thomlinson, W.

    1991-10-01

    The fields of medical imaging and medical imaging instrumentation are increasingly important. The state-of-the-art continues to advance at a very rapid pace. In fact, various medical imaging modalities are under development at the National Synchrotron Light Source (such as MECT and Transvenous Angiography.) It is important to understand how these techniques compare with today's more conventional imaging modalities. The purpose of this report is to provide some basic information about the various medical imaging technologies currently in use and their potential developments as a basis for this comparison. This report is by no means an in-depth study of the physics and instrumentation of the various imaging modalities; instead, it is an attempt to provide an explanation of the physical bases of these techniques and their principal clinical and research capabilities.

  9. A survey of medical diagnostic imaging technologies

    Energy Technology Data Exchange (ETDEWEB)

    Heese, V.; Gmuer, N.; Thomlinson, W.

    1991-10-01

    The fields of medical imaging and medical imaging instrumentation are increasingly important. The state-of-the-art continues to advance at a very rapid pace. In fact, various medical imaging modalities are under development at the National Synchrotron Light Source (such as MECT and Transvenous Angiography.) It is important to understand how these techniques compare with today`s more conventional imaging modalities. The purpose of this report is to provide some basic information about the various medical imaging technologies currently in use and their potential developments as a basis for this comparison. This report is by no means an in-depth study of the physics and instrumentation of the various imaging modalities; instead, it is an attempt to provide an explanation of the physical bases of these techniques and their principal clinical and research capabilities.

  10. Medical hyperspectral imaging: a review

    Science.gov (United States)

    Lu, Guolan; Fei, Baowei

    2014-01-01

    Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941

  11. Automated medical image segmentation techniques

    Directory of Open Access Journals (Sweden)

    Sharma Neeraj

    2010-01-01

    Full Text Available Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT and Magnetic resonance (MR imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

  12. Medical hyperspectral imaging: a review.

    Science.gov (United States)

    Lu, Guolan; Fei, Baowei

    2014-01-01

    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

  13. Grating-based interferometry and hybrid photon counting detectors: Towards a new era in X-ray medical imaging

    Energy Technology Data Exchange (ETDEWEB)

    Gkoumas, Spyridon, E-mail: spyridon.gkoumas@psi.ch [Swiss Light Source, Paul Scherrer Institut, Villigen 5232 (Switzerland); Wang, Zhentian; Abis, Matteo; Arboleda, Carolina [Swiss Light Source, Paul Scherrer Institut, Villigen 5232 (Switzerland); Institute for Biomedical Engineering,University and ETH Zurich, Zurich 8092 (Switzerland); Tudosie, George; Donath, Tilman; Brönnimann, Christian; Schulze-Briese, Clemens [Dectris Ltd., Neuenhoferstrasse 107, Baden 5400 (Switzerland); Stampanoni, Marco [Swiss Light Source, Paul Scherrer Institut, Villigen 5232 (Switzerland); Institute for Biomedical Engineering,University and ETH Zurich, Zurich 8092 (Switzerland)

    2016-02-11

    Progress in X-ray medical imaging and advances in detector developments have always been closely related. Similarly, a strong connection exists between innovations in synchrotron imaging and their implementation on table-top X-ray tube setups. The transfer of phase-based imaging to X-ray tubes can provide table-top setups with improved contrast between areas of low attenuation differences, by exploiting the unit decrement of the real part of the refractive index. Medical imaging is a potential application for such a system. Originally developed for synchrotron experiments, the novel generation of hybrid photon counting detectors is becoming increasingly popular due to their unique characteristics, such as small pixel size, negligible dark noise, fast counting and adjustable energy thresholds. Furthermore, novel room temperature semiconductor materials such as Cd(Zn)Te can provide higher quantum efficiency. In the first part of this article we review phase-contrast techniques and recent research towards medical applications. In the second part we present results and evaluate the potential of combining a table-top Talbot grating interferometry system with latest generation hybrid photon counting detectors.

  14. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease

    Science.gov (United States)

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  15. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

    Energy Technology Data Exchange (ETDEWEB)

    Bhateja, Vikrant, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja [Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh 226028 (India); Bao, Le Nguyen [Duytan University, Danang 550000 (Viet Nam); Lay-Ekuakille, Aimé [Department of Innovation Engineering, University of Salento, Lecce 73100 (Italy); Le, Dac-Nhuong, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn [Duytan University, Danang 550000 (Viet Nam); Haiphong University, Haiphong 180000 (Viet Nam)

    2016-07-15

    Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  16. Medical imaging technology and applications

    CERN Document Server

    Iniewski, Krzysztof

    2014-01-01

    The book has two intentions. First, it assembles the latest research in the field of medical imaging technology in one place. Detailed descriptions of current state-of-the-art medical imaging systems (comprised of x-ray CT, MRI, ultrasound, and nuclear medicine) and data processing techniques are discussed. Information is provided that will give interested engineers and scientists a solid foundation from which to build with additional resources. Secondly, it exposes the reader to myriad applications that medical imaging technology has enabled.

  17. Blind integrity verification of medical images.

    Science.gov (United States)

    Huang, Hui; Coatrieux, Gouenou; Shu, Huazhong; Luo, Limin; Roux, Christian

    2012-11-01

    This work presents the first method of digital blind forensics within the medical imaging field with the objective to detect whether an image has been modified by some processing (e.g. filtering, lossy compression and so on). It compares two image features: the Histogram statistics of Reorganized Block-based Discrete cosine transform coefficients (HRBD), originally proposed for steganalysis purposes, and the Histogram statistics of Reorganized Block-based Tchebichef moments (HRBT). Both features serve as input of a set of SVM classifiers built in order to discriminate tampered images from original ones as well as to identify the nature of the global modification one image may have undergone. Performance evaluation, conducted in application to different medical image modalities, shows that these image features can help, independently or jointly, to blindly distinguish image processing or modifications with a detection rate greater than 70%. They also underline the complementarity of these features.

  18. Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

    Science.gov (United States)

    Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Roh, Seungkuk

    2016-05-01

    In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.

  19. Feasibility study of propagation-based phase-contrast X-ray lung imaging on the Imaging and Medical beamline at the Australian Synchrotron.

    Science.gov (United States)

    Murrie, Rhiannon P; Stevenson, Andrew W; Morgan, Kaye S; Fouras, Andreas; Paganin, David M; Siu, Karen K W

    2014-03-01

    Propagation-based phase-contrast X-ray imaging (PB-PCXI) using synchrotron radiation has achieved high-resolution imaging of the lungs of small animals both in real time and in vivo. Current studies are applying such imaging techniques to lung disease models to aid in diagnosis and treatment development. At the Australian Synchrotron, the Imaging and Medical beamline (IMBL) is well equipped for PB-PCXI, combining high flux and coherence with a beam size sufficient to image large animals, such as sheep, due to a wiggler source and source-to-sample distances of over 137 m. This study aimed to measure the capabilities of PB-PCXI on IMBL for imaging small animal lungs to study lung disease. The feasibility of combining this technique with computed tomography for three-dimensional imaging and X-ray velocimetry for studies of airflow and non-invasive lung function testing was also investigated. Detailed analysis of the role of the effective source size and sample-to-detector distance on lung image contrast was undertaken as well as phase retrieval for sample volume analysis. Results showed that PB-PCXI of lung phantoms and mouse lungs produced high-contrast images, with successful computed tomography and velocimetry also being carried out, suggesting that live animal lung imaging will also be feasible at the IMBL.

  20. Carbon nanotube based X-ray sources: Applications in pre-clinical and medical imaging

    Science.gov (United States)

    Lee, Yueh Z.; Burk, Laurel; Wang, Ko-Han; Cao, Guohua; Lu, Jianping; Zhou, Otto

    2011-08-01

    Field emission offers an alternate method of electron production for Bremsstrahlung based X-ray tubes. Carbon nanotubes (CNTs) serve as very effective field emitters, allowing them to serve as electron sources for X-ray sources, with specific advantages over traditional thermionic tubes. CNT derived X-ray sources can create X-ray pulses of any duration and frequency, gate the X-ray pulse to any source and allow the placement of many sources in close proximity.We have constructed a number of micro-CT systems based on CNT X-ray sources for applications in small animal imaging, specifically focused on the imaging of the heart and lungs. This paper offers a review of the pre-clinical applications of the CNT based micro-CT that we have developed. We also discuss some of the current and potential clinical applications of the CNT X-ray sources.

  1. Medical image processing

    CERN Document Server

    Dougherty, Geoff

    2011-01-01

    This book is designed for end users in the field of digital imaging, who wish to update their skills and understanding with the latest techniques in image analysis. This book emphasizes the conceptual framework of image analysis and the effective use of image processing tools. It uses applications in a variety of fields to demonstrate and consolidate both specific and general concepts, and to build intuition, insight and understanding. Although the chapters are essentially self-contained they reference other chapters to form an integrated whole. Each chapter employs a pedagogical approach to e

  2. A web service system supporting three-dimensional post-processing of medical images based on WADO protocol.

    Science.gov (United States)

    He, Longjun; Xu, Lang; Ming, Xing; Liu, Qian

    2015-02-01

    Three-dimensional post-processing operations on the volume data generated by a series of CT or MR images had important significance on image reading and diagnosis. As a part of the DIOCM standard, WADO service defined how to access DICOM objects on the Web, but it didn't involve three-dimensional post-processing operations on the series images. This paper analyzed the technical features of three-dimensional post-processing operations on the volume data, and then designed and implemented a web service system for three-dimensional post-processing operations of medical images based on the WADO protocol. In order to improve the scalability of the proposed system, the business tasks and calculation operations were separated into two modules. As results, it was proved that the proposed system could support three-dimensional post-processing service of medical images for multiple clients at the same moment, which met the demand of accessing three-dimensional post-processing operations on the volume data on the web.

  3. Machine Learning for Medical Imaging.

    Science.gov (United States)

    Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

    2017-01-01

    Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. (©)RSNA, 2017.

  4. Compressive sensing in medical imaging.

    Science.gov (United States)

    Graff, Christian G; Sidky, Emil Y

    2015-03-10

    The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

  5. [Medical imaging: its medical economics and recent situation in Japan.].

    Science.gov (United States)

    Imai, Keiko

    2006-01-01

    Two fields of radiology, medical imaging and radiation therapy, are coded separately in medical fee system, and the health care statistics of 2003 shows that expenditure on the former was 5.2% of the whole medical cost and the latter 0.28%. Introduction of DPC, an abbreviation of Diagnostic Procedure Combination, was carried out in 2003, which was an essential reform of medical fee payment system that have been managed on fee-for-service base throughout, and 22% of beds for acute patients care are under the control of DPC payment in 2006. As medical imaging procedures are basically classified in inclusive payment in DPC system, their accurate statistics cannot be figured out because of the lack of description of individual procedures in DPC bills. Policy-making of medical economics will suffer a great loss from the deficiency of detailed data in published statistics. Important role in clinical diagnoses of CT and MR results an increase of fee paid for them up to more than half of total expenditure on medical imaging. So, dominant reduction of examination fee has been done for MR imaging, especially in 2002, to reduce the total cost of medical imaging. Follows could be featured as major topics of medical imaging in health insurance system, (a) fee is newly assigned for electronic handling of CT-and-MR images, and nuclear medicine, and (b) there is still a mismatch between actual payment and quality of medical facilities. As matters related to medical imaging, the followings should be stressed; (a) numbers of CT and MR units per population are dominantly high among OECD countries, but, those controlled by qualified radiologists are at the average level of those countries, (b) there is a big difference of MR examination quality among medical facilities, and (c) 76% of newly-installed high-end MR units are supplied by foreign industries. Hopefully, there will be an increase in the concern to medical fee payment system and health care cost because they possibly

  6. Internet-based ICRP resource for healthcare providers on the risks and benefits of medical imaging that uses ionising radiation.

    Science.gov (United States)

    Demeter, S; Applegate, K E; Perez, M

    2016-06-01

    The purpose of the International Commission on Radiological Protection (ICRP) Committee 3 Working Party was to update the 2001 web-based module 'Radiation and your patient: a guide for medical practitioners' from ICRP. The key elements of this task were: to clearly identify the target audience (such as healthcare providers with an emphasis on primary care); to review other reputable sources of information; and to succinctly publish the contribution made by ICRP to the various topics. A 'question-and-answer' format addressing practical topics was adopted. These topics included benefits and risks of imaging using ionising radiation in common medical situations, as well as pertaining to specific populations such as pregnant, breast-feeding, and paediatric patients. In general, the benefits of medical imaging and related procedures far outweigh the potential risks associated with ionising radiation exposure. However, it is still important to ensure that the examinations are clinically justified, that the procedure is optimised to deliver the lowest dose commensurate with the medical purpose, and that consideration is given to diagnostic reference levels for particular classes of examinations. © The International Society for Prosthetics and Orthotics.

  7. An efficient medical image compression scheme.

    Science.gov (United States)

    Li, Xiaofeng; Shen, Yi; Ma, Jiachen

    2005-01-01

    In this paper, a fast lossless compression scheme is presented for the medical image. This scheme consists of two stages. In the first stage, a Differential Pulse Code Modulation (DPCM) is used to decorrelate the raw image data, therefore increasing the compressibility of the medical image. In the second stage, an effective scheme based on the Huffman coding method is developed to encode the residual image. This newly proposed scheme could reduce the cost for the Huffman coding table while achieving high compression ratio. With this algorithm, a compression ratio higher than that of the lossless JPEG method for image can be obtained. At the same time, this method is quicker than the lossless JPEG2000. In other words, the newly proposed algorithm provides a good means for lossless medical image compression.

  8. Segmented medical images based simulations of Cardiac electrical activity and electrocardiogram: a model comparison

    OpenAIRE

    Pierre, Charles; Rousseau, Olivier; Bourgault, Yves

    2009-01-01

    The purposes of this work is to compare the action potential and electrocardiogram computed with the monodomain and bidomain models, using a patient-based two-dimensional geometry of the heart-torso. The pipeline from CT scans to image segmentation with an in-house level set method, then to mesh generation is detailed in the article. Our segmentation technique is based on a new iterative Chan-Vese method. The bidomain model and its approximation called the ``adapted'' monodomain model are nex...

  9. Medical image libraries: ICoS project

    Science.gov (United States)

    Honniball, John; Thomas, Peter

    1999-08-01

    FOr use of digital techniques for the production, manipulation and storage of images has resulted in the creation of digital image libraries. These libraries often store many thousands of images. While provision of storage media for such large amounts of data has been straightforward, provision of effective searching and retrieval tools has not. Medicine relies heavily on images as a diagnostic tool. The most obvious example is the x-ray, but many other image forms are in everyday use. Advances in technology are affecting the ways medical images are generated, stored and retrieved. The paper describes the work of the Image COding and Segmentation to Support Variable Rate Transmission Channels and Variable Resolution Platforms (ICoS) research project currently under way in Bristol, UK. ICoS is a project of the Mobile of England and Hewlett-Packard Research Laboratories Europe. Funding is provided by the Engineering and PHysical Sciences Research Council. The aim of the ICoS project is to demonstrate the practical application of computer networking to medical image libraries. Work at the University of the West of England concentrates on user interface and indexing issues. Metadata is used to organize the images, coded using the WWW Consortium standard Resource Description Framework. We are investigating the application of such standards to medical images, one outcome being to implement a metadata-based image library. This paper describes the ICoS project in detail and discuses both metadata system and user interfaces in the context of medical applications.

  10. Introduction to Medical Image Analysis

    DEFF Research Database (Denmark)

    Paulsen, Rasmus Reinhold; Moeslund, Thomas B.

    2011-01-01

    of the book is to present the fascinating world of medical image analysis in an easy and interesting way. Compared to many standard books on image analysis, the approach we have chosen is less mathematical and more casual. Some of the key algorithms are exemplified in C-code. Please note that the code...

  11. Introduction to Medical Image Analysis

    DEFF Research Database (Denmark)

    Paulsen, Rasmus Reinhold; Moeslund, Thomas B.

    of the book is to present the fascinating world of medical image analysis in an easy and interesting way. Compared to many standard books on image analysis, the approach we have chosen is less mathematical and more casual. Some of the key algorithms are exemplified in C-code. Please note that the code...

  12. Simulation-based joint estimation of body deformation and elasticity parameters for medical image analysis.

    Science.gov (United States)

    Lee, Huai-Ping; Foskey, Mark; Niethammer, Marc; Krajcevski, Pavel; Lin, Ming

    2012-11-01

    Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound orMR images) and known external forces.Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

  13. A comparative study on medical image segmentation methods

    Directory of Open Access Journals (Sweden)

    Praylin Selva Blessy SELVARAJ ASSLEY

    2014-03-01

    Full Text Available Image segmentation plays an important role in medical images. It has been a relevant research area in computer vision and image analysis. Many segmentation algorithms have been proposed for medical images. This paper makes a review on segmentation methods for medical images. In this survey, segmentation methods are divided into five categories: region based, boundary based, model based, hybrid based and atlas based. The five different categories with their principle ideas, advantages and disadvantages in segmenting different medical images are discussed.

  14. Markov random field driven region-based active contour model (MaRACel): application to medical image segmentation.

    Science.gov (United States)

    Xu, Jun; Monaco, James P; Madabhushi, Anant

    2010-01-01

    In this paper we present a Markov random field (MRF) driven region-based active contour model (MaRACel) for medical image segmentation. State-of-the-art region-based active contour (RAC) models assume that every spatial location in the image is statistically independent of the others, thereby ignoring valuable contextual information. To address this shortcoming we incorporate a MRF prior into the AC model, further generalizing Chan & Vese's (CV) and Rousson and Deriche's (RD) AC models. This incorporation requires a Markov prior that is consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analogue to the discrete Potts model. To demonstrate the effectiveness of MaRACel, we compare its performance to those of the CV and RD AC models in the following scenarios: (1) the qualitative segmentation of a cancerous lesion in a breast DCE-MR image and (2) the qualitative and quantitative segmentations of prostatic acini (glands) in 200 histopathology images. Across the 200 prostate needle core biopsy histology images, MaRACel yielded an average sensitivity, specificity, and positive predictive value of 71%, 95%, 74% with respect to the segmented gland boundaries; the CV and RD models have corresponding values of 19%, 81%, 20% and 53%, 88%, 56%, respectively.

  15. Medical Imaging Informatics.

    Science.gov (United States)

    Hsu, William; El-Saden, Suzie; Taira, Ricky K

    2016-01-01

    Imaging is one of the most important sources of clinically observable evidence that provides broad coverage, can provide insight on low-level scale properties, is noninvasive, has few side effects, and can be performed frequently. Thus, imaging data provides a viable observable that can facilitate the instantiation of a theoretical understanding of a disease for a particular patient context by connecting imaging findings to other biologic parameters in the model (e.g., genetic, molecular, symptoms, and patient survival). These connections can help inform their possible states and/or provide further coherent evidence. The field of radiomics is particularly dedicated to this task and seeks to extract quantifiable measures wherever possible. Example properties of investigation include genotype characterization, histopathology parameters, metabolite concentrations, vascular proliferation, necrosis, cellularity, and oxygenation. Important issues within the field include: signal calibration, spatial calibration, preprocessing methods (e.g., noise suppression, motion correction, and field bias correction), segmentation of target anatomic/pathologic entities, extraction of computed features, and inferencing methods connecting imaging features to biological states.

  16. Workflow-enabled distributed component-based information architecture for digital medical imaging enterprises.

    Science.gov (United States)

    Wong, Stephen T C; Tjandra, Donny; Wang, Huili; Shen, Weimin

    2003-09-01

    Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system.

  17. I2Cnet medical image annotation service.

    Science.gov (United States)

    Chronaki, C E; Zabulis, X; Orphanoudakis, S C

    1997-01-01

    I2Cnet (Image Indexing by Content network) aims to provide services related to the content-based management of images in healthcare over the World-Wide Web. Each I2Cnet server maintains an autonomous repository of medical images and related information. The annotation service of I2Cnet allows specialists to interact with the contents of the repository, adding comments or illustrations to medical images of interest. I2Cnet annotations may be communicated to other users via e-mail or posted to I2Cnet for inclusion in its local repositories. This paper discusses the annotation service of I2Cnet and argues that such services pave the way towards the evolution of active digital medical image libraries.

  18. Overview of deep learning in medical imaging.

    Science.gov (United States)

    Suzuki, Kenji

    2017-07-08

    The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

  19. Shape analysis in medical image analysis

    CERN Document Server

    Tavares, João

    2014-01-01

    This book contains thirteen contributions from invited experts of international recognition addressing important issues in shape analysis in medical image analysis, including techniques for image segmentation, registration, modelling and classification, and applications in biology, as well as in cardiac, brain, spine, chest, lung and clinical practice. This volume treats topics such as, anatomic and functional shape representation and matching; shape-based medical image segmentation; shape registration; statistical shape analysis; shape deformation; shape-based abnormity detection; shape tracking and longitudinal shape analysis; machine learning for shape modeling and analysis; shape-based computer-aided-diagnosis; shape-based medical navigation; benchmark and validation of shape representation, analysis and modeling algorithms. This work will be of interest to researchers, students, and manufacturers in the fields of artificial intelligence, bioengineering, biomechanics, computational mechanics, computationa...

  20. Generative Interpretation of Medical Images

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille

    2004-01-01

    This thesis describes, proposes and evaluates methods for automated analysis and quantification of medical images. A common theme is the usage of generative methods, which draw inference from unknown images by synthesising new images having shape, pose and appearance similar to the analysed image...... fraction from 4D cardiac cine MRI, myocardial perfusion in bolus passage cardiac perfusion MRI, corpus callosum shape and area in mid-sagittal brain MRI, and finally, lung, heart, clavicle location and cardiothoracic ratio in anterior-posterior chest radiographs....

  1. Image processing in medical ultrasound

    DEFF Research Database (Denmark)

    Hemmsen, Martin Christian

    as a double blinded study. The result of the pre-clinical trialmotivated for a larger scale clinical trial. Each of the two clinical trials were performed in collaboration with Copenhagen University Hospital, Rigshospitalet, and Copenhagen University, Department of Biostatistic. Evaluations were performed...... by medical doctors and experts in ultrasound, using the developed Image Quality assessment program (IQap). The study concludes that the image quality in terms of spatial resolution, contrast and unwanted artifacts is statistically better using SASB imaging than conventional imaging. The third and final...

  2. Improved Interactive Medical-Imaging System

    Science.gov (United States)

    Ross, Muriel D.; Twombly, Ian A.; Senger, Steven

    2003-01-01

    An improved computational-simulation system for interactive medical imaging has been invented. The system displays high-resolution, three-dimensional-appearing images of anatomical objects based on data acquired by such techniques as computed tomography (CT) and magnetic-resonance imaging (MRI). The system enables users to manipulate the data to obtain a variety of views for example, to display cross sections in specified planes or to rotate images about specified axes. Relative to prior such systems, this system offers enhanced capabilities for synthesizing images of surgical cuts and for collaboration by users at multiple, remote computing sites.

  3. The evaluation of non-ionizing radiation (near-infrared radiation) based medical imaging application: Diabetes foot

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Young Jin [Dept. of Radiological Science, Dongseo University, Busan (Korea, Republic of); Shin, Cheol Won; Ahn, Sung Min; Hong, Jun Yong; Ahn, Yun Jin; Lim, Cheong Hwan [Dept. of Radiological Science, Hanseo University, Seosan (Korea, Republic of)

    2016-09-15

    Near-infrared radiation (NIR) is non-ionizing, non-invasive, and deep tissue penetration in biological material, thereby increasing research interests as a medical imaging technique in the world. However, the use of current near-infrared medical image is extremely limited in Korea (ROK) since it is not well known among radiologic technologists and radiological researchers. Therefore to strengthen the knowledge for NIR medical imaging is necessary so as to prepare a qualified radiological professionals to serve medical images in high-quality on the clinical sites. In this study, an overview of the features and principles of N IR imaging was demonstrated. The latest research topics and worldwide research trends were introduced for radiologic technologist to reinforce their technical skills. In particular, wound care and diabetic foot which have high feasibility for clinical translation were introduced in order to contribute to accelerating NIR research for developing the field of radiological science.

  4. Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

    Directory of Open Access Journals (Sweden)

    Nour-Eddine El Harchaoui

    2013-01-01

    Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

  5. Research imaging in an academic medical center.

    Science.gov (United States)

    Armato, Samuel G; Gruszauskas, Nicholas P; Macmahon, Heber; Torno, Michael D; Li, Feng; Engelmann, Roger M; Starkey, Adam; Pudela, Caileigh L; Marino, Jonathan S; Santiago, Faustino; Chang, Paul J; Giger, Maryellen L

    2012-06-01

    Managing and supervising the complex imaging examinations performed for clinical research in an academic medical center can be a daunting task. Coordinating with both radiology and research staff to ensure that the necessary imaging is performed, analyzed, and delivered in accordance with the research protocol is nontrivial. The purpose of this communication is to report on the establishment of a new Human Imaging Research Office (HIRO) at our institution that provides a dedicated infrastructure to assist with these issues and improve collaborations between radiology and research staff. The HIRO was created with three primary responsibilities: 1) coordinate the acquisition of images for clinical research per the study protocol, 2) facilitate reliable and consistent assessment of disease response for clinical research, and 3) manage and distribute clinical research images in a compliant manner. The HIRO currently provides assistance for 191 clinical research studies from 14 sections and departments within our medical center and performs quality assessment of image-based measurements for six clinical research studies. The HIRO has fulfilled 1806 requests for medical images, delivering 81,712 imaging examinations (more than 44.1 million images) and related reports to investigators for research purposes. The ultimate goal of the HIRO is to increase the level of satisfaction and interaction among investigators, research subjects, radiologists, and other imaging professionals. Clinical research studies that use the HIRO benefit from a more efficient and accurate imaging process. The HIRO model could be adopted by other academic medical centers to support their clinical research activities; the details of implementation may differ among institutions, but the need to support imaging in clinical research through a dedicated, centralized initiative should apply to most academic medical centers. Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.

  6. Nonreference Medical Image Edge Map Measure

    Directory of Open Access Journals (Sweden)

    Karen Panetta

    2014-01-01

    Full Text Available Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

  7. A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

    Directory of Open Access Journals (Sweden)

    Abdallah Bengueddoudj

    2017-05-01

    Full Text Available In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT. The fusion of the detail 2D-SMCWT coefficients is performed via a Bayesian Maximum a Posteriori (MAP approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients. For the approximation coefficients, a new fusion rule based on the Principal Component Analysis (PCA is applied. We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method. The obtained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics. Robustness of the proposed method is further tested against different types of noise. The plots of fusion metrics establish the accuracy of the proposed fusion method.

  8. Semantic annotation of medical images

    Science.gov (United States)

    Seifert, Sascha; Kelm, Michael; Moeller, Manuel; Mukherjee, Saikat; Cavallaro, Alexander; Huber, Martin; Comaniciu, Dorin

    2010-03-01

    Diagnosis and treatment planning for patients can be significantly improved by comparing with clinical images of other patients with similar anatomical and pathological characteristics. This requires the images to be annotated using common vocabulary from clinical ontologies. Current approaches to such annotation are typically manual, consuming extensive clinician time, and cannot be scaled to large amounts of imaging data in hospitals. On the other hand, automated image analysis while being very scalable do not leverage standardized semantics and thus cannot be used across specific applications. In our work, we describe an automated and context-sensitive workflow based on an image parsing system complemented by an ontology-based context-sensitive annotation tool. An unique characteristic of our framework is that it brings together the diverse paradigms of machine learning based image analysis and ontology based modeling for accurate and scalable semantic image annotation.

  9. Study of quality perception in medical images based on comparison of contrast enhancement techniques in mammographic images

    Science.gov (United States)

    Matheus, B.; Verçosa, L. B.; Barufaldi, B.; Schiabel, H.

    2014-03-01

    With the absolute prevalence of digital images in mammography several new tools became available for radiologist; such as CAD schemes, digital zoom and contrast alteration. This work focuses in contrast variation and how the radiologist reacts to these changes when asked to evaluated image quality. Three contrast enhancing techniques were used in this study: conventional equalization, CCB Correction [1] - a digitization correction - and value subtraction. A set of 100 images was used in tests from some available online mammographic databases. The tests consisted of the presentation of all four versions of an image (original plus the three contrast enhanced images) to the specialist, requested to rank each one from the best up to worst quality for diagnosis. Analysis of results has demonstrated that CCB Correction [1] produced better images in almost all cases. Equalization, which mathematically produces a better contrast, was considered the worst for mammography image quality enhancement in the majority of cases (69.7%). The value subtraction procedure produced images considered better than the original in 84% of cases. Tests indicate that, for the radiologist's perception, it seems more important to guaranty full visualization of nuances than a high contrast image. Another result observed is that the "ideal" scanner curve does not yield the best result for a mammographic image. The important contrast range is the middle of the histogram, where nodules and masses need to be seen and clearly distinguished.

  10. Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine.

    Science.gov (United States)

    Qiao, Liang; Li, Ying; Chen, Xin; Yang, Sheng; Gao, Peng; Liu, Hongjun; Feng, Zhengquan; Nian, Yongjian; Qiu, Mingguo

    2015-09-01

    There are various medical image sharing and electronic whiteboard systems available for diagnosis and discussion purposes. However, most of these systems ask clients to install special software tools or web plug-ins to support whiteboard discussion, special medical image format, and customized decoding algorithm of data transmission of HRIs (high-resolution images). This limits the accessibility of the software running on different devices and operating systems. In this paper, we propose a solution based on pure web pages for medical HRIs lossless sharing and e-whiteboard discussion, and have set up a medical HRI sharing and e-whiteboard system, which has four-layered design: (1) HRIs access layer: we improved an tile-pyramid model named unbalanced ratio pyramid structure (URPS), to rapidly share lossless HRIs and to adapt to the reading habits of users; (2) format conversion layer: we designed a format conversion engine (FCE) on server side to real time convert and cache DICOM tiles which clients requesting with window-level parameters, to make browsers compatible and keep response efficiency to server-client; (3) business logic layer: we built a XML behavior relationship storage structure to store and share users' behavior, to keep real time co-browsing and discussion between clients; (4) web-user-interface layer: AJAX technology and Raphael toolkit were used to combine HTML and JavaScript to build client RIA (rich Internet application), to meet clients' desktop-like interaction on any pure webpage. This system can be used to quickly browse lossless HRIs, and support discussing and co-browsing smoothly on any web browser in a diversified network environment. The proposal methods can provide a way to share HRIs safely, and may be used in the field of regional health, telemedicine and remote education at a low cost.

  11. Classification of Medical Brain Images

    Institute of Scientific and Technical Information of China (English)

    Pan Haiwei(潘海为); Li Jianzhong; Zhang Wei

    2003-01-01

    Since brain tumors endanger people's living quality and even their lives, the accuracy of classification becomes more important. Conventional classifying techniques are used to deal with those datasets with characters and numbers. It is difficult, however, to apply them to datasets that include brain images and medical history (alphanumeric data), especially to guarantee the accuracy. For these datasets, this paper combines the knowledge of medical field and improves the traditional decision tree. The new classification algorithm with the direction of the medical knowledge not only adds the interaction with the doctors, but also enhances the quality of classification. The algorithm has been used on real brain CT images and a precious rule has been gained from the experiments. This paper shows that the algorithm works well for real CT data.

  12. A flexible multichannel FPGA and PC-Based ultrasound system for medical imaging research: initial phantom experiments

    Directory of Open Access Journals (Sweden)

    Amauri Amorin Assef

    Full Text Available IntroductionIn this paper, we present the initial results of a fully programmable 128-channel FPGA and PC-based system that has been developed for medical ultrasound (US imaging research in our University laboratory (Federal University of Technology - Paraná, Brazil.MethodsIn order to demonstrate the feasibility of the US research system, two applications involving unfocused plane wave transmission and conventional B-mode beamforming were evaluated using a commercial tissue-mimicking phantom and a 3.2 MHz 128-element convex array transducer.ResultsTesting results show that the hardware platform is able to synthesize arbitrary pulses up to 100 Vpp with second order harmonic distortion below 80 dB. For the first application, a 41-tap digital FIR bandpass filter was applied to the acquired RF echoes, sampled at 40 MHz with 12-bit resolution, to improve the noise suppression. In the second application, after offline apodization weighting, filtering, delay-and-sum processing, envelope detection, log compression and scan conversion, the reconstructed B-mode image is displayed over a 50 dB range.DiscussionThe presented results indicate that the open US imaging system can be used to support different ultrasonic transmission and reception strategies, which typically cannot be implemented in conventional data flow architectures that are mainly based on hardware.

  13. a Laboratory-Based X-Ray Phase Contrast Imaging Scanner with Applications in Biomedical and Non-Medical Disciplines

    Science.gov (United States)

    Hagen, C. K.; Diemoz, P. C.; Endrizzi, M.; Munro, P. R. T.; Szafraniec, M. B.; Millard, T. P.; Speller, R.; Olivo, D. A.

    2014-02-01

    X-ray phase contrast imaging (XPCi) provides a much higher visibility of low-absorbing details than conventional, attenuation-based radiography. This is due to the fact that image contrast is determined by the unit decrement of the real part of the complex refractive index of an object rather than by its imaginary part (the absorption coefficient), which can be up to 1000 times larger for energies in the X-ray regime. This finds applications in many areas, including medicine, biology, material testing, and homeland security. Until lately, XPCi has been restricted to synchrotron facilities due to its demanding coherence requirements on the radiation source. However, edge illumination XPCi, first developed by one of the authors at the ELETTRA Synchrotron in Italy, substantially relaxes these requirements and therefore provides options to overcome this problem. Our group has built a prototype scanner that adapts the edge-illumination concept to standard laboratory conditions and extends it to large fields of view. This is based on X-ray sources and detectors available off the shelf, and its use has led to impressive results in mammography, cartilage imaging, testing of composite materials and security inspection. This article presents the method and the scanner prototype, and reviews its applications in selected biomedical and non-medical disciplines.

  14. Building the Medical Imaging Resource Center Based on PACS to Improve the Teaching Medical Imaging%构建基于PACS的医学影像教学资源中心提升教学质量

    Institute of Scientific and Technical Information of China (English)

    王世威; 姜慧萍; 韩浙; 许茂盛

    2013-01-01

    [目的]构建基于图像存档与传输系统(picture archiving and communication system,PACS)的数字化医学影像教学资源中心,提升医学影像学教学质量。[方法]设计电子教案(electronic teaching file,ETF)生成模块,并把它整合在PACS系统报告工作站,配置电子教案服务器,建立电子教案数据库,并通过与PACS系统交互的接口模块,实现影像电子教案的制作,然后利用医院PACS系统中海量的数字化医学影像资料,建立数字化医学影像教学资源中心。[结果]通过整合在PACS系统报告工作站中的电子教案生成模块,选择典型病例、感兴趣病例,并经过简单的处理后自动快速地生成电子教案,成功构建数字化医学影像教学资源中心。[结论]基于PACS系统成功构建数字化医学影像教学资源中心,最大限度实现医学影像教学资源共享,将改变医学影像学教学模式,极大地提升医学影像学教学质量。%[Purpose] To build the digital Medical Imaging Resource Center based on the picture archiving and communication system(PACS) to improve the teaching medical imaging. [Methods] The generating module of the electronic teaching file(ETF) was developed and integrated into the report work-station of PACS. The ETF server was configurated. The ETF database was created. The ETF was created through the interface module with PACS, and then the medical imaging electronic teaching resources center was built. [Result] Using the generation module of the ETF integrated in report workstation of PACS, the ETF can be automatical y created quickly through simply processing after selecting typical cases or the cases of interest. The medical imaging electronic teaching resources center has been built successful y by using the great number of medical image data in the hospital PACS. [Conclusion]The medical imaging electronic teaching resources center can be built successful y based on PACS

  15. Fast Intra and Inter Prediction Mode Decision of H.264/AVC for Medical Image Compression Based on Region of Interest

    Directory of Open Access Journals (Sweden)

    Mehdi Jafari

    2012-01-01

    Full Text Available This paper aims at applying H.264 in medical video compression applications and improving the H.264 Compression performance with better perceptual quality and low coding complexity. In order to achieve higher compression of medical video, while maintaining high image quality in the region of interest, with low coding complexity, here we propose a new model using H.264/AVC that uses lossless compression in the region of interest, and very high rate, lossy compression in other regions. This paper proposes a new method to achieve fast intra and inter prediction mode decision that is based on coarse macroblocks for intra and inter prediction mode decision of the background region and finer macroblocks for region of interest. Also the macroblocks of the background region are encoded with the maximum quantization parameter allowed by H.264/AVC in order to maximize the number of null coefficients. Experimental results show that the proposed algorithm achieves a higher compression rate on medical videos with a higher quality of region of interest with low coding complexity when compared to our previous algorithm and other standard algorithms reported in the literature.

  16. Vessel Segmentation in Medical Imaging Using a Tight-Frame Based Algorithm

    CERN Document Server

    Cai, Xiaohao; Morigi, Serena; Sgallari, Fiorella

    2011-01-01

    Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the variational approach and the partial differential equation (PDE) modeling. In this paper, we propose to apply the tight-frame approach to automatically identify tube-like structures such as blood vessels in Magnetic Resonance Angiography (MRA) images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the tight-frame algorithm to denoise and smooth the possible boundary and sharpen the region. We prove the convergence of our algorithm. Numerical experiments on real 2D/3D MRA images demonstrate that our method is very efficient with convergence usually within a few iterations, ...

  17. Image Processing in Intelligent Medical Robotic Systems

    Directory of Open Access Journals (Sweden)

    Shashev Dmitriy

    2016-01-01

    Full Text Available The paper deals with the use of high-performance computing systems with the parallel-operation architecture in intelligent medical systems, such as medical robotic systems, based on a computer vision system, is an automatic control system with the strict requirements, such as high reliability, accuracy and speed of performance. It shows the basic block-diagram of an automatic control system based on a computer vision system. The author considers the possibility of using a reconfigurable computing environment in such systems. The design principles of the reconfigurable computing environment allows to improve a reliability, accuracy and performance of whole system many times. The article contains the brief overview and the theory of the research, demonstrates the use of reconfigurable computing environments for the image preprocessing, namely morphological image processing operations. Present results of the successful simulation of the reconfigurable computing environment and implementation of the morphological image processing operations on the test image in the MATLAB Simulink.

  18. Medical imaging, PACS, and imaging informatics: retrospective.

    Science.gov (United States)

    Huang, H K

    2014-01-01

    Historical reviews of PACS (picture archiving and communication system) and imaging informatics development from different points of view have been published in the past (Huang in Euro J Radiol 78:163-176, 2011; Lemke in Euro J Radiol 78:177-183, 2011; Inamura and Jong in Euro J Radiol 78:184-189, 2011). This retrospective attempts to look at the topic from a different angle by identifying certain basic medical imaging inventions in the 1960s and 1970s which had conceptually defined basic components of PACS guiding its course of development in the 1980s and 1990s, as well as subsequent imaging informatics research in the 2000s. In medical imaging, the emphasis was on the innovations at Georgetown University in Washington, DC, in the 1960s and 1970s. During the 1980s and 1990s, research and training support from US government agencies and public and private medical imaging manufacturers became available for training of young talents in biomedical physics and for developing the key components required for PACS development. In the 2000s, computer hardware and software as well as communication networks advanced by leaps and bounds, opening the door for medical imaging informatics to flourish. Because many key components required for the PACS operation were developed by the UCLA PACS Team and its collaborative partners in the 1980s, this presentation is centered on that aspect. During this period, substantial collaborative research efforts by many individual teams in the US and in Japan were highlighted. Credits are due particularly to the Pattern Recognition Laboratory at Georgetown University, and the computed radiography (CR) development at the Fuji Electric Corp. in collaboration with Stanford University in the 1970s; the Image Processing Laboratory at UCLA in the 1980s-1990s; as well as the early PACS development at the Hokkaido University, Sapporo, Japan, in the late 1970s, and film scanner and digital radiography developed by Konishiroku Photo Ind. Co. Ltd

  19. Fundamental mathematics and physics of medical imaging

    CERN Document Server

    Lancaster, Jack

    2016-01-01

    Authored by a leading educator, this book is ideal for medical imaging courses. Rather than focus on imaging modalities the book delves into the mechanisms of image formation and image quality common to all imaging systems: contrast mechanisms, noise, and spatial and temporal resolution. This is an extensively revised new edition of The Physics of Medical X-Ray Imaging by Bruce Hasegawa (Medical Physics Publishing, 1991). A wide range of modalities are covered including X-ray CT, MRI and SPECT.

  20. Viewpoints on Medical Image Processing: From Science to Application

    Science.gov (United States)

    Deserno (né Lehmann), Thomas M.; Handels, Heinz; Maier-Hein (né Fritzsche), Klaus H.; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

    2013-01-01

    Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment. PMID:24078804

  1. Medical image segmentation using improved FCM

    Institute of Scientific and Technical Information of China (English)

    ZHANG XiaoFeng; ZHANG CaiMing; TANG WenJing; WEI ZhenWen

    2012-01-01

    Image segmentation is one of the most important problems in medical image processing,and the existence of partial volume effect and other phenomena makes the problem much more complex. Fuzzy Cmeans,as an effective tool to deal with PVE,however,is faced with great challenges in efficiency.Aiming at this,this paper proposes one improved FCM algorithm based on the histogram of the given image,which will be denoted as HisFCM and divided into two phases.The first phase will retrieve several intervals on which to compute cluster centroids,and the second one will perform image segmentation based on improved FCM algorithm.Compared with FCM and other improved algorithms,HisFCM is of much higher efficiency with satisfying results.Experiments on medical images show that HisFCM can achieve good segmentation results in less than 0.1 second,and can satisfy real-time requirements of medical image processing.

  2. Medical image registration using sparse coding of image patches.

    Science.gov (United States)

    Afzali, Maryam; Ghaffari, Aboozar; Fatemizadeh, Emad; Soltanian-Zadeh, Hamid

    2016-06-01

    Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use "Analysis K-SVD" to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing.

  3. Resolution enhancement in medical ultrasound imaging.

    Science.gov (United States)

    Ploquin, Marie; Basarab, Adrian; Kouamé, Denis

    2015-01-01

    Image resolution enhancement is a problem of considerable interest in all medical imaging modalities. Unlike general purpose imaging or video processing, for a very long time, medical image resolution enhancement has been based on optimization of the imaging devices. Although some recent works purport to deal with image postprocessing, much remains to be done regarding medical image enhancement via postprocessing, especially in ultrasound imaging. We face a resolution improvement issue in the case of medical ultrasound imaging. We propose to investigate this problem using multidimensional autoregressive (AR) models. Noting that the estimation of the envelope of an ultrasound radio frequency (RF) signal is very similar to the estimation of classical Fourier-based power spectrum estimation, we theoretically show that a domain change and a multidimensional AR model can be used to achieve super-resolution in ultrasound imaging provided the order is estimated correctly. Here, this is done by means of a technique that simultaneously estimates the order and the parameters of a multidimensional model using relevant regression matrix factorization. Doing so, the proposed method specifically fits ultrasound imaging and provides an estimated envelope. Moreover, an expression that links the theoretical image resolution to both the image acquisition features (such as the point spread function) and a postprocessing feature (the AR model) order is derived. The overall contribution of this work is threefold. First, it allows for automatic resolution improvement. Through a simple model and without any specific manual algorithmic parameter tuning, as is used in common methods, the proposed technique simply and exclusively uses the ultrasound RF signal as input and provides the improved B-mode as output. Second, it allows for the a priori prediction of the improvement in resolution via the knowledge of the parametric model order before actual processing. Finally, to achieve the

  4. Content Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering Algorithms

    Directory of Open Access Journals (Sweden)

    K. R. Chandran

    2012-01-01

    Full Text Available Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, “Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM and k-Means Clustering algorithms”. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain

  5. Cloud computing in medical imaging.

    Science.gov (United States)

    Kagadis, George C; Kloukinas, Christos; Moore, Kevin; Philbin, Jim; Papadimitroulas, Panagiotis; Alexakos, Christos; Nagy, Paul G; Visvikis, Dimitris; Hendee, William R

    2013-07-01

    Over the past century technology has played a decisive role in defining, driving, and reinventing procedures, devices, and pharmaceuticals in healthcare. Cloud computing has been introduced only recently but is already one of the major topics of discussion in research and clinical settings. The provision of extensive, easily accessible, and reconfigurable resources such as virtual systems, platforms, and applications with low service cost has caught the attention of many researchers and clinicians. Healthcare researchers are moving their efforts to the cloud, because they need adequate resources to process, store, exchange, and use large quantities of medical data. This Vision 20/20 paper addresses major questions related to the applicability of advanced cloud computing in medical imaging. The paper also considers security and ethical issues that accompany cloud computing.

  6. Neural networks: Application to medical imaging

    Science.gov (United States)

    Clarke, Laurence P.

    1994-01-01

    The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

  7. Scale-Specific Multifractal Medical Image Analysis

    Directory of Open Access Journals (Sweden)

    Boris Braverman

    2013-01-01

    irregular complex tissue structures that do not lend themselves to straightforward analysis with traditional Euclidean geometry. In this study, we treat the nonfractal behaviour of medical images over large-scale ranges by considering their box-counting fractal dimension as a scale-dependent parameter rather than a single number. We describe this approach in the context of the more generalized Rényi entropy, in which we can also compute the information and correlation dimensions of images. In addition, we describe and validate a computational improvement to box-counting fractal analysis. This improvement is based on integral images, which allows the speedup of any box-counting or similar fractal analysis algorithm, including estimation of scale-dependent dimensions. Finally, we applied our technique to images of invasive breast cancer tissue from 157 patients to show a relationship between the fractal analysis of these images over certain scale ranges and pathologic tumour grade (a standard prognosticator for breast cancer. Our approach is general and can be applied to any medical imaging application in which the complexity of pathological image structures may have clinical value.

  8. Rough sets and near sets in medical imaging: a review.

    Science.gov (United States)

    Hassanien, Aboul Ella; Abraham, Ajith; Peters, James F; Schaefer, Gerald; Henry, Christopher

    2009-11-01

    This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

  9. An Improved Medical Image Fusion Algorithm for Anatomical and Functional Medical Images

    Institute of Scientific and Technical Information of China (English)

    CHEN Mei-ling; TAO Ling; QIAN Zhi-yu

    2009-01-01

    In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data,but,not an appropriate fusion algorithm for anatomical and functional medical images.In this paper,the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively.When choosing high-frequency coefficients,the global gradient of each sub-image is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy,so that the fused image can reserve the anatomical image's edge and texture feature.Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.

  10. A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity Inhomogeneity

    Directory of Open Access Journals (Sweden)

    Maryam Rastgarpour

    2014-01-01

    Full Text Available Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM. The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

  11. A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.

    Science.gov (United States)

    Rastgarpour, Maryam; Shanbehzadeh, Jamshid

    2014-01-01

    Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

  12. An online interactive simulation system for medical imaging education.

    Science.gov (United States)

    Dikshit, Aditya; Wu, Dawei; Wu, Chunyan; Zhao, Weizhao

    2005-09-01

    This report presents a recently developed web-based medical imaging simulation system for teaching students or other trainees who plan to work in the medical imaging field. The increased importance of computer and information technology widely applied to different imaging techniques in clinics and medical research necessitates a comprehensive medical imaging education program. A complete tutorial of simulations introducing popular imaging modalities, such as X-ray, MRI, CT, ultrasound and PET, forms an essential component of such an education. Internet technologies provide a vehicle to carry medical imaging education online. There exist a number of internet-based medical imaging hyper-books or online documentations. However, there are few providing interactive computational simulations. We focus on delivering knowledge of the physical principles and engineering implementation of medical imaging techniques through an interactive website environment. The online medical imaging simulation system presented in this report outlines basic principles underlying different imaging techniques and image processing algorithms and offers trainees an interactive virtual laboratory. For education purposes, this system aims to provide general understanding of each imaging modality with comprehensive explanations, ample illustrations and copious references as its thrust, rather than complex physics or detailed math. This report specifically describes the development of the tutorial for commonly used medical imaging modalities. An internet-accessible interface is used to simulate various imaging algorithms with user-adjustable parameters. The tutorial is under the MATLAB Web Server environment. Macromedia Director MX is used to develop interactive animations integrating theory with graphic-oriented simulations. HTML and JavaScript are used to enable a user to explore these modules online in a web browser. Numerous multiple choice questions, links and references for advanced study are

  13. Machine learning for medical images analysis.

    Science.gov (United States)

    Criminisi, A

    2016-10-01

    This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods.

  14. Visible Watermarking within the Region of Non-Interest of Medical Images Based on Fuzzy C-Means and Harris Corner Detection

    Directory of Open Access Journals (Sweden)

    Debalina Biswas

    2013-05-01

    Full Text Available Transfer of medical information amongst various hos pitals and diagnostic centers for mutual availability of diagnostic and therapeutic case stu dies is a very common process. Watermarking is adding “ownership” information in multimedia con tents to verify signal integrity, prove authenticity and achieve control over the copy proc ess. Distortion in Region of Interest (ROI of a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment. Therefore, proper selection of Region of Non-Intere st (RONI in a medical image is very crucial for adding watermark. First part of the present wor k proposes proper selection of Region of Non-Interest based on Fuzzy C-Means segmentation an d Harris corner detection, to improve retention of diagnostic value lost in embedding own ership information. The second part of the work presents watermark embedding in the selected a rea of RONI based on alpha blending technique. In this approach, the generated watermar ked image having an acceptable level of imperceptibility and distortion is compared to the original image. The Peak Signal to Noise Ratio (PSNR of the original image vs. watermarked image is calculated to prove the efficacy of the proposed method.

  15. The Mutual Beneficial Effect between Medical Imaging and Nanomedicine

    Directory of Open Access Journals (Sweden)

    Huiting Qiao

    2013-01-01

    Full Text Available The reports on medical imaging and nanomedicine are getting more and more prevalent. Many nanoparticles entering into the body act as contrast agents, or probes in medical imaging, which are parts of nanomedicines. The application extent and the quality of imaging have been improved by nanotechnique. On one hand, nanomedicines advance the sensitivity and specificity of molecular imaging. On the other hand, the biodistribution of nanomedicine can also be studied in vivo by medical imaging, which is necessary in the toxicological research. The toxicity of nanomedicine is a concern which may slow down the application of nanomedical. The quantitative description of the kinetic process is significant. Based on metabolic study on radioactivity tracer, a scheme of pharmacokinetic research of nanomedicine is proposed. In this review, we will discuss the potential advantage of medical imaging in toxicology of nanomedicine, as well as the advancement of medical imaging prompted by nanomedicine.

  16. Application of stereo-imaging technology to medical field.

    Science.gov (United States)

    Nam, Kyoung Won; Park, Jeongyun; Kim, In Young; Kim, Kwang Gi

    2012-09-01

    There has been continuous development in the area of stereoscopic medical imaging devices, and many stereoscopic imaging devices have been realized and applied in the medical field. In this article, we review past and current trends pertaining to the application stereo-imaging technologies in the medical field. We describe the basic principles of stereo vision and visual issues related to it, including visual discomfort, binocular disparities, vergence-accommodation mismatch, and visual fatigue. We also present a brief history of medical applications of stereo-imaging techniques, examples of recently developed stereoscopic medical devices, and patent application trends as they pertain to stereo-imaging medical devices. Three-dimensional (3D) stereo-imaging technology can provide more realistic depth perception to the viewer than conventional two-dimensional imaging technology. Therefore, it allows for a more accurate understanding and analysis of the morphology of an object. Based on these advantages, the significance of stereoscopic imaging in the medical field increases in accordance with the increase in the number of laparoscopic surgeries, and stereo-imaging technology plays a key role in the diagnoses of the detailed morphologies of small biological specimens. The application of 3D stereo-imaging technology to the medical field will help improve surgical accuracy, reduce operation times, and enhance patient safety. Therefore, it is important to develop more enhanced stereoscopic medical devices.

  17. Osiris: a medical image-manipulation system.

    Science.gov (United States)

    Ligier, Y; Ratib, O; Logean, M; Girard, C

    1994-01-01

    We designed a general-purpose computer program, Osiris, for the display, manipulation, and analysis of digital medical images. The program offers an intuitive, window-based interface with direct access to generic tools. Characterized by user-friendliness, portability, and extensibility, Osiris is compatible with both Unix-based and Macintosh-based platforms. It is readily modified and can be used to develop new tools. It is able to monitor the entries made during a work session and thus provide data on its use. Osiris and its source code are being distributed, free of charge, to universities and research groups around the world.

  18. 基于NSCT变换的医学图像融合研究%RESEARCH ON NSCT-BASED MEDICAL IMAGE FUSION

    Institute of Scientific and Technical Information of China (English)

    田秀华; 兴旺

    2013-01-01

    In order to improve the quality of medical images and provide reliable information basis for medical diagnosis, the NSCT transform with more advanced multi-resolution and multi-directional decomposition is introduced to CT and MRI medical image fusion for study, and in light of the characteristics of medical images, an improved fusion rule with central region energy weighted average is proposed. The simulative experiments of medical image fusion with different NSCT transform-based fusion rules are carried out, and the comparative analyses of them with Contourlet Transform are done. Experimental results show that, the improve NSCT transform-based the fusion rules algorithm makes the medical fused image have richer amount of information and higher contrast, the related evaluation shows the effectiveness of the algorithm.%为了提高医学图像的质量,为医学诊断提供可靠的信息依据,将更为先进的多分辨率、多方向分解的NSCT变换引入到CT和MRI医学图像融合中进行研究,并针对医学图像的特点,提出一种改进的中心区域能量加权平均融合规则.进行基于NSCT变换的不同融合规则的医学图像融合仿真实验,并与Contourlet变换进行比较分析.实验结果表明,基于NSCT变换的改进融合规则算法使医学融合图像具有更丰富的信息量和更高的对比度,相关评价指标表明了该算法的有效性.

  19. Magnetic resonance imaging-based endovascular versus medical stroke treatment for symptom onset up to 12 h.

    Science.gov (United States)

    Wouters, Anke; Lemmens, Robin; Christensen, Soren; Wilms, Guido; Dupont, Patrick; Mlynash, Michael; Schneider, Armin; Laage, Rico; Cereda, Carlo W; Lansberg, Maarten G; Albers, Gregory W; Thijs, Vincent

    2016-01-01

    Recent trials have shown a clear benefit of endovascular therapy for stroke patients presenting within 6 h after stroke onset. Imaging-based selection may identify a cohort with a favorable response to endovascular therapy, in an even later time window. We performed an indirect comparison between outcomes seen in DEFUSE 2, a prospective cohort study of patients who received a baseline MRI before endovascular therapy, and a control group from AXIS 2 receiving standard medical care up to 12 h after symptom onset. Patients from AXIS 2 with a confirmed large vessel occlusion were selected as a control group for DEFUSE 2-patients. The primary endpoint was good functional outcome at day 90 (Modified Rankin Score 0-2). We performed a stratified analysis based on the presence of the target mismatch for both studies and reperfusion status in DEFUSE 2. We compared good functional outcome in 108 patients from AXIS 2 and 99 patients from DEFUSE 2. In DEFUSE 2-patients with the target mismatch profile in whom reperfusion was achieved, the rate of good functional outcome was increased compared to target mismatch patients in AXIS 2, 54% versus 29% (OR 3.2, 95% CI 1.1-9.4). In target mismatch patients treated between 6 and 12 h after stroke onset, this association between study and good functional outcome remained present (OR 9.0, 95% CI 1.1-75.8). This indirect comparison suggests that endovascular treatment resulting in substantial reperfusion is associated with improved outcome in target mismatch patients even beyond 6 h after stroke onset. Confirmation is needed from future clinical trials that randomize patients beyond the 6 h time window. © 2016 World Stroke Organization.

  20. Instrumentation of the ESRF medical imaging facility

    CERN Document Server

    Elleaume, H; Berkvens, P; Berruyer, G; Brochard, T; Dabin, Y; Domínguez, M C; Draperi, A; Fiedler, S; Goujon, G; Le Duc, G; Mattenet, M; Nemoz, C; Pérez, M; Renier, M; Schulze, C; Spanne, P; Suortti, P; Thomlinson, W; Estève, F; Bertrand, B; Le Bas, J F

    1999-01-01

    At the European Synchrotron Radiation Facility (ESRF) a beamport has been instrumented for medical research programs. Two facilities have been constructed for alternative operation. The first one is devoted to medical imaging and is focused on intravenous coronary angiography and computed tomography (CT). The second facility is dedicated to pre-clinical microbeam radiotherapy (MRT). This paper describes the instrumentation for the imaging facility. Two monochromators have been designed, both are based on bent silicon crystals in the Laue geometry. A versatile scanning device has been built for pre-alignment and scanning of the patient through the X-ray beam in radiography or CT modes. An intrinsic germanium detector is used together with large dynamic range electronics (16 bits) to acquire the data. The beamline is now at the end of its commissioning phase; intravenous coronary angiography is intended to start in 1999 with patients and the CT pre-clinical program is underway on small animals. The first in viv...

  1. Temperature imaging with speed of ultrasonic transmission tomography for medical treatment control:A physical model-based method

    Institute of Scientific and Technical Information of China (English)

    储哲琦; 袁杰; 王学鼎; 刘晓峻

    2015-01-01

    Hyperthermia is a promising method to enhance chemo and radiation therapy of breast cancer. In the process of hyperthermia, temperature monitoring is of great importance to assure the effectiveness of treatment. The transmission speed of ultrasound in biomedical tissue changes with temperature. However, when mapping the speed of sound directly to temperature in each pixel as desired for using all speeds of ultrasound data, temperature bipolar edge enhancement artifacts occur near the boundary of two tissues with different speeds of ultrasound. After the analysis of the reasons for causing these artifacts, an optimized method is introduced to rebuild the temperature field image by using the continuity constraint as the judgment criterion. The significant smoothness of the rebuilding image in the transitional area shows that our proposed method can build a more precise temperature image for controlling the medical thermal treatment.

  2. Tooling Techniques Enhance Medical Imaging

    Science.gov (United States)

    2012-01-01

    mission. The manufacturing techniques developed to create the components have yielded innovations advancing medical imaging, transportation security, and even energy efficiency.

  3. Morphological Techniques for Medical Images: A Review

    Directory of Open Access Journals (Sweden)

    Isma Irum

    2012-08-01

    Full Text Available Image processing is playing a very important role in medical imaging with its versatile applications and features towards the development of computer aided diagnostic systems, automatic detections of abnormalities and enhancement in ultrasonic, computed tomography, magnetic resonance images and lots more applications. Medical images morphology is a field of study where the medical images are observed and processed on basis of geometrical and changing structures. Medical images morphological techniques has been reviewed in this study underlying the some human organ images, the associated diseases and processing techniques to address some anatomical problem detection. Images of Human brain, bone, heart, carotid, iris, lesion, liver and lung have been discussed in this study.

  4. Watermarking patient data in encrypted medical images

    Indian Academy of Sciences (India)

    A Lavanya; V Natarajan

    2012-12-01

    In this paper, we propose a method for watermarking medical images for data integrity which consists of image encryption, data embedding and image-recovery phases. Data embedding can be completely recovered from the watermarked image after the watermark has been extracted. In the proposed method, we utilize standard stream cipher for image encryption and selecting non-region of interest tile to embed patient data. We show that the lower bound of the PSNR (peak-signal-to-noise-ratio) values for medical images is about 48 dB. Experimental results demonstrate that the proposed scheme can embed a large amount of data while keeping high visual quality of test images.

  5. Comparison of two position sensitive gamma-ray detectors based on continuous YAP and pixellated NaI(TI) for nuclear medical imaging applications

    Science.gov (United States)

    Zhu, Jie; Ma, Hong-Guang; Ma, Wen-Yan; Zeng, Hui; Wang, Zhao-Min; Xu, Zi-Zhong

    2008-11-01

    Dedicated position sensitive gamma-ray detectors based on position sensitive photomultiplier tubes (PSPMTs) coupled to scintillation crystals, have been used for the construction of compact gamma-ray imaging systems, suitable for nuclear medical imaging applications such as small animal imaging and single organ imaging and scintimammography. In this work, the performance of two gamma-ray detectors: a continuous YAP scintillation crystal coupled to a Hamamastu R2486 PSPMT and a pixellated NaI(TI) scintillation array crystal coupled to the same PSPMT, is compared. The results show that the gamma-ray detector based on a pixellated NaI(TI) scintillation array crystal is a promising candidate for nuclear medical imaging applications, since their performance in terms of position linearity, spatial resolution and effective field of view (FOV) is superior than that of the gamma-ray detector based on a continuous YAP scintillation crystal. However, a better photodetector (Hamamatau H8500 Flat Panel PMT, for example) coupled to the continuous crystal is also likely a good selection for nuclear medicine imaging applications. Supported by National Nature Science Foundation of China (10275063)

  6. Comparison of two position sensitive gamma-ray detectors based on continuous YAP and pixellated NaI(TI) for nuclear medical imaging applications

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Dedicated position sensitive gamma-ray detectors based on position sensitive photomultiplier tubes (PSPMTs) coupled to scintillation crystals, have been used for the construction of compact gamma-ray imaging systems, suitable for nuclear medical imaging applications such as small animal imaging and single organ imaging and scintimammography. In this work, the performance of two gamma-ray detectors: a continuous YAP scintillation crystal coupled to a Hamamastu R2486 PSPMT and a pixellated NaI(TI) scintillation array crystal coupled to the same PSPMT, is compared. The results show that the gamma-ray detector based on a pixellated NaI(TI) scintillation array crystal is a promising candidate for nuclear medical imaging applications,since their performance in terms of position linearity, spatial resolution and effective field of view (FOV) is superior than that of the gamma-ray detector based on a continuous YAP scintillation crystal. However, a better photodetector (Hamamatau H8500 Flat Panel PMT, for example) coupled to the continuous crystal is also likely a good selection for nuclear medicine imaging applications.

  7. Radiation biology of medical imaging

    CERN Document Server

    Kelsey, Charles A; Sandoval, Daniel J; Chambers, Gregory D; Adolphi, Natalie L; Paffett, Kimberly S

    2014-01-01

    This book provides a thorough yet concise introduction to quantitative radiobiology and radiation physics, particularly the practical and medical application. Beginning with a discussion of the basic science of radiobiology, the book explains the fast processes that initiate damage in irradiated tissue and the kinetic patterns in which such damage is expressed at the cellular level. The final section is presented in a highly practical handbook style and offers application-based discussions in radiation oncology, fractionated radiotherapy, and protracted radiation among others. The text is also supplemented by a Web site.

  8. Photon-based medical imagery

    CERN Document Server

    Fanet, Herve

    2013-01-01

    This book describes the different principles and equipments used in medical imaging. Importance of medical imaging for diagnostic is strongly increasing and it is now necessary to have a good knowledge of the different physical possible principles. Researchers, clinicians, engineers and professionals in this area, along with postgraduate students in the signal and image processing field, will find this book of great interest.

  9. Medical image compression with embedded-wavelet transform

    Science.gov (United States)

    Cheng, Po-Yuen; Lin, Freddie S.; Jannson, Tomasz

    1997-10-01

    The need for effective medical image compression and transmission techniques continues to grow because of the huge volume of radiological images captured each year. The limited bandwidth and efficiency of current networking systems cannot meet this need. In response, Physical Optics Corporation devised an efficient medical image management system to significantly reduce the storage space and transmission bandwidth required for digitized medical images. The major functions of this system are: (1) compressing medical imagery, using a visual-lossless coder, to reduce the storage space required; (2) transmitting image data progressively, to use the transmission bandwidth efficiently; and (3) indexing medical imagery according to image characteristics, to enable automatic content-based retrieval. A novel scalable wavelet-based image coder was developed to implement the system. In addition to its high compression, this approach is scalable in both image size and quality. The system provides dramatic solutions to many medical image handling problems. One application is the efficient storage and fast transmission of medical images over picture archiving and communication systems. In addition to reducing costs, the potential impact on improving the quality and responsiveness of health care delivery in the US is significant.

  10. Fast fluid registration of medical images

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten; Gramkow, Claus

    1996-01-01

    This paper offers a new fast algorithm for non-rigid viscous fluid registration of medical images that is at least an order of magnitude faster than the previous method by (Christensen et al., 1994). The core algorithm in the fluid registration method is based on a linear elastic deformation...... of the velocity field of the fluid. Using the linearity of this deformation we derive a convolution filter which we use in a scale-space framework. We also demonstrate that the `demon'-based registration method of (Thirion, 1996) can be seen as an approximation to the fluid registration method and point...

  11. 基于本体的医学影像信息整合①%Ontology-Based Information Model for Integration of Medical Imaging Data

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

    A computer readable unified information model is the data foundation in medical imaging semantic retrieve . In this paper, some challenges including lacking of unified information model for medical imaging information, the terminology and syntax for describing the semantic content in medical imaging varying were discussed, and an ontology-based information scheme for medical imaging information integrating was developed. Based on the analysis of medical imaging data source and the relationship of them, a medical imaging information ontology model was developed using "seven-step" method proposed by Stanford University, and the persistence of ontology model, extracting original data and data integration were realized. The information model was used in medical imaging semantic retrieve.%  计算机可理解的统一信息模型是基于语义的医学影像检索研究的数据基础。讨论了医学影像及其相关信息使用中存在的数据异构、图像标注术语及语法不一致及数据格式不支持现有数据挖掘和图像语义检索的问题,提出了一种基于本体的医学影像信息集成方案。在分析医学影像信息来源及其关系基础上,结合领域专家知识,使用斯坦福大学提出的本体构建“七步法”设计了医学影像信息本体模型,实现了本体模型的持久化、原始数据提取和数据整合,解决了医学影像信息使用中存在的问题,该信息模型已用于医学影像检索系统中。

  12. The Computational Challenges of Medical Imaging

    Science.gov (United States)

    2004-02-01

    JASON will undertake a study for the DOE and the NIH National Institute for Bio- medical Imaging and Bio-engineering on the role of computation...broadly defined to include raw computational capabilities, mass storage needs, and connectivity) for medical imaging . This study will address the

  13. The Pediatric Urinary Tract and Medical Imaging.

    Science.gov (United States)

    Penny, Steven M

    2016-01-01

    The pediatric urinary tract often is assessed with medical imaging. Consequently, it is essential for medical imaging professionals to have a fundamental understanding of pediatric anatomy, physiology, and common pathology of the urinary tract to provide optimal patient care. This article provides an overview of fetal development, pediatric urinary anatomy and physiology, and common diseases and conditions of the pediatric urinary tract.

  14. Medically inoperable endometrial cancer in patients with a high body mass index (BMI): Patterns of failure after 3-D image-based high dose rate (HDR) brachytherapy

    DEFF Research Database (Denmark)

    Acharya, Sahaja; Esthappan, Jacqueline; Badiyan, Shahed

    2016-01-01

    BACKGROUND AND PURPOSE: High BMI is a reason for medical inoperability in patients with endometrial cancer in the United States. Definitive radiation is an alternative therapy for these patients; however, data on patterns of failure after definitive radiotherapy are lacking. We describe...... the patterns of failure after definitive treatment with 3-D image-based high dose rate (HDR) brachytherapy for medically inoperable endometrial cancer. MATERIALS AND METHODS: Forty-three consecutive patients with endometrial cancer FIGO stages I-III were treated definitively with HDR brachytherapy...

  15. Image analysis in medical imaging: recent advances in selected examples

    Science.gov (United States)

    Dougherty, G

    2010-01-01

    Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments. PMID:21611048

  16. Refraction-based 2D, 2.5D and 3D medical imaging: Stepping forward to a clinical trial

    Energy Technology Data Exchange (ETDEWEB)

    Ando, Masami [Tokyo University of Science, Research Institute for Science and Technology, Noda, Chiba 278-8510 (Japan)], E-mail: msm-ando@rs.noda.tus.ac.jp; Bando, Hiroko [Tsukuba University (Japan); Tokiko, Endo; Ichihara, Shu [Nagoya Medical Center (Japan); Hashimoto, Eiko [GUAS (Japan); Hyodo, Kazuyuki [KEK (Japan); Kunisada, Toshiyuki [Okayama University (Japan); Li Gang [BSRF (China); Maksimenko, Anton [Tokyo University of Science, Research Institute for Science and Technology, Noda, Chiba 278-8510 (Japan); KEK (Japan); Mori, Kensaku [Nagoya University (Japan); Shimao, Daisuke [IPU (Japan); Sugiyama, Hiroshi [KEK (Japan); Yuasa, Tetsuya [Yamagata University (Japan); Ueno, Ei [Tsukuba University (Japan)

    2008-12-15

    An attempt at refraction-based 2D, 2.5D and 3D X-ray imaging of articular cartilage and breast carcinoma is reported. We are developing very high contrast X-ray 2D imaging with XDFI (X-ray dark-field imaging), X-ray CT whose data are acquired by DEI (diffraction-enhanced imaging) and tomosynthesis due to refraction contrast. 2D and 2.5D images were taken with nuclear plates or with X-ray films. Microcalcification of breast cancer and articular cartilage are clearly visible. 3D data were taken with an X-ray sensitive CCD camera. The 3D image was successfully reconstructed by the use of an algorithm newly made by our group. This shows a distinctive internal structure of a ductus lactiferi (milk duct) that contains inner wall, intraductal carcinoma and multifocal calcification in the necrotic core of the continuous DCIS (ductal carcinoma in situ). Furthermore consideration of clinical applications of these contrasts made us to try tomosynthesis. This attempt was satisfactory from the view point of articular cartilage image quality and the skin radiation dose.

  17. Design And Implementation Of Multilevel Access Control In Medical Image Transmission Using Symmetric Polynomial Based Audio Steganography

    CERN Document Server

    Begum, J Nafeesa; Sumathy, V

    2010-01-01

    ...The steganography scheme makes it possible to hide the medical image in different bit locations of host media without inviting suspicion. The Secret file is embedded in a cover media with a key. At the receiving end the key can be derived by all the classes which are higher in the hierarchy using symmetric polynomial and the medical image file can be retrieved. The system is implemented and found to be secure, fast and scalable. Simulation results show that the system is dynamic in nature and allows any type of hierarchy. The proposed approach performs better even during frequent member joins and leaves. The computation cost is reduced as the same algorithm is used for key computation and descendant key derivation. Steganographic technique used in this paper does not use the conventional LSB's and uses two bit positions and the hidden data occurs only from a frame which is dictated by the key that is used. Hence the quality of stego data is improved.

  18. Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience.

    Science.gov (United States)

    Kapur, Tina; Pieper, Steve; Fedorov, Andriy; Fillion-Robin, J-C; Halle, Michael; O'Donnell, Lauren; Lasso, Andras; Ungi, Tamas; Pinter, Csaba; Finet, Julien; Pujol, Sonia; Jagadeesan, Jayender; Tokuda, Junichi; Norton, Isaiah; Estepar, Raul San Jose; Gering, David; Aerts, Hugo J W L; Jakab, Marianna; Hata, Nobuhiko; Ibanez, Luiz; Blezek, Daniel; Miller, Jim; Aylward, Stephen; Grimson, W Eric L; Fichtinger, Gabor; Wells, William M; Lorensen, William E; Schroeder, Will; Kikinis, Ron

    2016-10-01

    The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

  19. Medical image archive node simulation and architecture

    Science.gov (United States)

    Chiang, Ted T.; Tang, Yau-Kuo

    1996-05-01

    It is a well known fact that managed care and new treatment technologies are revolutionizing the health care provider world. Community Health Information Network and Computer-based Patient Record projects are underway throughout the United States. More and more hospitals are installing digital, `filmless' radiology (and other imagery) systems. They generate a staggering amount of information around the clock. For example, a typical 500-bed hospital might accumulate more than 5 terabytes of image data in a period of 30 years for conventional x-ray images and digital images such as Magnetic Resonance Imaging and Computer Tomography images. With several hospitals contributing to the archive, the storage required will be in the hundreds of terabytes. Systems for reliable, secure, and inexpensive storage and retrieval of digital medical information do not exist today. In this paper, we present a Medical Image Archive and Distribution Service (MIADS) concept. MIADS is a system shared by individual and community hospitals, laboratories, and doctors' offices that need to store and retrieve medical images. Due to the large volume and complexity of the data, as well as the diversified user access requirement, implementation of the MIADS will be a complex procedure. One of the key challenges to implementing a MIADS is to select a cost-effective, scalable system architecture to meet the ingest/retrieval performance requirements. We have performed an in-depth system engineering study, and developed a sophisticated simulation model to address this key challenge. This paper describes the overall system architecture based on our system engineering study and simulation results. In particular, we will emphasize system scalability and upgradability issues. Furthermore, we will discuss our simulation results in detail. The simulations study the ingest/retrieval performance requirements based on different system configurations and architectures for variables such as workload, tape

  20. Image registration method for medical image sequences

    Science.gov (United States)

    Gee, Timothy F.; Goddard, James S.

    2013-03-26

    Image registration of low contrast image sequences is provided. In one aspect, a desired region of an image is automatically segmented and only the desired region is registered. Active contours and adaptive thresholding of intensity or edge information may be used to segment the desired regions. A transform function is defined to register the segmented region, and sub-pixel information may be determined using one or more interpolation methods.

  1. A glass-sealed field emission x-ray tube based on carbon nanotube emitter for medical imaging

    Science.gov (United States)

    Yeo, Seung Jun; Jeong, Jaeik; Ahn, Jeung Sun; Park, Hunkuk; Kwak, Junghwan; Noh, Eunkyong; Paik, Sanghyun; Kim, Seung Hoon; Ryu, Jehwang

    2016-04-01

    We report the design and fabrication of a carbon nanotube based a glass-sealed field emission x-ray tube without vacuum pump. The x-ray tube consists of four electrodes with anode, focuser, gate, and cathode electrode. The shape of cathode is rectangular for isotropic focal spot size at anode target. The obtained x-ray images show clearly micrometer scale.

  2. Medical imaging in new drug clinical development.

    Science.gov (United States)

    Wang, Yi-Xiang; Deng, Min

    2010-12-01

    Medical imaging can help answer key questions that arise during the drug development process. The role of medical imaging in new drug clinical trials includes identification of likely responders; detection and diagnosis of lesions and evaluation of their severity; and therapy monitoring and follow-up. Nuclear imaging techniques such as PET can be used to monitor drug pharmacokinetics and distribution and study specific molecular endpoints. In assessing drug efficacy, imaging biomarkers and imaging surrogate endpoints can be more objective and faster to measure than clinical outcomes, and allow small group sizes, quick results and good statistical power. Imaging also has important role in drug safety monitoring, particularly when there is no other suitable biomarkers available. Despite the long history of radiological sciences, its application to the drug development process is relatively recent. This review highlights the processes, opportunities, and challenges of medical imaging in new drug development.

  3. Client-side Medical Image Colorization in a Collaborative Environment.

    Science.gov (United States)

    Virag, Ioan; Stoicu-Tivadar, Lăcrămioara; Crişan-Vida, Mihaela

    2015-01-01

    The paper presents an application related to collaborative medicine using a browser based medical visualization system with focus on the medical image colorization process and the underlying open source web development technologies involved. Browser based systems allow physicians to share medical data with their remotely located counterparts or medical students, assisting them during patient diagnosis, treatment monitoring, surgery planning or for educational purposes. This approach brings forth the advantage of ubiquity. The system can be accessed from a any device, in order to process the images, assuring the independence towards having a specific proprietary operating system. The current work starts with processing of DICOM (Digital Imaging and Communications in Medicine) files and ends with the rendering of the resulting bitmap images on a HTML5 (fifth revision of the HyperText Markup Language) canvas element. The application improves the image visualization emphasizing different tissue densities.

  4. Adaptive textural segmentation of medical images

    Science.gov (United States)

    Kuklinski, Walter S.; Frost, Gordon S.; MacLaughlin, Thomas

    1992-06-01

    A number of important problems in medical imaging can be described as segmentation problems. Previous fractal-based image segmentation algorithms have used either the local fractal dimension alone or the local fractal dimension and the corresponding image intensity as features for subsequent pattern recognition algorithms. An image segmentation algorithm that utilized the local fractal dimension, image intensity, and the correlation coefficient of the local fractal dimension regression analysis computation, to produce a three-dimension feature space that was partitioned to identify specific pixels of dental radiographs as being either bone, teeth, or a boundary between bone and teeth also has been reported. In this work we formulated the segmentation process as a configurational optimization problem and discuss the application of simulated annealing optimization methods to the solution of this specific optimization problem. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

  5. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  6. Medical image fusion using the convolution of Meridian distributions.

    Science.gov (United States)

    Agrawal, Mayank; Tsakalides, Panagiotis; Achim, Alin

    2010-01-01

    The aim of this paper is to introduce a novel non-Gaussian statistical model-based approach for medical image fusion based on the Meridian distribution. The paper also includes a new approach to estimate the parameters of generalized Cauchy distribution. The input images are first decomposed using the Dual-Tree Complex Wavelet Transform (DT-CWT) with the subband coefficients modelled as Meridian random variables. Then, the convolution of Meridian distributions is applied as a probabilistic prior to model the fused coefficients, and the weights used to combine the source images are optimised via Maximum Likelihood (ML) estimation. The superior performance of the proposed method is demonstrated using medical images.

  7. High-accuracy and real-time 3D positioning, tracking system for medical imaging applications based on 3D digital image correlation

    Science.gov (United States)

    Xue, Yuan; Cheng, Teng; Xu, Xiaohai; Gao, Zeren; Li, Qianqian; Liu, Xiaojing; Wang, Xing; Song, Rui; Ju, Xiangyang; Zhang, Qingchuan

    2017-01-01

    This paper presents a system for positioning markers and tracking the pose of a rigid object with 6 degrees of freedom in real-time using 3D digital image correlation, with two examples for medical imaging applications. Traditional DIC method was improved to meet the requirements of the real-time by simplifying the computations of integral pixel search. Experiments were carried out and the results indicated that the new method improved the computational efficiency by about 4-10 times in comparison with the traditional DIC method. The system was aimed for orthognathic surgery navigation in order to track the maxilla segment after LeFort I osteotomy. Experiments showed noise for the static point was at the level of 10-3 mm and the measurement accuracy was 0.009 mm. The system was demonstrated on skin surface shape evaluation of a hand for finger stretching exercises, which indicated a great potential on tracking muscle and skin movements.

  8. THz Medical Imaging: in vivo Hydration Sensing

    Science.gov (United States)

    Taylor, Zachary D.; Singh, Rahul S.; Bennett, David B.; Tewari, Priyamvada; Kealey, Colin P.; Bajwa, Neha; Culjat, Martin O.; Stojadinovic, Alexander; Lee, Hua; Hubschman, Jean-Pierre; Brown, Elliott R.; Grundfest, Warren S.

    2015-01-01

    The application of THz to medical imaging is experiencing a surge in both interest and federal funding. A brief overview of the field is provided along with promising and emerging applications and ongoing research. THz imaging phenomenology is discussed and tradeoffs are identified. A THz medical imaging system, operating at ~525 GHz center frequency with ~125 GHz of response normalized bandwidth is introduced and details regarding principles of operation are provided. Two promising medical applications of THz imaging are presented: skin burns and cornea. For burns, images of second degree, partial thickness burns were obtained in rat models in vivo over an 8 hour period. These images clearly show the formation and progression of edema in and around the burn wound area. For cornea, experimental data measuring the hydration of ex vivo porcine cornea under drying is presented demonstrating utility in ophthalmologic applications. PMID:26085958

  9. Multiview locally linear embedding for effective medical image retrieval.

    Directory of Open Access Journals (Sweden)

    Hualei Shen

    Full Text Available Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE, principal component analysis (PCA, or laplacian eigenmaps (LE can be employed to reduce the "curse of dimensionality". Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods.

  10. Study on the Medical Image Distributed Dynamic Processing Method

    Institute of Scientific and Technical Information of China (English)

    张全海; 施鹏飞

    2003-01-01

    To meet the challenge of implementing rapidly advanced, time-consuming medical image processing algorithms,it is necessary to develop a medical image processing technology to process a 2D or 3D medical image dynamically on the web. But in a premier system, only static image processing can be provided with the limitation of web technology. The development of Java and CORBA (common object request broker architecture) overcomes the shortcoming of the web static application and makes the dynamic processing of medical images on the web available. To develop an open solution of distributed computing, we integrate the Java, and web with the CORBA and present a web-based medical image dynamic processing methed, which adopts Java technology as the language to program application and components of the web and utilies the CORBA architecture to cope with heterogeneous property of a complex distributed system. The method also provides a platform-independent, transparent processing architecture to implement the advanced image routines and enable users to access large dataset and resources according to the requirements of medical applications. The experiment in this paper shows that the medical image dynamic processing method implemented on the web by using Java and the CORBA is feasible.

  11. Medical imaging technology reviews and computational applications

    CERN Document Server

    Dewi, Dyah

    2015-01-01

    This book presents the latest research findings and reviews in the field of medical imaging technology, covering ultrasound diagnostics approaches for detecting osteoarthritis, breast carcinoma and cardiovascular conditions, image guided biopsy and segmentation techniques for detecting lung cancer, image fusion, and simulating fluid flows for cardiovascular applications. It offers a useful guide for students, lecturers and professional researchers in the fields of biomedical engineering and image processing.

  12. Adaptive Beamforming for Medical Ultrasound Imaging

    DEFF Research Database (Denmark)

    Holfort, Iben Kraglund

    This dissertation investigates the application of adaptive beamforming for medical ultrasound imaging. The investigations have been concentrated primarily on the Minimum Variance (MV) beamformer. A broadband implementation of theMV beamformer is described, and simulated data have been used...... to demonstrate the performance. The MV beamformer has been applied to different sets of ultrasound imaging sequences; synthetic aperture ultrasound imaging and plane wave ultrasound imaging. And an approach for applying MV optimized apodization weights on both the transmitting and the receiving apertures...

  13. Special Issue on “Medical Imaging and Image Processing”

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2014-12-01

    Full Text Available Over the last decade, Medical Imaging has become an essential component in many fields of bio-medical research and clinical practice. Biologists study cells and generate 3D confocal microscopy data sets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors from MRI and CT scans, and neuroscientists detect regional metabolic brain activity from PET and functional MRI scans. On the other hand, Image Processing includes the analysis, enhancement, and display of images captured via various medical imaging technologies. Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. In addition, image processing and analysis can be used to determine the diameter, volume, and vasculature of a tumor or organ, flow parameters of blood or other fluids, and microscopic changes that have not previously been discernible.[...

  14. 3D thermal medical image visualization tool: Integration between MRI and thermographic images.

    Science.gov (United States)

    Abreu de Souza, Mauren; Chagas Paz, André Augusto; Sanches, Ionildo Jóse; Nohama, Percy; Gamba, Humberto Remigio

    2014-01-01

    Three-dimensional medical image reconstruction using different images modalities require registration techniques that are, in general, based on the stacking of 2D MRI/CT images slices. In this way, the integration of two different imaging modalities: anatomical (MRI/CT) and physiological information (infrared image), to generate a 3D thermal model, is a new methodology still under development. This paper presents a 3D THERMO interface that provides flexibility for the 3D visualization: it incorporates the DICOM parameters; different color scale palettes at the final 3D model; 3D visualization at different planes of sections; and a filtering option that provides better image visualization. To summarize, the 3D thermographc medical image visualization provides a realistic and precise medical tool. The merging of two different imaging modalities allows better quality and more fidelity, especially for medical applications in which the temperature changes are clinically significant.

  15. An information gathering system for medical image inspection

    Science.gov (United States)

    Lee, Young-Jin; Bajcsy, Peter

    2005-04-01

    We present an information gathering system for medical image inspection that consists of software tools for capturing computer-centric and human-centric information. Computer-centric information includes (1) static annotations, such as (a) image drawings enclosing any selected area, a set of areas with similar colors, a set of salient points, and (b) textual descriptions associated with either image drawings or links between pairs of image drawings, and (2) dynamic (or temporal) information, such as mouse movements, zoom level changes, image panning and frame selections from an image stack. Human-centric information is represented by video and audio signals that are acquired by computer-mounted cameras and microphones. The short-term goal of the presented system is to facilitate learning of medical novices from medical experts, while the long-term goal is to data mine all information about image inspection for assisting in making diagnoses. In this work, we built basic software functionality for gathering computer-centric and human-centric information of the aforementioned variables. Next, we developed the information playback capabilities of all gathered information for educational purposes. Finally, we prototyped text-based and image template-based search engines to retrieve information from recorded annotations, for example, (a) find all annotations containing the word "blood vessels", or (b) search for similar areas to a selected image area. The information gathering system for medical image inspection reported here has been tested with images from the Histology Atlas database.

  16. ENVISION, from particle detectors to medical imaging

    CERN Multimedia

    2013-01-01

    Technologies developed for particle physics detectors are increasingly used in medical imaging tools like Positron Emission Tomography (PET). Produced by: CERN KT/Life Sciences and ENVISION Project Management: Manuela Cirilli 3D animation: Jeroen Huijben, Nymus3d

  17. Medical image segmentation by MDP model

    Science.gov (United States)

    Lu, Yisu; Chen, Wufan

    2011-11-01

    MDP (Dirichlet Process Mixtures) model is applied to segment medical images in this paper. Segmentation can been automatically done without initializing segmentation class numbers. The MDP model segmentation algorithm is used to segment natural images and MR (Magnetic Resonance) images in the paper. To demonstrate the accuracy of the MDP model segmentation algorithm, many compared experiments, such as EM (Expectation Maximization) image segmentation algorithm, K-means image segmentation algorithm and MRF (Markov Field) image segmentation algorithm, have been done to segment medical MR images. All the methods are also analyzed quantitatively by using DSC (Dice Similarity Coefficients). The experiments results show that DSC of MDP model segmentation algorithm of all slices exceed 90%, which show that the proposed method is robust and accurate.

  18. Applied medical image processing a basic course

    CERN Document Server

    Birkfellner, Wolfgang

    2014-01-01

    A widely used, classroom-tested text, Applied Medical Image Processing: A Basic Course delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Avoiding excessive mathematical formalisms, the book presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data and illustrations on an accompanying CD-ROM or companion website. Organized as a complete textbook, it provides an overview of the physics of medical image processing and discusses image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction.

  19. Lossy Compression Color Medical Image Using CDF Wavelet Lifting Scheme

    Directory of Open Access Journals (Sweden)

    M. beladghem

    2013-09-01

    Full Text Available As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including color medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for color medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested color images. Our algorithm provides very important PSNR and MSSIM values for color medical images.

  20. Computing support for advanced medical data analysis and imaging

    CERN Document Server

    Wiślicki, W; Białas, P; Czerwiński, E; Kapłon, Ł; Kochanowski, A; Korcyl, G; Kowal, J; Kowalski, P; Kozik, T; Krzemień, W; Molenda, M; Moskal, P; Niedźwiecki, S; Pałka, M; Pawlik, M; Raczyński, L; Rudy, Z; Salabura, P; Sharma, N G; Silarski, M; Słomski, A; Smyrski, J; Strzelecki, A; Wieczorek, A; Zieliński, M; Zoń, N

    2014-01-01

    We discuss computing issues for data analysis and image reconstruction of PET-TOF medical scanner or other medical scanning devices producing large volumes of data. Service architecture based on the grid and cloud concepts for distributed processing is proposed and critically discussed.

  1. MULTIWAVELET TRANSFORM IN COMPRESSION OF MEDICAL IMAGES

    Directory of Open Access Journals (Sweden)

    V. K. Sudha

    2013-05-01

    Full Text Available This paper analyses performance of multiwavelets - a variant of wavelet transform on compression of medical images. To do so, two processes namely, transformation for decorrelation and encoding are done. In transformation stage medical images are subjected to multiwavelet transform using multiwavelets such as Geronimo- Hardin-Massopust, Chui Lian, Cardinal 2 Balanced (Cardbal2 and orthogonal symmetric/antsymmetric multiwavelet (SA4. Set partitioned Embedded Block Coder is used as a common platform for encoding the transformed coefficients. Peak Signal to noise ratio, bit rate and Structural Similarity Index are used as metrics for performance analysis. For experiment we have used various medical images such as Magnetic Resonance Image, Computed Tomography and X-ray images.

  2. Physics for Medical Imaging Applications

    CERN Document Server

    Caner, Alesssandra; Rahal, Ghita

    2007-01-01

    The book introduces the fundamental aspects of digital imaging and covers four main themes: Ultrasound techniques and imaging applications; Magnetic resonance and MPJ in hospital; Digital imaging with X-rays; and Emission tomography (PET and SPECT). Each of these topics is developed by analysing the underlying physics principles and their implementation, quality and safety aspects, clinical performance and recent advancements in the field. Some issues specific to the individual techniques are also treated, e.g. choice of radioisotopes or contrast agents, optimisation of data acquisition and st

  3. Medical imaging principles and practices

    CERN Document Server

    Bronzino, Joseph D; Peterson, Donald R

    2013-01-01

    This book offers a selective review of key imaging modalities focusing on modalities with established clinical utilization. It provides a detailed overview of x-ray imaging and computed tomography, fundamental concepts in signal acquisition and processes, followed by an overview of functional MRI (fMRI) and chemical shift imaging. It also covers topics in Magnetic Resonance Microcopy, the physics of instrumentation and signal collection, and their application in clinical practice. The selection of topics provides readers with an appreciation of the depth and breadth of the field and the challenges ahead of the technical and clinical community of researchers and practitioners.

  4. Medical Image Compression using Wavelet Decomposition for Prediction Method

    CERN Document Server

    Ramesh, S M

    2010-01-01

    In this paper offers a simple and lossless compression method for compression of medical images. Method is based on wavelet decomposition of the medical images followed by the correlation analysis of coefficients. The correlation analyses are the basis of prediction equation for each sub band. Predictor variable selection is performed through coefficient graphic method to avoid multicollinearity problem and to achieve high prediction accuracy and compression rate. The method is applied on MRI and CT images. Results show that the proposed approach gives a high compression rate for MRI and CT images comparing with state of the art methods.

  5. 21 CFR 892.2030 - Medical image digitizer.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

  6. Multispectral imaging for medical diagnosis

    Science.gov (United States)

    Anselmo, V. J.

    1977-01-01

    Photography technique determines amount of morbidity present in tissue. Imaging apparatus incorporates numerical filtering. Overall system operates in near-real time. Information gained from this system enables physician to understand extent of injury and leads to accelerated treatment.

  7. Multi-channel medical imaging system

    Science.gov (United States)

    Frangioni, John V

    2013-12-31

    A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remain in the subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may provide an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide used to capture images. The system may be configured for use in open surgical procedures by providing an operating area that is closed to ambient light. The systems described herein provide two or more diagnostic imaging channels for capture of multiple, concurrent diagnostic images and may be used where a visible light image may be usefully supplemented by two or more images that are independently marked for functional interest.

  8. Multi-channel medical imaging system

    Science.gov (United States)

    Frangioni, John V.

    2016-05-03

    A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remain in a subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may provide an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide used to capture images. The system may be configured for use in open surgical procedures by providing an operating area that is closed to ambient light. The systems described herein provide two or more diagnostic imaging channels for capture of multiple, concurrent diagnostic images and may be used where a visible light image may be usefully supplemented by two or more images that are independently marked for functional interest.

  9. Multi-channel medical imaging system

    Energy Technology Data Exchange (ETDEWEB)

    Frangioni, John V.

    2016-05-03

    A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remain in a subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may provide an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide used to capture images. The system may be configured for use in open surgical procedures by providing an operating area that is closed to ambient light. The systems described herein provide two or more diagnostic imaging channels for capture of multiple, concurrent diagnostic images and may be used where a visible light image may be usefully supplemented by two or more images that are independently marked for functional interest.

  10. Segmentation of elongated structures in medical images

    NARCIS (Netherlands)

    Staal, Jozef Johannes

    2004-01-01

    The research described in this thesis concerns the automatic detection, recognition and segmentation of elongated structures in medical images. For this purpose techniques have been developed to detect subdimensional pointsets (e.g. ridges, edges) in images of arbitrary dimension. These pointsets ar

  11. Intuitionistic fuzzy segmentation of medical images.

    Science.gov (United States)

    Chaira, Tamalika

    2010-06-01

    This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.

  12. CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS

    OpenAIRE

    Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

    2013-01-01

    Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and u...

  13. Cerenkov luminescence imaging of medical isotopes

    OpenAIRE

    Ruggiero, Alessandro; Holland, Jason P.; Lewis, Jason S.; Grimm, Jan

    2010-01-01

    The development of novel multimodality imaging agents and techniques represents the current frontier of research in the field of medical imaging science. However, the combination of nuclear tomography with optical techniques has yet to be established. Here, we report the use of the inherent optical emissions from the decay of radiopharmaceuticals for Cerenkov luminescence imaging (CLI) of tumors in vivo and correlate the results with those obtained from concordant immuno-PET studies.

  14. Deep Learning in Medical Image Analysis.

    Science.gov (United States)

    Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

    2017-03-09

    This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. Expected final online publication date for the Annual Review of Biomedical Engineering Volume 19 is June 4, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  15. Radiology and Enterprise Medical Imaging Extensions (REMIX).

    Science.gov (United States)

    Erdal, Barbaros S; Prevedello, Luciano M; Qian, Songyue; Demirer, Mutlu; Little, Kevin; Ryu, John; O'Donnell, Thomas; White, Richard D

    2017-08-24

    Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of "big imaging data," as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available.

  16. Photoacoustic Imaging: Opening New Frontiers in Medical Imaging

    Directory of Open Access Journals (Sweden)

    Keerthi S Valluru

    2011-01-01

    Full Text Available In today′s world, technology is advancing at an exponential rate and medical imaging is no exception. During the last hundred years, the field of medical imaging has seen a tremendous technological growth with the invention of imaging modalities including but not limited to X-ray, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography. These tools have led to better diagnosis and improved patient care. However, each of these modalities has its advantages as well as disadvantages and none of them can reveal all the information a physician would like to have. In the last decade, a new diagnostic technology called photoacoustic imaging has evolved which is moving rapidly from the research phase to the clinical trial phase. This article outlines the basics of photoacoustic imaging and describes our hands-on experience in developing a comprehensive photoacoustic imaging system to detect tissue abnormalities.

  17. Medical Image Feature, Extraction, Selection And Classification

    Directory of Open Access Journals (Sweden)

    M.VASANTHA,

    2010-06-01

    Full Text Available Breast cancer is the most common type of cancer found in women. It is the most frequent form of cancer and one in 22 women in India is likely to suffer from breast cancer. This paper proposes a image classifier to classify the mammogram images. Mammogram image is classified into normal image, benign image and malignant image. Totally 26 features including histogram intensity features and GLCM features are extracted from mammogram image. A hybrid approach of feature selection is proposed in this paper which reduces 75% of the features. Decision tree algorithms are applied to mammography lassification by using these reduced features. Experimental results have been obtained for a data set of 113 images taken from MIAS of different types. This technique of classification has not been attempted before and it reveals the potential of Data mining in medical treatment.

  18. Use of mobile devices for medical imaging.

    Science.gov (United States)

    Hirschorn, David S; Choudhri, Asim F; Shih, George; Kim, Woojin

    2014-12-01

    Mobile devices have fundamentally changed personal computing, with many people forgoing the desktop and even laptop computer altogether in favor of a smaller, lighter, and cheaper device with a touch screen. Doctors and patients are beginning to expect medical images to be available on these devices for consultative viewing, if not actual diagnosis. However, this raises serious concerns with regard to the ability of existing mobile devices and networks to quickly and securely move these images. Medical images often come in large sets, which can bog down a network if not conveyed in an intelligent manner, and downloaded data on a mobile device are highly vulnerable to a breach of patient confidentiality should that device become lost or stolen. Some degree of regulation is needed to ensure that the software used to view these images allows all relevant medical information to be visible and manipulated in a clinically acceptable manner. There also needs to be a quality control mechanism to ensure that a device's display accurately conveys the image content without loss of contrast detail. Furthermore, not all mobile displays are appropriate for all types of images. The smaller displays of smart phones, for example, are not well suited for viewing entire chest radiographs, no matter how small and numerous the pixels of the display may be. All of these factors should be taken into account when deciding where, when, and how to use mobile devices for the display of medical images.

  19. Design strategy and implementation of the medical diagnostic image support system at two large military medical centers

    Science.gov (United States)

    Smith, Donald V.; Smith, Stan M.; Sauls, F.; Cawthon, Michael A.; Telepak, Robert J.

    1992-07-01

    The Medical Diagnostic Imaging Support (MDIS) system contract for federal medical treatment facilities was awarded to Loral/Siemens in the Fall of 1991. This contract places ''filmless'' imaging in a variety of situations from small clients to large medical centers. The MDIS system approach is a ''turn-key'', performance based specification driven by clinical requirements.

  20. A lossless encryption method for medical images using edge maps.

    Science.gov (United States)

    Zhou, Yicong; Panetta, Karen; Agaian, Sos

    2009-01-01

    Image encryption is an effective approach for providing security and privacy protection for medical images. This paper introduces a new lossless approach, called EdgeCrypt, to encrypt medical images using the information contained within an edge map. The algorithm can fully protect the selected objects/regions within medical images or the entire medical images. It can also encrypt other types of images such as grayscale images or color images. The algorithm can be used for privacy protection in the real-time medical applications such as wireless medical networking and mobile medical services.

  1. Quantitative imaging features: extension of the oncology medical image database

    Science.gov (United States)

    Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

    2015-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

  2. A survey of medical image registration - under review.

    Science.gov (United States)

    Viergever, Max A; Maintz, J B Antoine; Klein, Stefan; Murphy, Keelin; Staring, Marius; Pluim, Josien P W

    2016-10-01

    A retrospective view on the past two decades of the field of medical image registration is presented, guided by the article "A survey of medical image registration" (Maintz and Viergever, 1998). It shows that the classification of the field introduced in that article is still usable, although some modifications to do justice to advances in the field would be due. The main changes over the last twenty years are the shift from extrinsic to intrinsic registration, the primacy of intensity-based registration, the breakthrough of nonlinear registration, the progress of inter-subject registration, and the availability of generic image registration software packages. Two problems that were called urgent already 20 years ago, are even more urgent nowadays: Validation of registration methods, and translation of results of image registration research to clinical practice. It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects.

  3. Real-time hand-held ultrasound medical-imaging device based on a new digital quadrature demodulation processor.

    Science.gov (United States)

    Levesque, Philippe; Sawan, Mohamad

    2009-08-01

    A fully hardware-based real-time digital wideband quadrature demodulation processor based on the Hilbert transform is proposed to process ultrasound radio frequency signals. The presented architecture combines 2 finite impulse response (FIR) filters to process in-phase and quadrature signals and includes a piecewise linear approximation architecture that performs the required square root operations. The proposed implementation enables flexibility to support different transducers with its ability to load on-the-fly different FIR filter coefficient sets. The complexity and accuracy of the demodulator processor are analyzed with simulated RF data; a normalized residual sum-of-squares cost function is used for comparison with the Matlab Hilbert function. Three implementations are integrated into a hand-held ultrasound system for experimental accuracy and performance evaluation. Real-time images were acquired from a reference phantom, demonstrating the feasibility of using the presented architecture to perform real-time digital quadrature demodulation of ultrasonic signal echoes. Experimental results show that the implementation, using only 2942 slices and 3 dedicated digital multipliers of a low-cost and low-power field-programmable gate array (FPGA) is accurate relative to a comparable software- based system; axial and lateral resolution of 1 mm and 2 mm, respectively, were obtained with a 12-mm piezoelectric transducer without postprocessing. Because the processing and sampling rates are the same, high-frequency ultrasound signals can be processed as well. For a 15-frame-per-second display, the hand-held ultrasonic imaging-processing core (FPGA, memory) requires only 45 mW (dynamic) when using a 5-MHz single-element piezoelectric transducer.

  4. Perspectives of medical X-ray imaging

    Energy Technology Data Exchange (ETDEWEB)

    Freudenberger, J. E-mail: joerg.freudenberger@med.siemens.de; Hell, E.; Knuepfer, W

    2001-06-21

    While X-ray image intensifiers (XII), storage phosphor screens and film-screen systems are still the work horses of medical imaging, large flat panel solid state detectors using either scintillators and amorphous silicon photo diode arrays (FD-Si), or direct X-ray conversion in amorphous selenium are reaching maturity. The main advantage with respect to image quality and low patient dose of the XII and FD-Si systems is caused by the rise of the Detector Quantum Efficiency originating from the application of thick needle-structured phosphor X-ray absorbers. With the detectors getting closer to an optimal state, further progress in medical X-ray imaging requires an improvement of the usable source characteristics. The development of clinical monochromatic X-ray sources of high power would not only allow an improved contrast-to-dose ratio by allowing smaller average photon energies in applications but would also lead to new imaging techniques.

  5. Perspectives of medical X-ray imaging

    Science.gov (United States)

    Freudenberger, J.; Hell, E.; Knüpfer, W.

    2001-06-01

    While X-ray image intensifiers (XII), storage phosphor screens and film-screen systems are still the work horses of medical imaging, large flat panel solid state detectors using either scintillators and amorphous silicon photo diode arrays (FD-Si), or direct X-ray conversion in amorphous selenium are reaching maturity. The main advantage with respect to image quality and low patient dose of the XII and FD-Si systems is caused by the rise of the Detector Quantum Efficiency originating from the application of thick needle-structured phosphor X-ray absorbers. With the detectors getting closer to an optimal state, further progress in medical X-ray imaging requires an improvement of the usable source characteristics. The development of clinical monochromatic X-ray sources of high power would not only allow an improved contrast-to-dose ratio by allowing smaller average photon energies in applications but would also lead to new imaging techniques.

  6. Multi-scale visual words for hierarchical medical image categorisation

    Science.gov (United States)

    Markonis, Dimitrios; Seco de Herrera, Alba G.; Eggel, Ivan; Müller, Henning

    2012-02-01

    The biomedical literature published regularly has increased strongly in past years and keeping updated even in narrow domains is difficult. Images represent essential information of their articles and can help to quicker browse through large volumes of articles in connection with keyword search. Content-based image retrieval is helping the retrieval of visual content. To facilitate retrieval of visual information, image categorisation can be an important first step. To represent scientific articles visually, medical images need to be separated from general images such as flowcharts or graphs to facilitate browsing, as graphs contain little information. Medical modality classification is a second step to focus search. The techniques described in this article first classify images into broad categories. In a second step the images are further classified into the exact medical modalities. The system combines the Scale-Invariant Feature Transform (SIFT) and density-based clustering (DENCLUE). Visual words are first created globally to differentiate broad categories and then within each category a new visual vocabulary is created for modality classification. The results show the difficulties to differentiate between some modalities by visual means alone. On the other hand the improvement of the accuracy of the two-step approach shows the usefulness of the method. The system is currently being integrated into the Goldminer image search engine of the ARRS (American Roentgen Ray Society) as a web service, allowing concentrating image search onto clinically relevant images automatically.

  7. Image-based medical expert teleconsultation in acute care of injuries. A systematic review of effects on information accuracy, diagnostic validity, clinical outcome, and user satisfaction.

    Directory of Open Access Journals (Sweden)

    Marie Hasselberg

    Full Text Available OBJECTIVE: To systematically review the literature on image-based telemedicine for medical expert consultation in acute care of injuries, considering system, user, and clinical aspects. DESIGN: Systematic review of peer-reviewed journal articles. DATA SOURCES: Searches of five databases and in eligible articles, relevant reviews, and specialized peer-reviewed journals. ELIGIBILITY CRITERIA: Studies were included that covered teleconsultation systems based on image capture and transfer with the objective of seeking medical expertise for the diagnostic and treatment of acute injury care and that presented the evaluation of one or several aspects of the system based on empirical data. Studies of systems not under routine practice or including real-time interactive video conferencing were excluded. METHOD: The procedures used in this review followed the PRISMA Statement. Predefined criteria were used for the assessment of the risk of bias. The DeLone and McLean Information System Success Model was used as a framework to synthesise the results according to system quality, user satisfaction, information quality and net benefits. All data extractions were done by at least two reviewers independently. RESULTS: Out of 331 articles, 24 were found eligible. Diagnostic validity and management outcomes were often studied; fewer studies focused on system quality and user satisfaction. Most systems were evaluated at a feasibility stage or during small-scale pilot testing. Although the results of the evaluations were generally positive, biases in the methodology of evaluation were concerning selection, performance and exclusion. Gold standards and statistical tests were not always used when assessing diagnostic validity and patient management. CONCLUSIONS: Image-based telemedicine systems for injury emergency care tend to support valid diagnosis and influence patient management. The evidence relates to a few clinical fields, and has substantial methodological

  8. 21 CFR 892.2040 - Medical image hardcopy device.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Medical image hardcopy device. 892.2040 Section... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2040 Medical image hardcopy device. (a) Identification. A medical image hardcopy device is a device that produces a visible printed record of a medical...

  9. Research on medical image encryption in telemedicine systems.

    Science.gov (United States)

    Dai, Yin; Wang, Huanzhen; Zhou, Zixia; Jin, Ziyi

    2016-04-29

    Recently, advances in computers and high-speed communication tools have led to enhancements in remote medical consultation research. Laws in some localities require hospitals to encrypt patient information (including images of the patient) before transferring the data over a network. Therefore, developing suitable encryption algorithms is quite important for modern medicine. This paper demonstrates a digital image encryption algorithm based on chaotic mapping, which uses the no-period and no-convergence properties of a chaotic sequence to create image chaos and pixel averaging. Then, the chaotic sequence is used to encrypt the image, thereby improving data security. With this method, the security of data and images can be improved.

  10. Medical Image Steganography: Study of Medical Image Quality Degradation when Embedding Data in the Frequency Domain

    Directory of Open Access Journals (Sweden)

    M.I.Khalil

    2017-02-01

    Full Text Available Steganography is the discipline of invisible communication by hiding the exchanged secret information (message in another digital information media (image, video or audio. The existence of the message is kept indiscernible in sense that no one, other than the intended recipient, suspects the existence of the message. The majority of steganography techniques are implemented either in spatial domain or in frequency domain of the digital images while the embedded information can be in the form of plain or cipher message. Medical image steganography is classified as a distinctive case of image steganography in such a way that both the image and the embedded information have special requirements such as achieving utmost clarity reading of the medical images and the embedded messages. There is a contention between the amount of hidden information and the caused detectable distortion of image. The current paper studies the degradation of the medical image when undergoes the steganography process in the frequency domain.

  11. APES Beamforming Applied to Medical Ultrasound Imaging

    DEFF Research Database (Denmark)

    Blomberg, Ann E. A.; Holfort, Iben Kraglund; Austeng, Andreas

    2009-01-01

    Recently, adaptive beamformers have been introduced to medical ultrasound imaging. The primary focus has been on the minimum variance (MV) (or Capon) beamformer. This work investigates an alternative but closely related beamformer, the Amplitude and Phase Estimation (APES) beamformer. APES offers...... added robustness at the expense of a slightly lower resolution. The purpose of this study was to evaluate the performance of the APES beamformer on medical imaging data, since correct amplitude estimation often is just as important as spatial resolution. In our simulations we have used a 3.5 MHz, 96...... element linear transducer array. When imaging two closely spaced point targets, APES displays nearly the same resolution as the MV, and at the same time improved amplitude control. When imaging cysts in speckle, APES offers speckle statistics similar to that of the DAS, without the need for temporal...

  12. Object-based modeling, identification, and labeling of medical images for content-based retrieval by querying on intervals of attribute values

    Science.gov (United States)

    Thies, Christian; Ostwald, Tamara; Fischer, Benedikt; Lehmann, Thomas M.

    2005-04-01

    The classification and measuring of objects in medical images is important in radiological diagnostics and education, especially when using large databases as knowledge resources, for instance a picture archiving and communication system (PACS). The main challenge is the modeling of medical knowledge and the diagnostic context to label the sought objects. This task is referred to as closing the semantic gap between low-level pixel information and high level application knowledge. This work describes an approach which allows labeling of a-priori unknown objects in an intuitive way. Our approach consists of four main components. At first an image is completely decomposed into all visually relevant partitions on different scales. This provides a hierarchical organized set of regions. Afterwards, for each of the obtained regions a set of descriptive features is computed. In this data structure objects are represented by regions with characteristic attributes. The actual object identification is the formulation of a query. It consists of attributes on which intervals are defined describing those regions that correspond to the sought objects. Since the objects are a-priori unknown, they are described by a medical expert by means of an intuitive graphical user interface (GUI). This GUI is the fourth component. It enables complex object definitions by browsing the data structure and examinating the attributes to formulate the query. The query is executed and if the sought objects have not been identified its parameterization is refined. By using this heuristic approach, object models for hand radiographs have been developed to extract bones from a single hand in different anatomical contexts. This demonstrates the applicability of the labeling concept. By using a rule for metacarpal bones on a series of 105 images, this type of bone could be retrieved with a precision of 0.53 % and a recall of 0.6%.

  13. Deep Transfer Learning for Modality Classification of Medical Images

    Directory of Open Access Journals (Sweden)

    Yuhai Yu

    2017-07-01

    Full Text Available Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classification are dependent on the choice of hand-crafted features and demand a clear awareness of prior domain knowledge. The feature learning approach may detect efficiently visual characteristics of different modalities, but it is limited to the number of training datasets. To overcome the absence of labeled data, on the one hand, we take deep convolutional neural networks (VGGNet, ResNet with different depths pre-trained on ImageNet, fix most of the earlier layers to reserve generic features of natural images, and only train their higher-level portion on ImageCLEF to learn domain-specific features of medical figures. Then, we train from scratch deep CNNs with only six weight layers to capture more domain-specific features. On the other hand, we employ two data augmentation methods to help CNNs to give the full scope to their potential characterizing image modality features. The final prediction is given by our voting system based on the outputs of three CNNs. After evaluating our proposed model on the subfigure classification task in ImageCLEF2015 and ImageCLEF2016, we obtain new, state-of-the-art results—76.87% in ImageCLEF2015 and 87.37% in ImageCLEF2016—which imply that CNNs, based on our proposed transfer learning methods and data augmentation skills, can identify more efficiently modalities of medical images.

  14. Automatic medical X-ray image classification using annotation.

    Science.gov (United States)

    Zare, Mohammad Reza; Mueen, Ahmed; Seng, Woo Chaw

    2014-02-01

    The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.

  15. Spatio-Temporal Encoding in Medical Ultrasound Imaging

    DEFF Research Database (Denmark)

    Gran, Fredrik

    2005-01-01

    In this dissertation two methods for spatio-temporal encoding in medical ultrasound imaging are investigated. The first technique is based on a frequency division approach. Here, the available spectrum of the transducer is divided into a set of narrow bands. A waveform is designed for each band...... the signal to noise ratio and simultaneously the penetration depth so that the medical doctor can image deeper lying structures. The method is tested both experimentally and in simulation and has also evaluated for the purpose of blood flow estimation. The work presented is based on four papers which...

  16. Medical Image Protection using steganography by crypto-image as cover Image

    Directory of Open Access Journals (Sweden)

    Vinay Pandey

    2012-09-01

    Full Text Available This paper presents securing the transmission of medical images. The presented algorithms will be applied to images. This work presents a new method that combines image cryptography, data hiding and Steganography technique for denoised and safe image transmission purpose. In This method we encrypt the original image with two shares mechanism encryption algorithm then embed the encrypted image with patient information by using lossless data embedding technique with data hiding method after that for more security. We apply steganography by encrypted image of any other medical image as cover image and embedded images as secrete image with the private key. In receiver side when the message is arrived then we apply the inverse methods in reverse order to get the original image and patient information and to remove noise we extract the image before the decryption of message. We have applied and showed the results of our method to medical images.

  17. SVM for density estimation and application to medical image segmentation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhao; ZHANG Su; ZHANG Chen-xi; CHEN Ya-zhu

    2006-01-01

    A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process.Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

  18. User Oriented Platform for Data Analytics in Medical Imaging Repositories.

    Science.gov (United States)

    Valerio, Miguel; Godinho, Tiago Marques; Costa, Carlos

    2016-01-01

    The production of medical imaging studies and associated data has been growing in the last decades. Their primary use is to support medical diagnosis and treatment processes. However, the secondary use of the tremendous amount of stored data is generally more limited. Nowadays, medical imaging repositories have turned into rich databanks holding not only the images themselves, but also a wide range of metadata related to the medical practice. Exploring these repositories through data analysis and business intelligence techniques has the potential of increasing the efficiency and quality of the medical practice. Nevertheless, the continuous production of tremendous amounts of data makes their analysis difficult by conventional approaches. This article proposes a novel automated methodology to derive knowledge from medical imaging repositories that does not disrupt the regular medical practice. Our method is able to apply statistical analysis and business intelligence techniques directly on top of live institutional repositories. It is a Web-based solution that provides extensive dashboard capabilities, including complete charting and reporting options, combined with data mining components. Moreover, it enables the operator to set a wide multitude of query parameters and operators through the use of an intuitive graphical interface.

  19. Model observers in medical imaging research.

    Science.gov (United States)

    He, Xin; Park, Subok

    2013-10-04

    Model observers play an important role in the optimization and assessment of imaging devices. In this review paper, we first discuss the basic concepts of model observers, which include the mathematical foundations and psychophysical considerations in designing both optimal observers for optimizing imaging systems and anthropomorphic observers for modeling human observers. Second, we survey a few state-of-the-art computational techniques for estimating model observers and the principles of implementing these techniques. Finally, we review a few applications of model observers in medical imaging research.

  20. Beat-Frequency/Microsphere Medical Ultrasonic Imaging

    Science.gov (United States)

    Yost, William T.; Cantrell, John H.; Pretlow, Robert A., III

    1995-01-01

    Medical ultrasonic imaging system designed to provide quantitative data on various flows of blood in chambers, blood vessels, muscles, and tissues of heart. Sensitive enough to yield readings on flows of blood in heart even when microspheres used as ultrasonic contrast agents injected far from heart and diluted by circulation of blood elsewhere in body.

  1. Curve Matching with Applications in Medical Imaging

    DEFF Research Database (Denmark)

    Bauer, Martin; Bruveris, Martins; Harms, Philipp

    2015-01-01

    In the recent years, Riemannian shape analysis of curves and surfaces has found several applications in medical image analysis. In this paper we present a numerical discretization of second order Sobolev metrics on the space of regular curves in Euclidean space. This class of metrics has several...

  2. [Promoting "well-treatment" in medical imaging].

    Science.gov (United States)

    Renouf, Nicole; Llop, Marc

    2012-12-01

    A project to promote "well-treatment" has been initiated in the medical imaging department of a Parisian hospital. With the aim of promoting the well-being of the patient and developing shared values of empathy and respect, the members of this medico-technical team have undertaken to build a culture of "well-treatment" which respects the patient's dignity and rights.

  3. Medical Imaging with Ultrasound: Some Basic Physics.

    Science.gov (United States)

    Gosling, R.

    1989-01-01

    Discussed are medical applications of ultrasound. The physics of the wave nature of ultrasound including its propagation and production, return by the body, spatial and contrast resolution, attenuation, image formation using pulsed echo ultrasound techniques, measurement of velocity and duplex scanning are described. (YP)

  4. Gestalt descriptions embodiments and medical image interpretation

    DEFF Research Database (Denmark)

    Friis, Jan Kyrre Berg Olsen

    2017-01-01

    In this paper I will argue that medical specialists interpret and diagnose through technological mediations like X-ray and fMRI images, and by actualizing embodied skills tacitly they are determining the identity of objects in the perceptual field. The initial phase of human interpretation of vis...

  5. Lesion Contrast Enhancement in Medical Ultrasound Imaging

    DEFF Research Database (Denmark)

    Stetson, Paul F.; Sommer, F.G.; Macovski, A.

    1997-01-01

    Methods for improving the contrast-to-noise ratio (CNR) of low-contrast lesions in medical ultrasound imaging are described. Differences in the frequency spectra and amplitude distributions of the lesion and its surroundings can be used to increase the CNR of the lesion relative to the background...

  6. Based on NSCT and the Human Visual Characteristics of Medical Image Fusion%基于NSCT及人眼视觉特性的医学图像融合

    Institute of Scientific and Technical Information of China (English)

    蒋媛

    2014-01-01

    Fusion problem for multi-modality medical images, presents an image fusion method based on Nonsubsampled Con⁃tourlet (NSCT) transform and human visual system (HVC) is . After transformation of the source image NSCT decomposition visibility measure low frequency based on the selection , the high frequency portion of the texture information based on the selec⁃tion , and then using the inverse transform to obtain a final blend NSCT image . Experimental results show that:the proposed fu⁃sion method can improve the spatial resolution , while maintaining spectral information and improved. Both visual and quantita⁃tive analysis in comparison with the conventional method , a good advantage.%针对多模态医学图像融合问题,提出一种基于非下采样Contourlet (NSCT)变换与人眼视觉特性(HVC)的图像融合方法。NSCT变换对源图像的分解后,低频部分基于可见性测度的选择,高频部分基于纹理信息的选择,然后采用NSCT逆变换获得最终融合图像。实验结果表明:所提出的融合方法可以提高空间分辨率,同时保持光谱信息,并有改善。无论是在视觉效果和定量分析与传统的方法相比较,有好的优越性。

  7. Estimating fractal dimension of medical images

    Science.gov (United States)

    Penn, Alan I.; Loew, Murray H.

    1996-04-01

    Box counting (BC) is widely used to estimate the fractal dimension (fd) of medical images on the basis of a finite set of pixel data. The fd is then used as a feature to discriminate between healthy and unhealthy conditions. We show that BC is ineffective when used on small data sets and give examples of published studies in which researchers have obtained contradictory and flawed results by using BC to estimate the fd of data-limited medical images. We present a new method for estimating fd of data-limited medical images. In the new method, fractal interpolation functions (FIFs) are used to generate self-affine models of the underlying image; each model, upon discretization, approximates the original data points. The fd of each FIF is analytically evaluated. The mean of the fds of the FIFs is the estimate of the fd of the original data. The standard deviation of the fds of the FIFs is a confidence measure of the estimate. The goodness-of-fit of the discretized models to the original data is a measure of self-affinity of the original data. In a test case, the new method generated a stable estimate of fd of a rib edge in a standard chest x-ray; box counting failed to generate a meaningful estimate of the same image.

  8. CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

    Science.gov (United States)

    Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

    2013-12-31

    Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

  9. Aliphatic polyesters for medical imaging and theranostic applications.

    Science.gov (United States)

    Nottelet, Benjamin; Darcos, Vincent; Coudane, Jean

    2015-11-01

    Medical imaging is a cornerstone of modern medicine. In that context the development of innovative imaging systems combining biomaterials and contrast agents (CAs)/imaging probes (IPs) for improved diagnostic and theranostic applications focuses intense research efforts. In particular, the classical aliphatic (co)polyesters poly(lactide) (PLA), poly(lactide-co-glycolide) (PLGA) and poly(ɛ-caprolactone) (PCL), attract much attention due to their long track record in the medical field. This review aims therefore at providing a state-of-the-art of polyester-based imaging systems. In a first section a rapid description of the various imaging modalities, including magnetic resonance imaging (MRI), optical imaging, computed tomography (CT), ultrasound (US) and radionuclide imaging (SPECT, PET) will be given. Then, the two main strategies used to combine the CAs/IPs and the polyesters will be discussed. In more detail we will first present the strategies relying on CAs/IPs encapsulation in nanoparticles, micelles, dendrimers or capsules. We will then present chemical modifications of polyesters backbones and/or polyester surfaces to yield macromolecular imaging agents. Finally, opportunities offered by these innovative systems will be illustrated with some recent examples in the fields of cell labeling, diagnostic or theranostic applications and medical devices. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Medical Image Classification Using Genetic Optimized Elman Network

    Directory of Open Access Journals (Sweden)

    T. Baranidharan

    2012-01-01

    Full Text Available Problem statement: Advancements in the internet and digital images have resulted in a huge database of images. Most of the current search engines found in the web depends only on images that can be retrieved using metadata, which generates a lot of unwanted results in the results got. Content-Based Image Retrieval (CBIR system is the utilization of computer vision techniques in the predicament of image retrieval. In other words, it is used for searching and retrieving of the right digital image among a huge database using query image. CBIR finds extensive applications in the field of medicine as it helps medical professionals in diagnosis and plan treatment. Approach: Various methods have been proposed for CBIR using the images low level features like histogram, color, texture and shape. Similarly various classification algorithms like Naive Bayes classifier, Support Vector Machine, Decision tree induction algorithms and Neural Network based classifiers have been studied extensively. In this study it is proposed to extract global features using Hilbert Transform (HT, select features based on the correlation of the extracted vectors with respect to the class label and propose a enhanced Elman Neural Network Genetic Algorithm Optimized Elman (GAOE Neural Network. Results and Conclusion: The proposed method for feature extraction and the classification algorithm was tested on a dataset consisting of 180 medical images. The classification accuracy of 92.22% was obtained in the proposed method.

  11. Medical Image distribution and visualization in a hospital using CORBA.

    Science.gov (United States)

    Moreno, Ramon Alfredo; do Santos, Marcelo; Bertozzo, Nivaldo; de Sa Rebelo, Marina; Furuie, Sergio S; Gutierrez, Marco A

    2008-01-01

    In this work it is presented the solution adopted by the Heart Institute (InCor) of Sao Paulo for medical image distribution and visualization inside the hospital's intranet as part of the PACS system. A CORBA-based image server was developed to distribute DICOM images across the hospital together with the images' report. The solution adopted allows the decoupling of the server implementation and the client. This gives the advantage of reusing the same solution in different implementation sites. Currently, the PACS system is being used on two different hospitals each one with three different environments: development, prototype and production.

  12. Medical Imaging Informatics: Towards a Personalized Computational Patient.

    Science.gov (United States)

    Ayache, N

    2016-05-20

    Medical Imaging Informatics has become a fast evolving discipline at the crossing of Informatics, Computational Sciences, and Medicine that is profoundly changing medical practices, for the patients' benefit.

  13. Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

    Institute of Scientific and Technical Information of China (English)

    Mahsa Chitsaz; Chaw Seng Woo

    2011-01-01

    Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.

  14. Flexible medical image management using service-oriented architecture.

    Science.gov (United States)

    Shaham, Oded; Melament, Alex; Barak-Corren, Yuval; Kostirev, Igor; Shmueli, Noam; Peres, Yardena

    2012-01-01

    Management of medical images increasingly involves the need for integration with a variety of information systems. To address this need, we developed Content Management Offering (CMO), a platform for medical image management supporting interoperability through compliance with standards. CMO is based on the principles of service-oriented architecture, implemented with emphasis on three areas: clarity of business process definition, consolidation of service configuration management, and system scalability. Owing to the flexibility of this platform, a small team is able to accommodate requirements of customers varying in scale and in business needs. We describe two deployments of CMO, highlighting the platform's value to customers. CMO represents a flexible approach to medical image management, which can be applied to a variety of information technology challenges in healthcare and life sciences organizations.

  15. Oral antioxidants for radioprotection during medical imaging examinations

    Science.gov (United States)

    Velauthapillai, Nivethan

    The oncogenic effect of ionizing radiation (IR) is clearly established and occurs in response to DNA damage. Many diagnostic imaging exams make use of IR and the oncogenic risk of IR-based imaging has been calculated. We hypothesized that the DNA damage sustained from IR exposure during medical imaging exams could be reduced by pre-medicating patients with antioxidants. First, we tested and validated a method for measuring DNA double-strand breaks (DSBs) in peripheral blood mononuclear cells (PBMCs) exposed to low doses of ionizing radiation. Afterwards, we conducted a pilot clinical study in which we administered oral antioxidants to patients undergoing bone scans, prior to radiotracer injection. We showed that oral antioxidant pre-medication reduced the number of DSBs in PBMCs induced by radiotracer injection. Our study shows proof-of-principle for this simple and inexpensive approach to radioprotection in the clinical setting.

  16. Creating New Medical Ontologies for Image Annotation A Case Study

    CERN Document Server

    Stanescu, Liana; Brezovan, Marius; Mihai, Cristian Gabriel

    2012-01-01

    Creating New Medical Ontologies for Image Annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. The original algorithms proposed by authors are compared with other efficient similar algorithms. In addition, the authors treat the problem of creating ontologies in an automatic way, starting from Medical Subject Headings (MESH). They have presented some efficient and relevant annotation models and also the basics of the annotation model used by the proposed system: Cross Media Relevance Models. Based on a text query the system will retrieve the images that contain objects described by the keywords.

  17. Wavelength conversion based spectral imaging

    DEFF Research Database (Denmark)

    Dam, Jeppe Seidelin

    There has been a strong, application driven development of Si-based cameras and spectrometers for imaging and spectral analysis of light in the visible and near infrared spectral range. This has resulted in very efficient devices, with high quantum efficiency, good signal to noise ratio and high...... resolution for this spectral region. Today, an increasing number of applications exists outside the spectral region covered by Si-based devices, e.g. within cleantech, medical or food imaging. We present a technology based on wavelength conversion which will extend the spectral coverage of state of the art...... visible or near infrared cameras and spectrometers to include other spectral regions of interest....

  18. HEP technologies to address medical imaging challenges

    CERN Document Server

    CERN. Geneva

    2016-01-01

    Developments in detector technologies aimed at solving challenges in present and future CERN experiments, particularly at the LHC, have triggered exceptional advances in the performance of medical imaging devices, allowing for a spectacular progress in in-vivo molecular imaging procedures, which are opening the way for tailored therapies of major diseases. This talk will briefly review the recent history of this prime example of technology transfer from HEP experiments to society, will describe the technical challenges being addressed by some ongoing projects, and will present a few new ideas for further developments and their foreseeable impact.

  19. Lossless Compression of Medical Images Using 3D Predictors.

    Science.gov (United States)

    Lucas, Luis; Rodrigues, Nuno; Cruz, Luis; Faria, Sergio

    2017-06-09

    This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3D-MRP, is based on the principle of minimum rate predictors (MRP), which is one of the state-of-the-art lossless compression technologies, presented in the data compression literature. The main features of the proposed method include the use of 3D predictors, 3D-block octree partitioning and classification, volume-based optimisation and support for 16 bit-depth images. Experimental results demonstrate the efficiency of the 3D-MRP algorithm for the compression of volumetric sets of medical images, achieving gains above 15% and 12% for 8 bit and 16 bit-depth contents, respectively, when compared to JPEG-LS, JPEG2000, CALIC, HEVC, as well as other proposals based on MRP algorithm.

  20. Deep Learning in Medical Imaging: General Overview

    Science.gov (United States)

    Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

    2017-01-01

    The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

  1. Deep Learning in Medical Imaging: General Overview.

    Science.gov (United States)

    Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

    2017-01-01

    The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

  2. Deep learning in medical imaging: General overview

    Energy Technology Data Exchange (ETDEWEB)

    Lee, June Goo; Jun, Sang Hoon; Cho, Young Won; Lee, Hyun Na; KIm, Guk Bae; Seo, Joon Beom; Kim, Nam Kug [University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of)

    2017-08-01

    The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and health care, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

  3. Medical Image Registration and Surgery Simulation

    DEFF Research Database (Denmark)

    Bro-Nielsen, Morten

    1996-01-01

    This thesis explores the application of physical models in medical image registration and surgery simulation. The continuum models of elasticity and viscous fluids are described in detail, and this knowledge is used as a basis for most of the methods described here. Real-time deformable models......, and the use of selective matrix vector multiplication. Fluid medical image registration A new and faster algorithm for non-rigid registration using viscous fluid models is presented. This algorithm replaces the core part of the original algorithm with multi-resolution convolution using a new filter, which...... for surgery simulation Real-time deformable models, using finite element models of linear elasticity, have been developed for surgery simulation. The time consumption of the finite element method is reduced dramaticly, by the use of condensation techniques, explicit inversion of the stiffness matrix...

  4. CERN crystals used in medical imaging

    CERN Multimedia

    Maximilien Brice

    2004-01-01

    This crystal is a type of material known as a scintillator. When a high energy charged particle or photon passes through a scintillator it glows. These materials are widely used in particle physics for particle detection, but their uses are being realized in further fields, such as Positron Emission Tomography (PET), an area of medical imaging that monitors the regions of energy use in the body.

  5. Survey on Digital Watermarking on Medical Images

    Directory of Open Access Journals (Sweden)

    Kavitha K J

    2013-12-01

    Full Text Available The rapid growth in information and communication technologies has advances the medical data management systems immensely. In this regard, many different techniques and also the advanced equipment like Magnetic Resonance Imaging (MRI Scanner, Computer Tomography (CT scanner, Positron Emission of Tomography (PET, mammography, ultrasound, radiography etc. are used. Nowadays there is a rise of various diseases, for which several diagnoses are insufficient; therefore to achieve a correct diagnostic, there is need to exchange the data over Internet, but the main problem is while exchanging the data over Internet, we need to maintain their authenticity, integrity and confidentiality. Therefore, we need a system for effective storage, transmission, controlled manipulation and access of medical data keeping its authenticity, integrity and confidentiality. In this article, we discuss various water marking techniques used for effective storage, transmission, controlled manipulation and access of medical data keeping its authenticity, integrity and confidentiality.

  6. 基于ICA的X射线医学图像目标提取%Object Separation from Medical X-Ray Images Based on ICA

    Institute of Scientific and Technical Information of China (English)

    李艳; 喻春雨; 缪亚健; 费彬; 庄凤云

    2015-01-01

    X 射线医学成像能观察到患者体内病变组织,对医学诊断有重要参考价值。针对传统医学 X 射线图像噪声强、层次感差和器官组织重叠的问题,提出利用多能谱 X 射线成像结合独立成分分析(independent component analysis,ICA)进行图像去噪和目标提取。首先 ICA 结合稀疏编码收缩法对图像降噪预处理以保证目标提取精度;然后根据图像中各目标组成特性,分离图像中每个像素对应的目标厚度矩阵;最后 ICA以盲分离理论获得收敛矩阵重建出目标对象。在 ICA 算法中,借助于主观评价标准,发现当收敛次数大于40时目标分离成功;当幅值尺度在[25,45]区间内,目标图像对比度高且失真较小。同时,通过观测实验得到的三维峰值信噪比图表明:ICA 算法中收敛次数和幅值对图像质量有较大影响,当重建图像的对比度和边缘信息均达到较好效果时,收敛次数与幅值为85和35。%X-ray medical image can examine diseased tissue of patients and has important reference value for medical diagnosis. With the problems that traditional X-ray images have noise,poor level sense and blocked aliasing organs,this paper proposes a method for the introduction of multi-spectrum X-ray imaging and independent component analysis (ICA)algorithm to separate the target object.Firstly image de-noising preprocessing ensures the accuracy of target extraction based on independent compo-nent analysis and sparse code shrinkage.Then according to the main proportion of organ in the images,aliasing thickness matrix of each pixel was isolated.Finally independent component analysis obtains convergence matrix to reconstruct the target object with blind separation theory.In the ICA algorithm,it found that when the number is more than 40,the target objects separate successfully with the aid of subjective evaluation standard.And when the amplitudes of the scale are in the [25

  7. Novel medical imaging technologies for disease diagnosis and treatment

    Science.gov (United States)

    Olego, Diego

    2009-03-01

    New clinical approaches for disease diagnosis, treatment and monitoring will rely on the ability of simultaneously obtaining anatomical, functional and biological information. Medical imaging technologies in combination with targeted contrast agents play a key role in delivering with ever increasing temporal and spatial resolution structural and functional information about conditions and pathologies in cardiology, oncology and neurology fields among others. This presentation will review the clinical motivations and physics challenges in on-going developments of new medical imaging techniques and the associated contrast agents. Examples to be discussed are: *The enrichment of computer tomography with spectral sensitivity for the diagnosis of vulnerable sclerotic plaque. *Time of flight positron emission tomography for improved resolution in metabolic characterization of pathologies. *Magnetic particle imaging -a novel imaging modality based on in-vivo measurement of the local concentration of iron oxide nano-particles - for blood perfusion measurement with better sensitivity, spatial resolution and 3D real time acquisition. *Focused ultrasound for therapy delivery.

  8. Teaching medical anatomy: what is the role of imaging today?

    Science.gov (United States)

    Grignon, Bruno; Oldrini, Guillaume; Walter, Frédéric

    2016-03-01

    Medical anatomy instruction has been an important issue of debate for many years and imaging anatomy has become an increasingly important component in the field, the role of which has not yet been clearly defined. The aim of the paper was to assess the current deployment of medical imaging in the teaching of anatomy by means of a review of the literature. A systematic search was performed using the electronic database PubMed, ScienceDirect and various publisher databases, with combinations of the relevant MeSH terms. A manual research was added. In most academic curricula, imaging anatomy has been integrated as a part of anatomical education, taught using a very wide variety of strategies. Considerable variation in the time allocation, content and delivery of medical imaging in teaching human anatomy was identified. Given this considerable variation, an objective assessment remains quite difficult. In most publications, students' perceptions regarding anatomical courses including imaging anatomy were investigated by means of questionnaires and, regardless of the method of teaching, it was globally concluded that imaging anatomy enhanced the quality and efficiency of instruction in human anatomy. More objective evaluation based on an increase in students' performance on course examinations or on specific tests performed before and after teaching sessions showed positive results in numerous cases, while mixed results were also indicated by other studies. A relative standardization could be useful in improving the teaching of imaging anatomy, to facilitate its assessment and reinforce its effectiveness.

  9. MIRMAID: A Content Management System for Medical Image Analysis Research.

    Science.gov (United States)

    Korfiatis, Panagiotis D; Kline, Timothy L; Blezek, Daniel J; Langer, Steve G; Ryan, William J; Erickson, Bradley J

    2015-01-01

    Today, a typical clinical study can involve thousands of participants, with imaging data acquired over several time points across multiple institutions. The additional associated information (metadata) accompanying these data can cause data management to be a study-hindering bottleneck. Consistent data management is crucial for large-scale modern clinical imaging research studies. If the study is to be used for regulatory submissions, such systems must be able to meet regulatory compliance requirements for systems that manage clinical image trials, including protecting patient privacy. Our aim was to develop a system to address these needs by leveraging the capabilities of an open-source content management system (CMS) that has a highly configurable workflow; has a single interface that can store, manage, and retrieve imaging-based studies; and can handle the requirement for data auditing and project management. We developed a Web-accessible CMS for medical images called Medical Imaging Research Management and Associated Information Database (MIRMAID). From its inception, MIRMAID was developed to be highly flexible and to meet the needs of diverse studies. It fulfills the need for a complete system for medical imaging research management.

  10. An adaptive nonlocal means scheme for medical image denoising

    Science.gov (United States)

    Thaipanich, Tanaphol; Kuo, C.-C. Jay

    2010-03-01

    Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this work, we investigate an adaptive denoising scheme based on the nonlocal (NL)-means algorithm for medical imaging applications. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means (ANL-means) denoising scheme has three unique features. First, it employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique for robust classification of blocks in noisy images. Second, the local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm is adopted for better similarity matching. Experimental results from both additive white Gaussian noise (AWGN) and Rician noise are given to demonstrate the superior performance of the proposed ANL denoising technique over various image denoising benchmarks in term of both PSNR and perceptual quality comparison.

  11. Watermarking of ultrasound medical images in teleradiology using compressed watermark.

    Science.gov (United States)

    Badshah, Gran; Liew, Siau-Chuin; Zain, Jasni Mohamad; Ali, Mushtaq

    2016-01-01

    The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.

  12. Medical image segmentation using level set and watershed transform

    Science.gov (United States)

    Zhu, Fuping; Tian, Jie

    2003-07-01

    One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation algorithm is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm"s speed improves greatly. Moreover, the speed function is redefined based on the statistical similarity degree of the nearby regions. We also extend our algorithm to 3D circumstance and segment medical image series. Experiments show that the algorithm can fast and accurately obtain segmentation results of medical images.

  13. Fuzzy Wavenet (FWN classifier for medical images

    Directory of Open Access Journals (Sweden)

    Entather Mahos

    2005-01-01

    Full Text Available The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success. In this work we proposed a fuzzy wavenet network (FWN, which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images. We have known that the wavelet transformation is more accurate in small dimension problem. But image processing is large dimension problem then we used neural network. Results are presented on the application on the three layer fuzzy wavenet to vision system. They demonstrate a considerable improvement in performance by proposed two table’s rule for fuzzy and deterministic dilation and translation in wavelet transformation techniques.

  14. Multimodal Medical Image Fusion by Adaptive Manifold Filter

    Directory of Open Access Journals (Sweden)

    Peng Geng

    2015-01-01

    Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

  15. An Improved Medical Image Segmentation Algorithm Based on Visual Perception Model%一种基于视觉感知的复合医学图像分割算法

    Institute of Scientific and Technical Information of China (English)

    刘再涛; 魏本征; 柳澄

    2011-01-01

    To improve the visual effect of the medical image segmentation, an improved medical image segmentation algorithm was presented based on the visual perception delaminated characters. Based the character of medical image, the local region features were described firstly, and then the local texture feature was designed consistently with visual perception of different image region. The Fuzzy-ART neural network as pixels classifier was used to segment the medical image based on the layer feature functions. The experiment results showed that this method effectively solved the local information and distribution of image local features, and achieved a good segmentation and visual effect.%为提高医学图像分割的视觉效果,依据人类视觉感知的分层特性,提出了一种新的复合医学图像分割方法.该方法通过提取医学图像的底层特征,利用Fuzzy-ART神经网络作为像素的分类器,对医学图像进行连续两次分割.实验结果表明,该医学图像分割方法能有效地解决局部信息与整体分布边缘淡化等相关问题,达到良好的分割视觉效果.

  16. Quantification of Structure from Medical Images

    DEFF Research Database (Denmark)

    Qazi, Arish Asif

    In this thesis, we present automated methods that quantify information from medical images; information that is intended to assist and enable clinicians gain a better understanding of the underlying pathology. The first part of the thesis presents methods that analyse the articular cartilage......, segmented from MR images of the knee. The cartilage tissue is considered to be a key determinant in the onset of Osteoarthritis (OA), a degenerative joint disease, with no known cure. The primary obstacle has been the dependence on radiography as the ‘gold standard’ for detecting the manifestation...... of cartilage changes. This is an indirect assessment, since the cartilage is not visible on xrays. We propose Cartilage Homogeneity, quantified from MR images, as a marker for detection of the early biochemical alterations in the articular cartilage. We show that homogeneity provides accuracy, sensitivity...

  17. Multiphase Systems for Medical Image Region Classification

    Science.gov (United States)

    Garamendi, J. F.; Malpica, N.; Schiavi, E.

    2009-05-01

    Variational methods for region classification have shown very promising results in medical image analysis. The Chan-Vese model is one of the most popular methods, but its numerical resolution is slow and it has serious drawbacks for most multiphase applications. In this work, we extend the link, stablished by Chambolle, between the two classes binary Chan-Vese model and the Rudin-Osher-Fatemi (ROF) model to a multiphase four classes minimal partition problem. We solve the ROF image restoration model and then we threshold the image by means of a genetic algorithm. This strategy allows for a more efficient algorithm due to the fact that only one well posed elliptic problem is solved instead of solving the coupled parabolic equations arising in the original multiphase Chan-Vese model.

  18. Survey: interpolation methods in medical image processing.

    Science.gov (United States)

    Lehmann, T M; Gönner, C; Spitzer, K

    1999-11-01

    Image interpolation techniques often are required in medical imaging for image generation (e.g., discrete back projection for inverse Radon transform) and processing such as compression or resampling. Since the ideal interpolation function spatially is unlimited, several interpolation kernels of finite size have been introduced. This paper compares 1) truncated and windowed sinc; 2) nearest neighbor; 3) linear; 4) quadratic; 5) cubic B-spline; 6) cubic; g) Lagrange; and 7) Gaussian interpolation and approximation techniques with kernel sizes from 1 x 1 up to 8 x 8. The comparison is done by: 1) spatial and Fourier analyses; 2) computational complexity as well as runtime evaluations; and 3) qualitative and quantitative interpolation error determinations for particular interpolation tasks which were taken from common situations in medical image processing. For local and Fourier analyses, a standardized notation is introduced and fundamental properties of interpolators are derived. Successful methods should be direct current (DC)-constant and interpolators rather than DC-inconstant or approximators. Each method's parameters are tuned with respect to those properties. This results in three novel kernels, which are introduced in this paper and proven to be within the best choices for medical image interpolation: the 6 x 6 Blackman-Harris windowed sinc interpolator, and the C2-continuous cubic kernels with N = 6 and N = 8 supporting points. For quantitative error evaluations, a set of 50 direct digital X rays was used. They have been selected arbitrarily from clinical routine. In general, large kernel sizes were found to be superior to small interpolation masks. Except for truncated sinc interpolators, all kernels with N = 6 or larger sizes perform significantly better than N = 2 or N = 3 point methods (p cubic 6 x 6 interpolator with continuous second derivatives, as defined in (24), can be recommended for most common interpolation tasks. It appears to be the fastest

  19. A Routing Mechanism for Cloud Outsourcing of Medical Imaging Repositories.

    Science.gov (United States)

    Godinho, Tiago Marques; Viana-Ferreira, Carlos; Bastião Silva, Luís A; Costa, Carlos

    2016-01-01

    Web-based technologies have been increasingly used in picture archive and communication systems (PACS), in services related to storage, distribution, and visualization of medical images. Nowadays, many healthcare institutions are outsourcing their repositories to the cloud. However, managing communications between multiple geo-distributed locations is still challenging due to the complexity of dealing with huge volumes of data and bandwidth requirements. Moreover, standard methodologies still do not take full advantage of outsourced archives, namely because their integration with other in-house solutions is troublesome. In order to improve the performance of distributed medical imaging networks, a smart routing mechanism was developed. This includes an innovative cache system based on splitting and dynamic management of digital imaging and communications in medicine objects. The proposed solution was successfully deployed in a regional PACS archive. The results obtained proved that it is better than conventional approaches, as it reduces remote access latency and also the required cache storage space.

  20. Imaging requirements for medical applications of additive manufacturing.

    Science.gov (United States)

    Huotilainen, Eero; Paloheimo, Markku; Salmi, Mika; Paloheimo, Kaija-Stiina; Björkstrand, Roy; Tuomi, Jukka; Markkola, Antti; Mäkitie, Antti

    2014-02-01

    Additive manufacturing (AM), formerly known as rapid prototyping, is steadily shifting its focus from industrial prototyping to medical applications as AM processes, bioadaptive materials, and medical imaging technologies develop, and the benefits of the techniques gain wider knowledge among clinicians. This article gives an overview of the main requirements for medical imaging affected by needs of AM, as well as provides a brief literature review from existing clinical cases concentrating especially on the kind of radiology they required. As an example application, a pair of CT images of the facial skull base was turned into 3D models in order to illustrate the significance of suitable imaging parameters. Additionally, the model was printed into a preoperative medical model with a popular AM device. Successful clinical cases of AM are recognized to rely heavily on efficient collaboration between various disciplines - notably operating surgeons, radiologists, and engineers. The single main requirement separating tangible model creation from traditional imaging objectives such as diagnostics and preoperative planning is the increased need for anatomical accuracy in all three spatial dimensions, but depending on the application, other specific requirements may be present as well. This article essentially intends to narrow the potential communication gap between radiologists and engineers who work with projects involving AM by showcasing the overlap between the two disciplines.

  1. A study for watermark methods appropriate to medical images.

    Science.gov (United States)

    Cho, Y; Ahn, B; Kim, J S; Kim, I Y; Kim, S I

    2001-06-01

    The network system, including the picture archiving and communication system (PACS), is essential in hospital and medical imaging fields these days. Many medical images are accessed and processed on the web, as well as in PACS. Therefore, any possible accidents caused by the illegal modification of medical images must be prevented. Digital image watermark techniques have been proposed as a method to protect against illegal copying or modification of copyrighted material. Invisible signatures made by a digital image watermarking technique can be a solution to these problems. However, medical images have some different characteristics from normal digital images in that one must not corrupt the information contained in the original medical images. In this study, we suggest modified watermark methods appropriate for medical image processing and communication system that prevent clinically important data contained in original images from being corrupted.

  2. Do medical images aid understanding and recall of medical information? An experimental study comparing the experience of viewing no image, a 2D medical image and a 3D medical image alongside a diagnosis.

    Science.gov (United States)

    Phelps, Emma Elizabeth; Wellings, Richard; Griffiths, Frances; Hutchinson, Charles; Kunar, Melina

    2017-06-01

    This study compared the experience of viewing 3D medical images, 2D medical images and no image presented alongside a diagnosis. We conducted two laboratory experiments, each with 126 healthy participants. Participants heard three diagnoses; one accompanied by 3D medical images, one accompanied by 2D medical images and one with no image. Participants completed a questionnaire after each diagnosis rating their experience. In Experiment 2, half of the participants were informed that image interpretation can be susceptible to errors. Participants preferred to view 3D images alongside a diagnosis (pmedical images may aid patient understanding, recall and trust in medical information. Medical images may be a powerful resource for patients that could be utilised by clinicians during consultations. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  3. Dictionary Based Image Segmentation

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2015-01-01

    We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets...

  4. Lossless wavelet compression on medical image

    Science.gov (United States)

    Zhao, Xiuying; Wei, Jingyuan; Zhai, Linpei; Liu, Hong

    2006-09-01

    An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS), as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery, while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image, thus facilitating accurate diagnosis, of course at the expense of higher bit rates, i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization. Wavelet coding, neural networks, and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1, or even more), they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image, but the achievable compression ratios are only of the order 2:1, up to 4:1. In our paper, we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time, we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance, so that all the low rate codes are included at the beginning of the bit stream. Typically, the encoding process stops when the target bit rate is met. Similarly, the decoder can interrupt the decoding process at any point in the bit stream, and still reconstruct the image. Therefore, a compression scheme generating an embedded code can

  5. Segmentation of Medical Image using Clustering and Watershed Algorithms

    OpenAIRE

    M. C.J. Christ; R.M.S Parvathi

    2011-01-01

    Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to...

  6. A New Hybrid Fuzzy Intelligent Filter for Medical Image Noise Reduction

    OpenAIRE

    Somaye Aliakbari Dehkordi; Mohammad Ghasemzadeh; Vali Derhami

    2014-01-01

    Medical imaging comprises different imaging modalities and processes to image human body for diagnostic and treatment purposes and, therefore has an important role in the improvement of public health in all population groups. In this paper, we present an intelligent hybrid noise reduction filter which is based on Neuro-Fuzzy systems. It is especially beneficial in medical image noise reduction. First stage we feed the input image into four general noise reduction filters in parallel. These ge...

  7. Open source tools for standardized privacy protection of medical images

    Science.gov (United States)

    Lien, Chung-Yueh; Onken, Michael; Eichelberg, Marco; Kao, Tsair; Hein, Andreas

    2011-03-01

    In addition to the primary care context, medical images are often useful for research projects and community healthcare networks, so-called "secondary use". Patient privacy becomes an issue in such scenarios since the disclosure of personal health information (PHI) has to be prevented in a sharing environment. In general, most PHIs should be completely removed from the images according to the respective privacy regulations, but some basic and alleviated data is usually required for accurate image interpretation. Our objective is to utilize and enhance these specifications in order to provide reliable software implementations for de- and re-identification of medical images suitable for online and offline delivery. DICOM (Digital Imaging and Communications in Medicine) images are de-identified by replacing PHI-specific information with values still being reasonable for imaging diagnosis and patient indexing. In this paper, this approach is evaluated based on a prototype implementation built on top of the open source framework DCMTK (DICOM Toolkit) utilizing standardized de- and re-identification mechanisms. A set of tools has been developed for DICOM de-identification that meets privacy requirements of an offline and online sharing environment and fully relies on standard-based methods.

  8. Medical image information system 2001. Development of the medical image information system to risk management- Medical exposure management

    Energy Technology Data Exchange (ETDEWEB)

    Kuranishi, Makoto; Kumagai, Michitomo; Shintani, Mitsuo [Toyama Medical and Pharmaceutical Univ. (Japan). Hospital

    2000-12-01

    This paper discusses the methods and systems for optimizing the following supplements 10 and 17 for national health and medical care. The supplements 10 and 17 of DICOM (digital imaging and communications in medicine) system, which is now under progress for the purpose to keep compatibility within medical image information system as an international standard, are important for making the cooperation between HIS (hospital information system)/RIS (radiation information system) and modality (imaging instruments). Supplement 10 concerns the system to send the information of patients and their orders through HIS/RIS to modality and 17, the information of modality performed procedure step (MPPS) to HIS/RIS. The latter defines to document patients' exposure, a part of which has not been recognized in Japan. Thus the medical information system can be useful for risk-management of medical exposure in future. (K.H.)

  9. [Tattoos and medical imaging: issues and myths].

    Science.gov (United States)

    Kluger, Nicolas

    2014-05-01

    Tattooing is characterized by the introduction in the dermis of exogenous pigments to obtain a permanent design. Whether it is a traditional tattoo applied on the skin or a cosmetic one (permanent make-up), its prevalence has boomed for the past 20 years. The increased prevalence of tattooed patients along with medical progresses, in the field of therapeutics or diagnostic means have lead to the discovery of "new" complications and unexpected issues. Medical imaging world has also been affected by the tattoo craze. It has been approximately 20 years when the first issues related to tattooing and permanent make-up aroused. However, cautions and questions as well as anecdotal severe case reports have sometimes led to an over-exaggerated response by some physicians such as the systematic avoidance of RMN imaging for tattooed individuals. This review is intended to summarize the risks but also the "myths" associated with tattoo in the daily practice of the radiologist for RMN, CT scan, mammography, Pet-scan and ultrasound imaging.

  10. Mining knowledge in medical image databases

    Science.gov (United States)

    Perner, Petra

    2000-04-01

    Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers. That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. In this paper, we present our work on data mining in medical picture archiving systems. We use decision tree induction in order to learn the knowledge for computer- assisted image analysis. We are applying our method to interpretation of x-ray images for lung cancer diagnosis. We are describing our methodology on how to perform data mining on picture archiving systems and our tool for data mining. Results are given. The method has shown very good results so that we are going on to apply it to other medical image diagnosis tasks such as lymph node diagnosis in MRI and investigation of breast MRI.

  11. Gadgetron: an open source framework for medical image reconstruction.

    Science.gov (United States)

    Hansen, Michael Schacht; Sørensen, Thomas Sangild

    2013-06-01

    This work presents a new open source framework for medical image reconstruction called the "Gadgetron." The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or "Gadgets" from raw data to reconstructed images. The data processing pipeline is configured dynamically at run-time based on an extensible markup language configuration description. The framework promotes reuse and sharing of reconstruction modules and new Gadgets can be added to the Gadgetron framework through a plugin-like architecture without recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its application to Cartesian and non-Cartesian parallel magnetic resonance imaging.

  12. Cerenkov luminescence imaging of medical isotopes.

    Science.gov (United States)

    Ruggiero, Alessandro; Holland, Jason P; Lewis, Jason S; Grimm, Jan

    2010-07-01

    The development of novel multimodality imaging agents and techniques represents the current frontier of research in the field of medical imaging science. However, the combination of nuclear tomography with optical techniques has yet to be established. Here, we report the use of the inherent optical emissions from the decay of radiopharmaceuticals for Cerenkov luminescence imaging (CLI) of tumors in vivo and correlate the results with those obtained from concordant immuno-PET studies. In vitro phantom studies were used to validate the visible light emission observed from a range of radionuclides including the positron emitters (18)F, (64)Cu, (89)Zr, and (124)I; beta-emitter (131)I; and alpha-particle emitter (225)Ac for potential use in CLI. The novel radiolabeled monoclonal antibody (89)Zr-desferrioxamine B [DFO]-J591 for immuno-PET of prostate-specific membrane antigen (PSMA) expression was used to coregister and correlate the CLI signal observed with the immuno-PET images and biodistribution studies. Phantom studies confirmed that Cerenkov radiation can be observed from a range of positron-, beta-, and alpha-emitting radionuclides using standard optical imaging devices. The change in light emission intensity versus time was concordant with radionuclide decay and was also found to correlate linearly with both the activity concentration and the measured PET signal (percentage injected dose per gram). In vivo studies conducted in male severe combined immune deficient mice bearing PSMA-positive, subcutaneous LNCaP tumors demonstrated that tumor-specific uptake of (89)Zr-DFO-J591 could be visualized by both immuno-PET and CLI. Optical and immuno-PET signal intensities were found to increase over time from 24 to 96 h, and biodistribution studies were found to correlate well with both imaging modalities. These studies represent the first, to our knowledge, quantitative assessment of CLI for measuring radiotracer uptake in vivo. Many radionuclides common to both nuclear

  13. The fuzzy Hough Transform-feature extraction in medical images

    Energy Technology Data Exchange (ETDEWEB)

    Philip, K.P.; Dove, E.L.; Stanford, W.; Chandran, K.B. (Univ. of Iowa, Iowa City, IA (United States)); McPherson, D.D.; Gotteiner, N.L. (Northwestern Univ., Chicago, IL (United States). Dept. of Internal Medicine)

    1994-06-01

    Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final estimate of the true borders with other image processing techniques. The authors present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough Transform algorithm as part of a larger procedures to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications.

  14. Communication software for physicians' workstations supporting medical imaging services

    Science.gov (United States)

    Orphanos, George; Kanellopoulos, Dimitris; Koubias, Stavros

    1993-09-01

    This paper describes a software communication architecture for medical imaging services. This work aims to provide to the physician the communication facilities to access and track a patient's record or to retrieve medical images from a remote database. The proposed architecture is comprised of a communication protocol and an application programming interface (API). The implemented protocol, namely the Telemedicine Network Services (TNS) protocol, has been designed in agreement with Open System Interconnection (OSI) upper layer protocols already standardized. Based on this concept an OSI-like interface has been developed capable of providing application services to the application developer, and thus facilitating the writing of medical application. TNS protocol has been implemented on top of TCP/IP communication protocols, by implementing OSI presentation and application services on top of the Transport Service Access Point (TSAP) which is provided by the socket abstraction on top of the TCP.

  15. Compressive Deconvolution in Medical Ultrasound Imaging.

    Science.gov (United States)

    Chen, Zhouye; Basarab, Adrian; Kouamé, Denis

    2016-03-01

    The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to US wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this paper, we propose a novel framework, named compressive deconvolution, that reconstructs enhanced RF images from compressed measurements. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of our approach is the joint data volume reduction and image quality improvement. The proposed optimization method, based on the Alternating Direction Method of Multipliers, is evaluated on both simulated and in vivo data.

  16. [Competence based medical education].

    Science.gov (United States)

    Bernabó, Jorge G; Buraschi, Jorge; Olcese, Juan; Buraschi, María; Duro, Eduardo

    2007-01-01

    The strategy of curriculum planning in the majority of the Schools of Medicine has shifted, in the past years, from curriculum models based in contents to outcome oriented curricula. Coincidently the interest in defining and evaluating the clinical competences that a graduate must have has grown. In our country, and particularly in the Associated Hospitals belonging to the Unidad Regional de Enseñanza IV of the UBA School of Medicine, evidence has been gathered showing that the acquisition of clinical competences during the grade is in general insufficient. The foundations and characteristics of PREM (Programa de Requisitos Esenciales Mínimos) are described. PREM is a tool to promote the apprenticeship of abilities and necessary skills for the practice of medicine. The objective of the program is to promote the apprenticeship of a well defined list of core competences considered indispensable for a general practitioner. An outcome oriented curriculum with a clear definition of the expected knowledge, skills and attitudes of a graduate of the programme, the promotion of learning experiences centered in the practice and evaluation tools based in direct observation of the student's performance should contribute to close the gap between what the Medicine Schools traditionally teach and evaluate, and what the doctor needs to know and needs to do to perform correctly its profession.

  17. Microfluidics-based single-step preparation of injection-ready polymeric nanosystems for medical imaging and drug delivery

    Science.gov (United States)

    Liu, Kegang; Zhu, Zhen; Wang, Xueya; Gonçalves, Daniel; Zhang, Bei; Hierlemann, Andreas; Hunziker, Patrick

    2015-10-01

    Translation of therapeutic polymeric nanosystems to patients and industry requires simplified, reproducible and scalable methods for assembly and loading. A single-step in-line process based on nanocoprecipitation of oxazoline-siloxane block copolymers in flow-focusing poly(dimethylsiloxane) microfluidics was designed to manufacture injection-ready nanosystems. Nanosystem characteristics could be controlled by copolymer concentration, block length and chemistry, microchannel geometry, flow rate, aqueous/organic flow rate ratio and payload concentration. The well-tolerated nanosystems exhibited differential cell binding and payload delivery and could confer sensitivity to photodynamic therapy to HeLa cancer cells. Such injection-ready nanosystems carrying drugs, diagnostic or functional materials may facilitate translation to clinical application.Translation of therapeutic polymeric nanosystems to patients and industry requires simplified, reproducible and scalable methods for assembly and loading. A single-step in-line process based on nanocoprecipitation of oxazoline-siloxane block copolymers in flow-focusing poly(dimethylsiloxane) microfluidics was designed to manufacture injection-ready nanosystems. Nanosystem characteristics could be controlled by copolymer concentration, block length and chemistry, microchannel geometry, flow rate, aqueous/organic flow rate ratio and payload concentration. The well-tolerated nanosystems exhibited differential cell binding and payload delivery and could confer sensitivity to photodynamic therapy to HeLa cancer cells. Such injection-ready nanosystems carrying drugs, diagnostic or functional materials may facilitate translation to clinical application. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr03543k

  18. Medical imaging projects meet at CERN

    CERN Multimedia

    CERN Bulletin

    2013-01-01

    ENTERVISION, the Research Training Network in 3D Digital Imaging for Cancer Radiation Therapy, successfully passed its mid-term review held at CERN on 11 January. This multidisciplinary project aims at qualifying experts in medical imaging techniques for improved hadron therapy.   ENTERVISION provides training in physics, medicine, electronics, informatics, radiobiology and engineering, as well as a wide range of soft skills, to 16 researchers of different backgrounds and nationalities. The network is funded by the European Commission within the Marie Curie Initial Training Network, and relies on the EU-funded research project ENVISION to provide a training platform for the Marie Curie researchers. The two projects hold their annual meetings jointly, allowing the young researchers to meet senior scientists and to have a full picture of the latest developments in the field beyond their individual research project. ENVISION and ENTERVISION are both co-ordinated by CERN, and the Laboratory hosts t...

  19. Content-based vessel image retrieval

    Science.gov (United States)

    Mukherjee, Satabdi; Cohen, Samuel; Gertner, Izidor

    2016-05-01

    This paper describes an approach to vessel classification from satellite images using content based image retrieval methodology. Content-based image retrieval is an important problem in both medical imaging and surveillance applications. In many cases the archived reference database is not fully structured, thus making content-based image retrieval a challenging problem. In addition, in surveillance applications, the query image may be affected by weather or/and geometric distortions. Our approach of content-based vessel image retrieval consists of two phases. First, we create a structured reference database, then for each new query image of a vessel we find the closest cluster of images in the structured reference database, thus identifying and classifying the vessel. Then we update the closest cluster with new query image.

  20. Microfluidics-based single-step preparation of injection-ready polymeric nanosystems for medical imaging and drug delivery.

    Science.gov (United States)

    Liu, Kegang; Zhu, Zhen; Wang, Xueya; Gonçalves, Daniel; Zhang, Bei; Hierlemann, Andreas; Hunziker, Patrick

    2015-10-28

    Translation of therapeutic polymeric nanosystems to patients and industry requires simplified, reproducible and scalable methods for assembly and loading. A single-step in-line process based on nanocoprecipitation of oxazoline-siloxane block copolymers in flow-focusing poly(dimethylsiloxane) microfluidics was designed to manufacture injection-ready nanosystems. Nanosystem characteristics could be controlled by copolymer concentration, block length and chemistry, microchannel geometry, flow rate, aqueous/organic flow rate ratio and payload concentration. The well-tolerated nanosystems exhibited differential cell binding and payload delivery and could confer sensitivity to photodynamic therapy to HeLa cancer cells. Such injection-ready nanosystems carrying drugs, diagnostic or functional materials may facilitate translation to clinical application.

  1. Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

    Science.gov (United States)

    Möller, Manuel; Sintek, Michael; Buitelaar, Paul; Mukherjee, Saikat; Zhou, Xiang Sean; Freund, Jörg

    Recent advances in medical imaging technology have dramatically increased the amount of clinical image data. In contrast, techniques for efficiently exploiting the rich semantic information in medical images have evolved much slower. Despite the research outcomes in image understanding, current image databases are still indexed by manually assigned subjective keywords instead of the semantics of the images. Indeed, most current content-based image search applications index image features that do not generalize well and use inflexible queries. This slow progress is due to the lack of scalable and generic information representation systems which can abstract over the high dimensional nature of medical images as well as semantically model the results of object recognition techniques. We propose a system combining medical imaging information with ontological formalized semantic knowledge that provides a basis for building universal knowledge repositories and gives clinicians fully cross-lingual and cross-modal access to biomedical information.

  2. MMSPix - A multimedia service (MMS) medical images weblog.

    Science.gov (United States)

    Fontelo, Paul; Liu, Fang; Muin, Michael; Ducut, Erick; Ackerman, Michael; Paalan-Vasquez, Franciene

    2007-01-01

    Smartphones with cameras have added a new dimension to augmenting medical image collections for education and teleconsultation. It allows healthcare personnel to instantly capture and send images through the multimedia messaging service (MMS) protocol. We developed a searchable archive, a mobile images Weblog of camera phone images for medical education. Registered users can view and comment on uploaded images. The archive is compartmentalized to allow sharing images with all viewers and by clinical specialty groups.

  3. Medical image segmentation based on statistical similarity feature%统计相似度特征的医学图像分割

    Institute of Scientific and Technical Information of China (English)

    郭艳蓉; 蒋建国; 郝世杰; 詹曙; 李鸿

    2013-01-01

    基于偏微分方程和图论两类图像分割方法的一个共同之处是将分割问题转换成了能量函数的模型建立及其最优化过程.从这一共同点出发,将图像的局部统计分布特征和Bhattacharyya相似度信息相结合并引入到测地线主动轮廓模型(GAC)和图切分(GC)模型的能量函数构造中.改进后GAC算法相当于为模型引入了一个基于似然比检验的回拉力,可有效阻止弱边界处泄露;基于非参数估计的能量函数构造更适用于小样本和分布函数不恒定的情况,使得改进GC模型更完整地提取图像目标的细节部分.将改进GAC和GC模型应用至膝关节MRI序列分割,提出完整分割各骨骼与半月板等结构的框架.在实验与分析部分,进行了定量与定性的实验对比.对噪声与局部体效应影响下的膝关节MRI序列及其他医学图像的实验,结果表明本文方法能够有效提高分割精度.%A common point of partial differential equation and graph theory based image segmentation methods lies in creating and optimizing their energy functions. From the viewpoint of creating energy models, statistical image features from nonparametric estimation are measured with Bhattacharyya metrics, which is further embedded into energy function construction in Geodesic Active Contour (GAC) and Graph Cuts ( GC) models in this paper. The improved GAC and GC models benefit from the energy function based on the aforementioned metric, which introduces a pull-back strength into the GAC to prevent boundary leaking and to help the GC model in accurately estimating the distribution from small samples and unstable distribution function as well as extracting objects in more detail. Then, the proposed methods are applied to the medical image segmentation scenario and a bone and meniscus segmentation framework on knee MRI sequence is presented. In the experimental section, quantitative and qualitative comparisons are conducted respectively

  4. An eSnake model for medical image segmentation

    Institute of Scientific and Technical Information of China (English)

    L(U) Hongyu; YUAN Kehong; BAO Shanglian; ZU Donglin; DUAN Chaijie

    2005-01-01

    A novel scheme of external force for detecting the object boundary of medical image based on Snakes (active contours)is introduced in the paper. In our new method, an electrostatic field on a template plane above the original image plane is designed to form the map of the external force. Compared with the method of Gradient Vector Flow (GVF), our approach has clear physical meanings. It has stronger ability to conform to boundary concavities, is simple to implement, and reliable for shape segmenting. Additionally, our method has larger capture range for the external force and is useful for medical image preprocessing in various applications. Finally, by adding the balloon force to the electrostatic field model, our Snake is able to represent long tube-like shapes or shapes with significant protrusions or bifurcations, and it has the specialty to prevent Snake leaking from large gaps on image edge by using a two-stage segmentation technique introduced in this paper. The test of our models proves that our methods are robust, precise in medical image segmentation.

  5. USING A BAG OF WORDS FOR AUTOMATIC MEDICAL IMAGE ANNOTATION WITH A LATENT SEMANTIC

    Directory of Open Access Journals (Sweden)

    Riadh Bouslimi

    2013-05-01

    Full Text Available We present in this paper a new approach for the automatic annotation of medical images, using the approach of "bag-of-words" to represent the visual content of the medical image combined with text descriptors based approach tf.idf and reduced by latent semantic to extract the co-occurrence between terms and visual terms. A medical report is composed of a text describing a medical image. First, we are interested to index the text and extract all relevant terms using a thesaurus containing MeSH medical concepts. In a second phase, the medical image is indexed while recovering areas of interest which are invariant to change in scale, light and tilt. To annotate a new medical image, we use the approach of "bagof-words" to recover the feature vector. Indeed, we use the vector space model to retrieve similar medical image from the database training. The calculation of the relevance value of an image to the query image is based on the cosine function. We conclude with an experiment carried out on five types of radiological imaging to evaluate the performance of our system of medical annotation. The results showed that our approach works better with more images from the radiology of the skull.

  6. Machine Learning Interface for Medical Image Analysis.

    Science.gov (United States)

    Zhang, Yi C; Kagen, Alexander C

    2016-10-11

    TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting.

  7. Comprehensive computerized medical imaging: interim hypothetical economic evaluation

    Science.gov (United States)

    Warburton, Rebecca N.; Fisher, Paul D.; Nosil, Josip

    1990-08-01

    The 422-bed Victoria General Hospital (VGH) and Siemens Electric Limited have since 1983 been piloting the implementation of comprehensive computerized medical imaging, including digital acquisition of diagnostic images, in British Columbia. Although full PACS is not yet in place at VGH, experience to date habeen used to project annual cost figures (including capital replacement) for a fully-computerized department. The resulting economic evaluation has been labelled hypothetical to emphasize that some key cost components were estimated rather than observed; this paper presents updated cost figures based on recent revisions to proposed departmental equipment configuration which raised the cost of conventional imaging equipment by 0.3 million* and lowered the cost of computerized imaging equipment by 0.8 million. Compared with conventional diagnostic imaging, computerized imaging appears to raise overall annual costs at VGH by nearly 0.7 million, or 11.6%; this is more favourable than the previous results, which indicated extra annual costs of 1 million (16.9%). Sensitivity analysis still indicates that all reasonable changes in the underlying assumptions result in higher costs for computerized imaging than for conventional imaging. Computerized imaging offers lower radiation exposure to patients, shorter waiting times, and other potential advantages, but as yet the price of obtaining these benefits remains substantial.

  8. Medical image of the week: alpha intrusion into REM sleep

    OpenAIRE

    Shetty S; Le T

    2015-01-01

    A 45-year-old woman with a past medical history of hypertension and chronic headaches was referred to the sleep laboratory for high clinical suspicion for sleep apnea based on a history of snoring, witnessed apnea and excessive daytime sleepiness. An overnight sleep study was performed. Images during N3 Sleep and REM sleep are shown (Figures 1 and 2). Alpha intrusion in delta sleep is seen in patients with fibromyalgia, depression, chronic fatigue syndrome, anxiety disorder, and primary sleep...

  9. ImageParser: a tool for finite element generation from three-dimensional medical images

    Directory of Open Access Journals (Sweden)

    Yamada T

    2004-10-01

    Full Text Available Abstract Background The finite element method (FEM is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures of interest (ROIs may be irregular and fuzzy. Methods A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements. Results The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues. Conclusion The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information.

  10. Crystal diffraction lens for medical imaging

    Science.gov (United States)

    Smither, Robert K.; Roa, Dante E.

    2000-04-01

    A crystal diffraction lens for focusing energetic gamma rays has been developed at Argonne National Laboratory for use in medical imaging of radioactivity in the human body. A common method for locating possible cancerous growths in the body is to inject radioactivity into the blood stream of the patient and then look for any concentration of radioactivity that could be associated with the fast growing cancer cells. Often there are borderline indications of possible cancers that could be due to statistical functions in the measured counting rates. In order to determine if these indications are false or real, one must resort to surgical means and take tissue samples in the suspect area. We are developing a system of crystal diffraction lenses that will be incorporated into a 3- D imaging system with better sensitivity (factors of 10 to 20) and better spatial resolution (a few mm in both vertical and horizontal directions) than most systems presently in use. The use of this new imaging system will allow one to eliminate 90 percent of the false indications and both locate and determine the size of the cancer with mm precision. The lens consists of 900 single crystals of copper, 4 mm X 4 mm on a side and 2 - 4 mm thick, mounted in 13 concentric rings.

  11. Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI.

    Science.gov (United States)

    Eswaraiah, R; Sreenivasa Reddy, E

    2014-01-01

    In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss.

  12. A New Application of MSPIHT for Medical Imaging

    Directory of Open Access Journals (Sweden)

    Athmane ZITOUNI

    2012-01-01

    Full Text Available In this paper, we propose a new application for medical imaging to image compression based on the principle of Set Partitioning In Hierarchical Tree algorithm (SPIHT. Our approach called , the modified SPIHT (MSPIHT, distributes entropy differently than SPIHT and also optimizes the coding. This approach can produce results that are a significant improvement on the Peak Signal-to-Noise Ratio (PSNR and compression ratio obtained by SPIHT algorithm, without affecting the computing time. These results are also comparable with those obtained using the Set Partitioning In Hierarchical Tree (SPIHT and Joint Photographic Experts Group 2000 (JPG2 algorithms.

  13. Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks.

    Science.gov (United States)

    Schlegl, Thomas; Waldstein, Sebastian M; Vogl, Wolf-Dieter; Schmidt-Erfurth, Ursula; Langs, Georg

    2015-01-01

    Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount of knowledge encoded in imaging data and corresponding reports generated during clinical routine. Weakly supervised learning approaches can link volume-level labels to image content but suffer from the typical label distributions in medical imaging data where only a small part consists of clinically relevant abnormal structures. In this paper we propose to use a semantic representation of clinical reports as a learning target that is predicted from imaging data by a convolutional neural network. We demonstrate how we can learn accurate voxel-level classifiers based on weak volume-level semantic descriptions on a set of 157 optical coherence tomography (OCT) volumes. We specifically show how semantic information increases classification accuracy for intraretinal cystoid fluid (IRC), subretinal fluid (SRF) and normal retinal tissue, and how the learning algorithm links semantic concepts to image content and geometry.

  14. Optimization of a single-phase liquid xenon Compton camera for 3γ medical imaging

    OpenAIRE

    Gallego Manzano, Lucia

    2016-01-01

    The work described in this thesis is focused on the characterization and optimization of a single-phaseliquid xenon Compton camera for medical imaging applications. The detector has been conceived to exploit the advantages of an innovative medical imaging technique called 3γ imaging, which aims to obtain aprecise 3D location of a radioactive source with high sensitivity and an important reduction of the dose administered to the patient. The 3γ imaging technique is based on the detection in co...

  15. A Novel Technique for Prealignment in Multimodality Medical Image Registration

    Directory of Open Access Journals (Sweden)

    Wu Zhou

    2014-01-01

    Full Text Available Image pair is often aligned initially based on a rigid or affine transformation before a deformable registration method is applied in medical image registration. Inappropriate initial registration may compromise the registration speed or impede the convergence of the optimization algorithm. In this work, a novel technique was proposed for prealignment in both monomodality and multimodality image registration based on statistical correlation of gradient information. A simple and robust algorithm was proposed to determine the rotational differences between two images based on orientation histogram matching accumulated from local orientation of each pixel without any feature extraction. Experimental results showed that it was effective to acquire the orientation angle between two unregistered images with advantages over the existed method based on edge-map in multimodalities. Applying the orientation detection into the registration of CT/MR, T1/T2 MRI, and monomadality images with respect to rigid and nonrigid deformation improved the chances of finding the global optimization of the registration and reduced the search space of optimization.

  16. Optical medical imaging: from glass to man

    Science.gov (United States)

    Bradley, Mark

    2016-11-01

    A formidable challenge in modern respiratory healthcare is the accurate and timely diagnosis of lung infection and inflammation. The EPSRC Interdisciplinary Research Collaboration (IRC) `Proteus' seeks to address this challenge by developing an optical fibre based healthcare technology platform that combines physiological sensing with multiplexed optical molecular imaging. This technology will enable in situ measurements deep in the human lung allowing the assessment of tissue function and characterization of the unique signatures of pulmonary disease and is illustrated here with our in-man application of Optical Imaging SmartProbes and our first device Versicolour.

  17. Quantification of Structure from Medical Images

    DEFF Research Database (Denmark)

    Qazi, Arish Asif

    , segmented from MR images of the knee. The cartilage tissue is considered to be a key determinant in the onset of Osteoarthritis (OA), a degenerative joint disease, with no known cure. The primary obstacle has been the dependence on radiography as the ‘gold standard’ for detecting the manifestation...... based on diffusion tensor imaging, a technique widely used for analysis of the white matter of the central nervous system in the living human brain. An inherent drawback of the traditional diffusion tensor model is its limited ability to provide detailed information about multi-directional fiber...

  18. MEDICAL IMAGE SEGMENTATION FOR ANATOMICAL KNOWLEDGE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Ms Maya Eapen

    2014-01-01

    Full Text Available Computed Tomography-Angiography (CTA images of the abdomen, followed by precise segmentation and subsequent computation of shape based features of liver play an important role in hepatic surgery, patient/donor diagnosis during liver transplantation and at various treatment stages. Nevertheless, the issues like intensity similarity and Partial Volume Effect (PVE between the neighboring organs; left the task of liver segmentation critical. The accurate segmentation of liver helps the surgeons to perfectly classify the patients based on their liver anatomy which in turn helps them in the treatment decision phase. In this study, we propose an effective Advanced Region Growing (ARG algorithm for segmentation of liver from CTA images. The performance of the proposed technique was tested with several CTA images acquired across a wide range of patients. The proposed ARG algorithm identifies the liver regions on the images based on the statistical features (intensity distribution and orientation value. The proposed technique addressed the aforementioned issues and been evaluated both quantitatively and qualitatively. For quantitative analysis proposed method was compared with manual segmentation (gold standard. The method was also compared with standard region growing.

  19. The masked educator-innovative simulation in an Australian undergraduate Medical Sonography and Medical Imaging program.

    Science.gov (United States)

    Reid-Searl, Kerry; Bowman, Anita; McAllister, Margaret; Cowling, Cynthia; Spuur, Kelly

    2014-12-01

    Clinical learning experiences for sonography and medical imaging students can sometimes involve the practice of technical procedures with less of a focus on developing communication skills with patients. Whilst patient-based simulation scenarios have been widely reported in other health education programmes, there is a paucity of research in sonography and medical imaging. The aim of this study was to explore the effectiveness of Mask-Ed™ (KRS Simulation) in the learning and teaching of clinical communication skills to undergraduate medical sonography and medical imaging students. Mask-Ed™ (KRS Simulation) is a simulation technique where the educator is hidden behind wearable realistic silicone body props including masks. Focus group interviews were conducted with 11 undergraduate medical sonography and medical imaging students at CQUniversity, Australia. The number of participants was limited to the size of the cohort of students enrolled in the course. Prior to these interviews participants were engaged in learning activities that featured the use of the Mask-Ed™ (KRS Simulation) method. Thematic analysis was employed to explore how the introduction of Mask-Ed™ (KRS Simulation) contributed to students' learning in relation to clinical communication skills. Key themes included: benefits of interacting with someone real rather than another student, learning made fun, awareness of empathy, therapeutic communication skills, engaged problem solving and purposeful reflection. Mask-Ed™ (KRS Simulation) combined with interactive sessions with an expert facilitator, contributed positively to students' learning in relation to clinical communication skills. Participants believed that interacting with someone real, as in the Mask-Ed characters was beneficial. In addition to the learning being described as fun, participants gained an awareness of empathy, therapeutic communication skills, engaged problem solving and purposeful reflection.

  20. Algorithms in radiology and medical imaging.

    Science.gov (United States)

    Athanasoulis, C A; Lee, A K

    1987-08-01

    As a tool in clinical decision making, algorithms deserve careful consideration. The potential use or abuse of algorithms in rationing health care renders such consideration essential. In radiology and medical imaging, algorithms have been applied as teaching tools in the conference room setting. These teaching decision trees, however, may not be applicable in the clinical situation. If an algorithmic approach to clinical radiology is pursued, several issues should be considered. Specifically, the application, design, designer, economics, and universality of the algorithms must be addressed. As an alternative to the wide dissemination of clinical algorithms, the authors propose the development of consensus opinions among specialists and the promulgation of the principle of radiologist-consultant-decision maker. A decision team is preferable to a decision tree.

  1. The state of the art of medical imaging technology: from creation to archive and back.

    Science.gov (United States)

    Gao, Xiaohong W; Qian, Yu; Hui, Rui

    2011-01-01

    Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations.

  2. Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique.

    Science.gov (United States)

    Badshah, Gran; Liew, Siau-Chuin; Zain, Jasni Mohd; Ali, Mushtaq

    2016-04-01

    In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.

  3. Medical Image Analysis by Cognitive Information Systems - a Review.

    Science.gov (United States)

    Ogiela, Lidia; Takizawa, Makoto

    2016-10-01

    This publication presents a review of medical image analysis systems. The paradigms of cognitive information systems will be presented by examples of medical image analysis systems. The semantic processes present as it is applied to different types of medical images. Cognitive information systems were defined on the basis of methods for the semantic analysis and interpretation of information - medical images - applied to cognitive meaning of medical images contained in analyzed data sets. Semantic analysis was proposed to analyzed the meaning of data. Meaning is included in information, for example in medical images. Medical image analysis will be presented and discussed as they are applied to various types of medical images, presented selected human organs, with different pathologies. Those images were analyzed using different classes of cognitive information systems. Cognitive information systems dedicated to medical image analysis was also defined for the decision supporting tasks. This process is very important for example in diagnostic and therapy processes, in the selection of semantic aspects/features, from analyzed data sets. Those features allow to create a new way of analysis.

  4. From analogue to apps--developing an app to prepare children for medical imaging procedures.

    Science.gov (United States)

    Williams, Gigi; Greene, Siobhan

    2015-01-01

    The Royal Children's Hospital (RCH) in Melbourne has launched a world-first app for children that will help reduce anxiety and the need for anesthesia during medical imaging procedures. The free, game-based app, "Okee in Medical Imaging", helps children aged from four to eight years to prepare for all medical imaging procedures--X-ray, CT, MRI, ultrasound, nuclear medicine, and fluoroscopy. The app is designed to reduce anticipatory fear of imaging procedures, while helping to ensure that children attend imaging appointments equipped with the skills required for efficient and effective scans to be performed. This paper describes how the app was developed.

  5. The Relationship Between Medical Imaging Technology and Medical Imaging Diagnosis%论医学影像技术与医学影像诊断的关系

    Institute of Scientific and Technical Information of China (English)

    杨东奇

    2015-01-01

    Objective To discuss and analysis the relationship between medical imaging technology and medical imaging diagnosis. Methods Based on our medical institution, combined with personal experience, elaborated the connections between medical image technology and medical image diagnose. Results Medical image technology and medical image diagnosis is a dialectical unity of the whole, both independent of the role wil have a certain impact on the other side. Conclusion Medical image diagnosis need the support of imaging technology, medical image diagnosis technology and medical imaging technology mutual y integral in clinic and they depend on each other.%目的:讨论并分析医学影像技术与医学影像诊断关系之间的联系。方法以我医疗机构为研究基准,结合个人实践经验,阐述医疗发展形势下医学影像技术和医学影像诊断之间的联系。结果医学影像技术与医学影像诊断是一个辩证统一的整体,两者独立作用均会对对方产生一定的影响。结论医学影像的诊断需要影像技术的支持,医学影像的诊断技术和医学影像的技术互为整体,在临床对于患者进行诊断的过程中互相依赖、制约以及促进。

  6. Computational observers and visualization methods for stereoscopic medical imaging.

    Science.gov (United States)

    Zafar, Fahad; Yesha, Yaacov; Badano, Aldo

    2014-09-22

    As stereoscopic display devices become common, their image quality assessment evaluation becomes increasingly important. Most studies conducted on 3D displays are based on psychophysics experiments with humans rating their experience based on detection tasks. The physical measurements do not map to effects on signal detection performance. Additionally, human observer study results are often subjective and difficult to generalize. We designed a computational stereoscopic observer approach inspired by the mechanisms of stereopsis in human vision for task-based image assessment that makes binary decisions based on a set of image pairs. The stereo-observer is constrained to a left and a right image generated using a visualization operator to render voxel datasets. We analyze white noise and lumpy backgrounds using volume rendering techniques. Our simulation framework generalizes many different types of model observers including existing 2D and 3D observers as well as providing flexibility to formulate a stereo model observer approach following the principles of stereoscopic viewing. This methodology has the potential to replace human observer studies when exploring issues with stereo display devices to be used in medical imaging. We show results quantifying the changes in performance when varying stereo angle as measured by an ideal linear stereoscopic observer. Our findings indicate that there is an increase in performance of about 13-18% for white noise and 20-46% for lumpy backgrounds, where the stereo angle is varied from 0 to 30. The applicability of this observer extends to stereoscopic displays used for in the areas of medical and entertainment imaging applications.

  7. Medical images data mining using classification algorithm based on association rule%基于关联分类算法的医学图像数据挖掘

    Institute of Scientific and Technical Information of China (English)

    邓薇薇; 卢延鑫

    2012-01-01

    Objective In order to assist clinicians in diagnosis and treatment of brain disease,a classifier for medical images which contains tumora inside,based on association rule data mining techniques was constructed.Methtoods After a pre-processing phase of the medical images,the related features from those images were extracted and discretized as the input of association rule,then the medical images classifier was constructed by improved Apriori algorithm.Results The medical images classifier was constructed.The known type of medical images was utilized to train the classifier so as to mine the association rules that satisfy the constraint conditions.Then the brain tumor in an unknown type of medical image was classified by the classifier constructed.Conclusion Classification algorithm based on association rule can be effectively used in mining image features,and constructing an image classifier to identify benign or malignant tumors.%目的 利用关联分类算法,构造医学图像分类器,对未知类型的脑肿瘤图像进行自动判别和分类,以帮助临床医生进行脑疾病的诊断和治疗.方法 对医学图像经过预处理后进行特征提取,再将提取的特征离散化后放到事务数据库中作为关联分类规则的输入,然后利用改进的Apriori算法构造医学图像分类器.结果 构造了医学图像分类器,用已知类型的图像训练分类器挖掘满足约束条件的关联规则,然后利用发现的关联规则对未知类型的医学图像进行分类以判断脑肿瘤的良恶性.结论 利用关联分类算法可以有效地挖掘医学图像特征,进而构造图像分类器,实现脑肿瘤良恶性的自动判别.

  8. Application of a medical image processing system in liver transplantation

    Institute of Scientific and Technical Information of China (English)

    Chi-Hua Fang; Xiao-Feng Li; Zhou Li; Ying-Fang Fan; Chao-Min Lu; Yan-Peng Huang; Feng-Ping Peng

    2010-01-01

    BACKGROUND: At present, imaging is used not only to show the form of images, but also to make three-dimensional (3D) reconstructions and visual simulations based on original data to guide clinical surgery. This study aimed to assess the use of a medical image-processing system in liver transplantation surgery. METHODS: The data of abdominal 64-slice spiral CT scan were collected from 200 healthy volunteers and 37 liver cancer patients in terms of hepatic arterial phase, portal phase, and hepatic venous phase. A 3D model of abdominal blood vessels including the abdominal aorta system, portal vein system, and inferior vena cava system was reconstructed by an abdominal image processing system to identify vascular variations. Then, a 3D model of the liver was reconstructed in terms of hepatic segmentation and liver volume was calculated. The FreeForm modeling system with a PHANTOM force feedback device was used to simulate the real liver transplantation environment, in which the total process of liver transplantation was completed. RESULTS: The reconstructed model of the abdominal blood vessels and the liver was clearly demonstrated to be three-dimensionally consistent with the anatomy of the liver, in which the variations of abdominal blood vessels were identiifed and liver segmentation was performed digitally. In the model, liver transplantation was simulated subsequently, and different modus operandi were selected successfully. CONCLUSION: The digitized medical image processing system may be valuable for liver transplantation.

  9. Wideband Optical Detector of Ultrasound for Medical Imaging Applications

    Science.gov (United States)

    Rosenthal, Amir; Kellnberger, Stephan; Omar, Murad; Razansky, Daniel; Ntziachristos, Vasilis

    2014-01-01

    Optical sensors of ultrasound are a promising alternative to piezoelectric techniques, as has been recently demonstrated in the field of optoacoustic imaging. In medical applications, one of the major limitations of optical sensing technology is its susceptibility to environmental conditions, e.g. changes in pressure and temperature, which may saturate the detection. Additionally, the clinical environment often imposes stringent limits on the size and robustness of the sensor. In this work, the combination of pulse interferometry and fiber-based optical sensing is demonstrated for ultrasound detection. Pulse interferometry enables robust performance of the readout system in the presence of rapid variations in the environmental conditions, whereas the use of all-fiber technology leads to a mechanically flexible sensing element compatible with highly demanding medical applications such as intravascular imaging. In order to achieve a short sensor length, a pi-phase-shifted fiber Bragg grating is used, which acts as a resonator trapping light over an effective length of 350 µm. To enable high bandwidth, the sensor is used for sideway detection of ultrasound, which is highly beneficial in circumferential imaging geometries such as intravascular imaging. An optoacoustic imaging setup is used to determine the response of the sensor for acoustic point sources at different positions. PMID:24895083

  10. Blackboard architecture for medical image interpretation

    Science.gov (United States)

    Davis, Darryl N.; Taylor, Christopher J.

    1991-06-01

    There is a growing interest in using sophisticated knowledge-based systems for biomedical image interpretation. We present a principled attempt to use artificial intelligence methodologies in interpreting lateral skull x-ray images. Such radiographs are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error prone and previous attempts to automate this analysis typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and image quality. An integrated model-based system has been developed which makes use of a blackboard architecture and multiple knowledge sources. A model definition interface allows quantitative models, of feature appearance and location, to be built from examples as well as more qualitative modelling constructs. Visual task definition and blackboard control modules allow task-specific knowledge sources to act on information available to the blackboard in a hypothesise and test reasoning cycle. Further knowledge-based modules include object selection, location hypothesis, intelligent segmentation, and constraint propagation systems. Alternative solutions to given tasks are permitted.

  11. Medical image segmentation based on guided filtering and multi-atlas%基于引导滤波的多图谱医学图像分割

    Institute of Scientific and Technical Information of China (English)

    温锐; 陈宏文; 张雷; 卢振泰

    2015-01-01

    A novel medical automatic image segmentation strategy based on guided filtering and multi-atlas is proposed to achieve accurate, smooth, robust, and reliable segmentation. This framework consists of 4 elements: the multi- atlas registration, which uses the atlas prior information;the label fusion, in which the similarity measure of the registration is used as the weight to fuse the warped label;the guided filtering, which uses the local information of the target image to correct the registration errors; and the threshold approaches used to obtain the segment result. The experimental results showed part among the 15 brain MRI images used to segment the hippocampus region, the proposed method achieved a median Dice coefficient of 86%on the left hippocampus and 87.4%on the right hippocampus. Compared with the traditional label fusion algorithm, the proposed algorithm outperforms the common brain image segmentation methods with a good efficiency and accuracy.%目的:为了有效的利用图谱的先验信息和待分割图像的灰度信息,并在融合标号图像的过程中校正配准引起的误差,得到光滑、准确的分割结果,提出了一种新的基于引导滤波的多图谱医学图像分割方法。方法本文将多图谱配准与引导滤波相结合。该方法包含4个部分:第一部分为多图谱配准,通过配准将图谱中存储的形状先验信息映射到待分割图像;第二部为标号融合,利用配准的相似性作为权重,将形变后的标号图像融合在一起;第三部分为引导滤波,利用引导滤波引入待分割图像的灰度信息,可以校正配准引起的误差;最后通过阈值处理,得到最终的分割结果。结果对15例脑部MR图像数据中的海马体进行分割实验,左、右海马体分别达到了86%及87.4%的分割精度,与传统的标号融合算法相比,平均分割精度提升了2.4%。结论本文方法结合多配谱配准与引导滤波的优势,

  12. Developments in medical image processing and computational vision

    CERN Document Server

    Jorge, Renato

    2015-01-01

    This book presents novel and advanced topics in Medical Image Processing and Computational Vision in order to solidify knowledge in the related fields and define their key stakeholders. It contains extended versions of selected papers presented in VipIMAGE 2013 – IV International ECCOMAS Thematic Conference on Computational Vision and Medical Image, which took place in Funchal, Madeira, Portugal, 14-16 October 2013.  The twenty-two chapters were written by invited experts of international recognition and address important issues in medical image processing and computational vision, including: 3D vision, 3D visualization, colour quantisation, continuum mechanics, data fusion, data mining, face recognition, GPU parallelisation, image acquisition and reconstruction, image and video analysis, image clustering, image registration, image restoring, image segmentation, machine learning, modelling and simulation, object detection, object recognition, object tracking, optical flow, pattern recognition, pose estimat...

  13. Review of Intelligent Techniques Applied for Classification and Preprocessing of Medical Image Data

    Directory of Open Access Journals (Sweden)

    H S Hota

    2013-01-01

    Full Text Available Medical image data like ECG, EEG and MRI, CT-scan images are the most important way to diagnose disease of human being in precise way and widely used by the physician. Problem can be clearly identified with the help of these medical images. A robust model can classify the medical image data in better way .In this paper intelligent techniques like neural network and fuzzy logic techniques are explored for MRI medical image data to identify tumor in human brain. Also need of preprocessing of medical image data is explored. Classification technique has been used extensively in the field of medical imaging. The conventional method in medical science for medical image data classification is done by human inspection which may result misclassification of data sometime this type of problem identification are impractical for large amounts of data and noisy data, a noisy data may be produced due to some technical fault of the machine or by human errors and can lead misclassification of medical image data. We have collected number of papers based on neural network and fuzzy logic along with hybrid technique to explore the efficiency and robustness of the model for brain MRI data. It has been analyzed that intelligent model along with data preprocessing using principal component analysis (PCA and segmentation may be the competitive model in this domain.

  14. Topics in medical image processing and computational vision

    CERN Document Server

    Jorge, Renato

    2013-01-01

      The sixteen chapters included in this book were written by invited experts of international recognition and address important issues in Medical Image Processing and Computational Vision, including: Object Recognition, Object Detection, Object Tracking, Pose Estimation, Facial Expression Recognition, Image Retrieval, Data Mining, Automatic Video Understanding and Management, Edges Detection, Image Segmentation, Modelling and Simulation, Medical thermography, Database Systems, Synthetic Aperture Radar and Satellite Imagery.   Different applications are addressed and described throughout the book, comprising: Object Recognition and Tracking, Facial Expression Recognition, Image Database, Plant Disease Classification, Video Understanding and Management, Image Processing, Image Segmentation, Bio-structure Modelling and Simulation, Medical Imaging, Image Classification, Medical Diagnosis, Urban Areas Classification, Land Map Generation.   The book brings together the current state-of-the-art in the various mul...

  15. 基于WADO的医学影像浏览系统研究与设计%Study and Design of a Medical Image Viewing System Based on WADO

    Institute of Scientific and Technical Information of China (English)

    梁炳进; 郭文明; 林国雄; 蔡荣杰

    2015-01-01

    目的:解决目前区域医疗、远程会诊和移动医疗等对Web下访问医学影像的需求问题。方法采用WADO技术(用于医学图像的传输和显示的技术)和URI(资源标志符)技术实现B/S架构的医学影像浏览系统。该系统基于DICOM标准,浏览器端通过HTTP方式请求要查看的医学影像, Web服务器端通过公共网关接口(CGI)技术实现医学影像处理并返回JPEG格式影像,客户端浏览器收到影像后进行显示。结果通过在Windows的IE、Firefox、Google Chrome、Apple Safari等主流浏览器上进行测试,系统均可以顺利运行。结论该系统与操作系统、浏览器版本无关,是跨操作系统、跨浏览器的医学影像浏览系统。%Objective To cope with the regional medical, remote consultation and mobile medical problems in Web access to medical images.Methods With adoption of WADO-URI (Web Access to DICOM Objects- Uniform Resource Identifier) technology, a medical image viewing system was constructed based on the B/S (Browser/Server) architecture. In line with the DICOM (Digital Imaging and Communications in Medicine) standard, the request of viewing medical images was sent out by the client browservia HTTP (Hyper Text Transfer Protocol). Then, the Web server realized the medical image processing through CGI (Common Gateway Interface) technology and returned with the JPEG format images. The images would be displayed after receipt.Results According to the test results, the system had proven its successful operation in Windows IE, FireFox, Google Chrome, Apple Safari and other mainstream browsers. Conclusion Being independent with the operating system and browser version, the system was a medical image viewing system across multiple operating systems and browsers.

  16. The fuzzy Hough transform-feature extraction in medical images.

    Science.gov (United States)

    Philip, K P; Dove, E L; McPherson, D D; Gotteiner, N L; Stanford, W; Chandran, K B

    1994-01-01

    Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final (improved) estimate of the true borders with other (subsequently used) image processing techniques. They present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough transform algorithm as part of a larger procedure to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications.

  17. Medical Image De-Noising Schemes using Wavelet Transform with Fixed form Thresholding

    Directory of Open Access Journals (Sweden)

    Nadir Mustafa

    2015-10-01

    Full Text Available Medical Imaging is currently a hot area of bio-medical engineers, researchers and medical doctors as it is extensively used in diagnosing of human health and by health care institutes. The imaging equipment is the device, which is used for better image processing and highlighting the important features. These images are affected by random noise during acquisition, analyzing and transmission process. This condition results in the blurry image visible in low contrast. The Image De-noising System (IDs is used as a tool for removing image noise and preserving important data. Image de-noising is one of the most interesting research areas among researchers of technology-giants and academic institutions. For Criminal Identification Systems (CIS & Magnetic Resonance Imaging (MRI, IDs is more beneficial in the field of medical imaging. This paper proposes an algorithm for de-noising medical images using different types of wavelet transform, such as Haar, Daubechies, Symlets and Bi-orthogonal. In this paper noise image quality has been evaluated using filter assessment parameters like Peak Signal to Noise Ratio (PSNR, Mean Square Error (MSE and Variance, It has been observed to form the numerical results that, the presentation of proposed algorithm reduced the mean square error and achieved best value of peak signal to noise ratio (PSNR. In this paper, the wavelet based de-noising algorithm has been investigated on medical images along with threshold.

  18. Competency-based medical education in postgraduate medical education

    NARCIS (Netherlands)

    Iobst, William F.; Sherbino, Jonathan; Ten Cate, Olle; Richardson, Denyse L.; Dath, Deepak; Swing, Susan R.; Harris, Peter; Mungroo, Rani; Holmboe, Eric S.; Frank, Jason R.

    2010-01-01

    With the introduction of Tomorrow's Doctors in 1993, medical education began the transition from a time-and process-based system to a competency-based training framework. Implementing competency-based training in postgraduate medical education poses many challenges but ultimately requires a demonstr

  19. The Design and Implementation of 3D Medical Image Reconstruction System Based on VTK and ITK%基于VTK和ITK的3D医学图像重建系统的设计与实现

    Institute of Scientific and Technical Information of China (English)

    刘鹰; 韩利凯

    2011-01-01

    三维图像重构是当前数字图像处理领域的一个热点,特别是其在医学图像处理中的应用.VascuView3D是一个基于VTK和ITK的3D医学图像重建系统,该系统实现了体绘制(VR)、表面绘制(SR)和多平面绘制(MPR)等3D视图,以及基于CLUT的三维灰度图像着色.%3D image reconstruction is an attractive Held generally in digital image processing techniques, especially in medical imaging. The design and implementation of a 3D medical image reconstruction system VascuView, which can be used to build 3D images from 2D image slice files produced by CT and MRI devices, is introduced. The volume rendering, surface rendering and Multi -Planar rendering are implemented and lots of the 3D operations such as coloring of 3D image based on CLUT can be performed with this software.

  20. Organizing and accessing methods for massive medical microscopic image data

    Science.gov (United States)

    Deng, Yan; Tang, Lixin

    2007-12-01

    The development of electronic medical archives requests to mosaic the medical microscopic images to a whole one, and the stitching result is usually a massive file hard to be stored or accessed. The paper proposes a file format named Medical TIFF to organize the massive microscopic image data. The Medical TIFF organizes the massive image data in tiles, appends the thumbnail of the result image at the end of the file, and offers the way to add medical information into the image file. Then the paper designs a three-layer system to access the file: the Physical Layer gathers the Medical TIFF components dispersed over the file and organizes them hierarchically, the Logical Layer uses a two dimensional dynamic array to deal with the tiles, and the Application Layer provides the interfaces for the applications developed on the basis of the system.

  1. Method for Surface Scanning in Medical Imaging and Related Apparatus

    DEFF Research Database (Denmark)

    2015-01-01

    A method and apparatus for surface scanning in medical imaging is provided. The surface scanning apparatus comprises an image source, a first optical fiber bundle comprising first optical fibers having proximal ends and distal ends, and a first optical coupler for coupling an image from the image...

  2. EAU standardised medical terminology for urologic imaging: a taxonomic approach.

    Science.gov (United States)

    Loch, Tillmann; Carey, Brendan; Walz, Jochen; Fulgham, Pat Fox

    2015-05-01

    The terminology and abbreviations used in urologic imaging have generally been adopted on an ad hoc basis by different speciality groups; however, there is a need for shared nomenclature to facilitate clinical communication and collaborative research. This work reviews the current nomenclature for urologic imaging used in clinical practice and proposes a taxonomy and terminology for urologic imaging studies. A list of terms used in urologic imaging were compiled from guidelines published by the European Association of Urology and the American Urological Association and from the American College of Radiology Appropriateness Criteria. Terms searched were grouped into broad categories based on technology, and imaging terms were further stratified based on the anatomic extent, contrast or phases, technique or modifiers, and combinations or fusions. Terms that had a high degree of utilisation were classified as accepted. We propose a new taxonomy to define a more useful and acceptable nomenclature model acceptable to all health professionals involved in urology. The major advantage of a taxonomic approach to the classification of urologic imaging studies is that it provides a flexible framework for classifying the modifications of current imaging modalities and allows the incorporation of new imaging modalities. The adoption of this hierarchical classification model ranging from the most general to the most detailed descriptions should facilitate hierarchical searches of the medical literature using both general and specific terms. This work is limited in its scope, as it is not currently all-inclusive. This will hopefully be addressed by future modification as others embrace the concept and work towards uniformity in nomenclature. This paper provides a noncomprehensive list of the most widely used terms across different specialties. This list can be used as the basis for further discussion, development, and enhancement. In this paper we describe a classification system

  3. Manchester medical society (imaging section) presidential address 2008

    Energy Technology Data Exchange (ETDEWEB)

    Blakeley, C. [University of Salford (United Kingdom); Manchester Royal Infirmary (CMFT) (United Kingdom)], E-mail: c.blakeley@salford.ac.uk; Hogg, P. [University of Salford (United Kingdom)

    2009-12-15

    This article is based partly upon the Presidential Address of the Manchester Medical Society (Imaging Section) in 2008. It reviews the development of radiology services in the Manchester (UK) area from their inception in 1896 to the installation of the first EMI body CT scanner in Europe. It considers some of the innovative people in the Manchester area and some milestone events that occurred in that area to help establish the role and value of X-ray in diagnostic imaging. In this article the first recorded case of when X-ray imaging was used in a forensic domiciliary case is also outlined; this occurred approximately 35 miles north of Manchester on 23rd April 1896. The article also explains some interesting background information on the development of the first EMI CT scanner, drawing particularly on the revenue stream generated by the music section of EMI through the success of The Beatles - a band which emanated 35 miles from Manchester in Liverpool.

  4. The mathematics of medical imaging a beginner’s guide

    CERN Document Server

    Feeman, Timothy G

    2015-01-01

    The basic mathematics of computerized tomography, the CT scan, are aptly presented for an audience of undergraduates in mathematics and engineering. Assuming no prior background in advanced mathematical analysis, topics such as the Fourier transform, sampling, and discrete approximation algorithms are introduced from scratch and are developed within the context of medical imaging. A chapter on magnetic resonance imaging focuses on manipulation of the Bloch equation, the system of differential equations that is the foundation of this important technology. Extending the ideas of the acclaimed first edition, new material has been added to render an even more accessible textbook for course usage. This edition includes new discussions of the Radon transform, the Dirac delta function and its role in X-ray imaging, Kacmarz’s method and least squares approximation, spectral filtering,  and more.  Copious examples and exercises, several new computer-based exercises, and additional graphics have been added to fur...

  5. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2006

    DEFF Research Database (Denmark)

    Nielsen, Mads; Sporring, Jon

    The two-volume set LNCS 4190 and LNCS 4191 constitute the refereed proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006, held in Copenhagen, Denmark in October 2006. The program committee carefully selected 39 revised full papers...... and 193 revised poster papers from 578 submissions for presentation in two volumes, based on a rigorous peer reviews. The first volume includes 114 contributions related to bone shape analysis, robotics and tracking, segmentation, analysis of diffusion tensor MRI, shape analysis and morphometry......, simulation and interaction, robotics and intervention, cardio-vascular applications, image analysis in oncology, brain atlases and segmentation, cardiac motion analysis, clinical applications, and registration. The second volume collects 118 papers related to segmentation, validation and quantitative image...

  6. Motion tracking in infrared imaging for quantitative medical diagnostic applications

    Science.gov (United States)

    Cheng, Tze-Yuan; Herman, Cila

    2014-01-01

    In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.

  7. An Approach to Integer Wavelet Transform for Medical Image Compression in PACS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    We study an approach to integer wavelet transform for lossless compression of medical image in medical picture archiving and communication system (PACS). By lifting scheme a reversible integer wavelet transform is generated, which has the similar features with the corresponding biorthogonal wavelet transform. Experimental results of the method based on integer wavelet transform are given to show better performance and great applicable potentiality in medical image compression.

  8. Medical image computing and computer science intervention. MICCAI 2005. Pt. 2. Proceedings

    Energy Technology Data Exchange (ETDEWEB)

    Duncan, J.S. [Yale Univ., New Haven, CT (United States). Dept. of Biomedical Engineering]|[Yale Univ., New Haven, CT (United States). Dept. of Diagnostic Radiology; Gerig, G. (eds.) [North Carolina Univ., Chapel Hill, NC (United States). Dept. of Computer Science

    2005-07-01

    The two-volume set LNCS 3749 and LNCS 3750 constitutes the refereed proceedings of the 8th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2005, held in Palm Springs, CA, USA, in October 2005. Based on rigorous peer reviews the program committee selected 237 carefully revised full papers from 632 submissions for presentation in two volumes. The first volume includes all the contributions related to image analysis and validation, vascular image segmentation, image registration, diffusion tensor image analysis, image segmentation and analysis, clinical applications - validation, imaging systems - visualization, computer assisted diagnosis, cellular and molecular image analysis, physically-based modeling, robotics and intervention, medical image computing for clinical applications, and biological imaging - simulation and modeling. The second volume collects the papers related to robotics, image-guided surgery and interventions, image registration, medical image computing, structural and functional brain analysis, model-based image analysis, image-guided intervention: simulation, modeling and display, and image segmentation and analysis. (orig.)

  9. Synthetic Aperture Imaging in Medical Ultrasound

    DEFF Research Database (Denmark)

    Nikolov, Svetoslav; Gammelmark, Kim; Pedersen, Morten

    2004-01-01

    with high precision, and the imaging is easily extended to real-time 3D scanning. This paper presents the work done at the Center for Fast Ultrasound Imaging in the area of SA imaging. Three areas that benefit from SA imaging are described. Firstly a preliminary in-vivo evaluation comparing conventional B......Synthetic Aperture (SA) ultrasound imaging is a relatively new and unexploited imaging technique. The images are perfectly focused both in transmit and receive, and have a better resolution and higher dynamic range than conventional ultrasound images. The blood flow can be estimated from SA images...

  10. Based on the Horizontal Noise Elimination of Medical Adhesion Image Edge Segmentation Algorithm%基于横噪声消除的医学粘连图像边缘分割算法

    Institute of Scientific and Technical Information of China (English)

    刘国宏

    2013-01-01

    According to the CCD type medical image, in cell adhesion area near signal in strong horizontal stripe noise interference, the correctness of the image information influence, the division will exist boundary fuzzy and sawtooth provi-sions. In order to improve the effect of medical image segmentation, this paper puts forward a based on the transverse stripe noise elimination of medical adhesion image edge segmentation algorithm. Analysis of the medical image edge transverse stripe noise reasons, by the use of combined with wold texture model and multi-scale markov random field model, using the certainty and uncertainty with the airport with the airport spectral properties of different features, will be the medical image edge interference characteristics apart. Is advantageous to adhesion medical image segmentation. Ex-perimental results prove that the method can effectively removed the transverse stripe noise and it is quite good to keep the image edge details and information, and at the same time operation complexity is low.%针对CCD型医学图像中,在细胞粘连区域信号附近出现的较强的横条纹噪声干扰,影响图像信息的正确性,分割后会存在边界模糊和锯齿条文的问题,为提高医学图像分割效果,提出了一种基于横条纹噪声消除的医学粘连图像边缘分割算法。分析了医学图像中边沿横条纹噪声的原因,通过wold纹理模型与多尺度马尔可夫随机场模型,利用确定性随机场和不确定性随机场的谱属性不同的特征,将医学图像边沿的干扰特征分离开,有利于对粘连医学图像进行分割。实验证明,方法有效地去除了横条纹噪声并很好地保留了图像的边缘和细节信息,同时运算复杂度低。

  11. Multiple-Instance Learning for Medical Image and Video Analysis.

    Science.gov (United States)

    Quellec, Gwenole; Cazuguel, Guy; Cochener, Beatrice; Lamard, Mathieu

    2017-01-10

    Multiple-Instance Learning (MIL) is a recent machine learning paradigm that is particularly well suited to Medical Image and Video Analysis (MIVA) tasks. Based solely on class labels assigned globally to images or videos, MIL algorithms learn to detect relevant patterns locally in images or videos. These patterns are then used for classification at a global level. Because supervision relies on global labels, manual segmentations are not needed to train MIL algorithms, unlike traditional Single-Instance Learning (SIL) algorithms. Consequently, these solutions are attracting increasing interest from the MIVA community: since the term was coined by Dietterich et al. in 1997, 73 research papers about MIL have been published in the MIVA literature. This paper reviews the existing strategies for modeling MIVA tasks as MIL problems, recommends generalpurpose MIL algorithms for each type of MIVA tasks and discusses MIVA-specific MIL algorithms. Various experiments performed in medical image and video datasets are compiled in order to back up these discussions. This meta-analysis shows that, besides being more convenient than SIL solutions, MIL algorithms are also more accurate in many cases. In other words, MIL is the ideal solution for many MIVA tasks. Recent trends are discussed and future directions are proposed for this emerging paradigm.

  12. Implementation of Novel Medical Image Compression Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Mohammad Al-Rababah

    2016-05-01

    Full Text Available The medical image processing process is one of the most important areas of research in medical applications in digitized medical information. A medical images have a large sizes. Since the coming of digital medical information, the important challenge is to care for the conduction and requirements of huge data, including medical images. Compression is considered as one of the necessary algorithm to explain this problem. A large amount of medical images must be compressed using lossless compression. This paper proposes a new medical image compression algorithm founded on lifting wavelet transform CDF 9/7 joined with SPIHT coding algorithm, this algorithm applied the lifting composition to confirm the benefit of the wavelet transform. To develop the proposed algorithm, the outcomes compared with other compression algorithm like JPEG codec. Experimental results proves that the anticipated algorithm is superior to another algorithm in both lossy and lossless compression for all medical images tested. The Wavelet-SPIHT algorithm provides PSNR very important values for MRI images.

  13. Glasses-free 3D viewing systems for medical imaging

    Science.gov (United States)

    Magalhães, Daniel S. F.; Serra, Rolando L.; Vannucci, André L.; Moreno, Alfredo B.; Li, Li M.

    2012-04-01

    In this work we show two different glasses-free 3D viewing systems for medical imaging: a stereoscopic system that employs a vertically dispersive holographic screen (VDHS) and a multi-autostereoscopic system, both used to produce 3D MRI/CT images. We describe how to obtain a VDHS in holographic plates optimized for this application, with field of view of 7 cm to each eye and focal length of 25 cm, showing images done with the system. We also describe a multi-autostereoscopic system, presenting how it can generate 3D medical imaging from viewpoints of a MRI or CT image, showing results of a 3D angioresonance image.

  14. Medical image of the week: pneumomediastinum

    Directory of Open Access Journals (Sweden)

    Franco R Jr

    2014-01-01

    Full Text Available No abstract available. Article truncated at 150 words. A 65 year old man presented with mild increase in shortness of breath. He had a past medical history of diabetes mellitus, hypertension, and severe malnutrition with percutaneous endoscopic gastrostomy (PEG placement after a colectomy and end ileostomy for sigmoid volvulus. CXR (Figure 1 suggested a pneumomediastinum with subsequent chest CT (Figure 2 confirming moderate sized pneumomediastinum. He had a chronic cough from chronic obstructive pulmonary disease (COPD as well as aspiration and chest CT also demonstrated emphysema with small blebs. He denied any significant chest pain. He was followed conservatively with imaging and discharged in stable condition. Pneumomediastinum can be caused by trauma, esophageal rupture after vomiting (Boerhaave’s syndrome and can be a spontaneous event if no obvious precipitating cause is identified (1. Valsalva maneuvers such as cough, sneeze, vomiting and childbirth, can all cause pneumomediastinum. Risk factors include asthma, COPD, interstitial lung disease and inhalational recreational drug use. …

  15. Medical image of the week: purpura fulminans

    Directory of Open Access Journals (Sweden)

    Power EP

    2016-12-01

    Full Text Available No abstract available. Article truncated at 150 words. A 54-year-old man with coronary artery disease, fibromyalgia and chronic sacral ulcers was brought to the emergency department due to acute changes in mentation and hypotension. He suffered a cardiac arrest shortly after arrival to the emergency department during emergent airway management. After successful resuscitation, he was admitted to the medical intensive care unit and treated for septic shock with fluid resuscitation, vasopressors and broad spectrum antibiotics. Laboratory results were significant for disseminated intravascular coagulopathy (DIC- thrombocytopenia, decreased fibrinogen and elevated PT, PTT and D-dimer levels. Profound metabolic acidosis and lactate elevation was also seen. Blood Cultures later revealed a multi-drug resistant E. coli bacteremia. Images of the lower extremities (Figure 1 were obtained at initial assessment and are consistent with purpura fulminans. He did not survive the stay. Purpura fulminans, also referred to as skin mottling, is an evolving skin condition which is characterized by an acutely worsening reticular …

  16. Medical image of the week: disseminated coccidioidomycosis

    Directory of Open Access Journals (Sweden)

    Ynosencio T

    2017-02-01

    Full Text Available No abstract available. Article truncated at 150 words. A 67-year-old African American man with no significant past medical history presented with shortness of breath and flu-like symptoms. On exam, he was noted to be profoundly hypoxemic with imaging showing diffuse thoracic changes (Figure 1 and a diffuse papular rash (Figure 2. Initial workup included coccidioidomycosis serologies which returned positive with a titer of 1:128. While exposure to coccidioidomycosis is very common in southern Arizona, dissemination is a rare occurrence. The incidence is estimated between 0.2 and 4.7 percent. Patients at highest risk include those that are immunosuppressed or that are of African or Filipino ancestry. Common extra-pulmonary sites include skin or subcutaneous tissue, meninges of brain or spinal cord, and bones. Even rarer sites include the eyes, liver, prostate, mediastinum, and kidneys. Treatment is usually the same as with pulmonary infection which is an azole agent. However, if the patient’s symptoms are severe or if the lesions involve …

  17. Medical image of the week: focal myopericaditis

    Directory of Open Access Journals (Sweden)

    Meenakshisundaram C

    2015-07-01

    Full Text Available No abstract available. Article truncated at 150 words. A 44-year-old man with no significant past medical history was admitted with a history of two episodes of substernal chest pain unrelated to exertion which had resolved spontaneously. Admission vital signs were within normal limits and physical examination was unremarkable. Basic lab tests were normal and urine toxicology was negative. Electrocardiogram was unremarkable with no ST/T changes. Troponin I was elevated at 4.19 which trended up to 6.57. An urgent cardiac angiogram was done which revealed normal patent coronaries. His transthoracic echocardiogram was also reported to be normal. He continued to have intermittent episodes of chest pain that was partially relieved by morphine. Erythrocyte sedimentation rate and C-reactive protein were elevated. Work up for autoimmune diseases, vasculitis, myocarditis panel were insignificant. Later, magnetic resonance imaging (MRI with gadolinium enhanced contrast (Figure 1 was obtained which showed abnormal epicardial/subepicardial myocardial enhancement within the inferolateral wall and cardiac apex consistent with focal ...

  18. Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation

    OpenAIRE

    Jasdeep Kaur; Manish Mahajan

    2015-01-01

    The procedure of partitioning an image into various segments to reform an image into somewhat that is more significant and easier to analyze, defined as image segmentation. In real world applications, noisy images exits and there could be some measurement errors too. These factors affect the quality of segmentation, which is of major concern in medical fields where decisions about patients’ treatment are based on information extracted from radiological images. Several algorithms and technique...

  19. A Survey on Deep Learning in Medical Image Analysis

    NARCIS (Netherlands)

    Litjens, G.J.; Kooi, T.; Ehteshami Bejnordi, B.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; Laak, J.A.W.M. van der; Ginneken, B. van; Sanchez, C.I.

    2017-01-01

    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared

  20. A framework for integration of heterogeneous medical imaging networks.

    Science.gov (United States)

    Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

    2014-01-01

    Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.

  1. Numerical Inversion of Integral Equations for Medical Imaging and Geophysics

    Science.gov (United States)

    1988-12-13

    Equations for Medical Imaging and Geophysics (Unclassified) 12 PERSONAL AUTHOR(S) Frank Stenger 13a. TYPE OF REPORT 13b TIME COVERED 14. DATE OF REPORT...9r~S NUMERICAL INVERSION OF INTEGRAL EQUATIONS FOR MEDICAL IMAGING AND GEOPHYSICS FINAL REPORT AUTHOR OF REPORT: Frank Stenger December 13, 1988

  2. Improved Strategies for Parallel Medical Image Processing Applications

    Institute of Scientific and Technical Information of China (English)

    WANG Kun; WANG Xiao-ying; LI San-li; CHEN Ying

    2008-01-01

    In order to meet the demands of high efficient and real-time computer assisted diagnosis as well as screening in medical area, to improve the efficacy of parallel medical image processing is of great importance. This article proposes improved strategies for parallel medical image processing applications,which is categorized into two genera. For each genus individual strategy is devised, including the theoretic algorithm for minimizing the exertion time. Experiment using mammograms not only justifies the validity of the theoretic analysis, with reasonable difference between the theoretic and measured value, but also shows that when adopting the improved strategies, efficacy of medical image parallel processing is improved greatly.

  3. Integrated ultrasound and gamma imaging probe for medical diagnosis

    Science.gov (United States)

    Pani, R.; Pellegrini, R.; Cinti, M. N.; Polito, C.; Orlandi, C.; Fabbri, A.; De Vincentis, G.

    2016-03-01

    In the last few years, integrated multi-modality systems have been developed, aimed at improving the accuracy of medical diagnosis correlating information from different imaging techniques. In this contest, a novel dual modality probe is proposed, based on an ultrasound detector integrated with a small field of view single photon emission gamma camera. The probe, dedicated to visualize small organs or tissues located at short depths, performs dual modality images and permits to correlate morphological and functional information. The small field of view gamma camera consists of a continuous NaI:Tl scintillation crystal coupled with two multi-anode photomultiplier tubes. Both detectors were characterized in terms of position linearity and spatial resolution performances in order to guarantee the spatial correspondence between the ultrasound and the gamma images. Finally, dual-modality images of custom phantoms are obtained highlighting the good co-registration between ultrasound and gamma images, in terms of geometry and image processing, as a consequence of calibration procedures.

  4. Machine learning approaches in medical image analysis: From detection to diagnosis

    NARCIS (Netherlands)

    M. de Bruijne (Marleen)

    2016-01-01

    textabstractMachine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging

  5. Machine learning approaches in medical image analysis: From detection to diagnosis.

    Science.gov (United States)

    de Bruijne, Marleen

    2016-10-01

    Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

  6. Case-based medical informatics

    Directory of Open Access Journals (Sweden)

    Arocha José F

    2004-11-01

    Full Text Available Abstract Background The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine. Discussion We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers. We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences

  7. Applications of the Generalized X-ray Diffraction Enhanced Imaging in the Medical Imaging

    Science.gov (United States)

    Maksimenko, Anton; Hashimoto, Eiko; Ando, Masami; Sugiyama, Hiroshi

    2007-01-01

    The X-ray Diffraction Enhanced Imaging (DEI) is the analyzer-based X-ray imaging technique which allows extraction of the "pure refraction" and "apparent absorption" contrasts from two images taken on the opposite sides of the rocking curve of the analyzing crystal. The refraction contrast obtained by this method shows many advantages over conventional absorption contrast. It was successfully applied in medicine, technique and other fields of science. However, information provided by the method is rather qualitative than quantitative. This happens because either side of the rocking curve of the analyzer is approximated as a straight line what limits the ranges of applicability and introduces additional error. One can easily overcome this problem considering the rocking curve as is instead of it's Taylor's expansion. This report is dedicated to the application of this idea in medical imaging and especially computed tomography based on the refraction contrast. The results obtained via both methods are presented and compared.

  8. Recent advances in medical imaging: anatomical and clinical applications.

    Science.gov (United States)

    Grignon, Bruno; Mainard, Laurence; Delion, Matthieu; Hodez, Claude; Oldrini, Guillaume

    2012-10-01

    The aim of this paper was to present an overview of the most important recent advances in medical imaging and their potential clinical and anatomical applications. Dramatic changes have been particularly observed in the field of computed tomography (CT) and magnetic resonance imaging (MRI). Computed tomography (CT) has been completely overturned by the successive development of helical acquisition, multidetector and large area-detector acquisition. Visualising brain function has become a new challenge for MRI, which is called functional MRI, currently based principally on blood oxygenation level-dependent sequences, which could be completed or replaced by other techniques such as diffusion MRI (DWI). Based on molecular diffusion due to the thermal energy of free water, DWI offers a spectrum of anatomical and clinical applications, ranging from brain ischemia to visualisation of large fibrous structures of the human body such as the anatomical bundles of white matter with diffusion tensor imaging and tractography. In the field of X-ray projection imaging, a new low-dose device called EOS has been developed through new highly sensitive detectors of X-rays, allowing for acquiring frontal and lateral images simultaneously. Other improvements have been briefly mentioned. Technical principles have been considered in order to understand what is most useful in clinical practice as well as in the field of anatomical applications. Nuclear medicine has not been included.

  9. Current trends in medical image registration and fusion

    Directory of Open Access Journals (Sweden)

    Fatma El-Zahraa Ahmed El-Gamal

    2016-03-01

    Full Text Available Recently, medical image registration and fusion processes are considered as a valuable assistant for the medical experts. The role of these processes arises from their ability to help the experts in the diagnosis, following up the diseases’ evolution, and deciding the necessary therapies regarding the patient’s condition. Therefore, the aim of this paper is to focus on medical image registration as well as medical image fusion. In addition, the paper presents a description of the common diagnostic images along with the main characteristics of each of them. The paper also illustrates most well-known toolkits that have been developed to help the working with the registration and fusion processes. Finally, the paper presents the current challenges associated with working with medical image registration and fusion through illustrating the recent diseases/disorders that were addressed through such an analyzing process.

  10. [Managing digital medical imaging projects in healthcare services: lessons learned].

    Science.gov (United States)

    Rojas de la Escalera, D

    2013-01-01

    Medical imaging is one of the most important diagnostic instruments in clinical practice. The technological development of digital medical imaging has enabled healthcare services to undertake large scale projects that require the participation and collaboration of many professionals of varied backgrounds and interests as well as substantial investments in infrastructures. Rather than focusing on systems for dealing with digital medical images, this article deals with the management of projects for implementing these systems, reviewing various organizational, technological, and human factors that are critical to ensure the success of these projects and to guarantee the compatibility and integration of digital medical imaging systems with other health information systems. To this end, the author relates several lessons learned from a review of the literature and the author's own experience in the technical coordination of digital medical imaging projects. Copyright © 2012 SERAM. Published by Elsevier Espana. All rights reserved.

  11. Medical image processing on the GPU - past, present and future.

    Science.gov (United States)

    Eklund, Anders; Dufort, Paul; Forsberg, Daniel; LaConte, Stephen M

    2013-12-01

    Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

  12. Medical image segmentation using object atlas versus object cloud models

    Science.gov (United States)

    Phellan, Renzo; Falcão, Alexandre X.; Udupa, Jayaram K.

    2015-03-01

    Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.

  13. A Multimodal Search Engine for Medical Imaging Studies.

    Science.gov (United States)

    Pinho, Eduardo; Godinho, Tiago; Valente, Frederico; Costa, Carlos

    2017-02-01

    The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks.

  14. Digital Signal Processing for Medical Imaging Using Matlab

    CERN Document Server

    Gopi, E S

    2013-01-01

    This book describes medical imaging systems, such as X-ray, Computed tomography, MRI, etc. from the point of view of digital signal processing. Readers will see techniques applied to medical imaging such as Radon transformation, image reconstruction, image rendering, image enhancement and restoration, and more. This book also outlines the physics behind medical imaging required to understand the techniques being described. The presentation is designed to be accessible to beginners who are doing research in DSP for medical imaging. Matlab programs and illustrations are used wherever possible to reinforce the concepts being discussed.  ·         Acts as a “starter kit” for beginners doing research in DSP for medical imaging; ·         Uses Matlab programs and illustrations throughout to make content accessible, particularly with techniques such as Radon transformation and image rendering; ·         Includes discussion of the basic principles behind the various medical imaging tec...

  15. Creating a classification of image types in the medical literature for visual categorization

    Science.gov (United States)

    Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

    2012-02-01

    Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation

  16. A New Method of Semantic Feature Extraction for Medical Images Data

    Institute of Scientific and Technical Information of China (English)

    XIE Conghua; SONG Yuqing; CHANG Jinyi

    2006-01-01

    In order to overcome the disadvantages of color, shape and texture-based features definition for medical images, this paper defines a new kind of semantic feature and its extraction algorithm. We firstly use kernel density estimation statistical model to describe the complicated medical image data, secondly, define some typical representative pixels of images as feature and finally, take hill-climbing strategy of Artificial Intelligence to extract those semantic features. Results of a content-based medial image retrieve system show that our semantic features have better distinguishing ability than those color, shape and texture-based features and can improve the ratios of recall and precision of this system smartly.

  17. 基于最小均方误差原理的医学X光影像滤波阈值选择%Threshold selection method for medical X-ray images filter based on minimum even-square error

    Institute of Scientific and Technical Information of China (English)

    刘光达; 赵立荣

    2001-01-01

    Disturbance noises in medical X-ray imaging systems consist of inherent and quantum noises, which obey random Gauss and Polsson distributions, respectively. This paper theoretically provided an optimum threshold selection method for medical X-ray images filter. Through practical process of CT images, medical X-ray images filter based on minimum even-square error has been accomplished in this work.%固有噪声和量子噪声构成了医学X光影像系统的干扰噪声。它们在统计规律上分别是依从高斯分布和泊松分布的随机空间波动。本文从理论上推导出了基于小波变换原理的医学X光影像的固有噪声抑制和消除处理中的最优滤波阈值选择。通过对实际CT影像的消噪处理应用,实现了基于最小均方误差原理的医学X光影像的滤波处理。

  18. Study on 3D Reconstruction for Medical Images Based on MITK%基于MITK的医学图像三维重建系统的研究与应用

    Institute of Scientific and Technical Information of China (English)

    马婧

    2011-01-01

    Objective To study the technology of three-dimensional reconstruction of medical image based on MITK and analyze the processing effect of medical image based on MITK. Methods An interactive three-dimensional reconstruction systern was implemented in the environment of VC++ 6.0. using the surface rendering technology with M(I)TX. Results The modified algorithm of medical image surface reconstruction was implemented by using the MITK platform. Conclusion Three-dimensional reconstruction of medical image is a multi-disciplinary subject, which is an important application of computer graphics and image processing in biomedical engineering. It is widely uaed in diagnostic, surgery planning and simulating. virtual endoscope and anatomy teaching, involving the relevant knowledge of digital image processing,mathematics, graphics and medical field, and so forth. Therefore, it will be of great theoretical significance and have great application value to study this area.[Chinese Medical Equipment Journal, 2011 ,32( 5) : 21-23]%目的:研究基于MITK的医学图像三维重建技术,分析基于MITK的医学图像处理效果.方法:利用MITK平台在VC++6.0环境下采用面绘制技术实现了交互式医学图像三维重建系统.结果:利用MITK(平台实现了改进算法下医学图像表面重建.结论:医学图像三维重建是一个多学科交叉的研究领域,是计算机图形学和图像处理在生物医学工程中的重要应用.它涉及数字图像处理、数学、图形学以及医学领域的相关知识,在诊断医学、手术规划及模拟仿真、虚拟内窥镜、解剖教学等方面都有重要应用.

  19. A New Method of CT MedicalImages Contrast Enhancement

    Institute of Scientific and Technical Information of China (English)

    SUNFeng-rong; LIUWei; WANGChang-yu; MEILiang-mo

    2004-01-01

    A new method of contrast enhancement is proposed in the paper using multiscale edge representation of images, and is applied to the field of CT medical image processing. Comparing to the traditional Window technique, our method is adaptive and meets the demand of radiology clinics more better. The clinical experiment results show the practicality and the potential applied value of our methodin the field of CT medical images contrast enhancement.

  20. A telemedicine system for remote cooperative medical imaging diagnosis.

    Science.gov (United States)

    Gómez, E J; del Pozo, F; Quiles, J A; Arredondo, M T; Rahms, H; Sanz, M; Cano, P

    1996-01-01

    Telemedicine is changing the classical form of health care delivery, by providing efficient solutions to an increasing number of new situations: here we consider those which require some type of computer-supported cooperative work (CSCW) between health care professionals located in different clinical sites. This paper presents the design and development of a telemedicine system for remote computer-supported cooperative medical imaging diagnosis. The main and novel component of our system is a new CSCW distributed architecture, comprised by a collaborative toolkit to add audioconferencing, telepointing, window sharing, user's coordination and application synchronization facilities, either to existing or new medical imaging diagnosis applications. In comparison with existing CSCW products, mainly based on centralized architectures, our distributed toolkit is specially designed for telemedicine applications: to allow different levels of sharing between participants, to improve user feedback in highly interactive user interfaces, and to optimize the required communication bandwidth in order to implement a telemedicine CSCW application on almost any telecommunication network. This telemedicine CSCW system has been applied to build a cooperative medical imaging diagnosis application, in which two doctors, located in different hospitals, need to achieve a cooperative diagnosis on haemodynamic studies using cardiac angiography images. The design of the graphical user interface for this kind of telemedicine CSCW systems, a critical component which conforms any telemedicine application, is also addressed with a new methodological approach, to assure the system usability and final user acceptance. The telemedicine cardiac angiography pilot has been implemented, tested and evaluated within the Research Project 'FEST-Framework for European Services in Telemedicine' funded by EU AIM Programme.

  1. [Current situations and problems of quality control for medical imaging display systems].

    Science.gov (United States)

    Shibutani, Takayuki; Setojima, Tsuyoshi; Ueda, Katsumi; Takada, Katsumi; Okuno, Teiichi; Onoguchi, Masahisa; Nakajima, Tadashi; Fujisawa, Ichiro

    2015-04-01

    Diagnostic imaging has been shifted rapidly from film to monitor diagnostic. Consequently, Japan medical imaging and radiological systems industries association (JIRA) have recommended methods of quality control (QC) for medical imaging display systems. However, in spite of its need by majority of people, executing rate is low. The purpose of this study was to validate the problem including check items about QC for medical imaging display systems. We performed acceptance test of medical imaging display monitors based on Japanese engineering standards of radiological apparatus (JESRA) X-0093*A-2005 to 2009, and performed constancy test based on JESRA X-0093*A-2010 from 2010 to 2012. Furthermore, we investigated the cause of trouble and repaired number. Medical imaging display monitors had 23 inappropriate monitors about visual estimation, and all these monitors were not criteria of JESRA about luminance uniformity. Max luminance was significantly lower year-by-year about measurement estimation, and the 29 monitors did not meet the criteria of JESRA about luminance deviation. Repaired number of medical imaging display monitors had 25, and the cause was failure liquid crystal panel. We suggested the problems about medical imaging display systems.

  2. Medical physics personnel for medical imaging: requirements, conditions of involvement and staffing levels-French recommendations.

    Science.gov (United States)

    Isambert, Aurélie; Le Du, Dominique; Valéro, Marc; Guilhem, Marie-Thérèse; Rousse, Carole; Dieudonné, Arnaud; Blanchard, Vincent; Pierrat, Noëlle; Salvat, Cécile

    2015-04-01

    The French regulations concerning the involvement of medical physicists in medical imaging procedures are relatively vague. In May 2013, the ASN and the SFPM issued recommendations regarding Medical Physics Personnel for Medical Imaging: Requirements, Conditions of Involvement and Staffing Levels. In these recommendations, the various areas of activity of medical physicists in radiology and nuclear medicine have been identified and described, and the time required to perform each task has been evaluated. Criteria for defining medical physics staffing levels are thus proposed. These criteria are defined according to the technical platform, the procedures and techniques practised on it, the number of patients treated and the number of persons in the medical and paramedical teams requiring periodic training. The result of this work is an aid available to each medical establishment to determine their own needs in terms of medical physics. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  3. 基于数据仓库的医疗影像检查KPI数据展示研究%Research on Medical Imaging KPI Data Display Based on Data Warehouse

    Institute of Scientific and Technical Information of China (English)

    吴佳峰; 徐哲; 何必仕

    2012-01-01

    本研究针对某三甲医院医疗影像信息系统(RIS/PACS)数年应用过程中所积累的海量数据,建立数据仓库,并且围绕医疗影像检查绩效开展关键指标数据展示研究,为决策者提供直观的图表数据,以辅助决策.%Aiming at mass data accumulated in application process of medical image information system(RIS/PACS) in a first-class Grade 3 hospital for several years, the data warehouse is built. And based on medical imaging performance to carry out demonstrate research of key index data, hospital leaders could get intuitive chart data which assist them to make decisions.

  4. AN OVERVIEW OF ELASTOGRAPHY - AN EMERGING BRANCH OF MEDICAL IMAGING.

    Science.gov (United States)

    Sarvazyan, Armen; Hall, Timothy J; Urban, Matthew W; Fatemi, Mostafa; Aglyamov, Salavat R; Garra, Brian S

    2011-11-01

    From times immemorial manual palpation served as a source of information on the state of soft tissues and allowed detection of various diseases accompanied by changes in tissue elasticity. During the last two decades, the ancient art of palpation gained new life due to numerous emerging elasticity imaging (EI) methods. Areas of applications of EI in medical diagnostics and treatment monitoring are steadily expanding. Elasticity imaging methods are emerging as commercial applications, a true testament to the progress and importance of the field.In this paper we present a brief history and theoretical basis of EI, describe various techniques of EI and, analyze their advantages and limitations, and overview main clinical applications. We present a classification of elasticity measurement and imaging techniques based on the methods used for generating a stress in the tissue (external mechanical force, internal ultrasound radiation force, or an internal endogenous force), and measurement of the tissue response. The measurement method can be performed using differing physical principles including magnetic resonance imaging (MRI), ultrasound imaging, X-ray imaging, optical and acoustic signals.Until recently, EI was largely a research method used by a few select institutions having the special equipment needed to perform the studies. Since 2005 however, increasing numbers of mainstream manufacturers have added EI to their ultrasound systems so that today the majority of manufacturers offer some sort of Elastography or tissue stiffness imaging on their clinical systems. Now it is safe to say that some sort of elasticity imaging may be performed on virtually all types of focal and diffuse disease. Most of the new applications are still in the early stages of research, but a few are becoming common applications in clinical practice.

  5. Monte Carlo PENRADIO software for dose calculation in medical imaging

    Science.gov (United States)

    Adrien, Camille; Lòpez Noriega, Mercedes; Bonniaud, Guillaume; Bordy, Jean-Marc; Le Loirec, Cindy; Poumarede, Bénédicte

    2014-06-01

    The increase on the collective radiation dose due to the large number of medical imaging exams has led the medical physics community to deeply consider the amount of dose delivered and its associated risks in these exams. For this purpose we have developed a Monte Carlo tool, PENRADIO, based on a modified version of PENELOPE code 2006 release, to obtain an accurate individualized radiation dose in conventional and interventional radiography and in computed tomography (CT). This tool has been validated showing excellent agreement between the measured and simulated organ doses in the case of a hip conventional radiography and a coronography. We expect the same accuracy in further results for other localizations and CT examinations.

  6. Radiation protection in medical imaging and radiation oncology

    CERN Document Server

    Stoeva, Magdalena S

    2016-01-01

    Radiation Protection in Medical Imaging and Radiation Oncology focuses on the professional, operational, and regulatory aspects of radiation protection. Advances in radiation medicine have resulted in new modalities and procedures, some of which have significant potential to cause serious harm. Examples include radiologic procedures that require very long fluoroscopy times, radiolabeled monoclonal antibodies, and intravascular brachytherapy. This book summarizes evidence supporting changes in consensus recommendations, regulations, and health physics practices associated with these recent advances in radiology, nuclear medicine, and radiation oncology. It supports intelligent and practical methods for protection of personnel, the public, and patients. The book is based on current recommendations by the International Commission on Radiological Protection and is complemented by detailed practical sections and professional discussions by the world’s leading medical and health physics professionals. It also ...

  7. Understanding by seeing before treating: present and future of medical imaging.

    Science.gov (United States)

    Bentourkia, M'hamed

    2012-10-01

    In the last three decades, the development of medical imaging gave a burst to modern medicine. A considerable budget has been affected to develop and equip departments of radiology, nuclear medicine, medical imaging, radiotherapy, and emergency services. Several imaging installations have become intensively and exclusively used by the clinic where most of the imaging exams are performed for diagnoses, while for other imaging installations, the time of usage is shared between the clinical and research departments. However, very few centers restrain their installations to the research groups only,as their budgets are not sufficient to maintain the devices. The increase in medical imaging demand is mainly attributed to: (1) the drastic increase in the technology of electronic and computing sciences, which has made the imaging devices efficient and easy to operate, and (2) to the public and private insurers who consent the reimbursement of the imaging fees for some determined medical exams. Because the imaging modalities are based on different physical properties, they can be used individually, complementary but distinctly, or jointly. Despite their beneficial contribution, the imaging devices should be used with care as they can provoke undesirable effects. The future of the imaging technologies is, a priori, to exploit the full potential of the actual instruments, to target experiments at the molecular level, and to be able to monitor a biological phenomenon at its time of occurrence. In this paper,rapid overview and perspectives are proposed as the field of medical imaging is vast and encompasses several domains of knowledge.

  8. Intrasubject registration for change analysis in medical imaging

    NARCIS (Netherlands)

    Staring, M.

    2008-01-01

    Image matching is important for the comparison of medical images. Comparison is of clinical relevance for the analysis of differences due to changes in the health of a patient. For example, when a disease is imaged at two time points, then one wants to know if it is stable, has regressed, or

  9. Mesh Processing in Medical-Image Analysis-a Tutorial

    DEFF Research Database (Denmark)

    Levine, Joshua A.; Paulsen, Rasmus Reinhold; Zhang, Yongjie

    2012-01-01

    Medical-image analysis requires an understanding of sophisticated scanning modalities, constructing geometric models, building meshes to represent domains, and downstream biological applications. These four steps form an image-to-mesh pipeline. For research in this field to progress, the imaging...

  10. An overview of medical image processing methods

    African Journals Online (AJOL)

    USER

    2010-06-14

    Jun 14, 2010 ... theoretical subjects about methods and algorithms used are explained. In the forth section, ... image processing techniques such as image segmentation, compression .... A convolution mask like -1 | 0 | 1 could be used in each.

  11. Bioassay Phantoms Using Medical Images and Computer Aided Manufacturing

    Energy Technology Data Exchange (ETDEWEB)

    Dr. X. Geroge Xu

    2011-01-28

    A radiation bioassay program relies on a set of standard human phantoms to calibrate and assess radioactivity levels inside a human body for radiation protection and nuclear medicine imaging purposes. However, the methodologies in the development and application of anthropomorphic phantoms, both physical and computational, had mostly remained the same for the past 40 years. We herein propose a 3-year research project to develop medical image-based physical and computational phantoms specifically for radiation bioassay applications involving internally deposited radionuclides. The broad, long-term objective of this research was to set the foundation for a systematic paradigm shift away from the anatomically crude phantoms in existence today to realistic and ultimately individual-specific bioassay methodologies. This long-term objective is expected to impact all areas of radiation bioassay involving nuclear power plants, U.S. DOE laboratories, and nuclear medicine clinics.

  12. Medical DR Image Adaptive Enhancement Based on Quantum Glowworm and Gain Beta%基于量子萤火虫和增益Beta的医学DR图像自适应增强

    Institute of Scientific and Technical Information of China (English)

    张剑飞; 杜晓昕; 王波

    2014-01-01

    为了解决医学DR图像对比度低、边缘模糊、细节不清晰和缺乏自适应增强的问题,提出一种基于量子萤火虫和增益Beta的医学DR图像自适应增强方法。该方法针对传统Beta变换的不足,提出了增益Beta变换方法,提出了应用于增益Beta变换的二分类别判定方法和参数约束修正方法。为实现自适应的增强,将量子计算和萤火虫群算法结合提出一种量子萤火虫群算法,提出一种FHCE图像增强质量评价标准作为算法的适应度函数,该算法可快速精确求解应用于医学DR图像自适应增强的增益Beta变换的最优增强参数值。实验结果表明这种方法提高了医学DR图像的对比度,边缘和细节更加的清晰,能够自适应增强各类型医学DR图像。%According to the problem about medical DR image that contrast is reduced ,edge is vague ,details are buried ,it is short of adaptive enhancement ,method of medical DR image adaptive enhancement based on quantum glowworm and gain Beta is proposed .A gain Beta transformation method is put forward aiming at the shortage of traditionalBeta transformation . A dichotomy category determination method and parameter constraint revision method applied to gain Beta transformation are given .In order to realize the adaptive enhancement ,a quantum glowworm swarm algorithm based on quantum computation and glowworm swarm algorithm is proposed .A HHCE image enhancement quality evaluation criteria is defined ,and it is used as fitness function of this algorithm .Optimal enhancement parameters of gain Beta transformation applied to medical DR image adaptive enhancement are computed fleetly and precisely .Simulation experiment on this method about medical DR image shows that contrast is raised ,edge and details are clearer ,adaptive enhancement on different kinds of medical DR images is realized .

  13. A New Approach To Embed Medical Information Into Medical Images

    Directory of Open Access Journals (Sweden)

    Esra Ayça Güzeldereli

    2013-08-01

    Full Text Available In recent years, under the light of developments in the field of computer, there has been an increasing demand for data processing in the health sector. Many different methods are being used to connect the personal information or diagnosis with the patient. These methods can differ from each other according to imaging techniques. In this thesis, this kind of data hiding/embedding techniques are mostly prefered in order to provide a privacy for patients. Also, useful to use compression techniques with data compressing for preserving the originality of the image which is damaged by large size of personal information saved in memory.

  14. Real Time Medical Image Consultation System Through Internet

    Directory of Open Access Journals (Sweden)

    D. Durga Prasad

    2010-01-01

    Full Text Available Teleconsultation among doctors using a telemedicine system typically involves dealing with and sharing medical images of the patients. This paper describes a software tool written in Java which enables the participating doctors to view medical images such as blood slides, X-Ray, USG, ECG etc. online and even allows them to mark and/or zoom specific areas. It is a multi-party secure image communication system tool that can be used by doctors and medical consultants over the Internet.

  15. Population Pharmacokinetics of Tracers: A New Tool for Medical Imaging?

    Science.gov (United States)

    Gandia, Peggy; Jaudet, Cyril; Chatelut, Etienne; Concordet, Didier

    2017-02-01

    Positron emission tomography-computed tomography is a medical imaging method measuring the activity of a radiotracer chosen to accumulate in cancer cells. A recent trend of medical imaging analysis is to account for the radiotracer's pharmacokinetic properties at a voxel (three-dimensional-pixel) level to separate the different tissues. These analyses are closely linked to population pharmacokinetic-pharmacodynamic modelling. Kineticists possess the cultural background to improve medical imaging analysis. This article stresses the common points with population pharmacokinetics and highlights the methodological locks that need to be lifted.

  16. I2C: a system for the indexing, storage, and retrieval of medical images by content.

    Science.gov (United States)

    Orphanoudakis, S C; Chronaki, C; Kostomanolakis, S

    1994-01-01

    Image indexing, storage, and retrieval based on pictorial content is a feature of image database systems which is becoming of increasing importance in many application domains. Medical image database systems, which support the retrieval of images generated by different modalities based on their pictorial content, will provide added value to future generation picture archiving and communication systems (PACS), and can be used as a diagnostic decision support tools and as a tool for medical research and training. We present the architecture and features of I2C, a system for the indexing, storage, and retrieval of medical images by content. A unique design feature of this architecture is that it also serves as a platform for the implementation and performance evaluation of image description methods and retrieval strategies. I2C is a modular and extensible system, which has been developed based on object-oriented principles. It consists of a set of cooperating modules which facilitate the addition of new graphical tools, image description and matching algorithms. These can be incorporated into the system at the application level. The core concept of I2C is an image class hierarchy. Image classes encapsulate different segmentation and image content description algorithms. Medical images are assigned to image classes based on a set of user-defined attributes such as imaging modality, type of study, anatomical characteristics, etc. This class-based treatment of images in the I2C system achieves increased accuracy and efficiency of content-based retrievals, by limiting the search space and allowing specific algorithms to be fine-tuned for images acquired by different modalities or representing different parts of the anatomy.

  17. Interpretation of medical imaging data with a mobile application: a mobile digital imaging processing environment

    Directory of Open Access Journals (Sweden)

    Meng Kuan eLin

    2013-07-01

    Full Text Available Digital Imaging Processing (DIP requires data extraction and output from a visualization tool to be consistent. Data handling and transmission between the server and a user is a systematic process in service interpretation. The use of integrated medical services for management and viewing of imaging data in combination with a mobile visualization tool can be greatly facilitated by data analysis and interpretation. This paper presents an integrated mobile application and digital imaging processing service, called M-DIP. The objective of the system is to (1 automate the direct data tiling, conversion, pre-tiling of brain images from Medical Imaging NetCDF (MINC, Neuroimaging Informatics Technology Initiative (NIFTI to RAW formats; (2 speed up querying of imaging measurement; and (3 display high level of images with three dimensions in real world coordinates. In addition, M-DIP provides the ability to work on a mobile or tablet device without any software installation using web-based protocols. M-DIP implements three levels of architecture with a relational middle- layer database, a stand-alone DIP server and a mobile application logic middle level realizing user interpretation for direct querying and communication. This imaging software has the ability to display biological imaging data a multiple zoom levels and to increase its quality to meet users expectations. Interpretation of bioimaging data is facilitated by an interface analogous to online mapping services using real world coordinate browsing. This allows mobile devices to display multiple datasets simultaneously from a remote site. M-DIP can be used as a measurement repository that can be accessed by any network environment, such as a portable mobile or tablet device. In addition, this system and combination with mobile applications are establishing a virtualization tool in the neuroinformatics field to speed interpretation services.

  18. An efficient similarity measure technique for medical image registration

    Indian Academy of Sciences (India)

    Vilas H Gaidhane; Yogesh V Hote; Vijander Singh

    2012-12-01

    In this paper, an efficient similarity measure technique is proposed for medical image registration. The proposed approach is based on the Gerschgorin circles theorem. In this approach, image registration is carried out by considering Gerschgorin bounds of a covariance matrix of two compared images with normalized energy. The beauty of this approach is that there is no need to calculate image features like eigenvalues and eigenvectors. This technique is superior to other well-known techniques such as normalized cross-correlation method and eigenvalue-based similarity measures since it avoids the false registration and requires less computation. The proposed approach is sensitive to small defects and robust to change in illuminations and noise. Experimental results on various synthetic medical images have shown the effectiveness of the proposed technique for detecting and locating the disease in the complicated medical images.

  19. Medical Technology Base Master Plan

    Science.gov (United States)

    1990-03-01

    action of candidate mnedical1 countermeasures - Analysis and characterization of candidate compounds and their inetabolites - Application of molecular ...expected that research in molecular biology will lead to medical ,.vphylaes and treatments that ofter improved speclicity and potency, thus increasing...Disease Hazards Research (Inlectious Disease, Medical Biologia Defense, and Military AIDS), Conbat Casualty Care Research, Medical Chem"ca Defense Research

  20. Medical image of the week: eosphageal perforation

    Directory of Open Access Journals (Sweden)

    Bilal J

    2015-04-01

    Full Text Available No abstract available. Article truncated after 150 words. A 74 year old man with a past medical history of esophageal strictures status post dilatation, coronary artery disease status post CABG, and atrial fibrillation presented to hospital with complaints of severe chest pain that began after the consumption of tortilla chips one hour prior to presentation. Electrocardiogram and cardiac enzymes were not consistent with acute coronary syndrome. Chest X-ray was consistent with a widened mediastinal silhouette. Contrast esophogram was negative for extra luminal extravasation. CT scan of the chest with oral contrast demonstrated thickening of the mid-thoracic esophagus with an extra-luminal focus of gas in the mediastinum along with fluid along the inferior aspect of the esophagus (Figures 1 and 2. These findings were concerning for esophageal perforation. The patient was taken to the operating room for endoscopy which showed micro perforation in mid-esophagus. Esophageal perforation remains a highly morbid condition. Mortality rates are based predominantly on time of ...