Double-compression method for biomedical images

Science.gov (United States)

Antonenko, Yevhenii A.; Mustetsov, Timofey N.; Hamdi, Rami R.; Małecka-Massalska, Teresa; Orshubekov, Nurbek; DzierŻak, RóŻa; Uvaysova, Svetlana

2017-08-01

This paper describes a double compression method (DCM) of biomedical images. A comparison of image compression factors in size JPEG, PNG and developed DCM was carried out. The main purpose of the DCM - compression of medical images while maintaining the key points that carry diagnostic information. To estimate the minimum compression factor an analysis of the coding of random noise image is presented.

The research progress of dual-modality probes for molecular imaging

International Nuclear Information System (INIS)

Cao Feng; Chen Yue

2010-01-01

Various imaging modalities have been exploited to investigate the anatomic or functional dissemination of tissues in the body. However, no single imaging modality allows overall structural, functional, and molecular information as each imaging modality has its own unique strengths and weaknesses. The combination of two imaging modalities that investigates the strengths of different methods might offer the prospect of improved diagnostic abilities. As more and more dual-modality imaging system have become clinically adopted, significant progress has been made toward the creation of dual-modality imaging probes, which can be used as novel tools for future multimodality systems. These all-in-one probes take full advantage of two different imaging modalities and could provide comprehensive information for clinical diagnostics. This review discusses the advantages and challenges in developing dual-modality imaging probes. (authors)

Perceptual Image Compression in Telemedicine

Science.gov (United States)

Watson, Andrew B.; Ahumada, Albert J., Jr.; Eckstein, Miguel; Null, Cynthia H. (Technical Monitor)

1996-01-01

The next era of space exploration, especially the "Mission to Planet Earth" will generate immense quantities of image data. For example, the Earth Observing System (EOS) is expected to generate in excess of one terabyte/day. NASA confronts a major technical challenge in managing this great flow of imagery: in collection, pre-processing, transmission to earth, archiving, and distribution to scientists at remote locations. Expected requirements in most of these areas clearly exceed current technology. Part of the solution to this problem lies in efficient image compression techniques. For much of this imagery, the ultimate consumer is the human eye. In this case image compression should be designed to match the visual capacities of the human observer. We have developed three techniques for optimizing image compression for the human viewer. The first consists of a formula, developed jointly with IBM and based on psychophysical measurements, that computes a DCT quantization matrix for any specified combination of viewing distance, display resolution, and display brightness. This DCT quantization matrix is used in most recent standards for digital image compression (JPEG, MPEG, CCITT H.261). The second technique optimizes the DCT quantization matrix for each individual image, based on the contents of the image. This is accomplished by means of a model of visual sensitivity to compression artifacts. The third technique extends the first two techniques to the realm of wavelet compression. Together these two techniques will allow systematic perceptual optimization of image compression in NASA imaging systems. Many of the image management challenges faced by NASA are mirrored in the field of telemedicine. Here too there are severe demands for transmission and archiving of large image databases, and the imagery is ultimately used primarily by human observers, such as radiologists. In this presentation I will describe some of our preliminary explorations of the applications

Composite Techniques Based Color Image Compression

Directory of Open Access Journals (Sweden)

Zainab Ibrahim Abood

2017-03-01

Full Text Available Compression for color image is now necessary for transmission and storage in the data bases since the color gives a pleasing nature and natural for any object, so three composite techniques based color image compression is implemented to achieve image with high compression, no loss in original image, better performance and good image quality. These techniques are composite stationary wavelet technique (S, composite wavelet technique (W and composite multi-wavelet technique (M. For the high energy sub-band of the 3rd level of each composite transform in each composite technique, the compression parameters are calculated. The best composite transform among the 27 types is the three levels of multi-wavelet transform (MMM in M technique which has the highest values of energy (En and compression ratio (CR and least values of bit per pixel (bpp, time (T and rate distortion R(D. Also the values of the compression parameters of the color image are nearly the same as the average values of the compression parameters of the three bands of the same image.

Subjective evaluation of compressed image quality

Science.gov (United States)

Lee, Heesub; Rowberg, Alan H.; Frank, Mark S.; Choi, Hyung-Sik; Kim, Yongmin

1992-05-01

Lossy data compression generates distortion or error on the reconstructed image and the distortion becomes visible as the compression ratio increases. Even at the same compression ratio, the distortion appears differently depending on the compression method used. Because of the nonlinearity of the human visual system and lossy data compression methods, we have evaluated subjectively the quality of medical images compressed with two different methods, an intraframe and interframe coding algorithms. The evaluated raw data were analyzed statistically to measure interrater reliability and reliability of an individual reader. Also, the analysis of variance was used to identify which compression method is better statistically, and from what compression ratio the quality of a compressed image is evaluated as poorer than that of the original. Nine x-ray CT head images from three patients were used as test cases. Six radiologists participated in reading the 99 images (some were duplicates) compressed at four different compression ratios, original, 5:1, 10:1, and 15:1. The six readers agree more than by chance alone and their agreement was statistically significant, but there were large variations among readers as well as within a reader. The displacement estimated interframe coding algorithm is significantly better in quality than that of the 2-D block DCT at significance level 0.05. Also, 10:1 compressed images with the interframe coding algorithm do not show any significant differences from the original at level 0.05.

HVS-based medical image compression

Energy Technology Data Exchange (ETDEWEB)

Kai Xie [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China)]. E-mail: xie_kai2001@sjtu.edu.cn; Jie Yang [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China); Min Zhuyue [CREATIS-CNRS Research Unit 5515 and INSERM Unit 630, 69621 Villeurbanne (France); Liang Lixiao [Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai (China)

2005-07-01

Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time.

HVS-based medical image compression

International Nuclear Information System (INIS)

Kai Xie; Jie Yang; Min Zhuyue; Liang Lixiao

2005-01-01

Introduction: With the promotion and application of digital imaging technology in the medical domain, the amount of medical images has grown rapidly. However, the commonly used compression methods cannot acquire satisfying results. Methods: In this paper, according to the existed and stated experiments and conclusions, the lifting step approach is used for wavelet decomposition. The physical and anatomic structure of human vision is combined and the contrast sensitivity function (CSF) is introduced as the main research issue in human vision system (HVS), and then the main designing points of HVS model are presented. On the basis of multi-resolution analyses of wavelet transform, the paper applies HVS including the CSF characteristics to the inner correlation-removed transform and quantization in image and proposes a new HVS-based medical image compression model. Results: The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging (MRI). At the same bit rate, the performance of SPIHT, with respect to the PSNR metric, is significantly higher than that of our algorithm. But the visual quality of the SPIHT-compressed image is roughly the same as that of the image compressed with our approach. Our algorithm obtains the same visual quality at lower bit rates and the coding/decoding time is less than that of SPIHT. Conclusions: The results show that under common objective conditions, our compression algorithm can achieve better subjective visual quality, and performs better than that of SPIHT in the aspects of compression ratios and coding/decoding time

Evaluation of a new image compression technique

International Nuclear Information System (INIS)

Algra, P.R.; Kroon, H.M.; Noordveld, R.B.; DeValk, J.P.J.; Seeley, G.W.; Westerink, P.H.

1988-01-01

The authors present the evaluation of a new image compression technique, subband coding using vector quantization, on 44 CT examinations of the upper abdomen. Three independent radiologists reviewed the original images and compressed versions. The compression ratios used were 16:1 and 20:1. Receiver operating characteristic analysis showed no difference in the diagnostic contents between originals and their compressed versions. Subjective visibility of anatomic structures was equal. Except for a few 20:1 compressed images, the observers could not distinguish compressed versions from original images. They conclude that subband coding using vector quantization is a valuable method for data compression in CT scans of the abdomen

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

Energy Technology Data Exchange (ETDEWEB)

Shih, Tzu-Ching [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, 40402, Taiwan (China); Chen, Jeon-Hor; Nie Ke; Lin Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying [Tu and Yuen Center for Functional Onco-Imaging and Radiological Sciences, University of California, Irvine, CA 92697 (United States); Liu Dongxu; Sun Lizhi, E-mail: shih@mail.cmu.edu.t [Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697 (United States)

2010-07-21

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo (registered) 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc (registered) software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under

An efficient adaptive arithmetic coding image compression technology

International Nuclear Information System (INIS)

Wang Xing-Yuan; Yun Jiao-Jiao; Zhang Yong-Lei

2011-01-01

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

ROI-based DICOM image compression for telemedicine

Indian Academy of Sciences (India)

ground and reconstruct the image portions losslessly. The compressed image can ... If the image is compressed by 8:1 compression without any perceptual distortion, the ... Figure 2. Cross-sectional view of medical image (statistical representation). ... The Integer Wavelet Transform (IWT) is used to have lossless processing.

Multimodal Image Alignment via Linear Mapping between Feature Modalities.

Science.gov (United States)

Jiang, Yanyun; Zheng, Yuanjie; Hou, Sujuan; Chang, Yuchou; Gee, James

2017-01-01

We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

Implementation and applications of dual-modality imaging

Science.gov (United States)

Hasegawa, Bruce H.; Barber, William C.; Funk, Tobias; Hwang, Andrew B.; Taylor, Carmen; Sun, Mingshan; Seo, Youngho

2004-06-01

In medical diagnosis, functional or physiological data can be acquired using radionuclide imaging with positron emission tomography or with single-photon emission computed tomography. However, anatomical or structural data can be acquired using X-ray computed tomography. In dual-modality imaging, both radionuclide and X-ray detectors are incorporated in an imaging system to allow both functional and structural data to be acquired in a single procedure without removing the patient from the imaging system. In a clinical setting, dual-modality imaging systems commonly are used to localize radiopharmaceutical uptake with respect to the patient's anatomy. This helps the clinician to differentiate disease from regions of normal radiopharmaceutical accumulation, to improve diagnosis or cancer staging, or to facilitate planning for radiation therapy or surgery. While initial applications of dual-modality imaging were developed for clinical imaging on humans, it now is recognized that these systems have potentially important applications for imaging small animals involved in experimental studies including basic investigations of mammalian biology and development of new pharmaceuticals for diagnosis or treatment of disease.

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

Application of content-based image compression to telepathology

Science.gov (United States)

Varga, Margaret J.; Ducksbury, Paul G.; Callagy, Grace

2002-05-01

Telepathology is a means of practicing pathology at a distance, viewing images on a computer display rather than directly through a microscope. Without compression, images take too long to transmit to a remote location and are very expensive to store for future examination. However, to date the use of compressed images in pathology remains controversial. This is because commercial image compression algorithms such as JPEG achieve data compression without knowledge of the diagnostic content. Often images are lossily compressed at the expense of corrupting informative content. None of the currently available lossy compression techniques are concerned with what information has been preserved and what data has been discarded. Their sole objective is to compress and transmit the images as fast as possible. By contrast, this paper presents a novel image compression technique, which exploits knowledge of the slide diagnostic content. This 'content based' approach combines visually lossless and lossy compression techniques, judiciously applying each in the appropriate context across an image so as to maintain 'diagnostic' information while still maximising the possible compression. Standard compression algorithms, e.g. wavelets, can still be used, but their use in a context sensitive manner can offer high compression ratios and preservation of diagnostically important information. When compared with lossless compression the novel content-based approach can potentially provide the same degree of information with a smaller amount of data. When compared with lossy compression it can provide more information for a given amount of compression. The precise gain in the compression performance depends on the application (e.g. database archive or second opinion consultation) and the diagnostic content of the images.

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.

Task-oriented lossy compression of magnetic resonance images

Science.gov (United States)

Anderson, Mark C.; Atkins, M. Stella; Vaisey, Jacques

1996-04-01

A new task-oriented image quality metric is used to quantify the effects of distortion introduced into magnetic resonance images by lossy compression. This metric measures the similarity between a radiologist's manual segmentation of pathological features in the original images and the automated segmentations performed on the original and compressed images. The images are compressed using a general wavelet-based lossy image compression technique, embedded zerotree coding, and segmented using a three-dimensional stochastic model-based tissue segmentation algorithm. The performance of the compression system is then enhanced by compressing different regions of the image volume at different bit rates, guided by prior knowledge about the location of important anatomical regions in the image. Application of the new system to magnetic resonance images is shown to produce compression results superior to the conventional methods, both subjectively and with respect to the segmentation similarity metric.

Halftoning processing on a JPEG-compressed image

Science.gov (United States)

Sibade, Cedric; Barizien, Stephane; Akil, Mohamed; Perroton, Laurent

2003-12-01

Digital image processing algorithms are usually designed for the raw format, that is on an uncompressed representation of the image. Therefore prior to transforming or processing a compressed format, decompression is applied; then, the result of the processing application is finally re-compressed for further transfer or storage. The change of data representation is resource-consuming in terms of computation, time and memory usage. In the wide format printing industry, this problem becomes an important issue: e.g. a 1 m2 input color image, scanned at 600 dpi exceeds 1.6 GB in its raw representation. However, some image processing algorithms can be performed in the compressed-domain, by applying an equivalent operation on the compressed format. This paper is presenting an innovative application of the halftoning processing operation by screening, to be applied on JPEG-compressed image. This compressed-domain transform is performed by computing the threshold operation of the screening algorithm in the DCT domain. This algorithm is illustrated by examples for different halftone masks. A pre-sharpening operation, applied on a JPEG-compressed low quality image is also described; it allows to de-noise and to enhance the contours of this image.

Established rheumatoid arthritis - new imaging modalities

DEFF Research Database (Denmark)

McQueen, Fiona M; Østergaard, Mikkel

2007-01-01

in real-time and facilitates diagnostic and therapeutic interventions such as joint aspiration and injection. Exciting experimental modalities are also being developed with the potential to provide not just morphological but functional imaging. Techniques such as positron emission tomography (PET......) and high-resolution computerized tomography. Erosions are very clearly depicted using these modalities and MRI also allows imaging of soft tissues with assessment of joint inflammation. High-resolution ultrasound is a convenient clinical technique for the assessment of erosions, synovitis and tenosynovitis...

High Bit-Depth Medical Image Compression With HEVC.

Science.gov (United States)

Parikh, Saurin S; Ruiz, Damian; Kalva, Hari; Fernandez-Escribano, Gerardo; Adzic, Velibor

2018-03-01

Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3-D imagery. Using HEVC, a single format can support all forms of medical images. This paper presents the use of HEVC for diagnostically acceptable medical image compression, focusing on compression efficiency compared to JPEG 2000. Diagnostically acceptable lossy compression and complexity of high bit-depth medical image compression are studied. Based on an established medically acceptable compression range for JPEG 2000, this paper establishes acceptable HEVC compression range for medical imaging applications. Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%. Along with this, a new method for reducing computational complexity of HEVC encoding for medical images is proposed. Results show that HEVC intra encoding complexity can be reduced by over 55% with negligible increase in file size.

TU-C-BRD-01: Image Guided SBRT I: Multi-Modality 4D Imaging

International Nuclear Information System (INIS)

Cai, J; Mageras, G; Pan, T

2014-01-01

Motion management is one of the critical technical challenges for radiation therapy. 4D imaging has been rapidly adopted as essential tool to assess organ motion associated with respiratory breathing. A variety of 4D imaging techniques have been developed and are currently under development based on different imaging modalities such as CT, MRI, PET, and CBCT. Each modality provides specific and complementary information about organ and tumor respiratory motion. Effective use of each different technique or combined use of different techniques can introduce a comprehensive management of tumor motion. Specifically, these techniques have afforded tremendous opportunities to better define and delineate tumor volumes, more accurately perform patient positioning, and effectively apply highly conformal therapy techniques such as IMRT and SBRT. Successful implementation requires good understanding of not only each technique, including unique features, limitations, artifacts, imaging acquisition and process, but also how to systematically apply the information obtained from different imaging modalities using proper tools such as deformable image registration. Furthermore, it is important to understand the differences in the effects of breathing variation between different imaging modalities. A comprehensive motion management strategy using multi-modality 4D imaging has shown promise in improving patient care, but at the same time faces significant challenges. This session will focuses on the current status and advances in imaging respiration-induced organ motion with different imaging modalities: 4D-CT, 4D-MRI, 4D-PET, and 4D-CBCT/DTS. Learning Objectives: Understand the need and role of multimodality 4D imaging in radiation therapy. Understand the underlying physics behind each 4D imaging technique. Recognize the advantages and limitations of each 4D imaging technique

Mathematical transforms and image compression: A review

Directory of Open Access Journals (Sweden)

Satish K. Singh

2010-07-01

Full Text Available It is well known that images, often used in a variety of computer and other scientific and engineering applications, are difficult to store and transmit due to their sizes. One possible solution to overcome this problem is to use an efficient digital image compression technique where an image is viewed as a matrix and then the operations are performed on the matrix. All the contemporary digital image compression systems use various mathematical transforms for compression. The compression performance is closely related to the performance by these mathematical transforms in terms of energy compaction and spatial frequency isolation by exploiting inter-pixel redundancies present in the image data. Through this paper, a comprehensive literature survey has been carried out and the pros and cons of various transform-based image compression models have also been discussed.

Compression and archiving of digital images

International Nuclear Information System (INIS)

Huang, H.K.

1988-01-01

This paper describes the application of a full-frame bit-allocation image compression technique to a hierarchical digital image archiving system consisting of magnetic disks, optical disks and an optical disk library. The digital archiving system without the compression has been in clinical operation in the Pediatric Radiology for more than half a year. The database in the system consists of all pediatric inpatients including all images from computed radiography, digitized x-ray films, CT, MR, and US. The rate of image accumulation is approximately 1,900 megabytes per week. The hardware design of the compression module is based on a Motorola 68020 microprocessor, A VME bus, a 16 megabyte image buffer memory board, and three Motorola digital signal processing 56001 chips on a VME board for performing the two-dimensional cosine transform and the quantization. The clinical evaluation of the compression module with the image archiving system is expected to be in February 1988

Lossless medical image compression with a hybrid coder

Science.gov (United States)

Way, Jing-Dar; Cheng, Po-Yuen

1998-10-01

The volume of medical image data is expected to increase dramatically in the next decade due to the large use of radiological image for medical diagnosis. The economics of distributing the medical image dictate that data compression is essential. While there is lossy image compression, the medical image must be recorded and transmitted lossless before it reaches the users to avoid wrong diagnosis due to the image data lost. Therefore, a low complexity, high performance lossless compression schematic that can approach the theoretic bound and operate in near real-time is needed. In this paper, we propose a hybrid image coder to compress the digitized medical image without any data loss. The hybrid coder is constituted of two key components: an embedded wavelet coder and a lossless run-length coder. In this system, the medical image is compressed with the lossy wavelet coder first, and the residual image between the original and the compressed ones is further compressed with the run-length coder. Several optimization schemes have been used in these coders to increase the coding performance. It is shown that the proposed algorithm is with higher compression ratio than run-length entropy coders such as arithmetic, Huffman and Lempel-Ziv coders.

Images compression in nuclear medicine

International Nuclear Information System (INIS)

Rebelo, M.S.; Furuie, S.S.; Moura, L.

1992-01-01

The performance of two methods for images compression in nuclear medicine was evaluated. The LZW precise, and Cosine Transformed, approximate, methods were analyzed. The results were obtained, showing that the utilization of approximated method produced images with an agreeable quality for visual analysis and compression rates, considerably high than precise method. (C.G.C.)

Efficient predictive algorithms for image compression

CERN Document Server

Rosário Lucas, Luís Filipe; Maciel de Faria, Sérgio Manuel; Morais Rodrigues, Nuno Miguel; Liberal Pagliari, Carla

2017-01-01

This book discusses efficient prediction techniques for the current state-of-the-art High Efficiency Video Coding (HEVC) standard, focusing on the compression of a wide range of video signals, such as 3D video, Light Fields and natural images. The authors begin with a review of the state-of-the-art predictive coding methods and compression technologies for both 2D and 3D multimedia contents, which provides a good starting point for new researchers in the field of image and video compression. New prediction techniques that go beyond the standardized compression technologies are then presented and discussed. In the context of 3D video, the authors describe a new predictive algorithm for the compression of depth maps, which combines intra-directional prediction, with flexible block partitioning and linear residue fitting. New approaches are described for the compression of Light Field and still images, which enforce sparsity constraints on linear models. The Locally Linear Embedding-based prediction method is in...

Biomedical imaging modality classification using combined visual features and textual terms.

Science.gov (United States)

Han, Xian-Hua; Chen, Yen-Wei

2011-01-01

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

Compressive Transient Imaging

KAUST Repository

Sun, Qilin

2017-04-01

High resolution transient/3D imaging technology is of high interest in both scientific research and commercial application. Nowadays, all of the transient imaging methods suffer from low resolution or time consuming mechanical scanning. We proposed a new method based on TCSPC and Compressive Sensing to achieve a high resolution transient imaging with a several seconds capturing process. Picosecond laser sends a serious of equal interval pulse while synchronized SPAD camera\\'s detecting gate window has a precise phase delay at each cycle. After capturing enough points, we are able to make up a whole signal. By inserting a DMD device into the system, we are able to modulate all the frames of data using binary random patterns to reconstruct a super resolution transient/3D image later. Because the low fill factor of SPAD sensor will make a compressive sensing scenario ill-conditioned, We designed and fabricated a diffractive microlens array. We proposed a new CS reconstruction algorithm which is able to denoise at the same time for the measurements suffering from Poisson noise. Instead of a single SPAD senor, we chose a SPAD array because it can drastically reduce the requirement for the number of measurements and its reconstruction time. Further more, it not easy to reconstruct a high resolution image with only one single sensor while for an array, it just needs to reconstruct small patches and a few measurements. In this thesis, we evaluated the reconstruction methods using both clean measurements and the version corrupted by Poisson noise. The results show how the integration over the layers influence the image quality and our algorithm works well while the measurements suffer from non-trival Poisson noise. It\\'s a breakthrough in the areas of both transient imaging and compressive sensing.

Automatic intra-modality brain image registration method

International Nuclear Information System (INIS)

Whitaker, J.M.; Ardekani, B.A.; Braun, M.

1996-01-01

Full text: Registration of 3D images of brain of the same or different subjects has potential importance in clinical diagnosis, treatment planning and neurological research. The broad aim of our work is to produce an automatic and robust intra-modality, brain image registration algorithm for intra-subject and inter-subject studies. Our algorithm is composed of two stages. Initial alignment is achieved by finding the values of nine transformation parameters (representing translation, rotation and scale) that minimise the nonoverlapping regions of the head. This is achieved by minimisation of the sum of the exclusive OR of two binary head images, produced using the head extraction procedure described by Ardekani et al. (J Comput Assist Tomogr, 19:613-623, 1995). The initial alignment successfully determines the scale parameters and gross translation and rotation parameters. Fine alignment uses an objective function described for inter-modality registration in Ardekani et al. (ibid.). The algorithm segments one of the images to be aligned into a set of connected components using K-means clustering. Registration is achieved by minimising the K-means variance of the segmentation induced in the other image. Similarity of images of the same modality makes the method attractive for intra-modality registration. A 3D MR image, with voxel dimensions, 2x2x6 mm, was misaligned. The registered image shows visually accurate registration. The average displacement of a pixel from its correct location was measured to be 3.3 mm. The algorithm was tested on intra-subject MR images and was found to produce good qualitative results. Using the data available, the algorithm produced promising qualitative results in intra-subject registration. Further work is necessary in its application to intersubject registration, due to large variability in brain structure between subjects. Clinical evaluation of the algorithm for selected applications is required

Cloud Optimized Image Format and Compression

Science.gov (United States)

Becker, P.; Plesea, L.; Maurer, T.

2015-04-01

Cloud based image storage and processing requires revaluation of formats and processing methods. For the true value of the massive volumes of earth observation data to be realized, the image data needs to be accessible from the cloud. Traditional file formats such as TIF and NITF were developed in the hay day of the desktop and assumed fast low latency file access. Other formats such as JPEG2000 provide for streaming protocols for pixel data, but still require a server to have file access. These concepts no longer truly hold in cloud based elastic storage and computation environments. This paper will provide details of a newly evolving image storage format (MRF) and compression that is optimized for cloud environments. Although the cost of storage continues to fall for large data volumes, there is still significant value in compression. For imagery data to be used in analysis and exploit the extended dynamic range of the new sensors, lossless or controlled lossy compression is of high value. Compression decreases the data volumes stored and reduces the data transferred, but the reduced data size must be balanced with the CPU required to decompress. The paper also outlines a new compression algorithm (LERC) for imagery and elevation data that optimizes this balance. Advantages of the compression include its simple to implement algorithm that enables it to be efficiently accessed using JavaScript. Combing this new cloud based image storage format and compression will help resolve some of the challenges of big image data on the internet.

Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

Science.gov (United States)

Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

2016-07-01

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

Iris Recognition: The Consequences of Image Compression

Directory of Open Access Journals (Sweden)

Bishop DanielA

2010-01-01

Full Text Available Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected.

Iris Recognition: The Consequences of Image Compression

Science.gov (United States)

Ives, Robert W.; Bishop, Daniel A.; Du, Yingzi; Belcher, Craig

2010-12-01

Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected.

Established rheumatoid arthritis - new imaging modalities

DEFF Research Database (Denmark)

McQueen, Fiona M; Østergaard, Mikkel

2007-01-01

New imaging modalities are assuming an increasingly important role in the investigation and management of rheumatoid arthritis. It is now possible to obtain information about all tissues within the joint in three dimensions using tomographic techniques such as magnetic resonance imaging (MRI...

Magnetic resonance imaging validation of pituitary gland compression and distortion by typical sellar pathology.

Science.gov (United States)

Cho, Charles H; Barkhoudarian, Garni; Hsu, Liangge; Bi, Wenya Linda; Zamani, Amir A; Laws, Edward R

2013-12-01

Identification of the normal pituitary gland is an important component of presurgical planning, defining many aspects of the surgical approach and facilitating normal gland preservation. Magnetic resonance imaging is a proven imaging modality for optimal soft-tissue contrast discrimination in the brain. This study is designed to validate the accuracy of localization of the normal pituitary gland with MRI in a cohort of surgical patients with pituitary mass lesions, and to evaluate for correlation between presurgical pituitary hormone values and pituitary gland characteristics on neuroimaging. Fifty-eight consecutive patients with pituitary mass lesions were included in the study. Anterior pituitary hormone levels were measured preoperatively in all patients. Video recordings from the endoscopic or microscopic surgical procedures were available for evaluation in 47 cases. Intraoperative identification of the normal gland was possible in 43 of 58 cases. Retrospective MR images were reviewed in a blinded fashion for the 43 cases, emphasizing the position of the normal gland and the extent of compression and displacement by the lesion. There was excellent agreement between imaging and surgery in 84% of the cases for normal gland localization, and in 70% for compression or noncompression of the normal gland. There was no consistent correlation between preoperative pituitary dysfunction and pituitary gland localization on imaging, gland identification during surgery, or pituitary gland compression. Magnetic resonance imaging proved to be accurate in identifying the normal gland in patients with pituitary mass lesions, and was useful for preoperative surgical planning.

Combined Sparsifying Transforms for Compressive Image Fusion

Directory of Open Access Journals (Sweden)

ZHAO, L.

2013-11-01

Full Text Available In this paper, we present a new compressive image fusion method based on combined sparsifying transforms. First, the framework of compressive image fusion is introduced briefly. Then, combined sparsifying transforms are presented to enhance the sparsity of images. Finally, a reconstruction algorithm based on the nonlinear conjugate gradient is presented to get the fused image. The simulations demonstrate that by using the combined sparsifying transforms better results can be achieved in terms of both the subjective visual effect and the objective evaluation indexes than using only a single sparsifying transform for compressive image fusion.

Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

Directory of Open Access Journals (Sweden)

Xian-Hua Han

2011-01-01

extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

Recognizable or Not: Towards Image Semantic Quality Assessment for Compression

Science.gov (United States)

Liu, Dong; Wang, Dandan; Li, Houqiang

2017-12-01

Traditionally, image compression was optimized for the pixel-wise fidelity or the perceptual quality of the compressed images given a bit-rate budget. But recently, compressed images are more and more utilized for automatic semantic analysis tasks such as recognition and retrieval. For these tasks, we argue that the optimization target of compression is no longer perceptual quality, but the utility of the compressed images in the given automatic semantic analysis task. Accordingly, we propose to evaluate the quality of the compressed images neither at pixel level nor at perceptual level, but at semantic level. In this paper, we make preliminary efforts towards image semantic quality assessment (ISQA), focusing on the task of optical character recognition (OCR) from compressed images. We propose a full-reference ISQA measure by comparing the features extracted from text regions of original and compressed images. We then propose to integrate the ISQA measure into an image compression scheme. Experimental results show that our proposed ISQA measure is much better than PSNR and SSIM in evaluating the semantic quality of compressed images; accordingly, adopting our ISQA measure to optimize compression for OCR leads to significant bit-rate saving compared to using PSNR or SSIM. Moreover, we perform subjective test about text recognition from compressed images, and observe that our ISQA measure has high consistency with subjective recognizability. Our work explores new dimensions in image quality assessment, and demonstrates promising direction to achieve higher compression ratio for specific semantic analysis tasks.

Contributions in compression of 3D medical images and 2D images; Contributions en compression d'images medicales 3D et d'images naturelles 2D

Energy Technology Data Exchange (ETDEWEB)

Gaudeau, Y

2006-12-15

The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)

High-quality compressive ghost imaging

Science.gov (United States)

Huang, Heyan; Zhou, Cheng; Tian, Tian; Liu, Dongqi; Song, Lijun

2018-04-01

We propose a high-quality compressive ghost imaging method based on projected Landweber regularization and guided filter, which effectively reduce the undersampling noise and improve the resolution. In our scheme, the original object is reconstructed by decomposing of regularization and denoising steps instead of solving a minimization problem in compressive reconstruction process. The simulation and experimental results show that our method can obtain high ghost imaging quality in terms of PSNR and visual observation.

The task of control digital image compression

OpenAIRE

TASHMANOV E.B.; МАМАTOV М.S.

2014-01-01

In this paper we consider the relationship of control tasks and image compression losses. The main idea of this approach is to allocate structural lines simplified image and further compress the selected data

A new hyperspectral image compression paradigm based on fusion

Science.gov (United States)

Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto

2016-10-01

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

Effects on MR images compression in tissue classification quality

International Nuclear Information System (INIS)

Santalla, H; Meschino, G; Ballarin, V

2007-01-01

It is known that image compression is required to optimize the storage in memory. Moreover, transmission speed can be significantly improved. Lossless compression is used without controversy in medicine, though benefits are limited. If we compress images lossy, where image can not be totally recovered; we can only recover an approximation. In this point definition of 'quality' is essential. What we understand for 'quality'? How can we evaluate a compressed image? Quality in images is an attribute whit several definitions and interpretations, which actually depend on the posterior use we want to give them. This work proposes a quantitative analysis of quality for lossy compressed Magnetic Resonance (MR) images, and their influence in automatic tissue classification, accomplished with these images

Applications of Novel X-Ray Imaging Modalities in Food Science

DEFF Research Database (Denmark)

Nielsen, Mikkel Schou

science for understanding and designing food products. In both of these aspects, X-ray imaging methods such as radiography and computed tomography provide a non-destructive solution. However, since the conventional attenuation-based modality suers from poor contrast in soft matter materials, modalities...... with improved contrast are needed. Two possible candidates in this regard are the novel X-ray phase-contrast and X-ray dark-eld imaging modalities. The contrast in phase-contrast imaging is based on dierences in electron density which is especially useful for soft matter materials whereas dark-eld imaging....... Furthermore, the process of translating the image in image analysis was addressed. For improved handling of multimodal image data, a multivariate segmentation scheme of multimodal X-ray tomography data was implemented. Finally, quantitative data analysis was applied for treating the images. Quantitative...

Correlation and image compression for limited-bandwidth CCD.

Energy Technology Data Exchange (ETDEWEB)

Thompson, Douglas G.

2005-07-01

As radars move to Unmanned Aerial Vehicles with limited-bandwidth data downlinks, the amount of data stored and transmitted with each image becomes more significant. This document gives the results of a study to determine the effect of lossy compression in the image magnitude and phase on Coherent Change Detection (CCD). We examine 44 lossy compression types, plus lossless zlib compression, and test each compression method with over 600 CCD image pairs. We also derive theoretical predictions for the correlation for most of these compression schemes, which compare favorably with the experimental results. We recommend image transmission formats for limited-bandwidth programs having various requirements for CCD, including programs which cannot allow performance degradation and those which have stricter bandwidth requirements at the expense of CCD performance.

Optimum image compression rate maintaining diagnostic image quality of digital intraoral radiographs

International Nuclear Information System (INIS)

Song, Ju Seop; Koh, Kwang Joon

2000-01-01

The aims of the present study are to determine the optimum compression rate in terms of file size reduction and diagnostic quality of the images after compression and evaluate the transmission speed of original or each compressed images. The material consisted of 24 extracted human premolars and molars. The occlusal surfaces and proximal surfaces of the teeth had a clinical disease spectrum that ranged from sound to varying degrees of fissure discoloration and cavitation. The images from Digora system were exported in TIFF and the images from conventional intraoral film were scanned and digitalized in TIFF by Nikon SF-200 scanner(Nikon, Japan). And six compression factors were chosen and applied on the basis of the results from a pilot study. The total number of images to be assessed were 336. Three radiologists assessed the occlusal and proximal surfaces of the teeth with 5-rank scale. Finally diagnosed as either sound or carious lesion by one expert oral pathologist. And sensitivity and specificity and kappa value for diagnostic agreement was calculated. Also the area (Az) values under the ROC curve were calculated and paired t-test and oneway ANOVA test was performed. Thereafter, transmission time of the image files of the each compression level were compared with that of the original image files. No significant difference was found between original and the corresponding images up to 7% (1:14) compression ratio for both the occlusal and proximal caries (p<0.05). JPEG3 (1:14) image files are transmitted fast more than 10 times, maintained diagnostic information in image, compared with original image files. 1:14 compressed image file may be used instead of the original image and reduce storage needs and transmission time.

Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation.

Science.gov (United States)

Sturgeon, Gregory M; Kiarashi, Nooshin; Lo, Joseph Y; Samei, E; Segars, W P

2016-05-01

The authors are developing a series of computational breast phantoms based on breast CT data for imaging research. In this work, the authors develop a program that will allow a user to alter the phantoms to simulate the effect of gravity and compression of the breast (craniocaudal or mediolateral oblique) making the phantoms applicable to multimodality imaging. This application utilizes a template finite-element (FE) breast model that can be applied to their presegmented voxelized breast phantoms. The FE model is automatically fit to the geometry of a given breast phantom, and the material properties of each element are set based on the segmented voxels contained within the element. The loading and boundary conditions, which include gravity, are then assigned based on a user-defined position and compression. The effect of applying these loads to the breast is computed using a multistage contact analysis in FEBio, a freely available and well-validated FE software package specifically designed for biomedical applications. The resulting deformation of the breast is then applied to a boundary mesh representation of the phantom that can be used for simulating medical images. An efficient script performs the above actions seamlessly. The user only needs to specify which voxelized breast phantom to use, the compressed thickness, and orientation of the breast. The authors utilized their FE application to simulate compressed states of the breast indicative of mammography and tomosynthesis. Gravity and compression were simulated on example phantoms and used to generate mammograms in the craniocaudal or mediolateral oblique views. The simulated mammograms show a high degree of realism illustrating the utility of the FE method in simulating imaging data of repositioned and compressed breasts. The breast phantoms and the compression software can become a useful resource to the breast imaging research community. These phantoms can then be used to evaluate and compare imaging

Musculoskeletal ultrasound and other imaging modalities in rheumatoid arthritis.

Science.gov (United States)

Ohrndorf, Sarah; Werner, Stephanie G; Finzel, Stephanie; Backhaus, Marina

2013-05-01

This review refers to the use of musculoskeletal ultrasound in patients with rheumatoid arthritis (RA) both in clinical practice and research. Furthermore, other novel sensitive imaging modalities (high resolution peripheral quantitative computed tomography and fluorescence optical imaging) are introduced in this article. Recently published ultrasound studies presented power Doppler activity by ultrasound highly predictive for later radiographic erosions in patients with RA. Another study presented synovitis detected by ultrasound being predictive of subsequent structural radiographic destruction irrespective of the ultrasound modality (grayscale ultrasound/power Doppler ultrasound). Further studies are currently under way which prove ultrasound findings as imaging biomarkers in the destructive process of RA. Other introduced novel imaging modalities are in the validation process to prove their impact and significance in inflammatory joint diseases. The introduced imaging modalities show different sensitivities and specificities as well as strength and weakness belonging to the assessment of inflammation, differentiation of the involved structures and radiological progression. The review tries to give an answer regarding how to best integrate them into daily clinical practice with the aim to improve the diagnostic algorithms, the daily patient care and, furthermore, the disease's outcome.

Autonomy of image and use of single or multiple sense modalities in original verbal image production.

Science.gov (United States)

Khatena, J

1978-06-01

The use of a single or of multiple sense modalities in the production of original verbal images as related to autonomy of imagery was explored. 72 college adults were administered Onomatopoeia and Images and the Gordon Test of Visual Imagery Control. A modified scoring procedure for the Gordon scale differentiated imagers who were moderate or low in autonomy. The two groups produced original verbal images using multiple sense modalities more frequently than a single modality.

Wavelet/scalar quantization compression standard for fingerprint images

Energy Technology Data Exchange (ETDEWEB)

Brislawn, C.M.

1996-06-12

US Federal Bureau of Investigation (FBI) has recently formulated a national standard for digitization and compression of gray-scale fingerprint images. Fingerprints are scanned at a spatial resolution of 500 dots per inch, with 8 bits of gray-scale resolution. The compression algorithm for the resulting digital images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition (wavelet/scalar quantization method). The FBI standard produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. The compression standard specifies a class of potential encoders and a universal decoder with sufficient generality to reconstruct compressed images produced by any compliant encoder, allowing flexibility for future improvements in encoder technology. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations.

Hyperspectral image compressing using wavelet-based method

Science.gov (United States)

Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng

2017-10-01

Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

Wavelet compression algorithm applied to abdominal ultrasound images

International Nuclear Information System (INIS)

Lin, Cheng-Hsun; Pan, Su-Feng; LU, Chin-Yuan; Lee, Ming-Che

2006-01-01

We sought to investigate acceptable compression ratios of lossy wavelet compression on 640 x 480 x 8 abdominal ultrasound (US) images. We acquired 100 abdominal US images with normal and abnormal findings from the view station of a 932-bed teaching hospital. The US images were then compressed at quality factors (QFs) of 3, 10, 30, and 50 followed outcomes of a pilot study. This was equal to the average compression ratios of 4.3:1, 8.5:1, 20:1 and 36.6:1, respectively. Four objective measurements were carried out to examine and compare the image degradation between original and compressed images. Receiver operating characteristic (ROC) analysis was also introduced for subjective assessment. Five experienced and qualified radiologists as reviewers blinded to corresponding pathological findings, analysed paired 400 randomly ordered images with two 17-inch thin film transistor/liquid crystal display (TFT/LCD) monitors. At ROC analysis, the average area under curve (Az) for US abdominal image was 0.874 at the ratio of 36.6:1. The compressed image size was only 2.7% for US original at this ratio. The objective parameters showed the higher the mean squared error (MSE) or root mean squared error (RMSE) values, the poorer the image quality. The higher signal-to-noise ratio (SNR) or peak signal-to-noise ratio (PSNR) values indicated better image quality. The average RMSE, PSNR at 36.6:1 for US were 4.84 ± 0.14, 35.45 dB, respectively. This finding suggests that, on the basis of the patient sample, wavelet compression of abdominal US to a ratio of 36.6:1 did not adversely affect diagnostic performance or evaluation error for radiologists' interpretation so as to risk affecting diagnosis

Contributions in compression of 3D medical images and 2D images; Contributions en compression d'images medicales 3D et d'images naturelles 2D

Energy Technology Data Exchange (ETDEWEB)

Gaudeau, Y

2006-12-15

The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)

Wavelet-based compression of pathological images for telemedicine applications

Science.gov (United States)

Chen, Chang W.; Jiang, Jianfei; Zheng, Zhiyong; Wu, Xue G.; Yu, Lun

2000-05-01

In this paper, we present the performance evaluation of wavelet-based coding techniques as applied to the compression of pathological images for application in an Internet-based telemedicine system. We first study how well suited the wavelet-based coding is as it applies to the compression of pathological images, since these images often contain fine textures that are often critical to the diagnosis of potential diseases. We compare the wavelet-based compression with the DCT-based JPEG compression in the DICOM standard for medical imaging applications. Both objective and subjective measures have been studied in the evaluation of compression performance. These studies are performed in close collaboration with expert pathologists who have conducted the evaluation of the compressed pathological images and communication engineers and information scientists who designed the proposed telemedicine system. These performance evaluations have shown that the wavelet-based coding is suitable for the compression of various pathological images and can be integrated well with the Internet-based telemedicine systems. A prototype of the proposed telemedicine system has been developed in which the wavelet-based coding is adopted for the compression to achieve bandwidth efficient transmission and therefore speed up the communications between the remote terminal and the central server of the telemedicine system.

Role of magnetic resonance imaging in entrapment and compressive neuropathy - what, where, and how to see the peripheral nerves on the musculoskeletal magnetic resonance image: part 1. Overview and lower extremity

Energy Technology Data Exchange (ETDEWEB)

Kim, Sungjun [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea); Hanyang University, Kuri Hospital, Department of Diagnostic Radiology, College of Medicine, Kuri City, Kyunggi-do (Korea); Choi, Jin-Young; Huh, Yong-Min; Song, Ho-Taek; Lee, Sung-Ah [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea); Kim, Seung Min [Yonsei University, Department of Neurology, College of Medicine, Seoul (Korea); Suh, Jin-Suck [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea); Yonsei University, Research Institute of Radiological Science, College of Medicine, Seoul (Korea)

2007-01-15

The diagnosis of nerve entrapment and compressive neuropathy has been traditionally based on the clinical and electrodiagnostic examinations. As a result of improvements in the magnetic resonance (MR) imaging modality, it plays not only a fundamental role in the detection of space-occupying lesions but also a compensatory role in clinically and electrodiagnostically inconclusive cases. Although ultrasound has undergone further development in the past decades and shows high resolution capabilities, it has inherent limitations due to its operator dependency. We review the general concepts that should be known to evaluate the entrapment and compressive neuropathy in MR imaging. We also review the course of normal peripheral nerves, as well as various clinical demonstrations and pathological features of compressed and entrapped nerves in the lower extremities on MR imaging, according to the nerves involved. The common sites of nerve entrapment of the lower extremity are as follows: sciatic nerve around the piriformis muscle; tibial nerve at the popliteal fossa and tarsal tunnel, common peroneal nerve around the fibular neck, and digital nerve near the metatarsal head. Although MR imaging can depict the peripheral nerves in the extremities effectively, radiologists should be familiar with nerve pathways, common sites of nerve compression, and common space-occupying lesions resulting in nerve compression in MR imaging. (orig.)

Role of magnetic resonance imaging in entrapment and compressive neuropathy - what, where, and how to see the peripheral nerves on the musculoskeletal magnetic resonance image: part 1. Overview and lower extremity

International Nuclear Information System (INIS)

Kim, Sungjun; Choi, Jin-Young; Huh, Yong-Min; Song, Ho-Taek; Lee, Sung-Ah; Kim, Seung Min; Suh, Jin-Suck

2007-01-01

The diagnosis of nerve entrapment and compressive neuropathy has been traditionally based on the clinical and electrodiagnostic examinations. As a result of improvements in the magnetic resonance (MR) imaging modality, it plays not only a fundamental role in the detection of space-occupying lesions but also a compensatory role in clinically and electrodiagnostically inconclusive cases. Although ultrasound has undergone further development in the past decades and shows high resolution capabilities, it has inherent limitations due to its operator dependency. We review the general concepts that should be known to evaluate the entrapment and compressive neuropathy in MR imaging. We also review the course of normal peripheral nerves, as well as various clinical demonstrations and pathological features of compressed and entrapped nerves in the lower extremities on MR imaging, according to the nerves involved. The common sites of nerve entrapment of the lower extremity are as follows: sciatic nerve around the piriformis muscle; tibial nerve at the popliteal fossa and tarsal tunnel, common peroneal nerve around the fibular neck, and digital nerve near the metatarsal head. Although MR imaging can depict the peripheral nerves in the extremities effectively, radiologists should be familiar with nerve pathways, common sites of nerve compression, and common space-occupying lesions resulting in nerve compression in MR imaging. (orig.)

Image compression with Iris-C

Science.gov (United States)

Gains, David

2009-05-01

Iris-C is an image codec designed for streaming video applications that demand low bit rate, low latency, lossless image compression. To achieve compression and low latency the codec features the discrete wavelet transform, Exp-Golomb coding, and online processes that construct dynamic models of the input video. Like H.264 and Dirac, the Iris-C codec accepts input video from both the YUV and YCOCG colour spaces, but the system can also operate on Bayer RAW data read directly from an image sensor. Testing shows that the Iris-C codec is competitive with the Dirac low delay syntax codec which is typically regarded as the state-of-the-art low latency, lossless video compressor.

Contributions in compression of 3D medical images and 2D images

International Nuclear Information System (INIS)

Gaudeau, Y.

2006-12-01

The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)

Modeling decision-making in single- and multi-modal medical images

Science.gov (United States)

Canosa, R. L.; Baum, K. G.

2009-02-01

This research introduces a mode-specific model of visual saliency that can be used to highlight likely lesion locations and potential errors (false positives and false negatives) in single-mode PET and MRI images and multi-modal fused PET/MRI images. Fused-modality digital images are a relatively recent technological improvement in medical imaging; therefore, a novel component of this research is to characterize the perceptual response to these fused images. Three different fusion techniques were compared to single-mode displays in terms of observer error rates using synthetic human brain images generated from an anthropomorphic phantom. An eye-tracking experiment was performed with naÃve (non-radiologist) observers who viewed the single- and multi-modal images. The eye-tracking data allowed the errors to be classified into four categories: false positives, search errors (false negatives never fixated), recognition errors (false negatives fixated less than 350 milliseconds), and decision errors (false negatives fixated greater than 350 milliseconds). A saliency model consisting of a set of differentially weighted low-level feature maps is derived from the known error and ground truth locations extracted from a subset of the test images for each modality. The saliency model shows that lesion and error locations attract visual attention according to low-level image features such as color, luminance, and texture.

On-board image compression for the RAE lunar mission

Science.gov (United States)

Miller, W. H.; Lynch, T. J.

1976-01-01

The requirements, design, implementation, and flight performance of an on-board image compression system for the lunar orbiting Radio Astronomy Explorer-2 (RAE-2) spacecraft are described. The image to be compressed is a panoramic camera view of the long radio astronomy antenna booms used for gravity-gradient stabilization of the spacecraft. A compression ratio of 32 to 1 is obtained by a combination of scan line skipping and adaptive run-length coding. The compressed imagery data are convolutionally encoded for error protection. This image compression system occupies about 1000 cu cm and consumes 0.4 W.

Exogenous Molecular Probes for Targeted Imaging in Cancer: Focus on Multi-modal Imaging

International Nuclear Information System (INIS)

Joshi, Bishnu P.; Wang, Thomas D.

2010-01-01

Cancer is one of the major causes of mortality and morbidity in our healthcare system. Molecular imaging is an emerging methodology for the early detection of cancer, guidance of therapy, and monitoring of response. The development of new instruments and exogenous molecular probes that can be labeled for multi-modality imaging is critical to this process. Today, molecular imaging is at a crossroad, and new targeted imaging agents are expected to broadly expand our ability to detect and manage cancer. This integrated imaging strategy will permit clinicians to not only localize lesions within the body but also to manage their therapy by visualizing the expression and activity of specific molecules. This information is expected to have a major impact on drug development and understanding of basic cancer biology. At this time, a number of molecular probes have been developed by conjugating various labels to affinity ligands for targeting in different imaging modalities. This review will describe the current status of exogenous molecular probes for optical, scintigraphic, MRI and ultrasound imaging platforms. Furthermore, we will also shed light on how these techniques can be used synergistically in multi-modal platforms and how these techniques are being employed in current research

Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing

Science.gov (United States)

Zhou, Nanrun; Pan, Shumin; Cheng, Shan; Zhou, Zhihong

2016-08-01

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Spatial compression algorithm for the analysis of very large multivariate images

Science.gov (United States)

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

Acceptable levels of digital image compression in chest radiology

International Nuclear Information System (INIS)

Smith, I.

2000-01-01

The introduction of picture archival and communications systems (PACS) and teleradiology has prompted an examination of techniques that optimize the storage capacity and speed of digital storage and distribution networks. The general acceptance of the move to replace conventional screen-film capture with computed radiography (CR) is an indication that clinicians within the radiology community are willing to accept images that have been 'compressed'. The question to be answered, therefore, is what level of compression is acceptable. The purpose of the present study is to provide an assessment of the ability of a group of imaging professionals to determine whether an image has been compressed. To undertake this study a single mobile chest image, selected for the presence of some subtle pathology in the form of a number of septal lines in both costphrenic angles, was compressed to levels of 10:1, 20:1 and 30:1. These images were randomly ordered and shown to the observers for interpretation. Analysis of the responses indicates that in general it was not possible to distinguish the original image from its compressed counterparts. Furthermore, a preference appeared to be shown for images that have undergone low levels of compression. This preference can most likely be attributed to the 'de-noising' effect of the compression algorithm at low levels. Copyright (1999) Blackwell Science Pty. Ltd

Reevaluation of JPEG image compression to digitalized gastrointestinal endoscopic color images: a pilot study

Science.gov (United States)

Kim, Christopher Y.

1999-05-01

Endoscopic images p lay an important role in describing many gastrointestinal (GI) disorders. The field of radiology has been on the leading edge of creating, archiving and transmitting digital images. With the advent of digital videoendoscopy, endoscopists now have the ability to generate images for storage and transmission. X-rays can be compressed 30-40X without appreciable decline in quality. We reported results of a pilot study using JPEG compression of 24-bit color endoscopic images. For that study, the result indicated that adequate compression ratios vary according to the lesion and that images could be compressed to between 31- and 99-fold smaller than the original size without an appreciable decline in quality. The purpose of this study was to expand upon the methodology of the previous sty with an eye towards application for the WWW, a medium which would expand both clinical and educational purposes of color medical imags. The results indicate that endoscopists are able to tolerate very significant compression of endoscopic images without loss of clinical image quality. This finding suggests that even 1 MB color images can be compressed to well under 30KB, which is considered a maximal tolerable image size for downloading on the WWW.

Image and Dose Simulation in Support of New Imaging Modalities

International Nuclear Information System (INIS)

Kuruvilla Verghese

2002-01-01

This report summarizes the highlights of the research performed under the 2-year NEER grant from the Department of Energy. The primary outcome of the work was a new Monte Carlo code, MCMIS-DS, for Monte Carlo for Mammography Image Simulation including Differential Sampling. The code was written to generate simulated images and dose distributions from two different new digital x-ray imaging modalities, namely, synchrotron imaging (SI) and a slot geometry digital mammography system called Fisher Senoscan. A differential sampling scheme was added to the code to generate multiple images that included variations in the parameters of the measurement system and the object in a single execution of the code. The code is to serve multiple purposes; (1) to answer questions regarding the contribution of scattered photons to images, (2) for use in design optimization studies, and (3) to do up to second-order perturbation studies to assess the effects of design parameter variations and/or physical parameters of the object (the breast) without having to re-run the code for each set of varied parameters. The accuracy and fidelity of the code were validated by a large variety of benchmark studies using published data and also using experimental results from mammography phantoms on both imaging modalities

Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis of Microarray Images

Directory of Open Access Journals (Sweden)

Jianping Hua

2004-01-01

Full Text Available This paper presents microarray BASICA: an integrated image processing tool for background adjustment, segmentation, image compression, and analysis of cDNA microarray images. BASICA uses a fast Mann-Whitney test-based algorithm to segment cDNA microarray images, and performs postprocessing to eliminate the segmentation irregularities. The segmentation results, along with the foreground and background intensities obtained with the background adjustment, are then used for independent compression of the foreground and background. We introduce a new distortion measurement for cDNA microarray image compression and devise a coding scheme by modifying the embedded block coding with optimized truncation (EBCOT algorithm (Taubman, 2000 to achieve optimal rate-distortion performance in lossy coding while still maintaining outstanding lossless compression performance. Experimental results show that the bit rate required to ensure sufficiently accurate gene expression measurement varies and depends on the quality of cDNA microarray images. For homogeneously hybridized cDNA microarray images, BASICA is able to provide from a bit rate as low as 5 bpp the gene expression data that are 99% in agreement with those of the original 32 bpp images.

Compositional-prior-guided image reconstruction algorithm for multi-modality imaging

Science.gov (United States)

Fang, Qianqian; Moore, Richard H.; Kopans, Daniel B.; Boas, David A.

2010-01-01

The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information. We propose a composition-based image segmentation method for X-ray digital breast tomosynthesis (DBT) and a structural-prior-guided image reconstruction for a combined DBT and diffuse optical tomography (DOT) breast imaging system. Using the 3D DBT images from 31 clinically measured healthy breasts, we create an empirical relationship between the X-ray intensities for adipose and fibroglandular tissue. We use this relationship to then segment another 58 healthy breast DBT images from 29 subjects into compositional maps of different tissue types. For each breast, we build a weighted-graph in the compositional space and construct a regularization matrix to incorporate the structural priors into a finite-element-based DOT image reconstruction. Use of the compositional priors enables us to fuse tissue anatomy into optical images with less restriction than when using a binary segmentation. This allows us to recover the image contrast captured by DOT but not by DBT. We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter. By estimating the optical properties for adipose and fibroglandular tissue using the proposed algorithm, we found the results are comparable or superior to those estimated with expert-segmentations, but does not involve the time-consuming manual selection of regions-of-interest. PMID:21258460

Comparison of JPEG and wavelet compression on intraoral digital radiographic images

International Nuclear Information System (INIS)

Kim, Eun Kyung

2004-01-01

To determine the proper image compression method and ratio without image quality degradation in intraoral digital radiographic images, comparing the discrete cosine transform (DCT)-based JPEG with the wavelet-based JPEG 2000 algorithm. Thirty extracted sound teeth and thirty extracted teeth with occlusal caries were used for this study. Twenty plaster blocks were made with three teeth each. They were radiographically exposed using CDR sensors (Schick Inc., Long Island, USA). Digital images were compressed to JPEG format, using Adobe Photoshop v. 7.0 and JPEG 2000 format using Jasper program with compression ratios of 5 : 1, 9 : 1, 14 : 1, 28 : 1 each. To evaluate the lesion detectability, receiver operating characteristic (ROC) analysis was performed by the three oral and maxillofacial radiologists. To evaluate the image quality, all the compressed images were assessed subjectively using 5 grades, in comparison to the original uncompressed images. Compressed images up to compression ratio of 14: 1 in JPEG and 28 : 1 in JPEG 2000 showed nearly the same the lesion detectability as the original images. In the subjective assessment of image quality, images up to compression ratio of 9 : 1 in JPEG and 14 : 1 in JPEG 2000 showed minute mean paired differences from the original images. The results showed that the clinically acceptable compression ratios were up to 9 : 1 for JPEG and 14 : 1 for JPEG 2000. The wavelet-based JPEG 2000 is a better compression method, comparing to DCT-based JPEG for intraoral digital radiographic images.

Extreme compression for extreme conditions: pilot study to identify optimal compression of CT images using MPEG-4 video compression.

Science.gov (United States)

Peterson, P Gabriel; Pak, Sung K; Nguyen, Binh; Jacobs, Genevieve; Folio, Les

2012-12-01

This study aims to evaluate the utility of compressed computed tomography (CT) studies (to expedite transmission) using Motion Pictures Experts Group, Layer 4 (MPEG-4) movie formatting in combat hospitals when guiding major treatment regimens. This retrospective analysis was approved by Walter Reed Army Medical Center institutional review board with a waiver for the informed consent requirement. Twenty-five CT chest, abdomen, and pelvis exams were converted from Digital Imaging and Communications in Medicine to MPEG-4 movie format at various compression ratios. Three board-certified radiologists reviewed various levels of compression on emergent CT findings on 25 combat casualties and compared with the interpretation of the original series. A Universal Trauma Window was selected at -200 HU level and 1,500 HU width, then compressed at three lossy levels. Sensitivities and specificities for each reviewer were calculated along with 95 % confidence intervals using the method of general estimating equations. The compression ratios compared were 171:1, 86:1, and 41:1 with combined sensitivities of 90 % (95 % confidence interval, 79-95), 94 % (87-97), and 100 % (93-100), respectively. Combined specificities were 100 % (85-100), 100 % (85-100), and 96 % (78-99), respectively. The introduction of CT in combat hospitals with increasing detectors and image data in recent military operations has increased the need for effective teleradiology; mandating compression technology. Image compression is currently used to transmit images from combat hospital to tertiary care centers with subspecialists and our study demonstrates MPEG-4 technology as a reasonable means of achieving such compression.

High bit depth infrared image compression via low bit depth codecs

Science.gov (United States)

Belyaev, Evgeny; Mantel, Claire; Forchhammer, Søren

2017-08-01

Future infrared remote sensing systems, such as monitoring of the Earth's environment by satellites, infrastructure inspection by unmanned airborne vehicles etc., will require 16 bit depth infrared images to be compressed and stored or transmitted for further analysis. Such systems are equipped with low power embedded platforms where image or video data is compressed by a hardware block called the video processing unit (VPU). However, in many cases using two 8-bit VPUs can provide advantages compared with using higher bit depth image compression directly. We propose to compress 16 bit depth images via 8 bit depth codecs in the following way. First, an input 16 bit depth image is mapped into 8 bit depth images, e.g., the first image contains only the most significant bytes (MSB image) and the second one contains only the least significant bytes (LSB image). Then each image is compressed by an image or video codec with 8 bits per pixel input format. We analyze how the compression parameters for both MSB and LSB images should be chosen to provide the maximum objective quality for a given compression ratio. Finally, we apply the proposed infrared image compression method utilizing JPEG and H.264/AVC codecs, which are usually available in efficient implementations, and compare their rate-distortion performance with JPEG2000, JPEG-XT and H.265/HEVC codecs supporting direct compression of infrared images in 16 bit depth format. A preliminary result shows that two 8 bit H.264/AVC codecs can achieve similar result as 16 bit HEVC codec.

Imaging Breast Density: Established and Emerging Modalities

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Jeon-Hor Chen

2015-12-01

Full Text Available Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature.

Role of magnetic resonance imaging in entrapment and compressive neuropathy - what, where, and how to see the peripheral nerves on the musculoskeletal magnetic resonance image: Part 2. Upper extremity

Energy Technology Data Exchange (ETDEWEB)

Kim, Sungjun [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea); Hanyang University, Kuri Hospital, Department of Diagnostic Radiology, College of Medicine, Kuri City, Kyunggi-do (Korea); Choi, Jin-Young; Huh, Yong-Min; Song, Ho-Taek; Lee, Sung-Ah [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea); Kim, Seung Min [Yonsei University, Department of Neurology, College of Medicine, Seoul (Korea); Suh, Jin-Suck [Yonsei University, Department of Diagnostic Radiology, College of Medicine, Seoul (Korea); Yonsei University, Research Institute of Radiological Science, College of Medicine, Seoul (Korea)

2007-02-15

The diagnosis of nerve entrapment and compressive neuropathy has been traditionally based on the clinical and electrodiagnostic examinations. As a result of improvements in the magnetic resonance (MR) imaging modality, it plays not only a fundamental role in the detection of space-occupying lesions, but also a compensatory role in clinically and electrodiagnostically inconclusive cases. Although ultrasound has undergone further development in the past decades and shows high resolution capabilities, it has inherent limitations due to its operator dependency. We review the course of normal peripheral nerves, as well as various clinical demonstrations and pathological features of compressed and entrapped nerves in the upper extremities on MR imaging, according to the nerves involved. The common sites of nerve entrapment of the upper extremity are as follows: the brachial plexus of the thoracic outlet; axillary nerve of the quadrilateral space; radial nerve of the radial tunnel; ulnar nerve of the cubital tunnel and Guyon's canal; median nerve of the pronator syndrome, anterior interosseous nerve syndrome, and carpal tunnel syndrome. Although MR imaging can depict the peripheral nerves in the extremities effectively, radiologists should be familiar with nerve pathways, common sites of nerve compression, and common space-occupying lesions resulting in nerve compression in MR imaging. (orig.)

Role of magnetic resonance imaging in entrapment and compressive neuropathy - what, where, and how to see the peripheral nerves on the musculoskeletal magnetic resonance image: Part 2. Upper extremity

International Nuclear Information System (INIS)

Kim, Sungjun; Choi, Jin-Young; Huh, Yong-Min; Song, Ho-Taek; Lee, Sung-Ah; Kim, Seung Min; Suh, Jin-Suck

2007-01-01

The diagnosis of nerve entrapment and compressive neuropathy has been traditionally based on the clinical and electrodiagnostic examinations. As a result of improvements in the magnetic resonance (MR) imaging modality, it plays not only a fundamental role in the detection of space-occupying lesions, but also a compensatory role in clinically and electrodiagnostically inconclusive cases. Although ultrasound has undergone further development in the past decades and shows high resolution capabilities, it has inherent limitations due to its operator dependency. We review the course of normal peripheral nerves, as well as various clinical demonstrations and pathological features of compressed and entrapped nerves in the upper extremities on MR imaging, according to the nerves involved. The common sites of nerve entrapment of the upper extremity are as follows: the brachial plexus of the thoracic outlet; axillary nerve of the quadrilateral space; radial nerve of the radial tunnel; ulnar nerve of the cubital tunnel and Guyon's canal; median nerve of the pronator syndrome, anterior interosseous nerve syndrome, and carpal tunnel syndrome. Although MR imaging can depict the peripheral nerves in the extremities effectively, radiologists should be familiar with nerve pathways, common sites of nerve compression, and common space-occupying lesions resulting in nerve compression in MR imaging. (orig.)

Diagnostic imaging of compression neuropathy

International Nuclear Information System (INIS)

Weishaupt, D.; Andreisek, G.

2007-01-01

Compression-induced neuropathy of peripheral nerves can cause severe pain of the foot and ankle. Early diagnosis is important to institute prompt treatment and to minimize potential injury. Although clinical examination combined with electrophysiological studies remain the cornerstone of the diagnostic work-up, in certain cases, imaging may provide key information with regard to the exact anatomic location of the lesion or aid in narrowing the differential diagnosis. In other patients with peripheral neuropathies of the foot and ankle, imaging may establish the etiology of the condition and provide information crucial for management and/or surgical planning. MR imaging and ultrasound provide direct visualization of the nerve and surrounding abnormalities. Bony abnormalities contributing to nerve compression are best assessed by radiographs and CT. Knowledge of the anatomy, the etiology, typical clinical findings, and imaging features of peripheral neuropathies affecting the peripheral nerves of the foot and ankle will allow for a more confident diagnosis. (orig.) [de

[Medical image compression: a review].

Science.gov (United States)

Noreña, Tatiana; Romero, Eduardo

2013-01-01

Modern medicine is an increasingly complex activity , based on the evidence ; it consists of information from multiple sources : medical record text , sound recordings , images and videos generated by a large number of devices . Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up . However , the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services , generating additional costs in devices with greater storage capacity . Besides , the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet . In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs . In this paper we present a review of compression techniques used for image storage , and a critical analysis of them from the point of view of their use in clinical settings.

An efficient algorithm for MR image reconstruction and compression

International Nuclear Information System (INIS)

Wang, Hang; Rosenfeld, D.; Braun, M.; Yan, Hong

1992-01-01

In magnetic resonance imaging (MRI), the original data are sampled in the spatial frequency domain. The sampled data thus constitute a set of discrete Fourier transform (DFT) coefficients. The image is usually reconstructed by taking inverse DFT. The image data may then be efficiently compressed using the discrete cosine transform (DCT). A method of using DCT to treat the sampled data is presented which combines two procedures, image reconstruction and data compression. This method may be particularly useful in medical picture archiving and communication systems where both image reconstruction and compression are important issues. 11 refs., 3 figs

The relationship between compression force, image quality and ...

African Journals Online (AJOL)

Theoretically, an increase in breast compression gives a reduction in thickness without changing the density, resulting in improved image quality and reduced radiation dose. Aim. This study investigates the relationship between compression force, phantom thickness, image quality and radiation dose. The existence of a ...

Evaluation of compression ratio using JPEG 2000 on diagnostic images in dentistry

International Nuclear Information System (INIS)

Jung, Gi Hun; Han, Won Jeong; Yoo, Dong Soo; Kim, Eun Kyung; Choi, Soon Chul

2005-01-01

To find out the proper compression ratios without degrading image quality and affecting lesion detectability on diagnostic images used in dentistry compressed with JPEG 2000 algorithm. Sixty Digora peri apical images, sixty panoramic computed radiographic (CR) images, sixty computed tomography (CT) images, and sixty magnetic resonance (MR) images were compressed into JPEG 2000 with ratios of 10 levels from 5:1 to 50:1. To evaluate the lesion detectability, the images were graded with 5 levels (1 : definitely absent ; 2 : probably absent ; 3 : equivocal ; 4 : probably present ; 5 : definitely present), and then receiver operating characteristic analysis was performed using the original image as a gold standard. Also to evaluate subjectively the image quality, the images were graded with 5 levels (1 : definitely unacceptable ; 2 : probably unacceptable ; 3 : equivocal ; 4 : probably acceptable ; 5 : definitely acceptable), and then paired t-test was performed. In Digora, CR panoramic and CT images, compressed images up to ratios of 15:1 showed nearly the same lesion detectability as original images, and in MR images, compressed images did up to ratios of 25:1. In Digora and CR panoramic images, compressed images up to ratios of 5:1 showed little difference between the original and reconstructed images in subjective assessment of image quality. In CT images, compressed images did up to ratios of 10:1 and in MR images up to ratios of 15:1. We considered compression ratios up to 5:1 in Digora and CR panoramic images, up to 10:1 in CT images, up to 15:1 in MR images as clinically applicable compression ratios.

Multi-modality molecular imaging: pre-clinical laboratory configuration

Science.gov (United States)

Wu, Yanjun; Wellen, Jeremy W.; Sarkar, Susanta K.

2006-02-01

In recent years, the prevalence of in vivo molecular imaging applications has rapidly increased. Here we report on the construction of a multi-modality imaging facility in a pharmaceutical setting that is expected to further advance existing capabilities for in vivo imaging of drug distribution and the interaction with their target. The imaging instrumentation in our facility includes a microPET scanner, a four wavelength time-domain optical imaging scanner, a 9.4T/30cm MRI scanner and a SPECT/X-ray CT scanner. An electronics shop and a computer room dedicated to image analysis are additional features of the facility. The layout of the facility was designed with a central animal preparation room surrounded by separate laboratory rooms for each of the major imaging modalities to accommodate the work-flow of simultaneous in vivo imaging experiments. This report will focus on the design of and anticipated applications for our microPET and optical imaging laboratory spaces. Additionally, we will discuss efforts to maximize the daily throughput of animal scans through development of efficient experimental work-flows and the use of multiple animals in a single scanning session.

MEDICAL IMAGE COMPRESSION USING HYBRID CODER WITH FUZZY EDGE DETECTION

Directory of Open Access Journals (Sweden)

K. Vidhya

2011-02-01

Full Text Available Medical imaging techniques produce prohibitive amounts of digitized clinical data. Compression of medical images is a must due to large memory space required for transmission and storage. This paper presents an effective algorithm to compress and to reconstruct medical images. The proposed algorithm first extracts edge information of medical images by using fuzzy edge detector. The images are decomposed using Cohen-Daubechies-Feauveau (CDF wavelet. The hybrid technique utilizes the efficient wavelet based compression algorithms such as JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT. The wavelet coefficients in the approximation sub band are encoded using tier 1 part of JPEG2000. The wavelet coefficients in the detailed sub bands are encoded using SPIHT. Consistent quality images are produced by this method at a lower bit rate compared to other standard compression algorithms. Two main approaches to assess image quality are objective testing and subjective testing. The image quality is evaluated by objective quality measures. Objective measures correlate well with the perceived image quality for the proposed compression algorithm.

Blind compressed sensing image reconstruction based on alternating direction method

Science.gov (United States)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

Encryption of Stereo Images after Compression by Advanced Encryption Standard (AES

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Marwah k Hussien

2018-04-01

Full Text Available New partial encryption schemes are proposed, in which a secure encryption algorithm is used to encrypt only part of the compressed data. Partial encryption applied after application of image compression algorithm. Only 0.0244%-25% of the original data isencrypted for two pairs of dif-ferent grayscale imageswiththe size (256 ´ 256 pixels. As a result, we see a significant reduction of time in the stage of encryption and decryption. In the compression step, the Orthogonal Search Algorithm (OSA for motion estimation (the dif-ferent between stereo images is used. The resulting disparity vector and the remaining image were compressed by Discrete Cosine Transform (DCT, Quantization and arithmetic encoding. The image compressed was encrypted by Advanced Encryption Standard (AES. The images were then decoded and were compared with the original images. Experimental results showed good results in terms of Peak Signal-to-Noise Ratio (PSNR, Com-pression Ratio (CR and processing time. The proposed partial encryption schemes are fast, se-cure and do not reduce the compression performance of the underlying selected compression methods

A tri-modality image fusion method for target delineation of brain tumors in radiotherapy.

Directory of Open Access Journals (Sweden)

Lu Guo

Full Text Available To develop a tri-modality image fusion method for better target delineation in image-guided radiotherapy for patients with brain tumors.A new method of tri-modality image fusion was developed, which can fuse and display all image sets in one panel and one operation. And a feasibility study in gross tumor volume (GTV delineation using data from three patients with brain tumors was conducted, which included images of simulation CT, MRI, and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET examinations before radiotherapy. Tri-modality image fusion was implemented after image registrations of CT+PET and CT+MRI, and the transparency weight of each modality could be adjusted and set by users. Three radiation oncologists delineated GTVs for all patients using dual-modality (MRI/CT and tri-modality (MRI/CT/PET image fusion respectively. Inter-observer variation was assessed by the coefficient of variation (COV, the average distance between surface and centroid (ADSC, and the local standard deviation (SDlocal. Analysis of COV was also performed to evaluate intra-observer volume variation.The inter-observer variation analysis showed that, the mean COV was 0.14(± 0.09 and 0.07(± 0.01 for dual-modality and tri-modality respectively; the standard deviation of ADSC was significantly reduced (p<0.05 with tri-modality; SDlocal averaged over median GTV surface was reduced in patient 2 (from 0.57 cm to 0.39 cm and patient 3 (from 0.42 cm to 0.36 cm with the new method. The intra-observer volume variation was also significantly reduced (p = 0.00 with the tri-modality method as compared with using the dual-modality method.With the new tri-modality image fusion method smaller inter- and intra-observer variation in GTV definition for the brain tumors can be achieved, which improves the consistency and accuracy for target delineation in individualized radiotherapy.

Medical image compression and its application to TDIS-FILE equipment

International Nuclear Information System (INIS)

Tsubura, Shin-ichi; Nishihara, Eitaro; Iwai, Shunsuke

1990-01-01

In order to compress medical images for filing and communication, we have developed a compression algorithm which compresses images with remarkable quality using a high-pass filtering method. Hardware for this compression algorithm was also developed and applied to TDIS (total digital imaging system)-FILE equipment. In the future, hardware based on this algorithm will be developed for various types of diagnostic equipment and PACS. This technique has the following characteristics: (1) significant reduction of artifacts; (2) acceptable quality for clinical evaluation at 15:1 to 20:1 compression ratio; and (3) high-speed processing and compact hardware. (author)

Performance evaluation of emerging JPEGXR compression standard for medical images

International Nuclear Information System (INIS)

Basit, M.A.

2012-01-01

Medical images require loss less compression as a small error due to lossy compression may be considered as a diagnostic error. JPEG XR is the latest image compression standard designed for variety of applications and has a support for lossy and loss less modes. This paper provides in-depth performance evaluation of latest JPEGXR with existing image coding standards for medical images using loss less compression. Various medical images are used for evaluation and ten images of each organ are tested. Performance of JPEGXR is compared with JPEG2000 and JPEGLS using mean square error, peak signal to noise ratio, mean absolute error and structural similarity index. JPEGXR shows improvement of 20.73 dB and 5.98 dB over JPEGLS and JPEG2000 respectively for various test images used in experimentation. (author)

The FBI compression standard for digitized fingerprint images

Energy Technology Data Exchange (ETDEWEB)

Brislawn, C.M.; Bradley, J.N. [Los Alamos National Lab., NM (United States); Onyshczak, R.J. [National Inst. of Standards and Technology, Gaithersburg, MD (United States); Hopper, T. [Federal Bureau of Investigation, Washington, DC (United States)

1996-10-01

The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the current status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.

Image Compression Based On Wavelet, Polynomial and Quadtree

Directory of Open Access Journals (Sweden)

Bushra A. SULTAN

2011-01-01

Full Text Available In this paper a simple and fast image compression scheme is proposed, it is based on using wavelet transform to decompose the image signal and then using polynomial approximation to prune the smoothing component of the image band. The architect of proposed coding scheme is high synthetic where the error produced due to polynomial approximation in addition to the detail sub-band data are coded using both quantization and Quadtree spatial coding. As a last stage of the encoding process shift encoding is used as a simple and efficient entropy encoder to compress the outcomes of the previous stage.The test results indicate that the proposed system can produce a promising compression performance while preserving the image quality level.

Image Quality Assessment of JPEG Compressed Mars Science Laboratory Mastcam Images using Convolutional Neural Networks

Science.gov (United States)

Kerner, H. R.; Bell, J. F., III; Ben Amor, H.

2017-12-01

The Mastcam color imaging system on the Mars Science Laboratory Curiosity rover acquires images within Gale crater for a variety of geologic and atmospheric studies. Images are often JPEG compressed before being downlinked to Earth. While critical for transmitting images on a low-bandwidth connection, this compression can result in image artifacts most noticeable as anomalous brightness or color changes within or near JPEG compression block boundaries. In images with significant high-frequency detail (e.g., in regions showing fine layering or lamination in sedimentary rocks), the image might need to be re-transmitted losslessly to enable accurate scientific interpretation of the data. The process of identifying which images have been adversely affected by compression artifacts is performed manually by the Mastcam science team, costing significant expert human time. To streamline the tedious process of identifying which images might need to be re-transmitted, we present an input-efficient neural network solution for predicting the perceived quality of a compressed Mastcam image. Most neural network solutions require large amounts of hand-labeled training data for the model to learn the target mapping between input (e.g. distorted images) and output (e.g. quality assessment). We propose an automatic labeling method using joint entropy between a compressed and uncompressed image to avoid the need for domain experts to label thousands of training examples by hand. We use automatically labeled data to train a convolutional neural network to estimate the probability that a Mastcam user would find the quality of a given compressed image acceptable for science analysis. We tested our model on a variety of Mastcam images and found that the proposed method correlates well with image quality perception by science team members. When assisted by our proposed method, we estimate that a Mastcam investigator could reduce the time spent reviewing images by a minimum of 70%.

Expandable image compression system: A modular approach

International Nuclear Information System (INIS)

Ho, B.K.T.; Lo, S.C.; Huang, H.K.

1986-01-01

The full-frame bit-allocation algorithm for radiological image compression can achieve an acceptable compression ratio as high as 30:1. It involves two stages of operation: a two-dimensional discrete cosine transform and pixel quantization in the transformed space with pixel depth kept accountable by a bit-allocation table. The cosine transform hardware design took an expandable modular approach based on the VME bus system with a maximum data transfer rate of 48 Mbytes/sec and a microprocessor (Motorola 68000 family). The modules are cascadable and microprogrammable to perform 1,024-point butterfly operations. A total of 18 stages would be required for transforming a 1,000 x 1,000 image. Multiplicative constants and addressing sequences are to be software loaded into the parameter buffers of each stage prior to streaming data through the processor stages. The compression rate for 1K x 1K images is expected to be faster than one image per sec

Imaging Modalities for Cervical Spondylotic Stenosis and Myelopathy

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C. Green

2012-01-01

Full Text Available Cervical spondylosis is a spectrum of pathology presenting as neck pain, radiculopathy, and myelopathy or all in combination. Diagnostic imaging is essential to diagnosis and preoperative planning. We discuss the modalities of imaging in common practice. We examine the use of imaging to differentiate among central, subarticular, and lateral stenosis and in the assessment of myelopathy.

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

Directory of Open Access Journals (Sweden)

Yuri Álvarez López

2017-01-01

Full Text Available One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated.

CoGI: Towards Compressing Genomes as an Image.

Science.gov (United States)

Xie, Xiaojing; Zhou, Shuigeng; Guan, Jihong

2015-01-01

Genomic science is now facing an explosive increase of data thanks to the fast development of sequencing technology. This situation poses serious challenges to genomic data storage and transferring. It is desirable to compress data to reduce storage and transferring cost, and thus to boost data distribution and utilization efficiency. Up to now, a number of algorithms / tools have been developed for compressing genomic sequences. Unlike the existing algorithms, most of which treat genomes as one-dimensional text strings and compress them based on dictionaries or probability models, this paper proposes a novel approach called CoGI (the abbreviation of Compressing Genomes as an Image) for genome compression, which transforms the genomic sequences to a two-dimensional binary image (or bitmap), then applies a rectangular partition coding algorithm to compress the binary image. CoGI can be used as either a reference-based compressor or a reference-free compressor. For the former, we develop two entropy-based algorithms to select a proper reference genome. Performance evaluation is conducted on various genomes. Experimental results show that the reference-based CoGI significantly outperforms two state-of-the-art reference-based genome compressors GReEn and RLZ-opt in both compression ratio and compression efficiency. It also achieves comparable compression ratio but two orders of magnitude higher compression efficiency in comparison with XM--one state-of-the-art reference-free genome compressor. Furthermore, our approach performs much better than Gzip--a general-purpose and widely-used compressor, in both compression speed and compression ratio. So, CoGI can serve as an effective and practical genome compressor. The source code and other related documents of CoGI are available at: http://admis.fudan.edu.cn/projects/cogi.htm.

Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

Science.gov (United States)

Huang, Yawen; Shao, Ling; Frangi, Alejandro F

2018-03-01

Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.

EBLAST: an efficient high-compression image transformation 3. application to Internet image and video transmission

Science.gov (United States)

Schmalz, Mark S.; Ritter, Gerhard X.; Caimi, Frank M.

2001-12-01

A wide variety of digital image compression transforms developed for still imaging and broadcast video transmission are unsuitable for Internet video applications due to insufficient compression ratio, poor reconstruction fidelity, or excessive computational requirements. Examples include hierarchical transforms that require all, or large portion of, a source image to reside in memory at one time, transforms that induce significant locking effect at operationally salient compression ratios, and algorithms that require large amounts of floating-point computation. The latter constraint holds especially for video compression by small mobile imaging devices for transmission to, and compression on, platforms such as palmtop computers or personal digital assistants (PDAs). As Internet video requirements for frame rate and resolution increase to produce more detailed, less discontinuous motion sequences, a new class of compression transforms will be needed, especially for small memory models and displays such as those found on PDAs. In this, the third series of papers, we discuss the EBLAST compression transform and its application to Internet communication. Leading transforms for compression of Internet video and still imagery are reviewed and analyzed, including GIF, JPEG, AWIC (wavelet-based), wavelet packets, and SPIHT, whose performance is compared with EBLAST. Performance analysis criteria include time and space complexity and quality of the decompressed image. The latter is determined by rate-distortion data obtained from a database of realistic test images. Discussion also includes issues such as robustness of the compressed format to channel noise. EBLAST has been shown to perform superiorly to JPEG and, unlike current wavelet compression transforms, supports fast implementation on embedded processors with small memory models.

Molecular imaging needles: dual-modality optical coherence tomography and fluorescence imaging of labeled antibodies deep in tissue

Science.gov (United States)

Scolaro, Loretta; Lorenser, Dirk; Madore, Wendy-Julie; Kirk, Rodney W.; Kramer, Anne S.; Yeoh, George C.; Godbout, Nicolas; Sampson, David D.; Boudoux, Caroline; McLaughlin, Robert A.

2015-01-01

Molecular imaging using optical techniques provides insight into disease at the cellular level. In this paper, we report on a novel dual-modality probe capable of performing molecular imaging by combining simultaneous three-dimensional optical coherence tomography (OCT) and two-dimensional fluorescence imaging in a hypodermic needle. The probe, referred to as a molecular imaging (MI) needle, may be inserted tens of millimeters into tissue. The MI needle utilizes double-clad fiber to carry both imaging modalities, and is interfaced to a 1310-nm OCT system and a fluorescence imaging subsystem using an asymmetrical double-clad fiber coupler customized to achieve high fluorescence collection efficiency. We present, to the best of our knowledge, the first dual-modality OCT and fluorescence needle probe with sufficient sensitivity to image fluorescently labeled antibodies. Such probes enable high-resolution molecular imaging deep within tissue. PMID:26137379

Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB

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Wei Jin

2015-01-01

Full Text Available An ideal compression method for neutron radiation image should have high compression ratio while keeping more details of the original image. Compressed sensing (CS, which can break through the restrictions of sampling theorem, is likely to offer an efficient compression scheme for the neutron radiation image. Combining wavelet transform with directional filter banks, a novel nonredundant multiscale geometry analysis transform named Wavelet Directional Filter Banks (WDFB is constructed and applied to represent neutron radiation image sparsely. Then, the block-based CS technique is introduced and a high performance CS scheme for neutron radiation image is proposed. By performing two-step iterative shrinkage algorithm the problem of L1 norm minimization is solved to reconstruct neutron radiation image from random measurements. The experiment results demonstrate that the scheme not only improves the quality of reconstructed image obviously but also retains more details of original image.

Wavelets: Applications to Image Compression-II

Indian Academy of Sciences (India)

Wavelets: Applications to Image Compression-II. Sachin P ... successful application of wavelets in image com- ... b) Soft threshold: In this case, all the coefficients x ..... [8] http://www.jpeg.org} Official site of the Joint Photographic Experts Group.

Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme

Science.gov (United States)

Li, Xianye; Meng, Xiangfeng; Yang, Xiulun; Wang, Yurong; Yin, Yongkai; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

2018-03-01

A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method.

Comparisons of three alternative breast modalities in a common phantom imaging experiment

International Nuclear Information System (INIS)

Li Dun; Meaney, Paul M.; Tosteson, Tor D.; Jiang Shudong; Kerner, Todd E.; McBride, Troy O.; Pogue, Brian W.; Hartov, Alexander; Paulsen, Keith D.

2003-01-01

Four model-based imaging systems are currently being developed for breast cancer detection at Dartmouth College. A potential advantage of multimodality imaging is the prospect of combining information collected from each system to provide a more complete diagnostic tool that covers the full range of the patient and pathology spectra. In this paper it is shown through common phantom experiments on three of these imaging systems that it was possible to correlate different types of image information to potentially improve the reliability of tumor detection. Imaging experiments were conducted with common phantoms which mimic both dielectric and optical properties of the human breast. Cross modality comparison was investigated through a statistical study based on the repeated data sets of reconstructed parameters for each modality. The system standard error between all methods was generally less than 10% and the correlation coefficient across modalities ranged from 0.68 to 0.91. Future work includes the minimization of bias (artifacts) on the periphery of electrical impedance spectroscopy images to improve cross modality correlation and implementation of the multimodality diagnosis for breast cancer detection

Medical image compression by using three-dimensional wavelet transformation

International Nuclear Information System (INIS)

Wang, J.; Huang, H.K.

1996-01-01

This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB. In general, the smaller the slice distance, the better the 3-D compression performance

Single exposure optically compressed imaging and visualization using random aperture coding

Energy Technology Data Exchange (ETDEWEB)

Stern, A [Electro Optical Unit, Ben Gurion University of the Negev, Beer-Sheva 84105 (Israel); Rivenson, Yair [Department of Electrical and Computer Engineering, Ben Gurion University of the Negev, Beer-Sheva 84105 (Israel); Javidi, Bahrain [Department of Electrical and Computer Engineering, University of Connecticut, Storrs, Connecticut 06269-1157 (United States)], E-mail: stern@bgu.ac.il

2008-11-01

The common approach in digital imaging follows the sample-then-compress framework. According to this approach, in the first step as many pixels as possible are captured and in the second step the captured image is compressed by digital means. The recently introduced theory of compressed sensing provides the mathematical foundation necessary to combine these two steps in a single one, that is, to compress the information optically before it is recorded. In this paper we overview and extend an optical implementation of compressed sensing theory that we have recently proposed. With this new imaging approach the compression is accomplished inherently in the optical acquisition step. The primary feature of this imaging approach is a randomly encoded aperture realized by means of a random phase screen. The randomly encoded aperture implements random projection of the object field in the image plane. Using a single exposure, a randomly encoded image is captured which can be decoded by proper decoding algorithm.

A JPEG backward-compatible HDR image compression

Science.gov (United States)

Korshunov, Pavel; Ebrahimi, Touradj

2012-10-01

High Dynamic Range (HDR) imaging is expected to become one of the technologies that could shape next generation of consumer digital photography. Manufacturers are rolling out cameras and displays capable of capturing and rendering HDR images. The popularity and full public adoption of HDR content is however hindered by the lack of standards in evaluation of quality, file formats, and compression, as well as large legacy base of Low Dynamic Range (LDR) displays that are unable to render HDR. To facilitate wide spread of HDR usage, the backward compatibility of HDR technology with commonly used legacy image storage, rendering, and compression is necessary. Although many tone-mapping algorithms were developed for generating viewable LDR images from HDR content, there is no consensus on which algorithm to use and under which conditions. This paper, via a series of subjective evaluations, demonstrates the dependency of perceived quality of the tone-mapped LDR images on environmental parameters and image content. Based on the results of subjective tests, it proposes to extend JPEG file format, as the most popular image format, in a backward compatible manner to also deal with HDR pictures. To this end, the paper provides an architecture to achieve such backward compatibility with JPEG and demonstrates efficiency of a simple implementation of this framework when compared to the state of the art HDR image compression.

Dictionary Approaches to Image Compression and Reconstruction

Science.gov (United States)

Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

1998-01-01

This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as phi(sub gamma), are discrete time signals, where gamma represents the dictionary index. A dictionary with a collection of these waveforms is typically complete or overcomplete. Given such a dictionary, the goal is to obtain a representation image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

Optimal Image Data Compression For Whole Slide Images

Directory of Open Access Journals (Sweden)

J. Isola

2016-06-01

Differences in WSI file sizes of scanned images deemed “visually lossless” were significant. If we set Hamamatsu Nanozoomer .NDPI file size (using its default “jpeg80 quality” as 100%, the size of a “visually lossless” JPEG2000 file was only 15-20% of that. Comparisons to Aperio and 3D-Histech files (.svs and .mrxs at their default settings yielded similar results. A further optimization of JPEG2000 was done by treating empty slide area as uniform white-grey surface, which could be maximally compressed. Using this algorithm, JPEG2000 file sizes were only half, or even smaller, of original JPEG2000. Variation was due to the proportion of empty slide area on the scan. We anticipate that wavelet-based image compression methods, such as JPEG2000, have a significant advantage in saving storage costs of scanned whole slide image. In routine pathology laboratories applying WSI technology widely to their histology material, absolute cost savings can be substantial.  

Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence

International Nuclear Information System (INIS)

Fukatsu, Hiroshi; Naganawa, Shinji; Yumura, Shinnichiro

2008-01-01

This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block; a residual data calculation block; a transformation and quantization block; an organization and modification block; and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching; and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method; and the compression rates were calculated. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data. (author)

Radiological Evaluation of Ambiguous Genitalia with Various Imaging Modalities

Science.gov (United States)

Ravi, N.; Bindushree, Kadakola

2012-07-01

Disorders of sex development (DSDs) are congenital conditions in which the development of chromosomal, gonadal, or anatomic sex is atypical. These can be classified broadly into four categories on the basis of gonadal histologic features: female pseudohermaphroditism (46,XX with two ovaries); male pseudohermaphroditism (46,XY with two testes); true hermaphroditism (ovotesticular DSD) (both ovarian and testicular tissues); and gonadal dysgenesis, either mixed (a testis and a streak gonad) or pure (bilateral streak gonads). Imaging plays an important role in demonstrating the anatomy and associated anomalies. Ultrasonography is the primary modality for demonstrating internal organs and magnetic resonance imaging is used as an adjunct modality to assess for internal gonads and genitalia. Early and appropriate gender assignment is necessary for healthy physical and psychologic development of children with ambiguous genitalia. Gender assignment can be facilitated with a team approach that involves a pediatric endocrinologist, geneticist, urologist, psychiatrist, social worker, neonatologist, nurse, and radiologist, allowing timely diagnosis and proper management. We describe case series on ambiguous genitalia presented to our department who were evaluated with multiple imaging modalities.

Establishment study of the in vivo imaging analysis with small animal imaging modalities for bio-durg development

International Nuclear Information System (INIS)

Jang, Beomsu; Park, Sanghyeon; Choi, Dae Seong; Park, Jeonghoon; Jung, Uhee; Lee, Yun Jong

2012-01-01

In this study, we established the image modalities (micro-PET, SPECT/CT) using the experimental animal (mouse) for the development of imaging assessment method for the bio-durg and extramural collaboration proposal. We examined the micro-SPECT/CT, PET imaging study using the Siemens Inveon micro-multimodality system (SPECT/CT) and imaging study using the Siemens Inveon micro-multimodality system (SPECT/CT) and micro-PET with 99m Tc tricarbonyl bifunctional chelators and 18 F-clotrimazole derivative. SPECT imaging studies were performed with 99m Tc tricarbonyl BFCs. PET imaging study was performed with 18 F-clotrimazole derivatives. We performed the PET image study of 18 F-clotrimazole derivatives using U87MG tumor bearing mice. Also we tested the intramural and extramural collaboration using small animal imaging modalities and prepared the draft of extramural R and D operation manual for small animal imaging modalities and the experimental animal imaging facility. These research results can be utilized as a basic image study protocols and data for the image assessment of drugs including biological drug

Pornographic image recognition and filtering using incremental learning in compressed domain

Science.gov (United States)

Zhang, Jing; Wang, Chao; Zhuo, Li; Geng, Wenhao

2015-11-01

With the rapid development and popularity of the network, the openness, anonymity, and interactivity of networks have led to the spread and proliferation of pornographic images on the Internet, which have done great harm to adolescents' physical and mental health. With the establishment of image compression standards, pornographic images are mainly stored with compressed formats. Therefore, how to efficiently filter pornographic images is one of the challenging issues for information security. A pornographic image recognition and filtering method in the compressed domain is proposed by using incremental learning, which includes the following steps: (1) low-resolution (LR) images are first reconstructed from the compressed stream of pornographic images, (2) visual words are created from the LR image to represent the pornographic image, and (3) incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples after the covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic images. The experimental results show that the proposed pornographic image recognition method using incremental learning has a higher recognition rate as well as costing less recognition time in the compressed domain.

Multi-Modality Medical Image Fusion Based on Wavelet Analysis and Quality Evaluation

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

Multi-modality medical image fusion has more and more important applications in medical image analysisand understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fusemedical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusionresults when applying different selection rules and obtain optimum combination of fusion parameters.

Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

Directory of Open Access Journals (Sweden)

Jin Li

2014-01-01

Full Text Available Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC combined with image data compression (IDC approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE. Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS-based algorithm has better compression performance than the traditional compression approaches.

Multispectral image compression based on DSC combined with CCSDS-IDC.

Science.gov (United States)

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

Cloud solution for histopathological image analysis using region of interest based compression.

Science.gov (United States)

Kanakatte, Aparna; Subramanya, Rakshith; Delampady, Ashik; Nayak, Rajarama; Purushothaman, Balamuralidhar; Gubbi, Jayavardhana

2017-07-01

Recent technological gains have led to the adoption of innovative cloud based solutions in medical imaging field. Once the medical image is acquired, it can be viewed, modified, annotated and shared on many devices. This advancement is mainly due to the introduction of Cloud computing in medical domain. Tissue pathology images are complex and are normally collected at different focal lengths using a microscope. The single whole slide image contains many multi resolution images stored in a pyramidal structure with the highest resolution image at the base and the smallest thumbnail image at the top of the pyramid. Highest resolution image will be used for tissue pathology diagnosis and analysis. Transferring and storing such huge images is a big challenge. Compression is a very useful and effective technique to reduce the size of these images. As pathology images are used for diagnosis, no information can be lost during compression (lossless compression). A novel method of extracting the tissue region and applying lossless compression on this region and lossy compression on the empty regions has been proposed in this paper. The resulting compression ratio along with lossless compression on tissue region is in acceptable range allowing efficient storage and transmission to and from the Cloud.

An Image Compression Scheme in Wireless Multimedia Sensor Networks Based on NMF

Directory of Open Access Journals (Sweden)

Shikang Kong

2017-02-01

Full Text Available With the goal of addressing the issue of image compression in wireless multimedia sensor networks with high recovered quality and low energy consumption, an image compression and transmission scheme based on non-negative matrix factorization (NMF is proposed in this paper. First, the NMF algorithm theory is studied. Then, a collaborative mechanism of image capture, block, compression and transmission is completed. Camera nodes capture images and send them to ordinary nodes which use an NMF algorithm for image compression. Compressed images are transmitted to the station by the cluster head node and received from ordinary nodes. The station takes on the image restoration. Simulation results show that, compared with the JPEG2000 and singular value decomposition (SVD compression schemes, the proposed scheme has a higher quality of recovered images and lower total node energy consumption. It is beneficial to reduce the burden of energy consumption and prolong the life of the whole network system, which has great significance for practical applications of WMSNs.

High speed fluorescence imaging with compressed ultrafast photography

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Thompson, J. V.; Mason, J. D.; Beier, H. T.; Bixler, J. N.

2017-02-01

Fluorescent lifetime imaging is an optical technique that facilitates imaging molecular interactions and cellular functions. Because the excited lifetime of a fluorophore is sensitive to its local microenvironment,1, 2 measurement of fluorescent lifetimes can be used to accurately detect regional changes in temperature, pH, and ion concentration. However, typical state of the art fluorescent lifetime methods are severely limited when it comes to acquisition time (on the order of seconds to minutes) and video rate imaging. Here we show that compressed ultrafast photography (CUP) can be used in conjunction with fluorescent lifetime imaging to overcome these acquisition rate limitations. Frame rates up to one hundred billion frames per second have been demonstrated with compressed ultrafast photography using a streak camera.3 These rates are achieved by encoding time in the spatial direction with a pseudo-random binary pattern. The time domain information is then reconstructed using a compressed sensing algorithm, resulting in a cube of data (x,y,t) for each readout image. Thus, application of compressed ultrafast photography will allow us to acquire an entire fluorescent lifetime image with a single laser pulse. Using a streak camera with a high-speed CMOS camera, acquisition rates of 100 frames per second can be achieved, which will significantly enhance our ability to quantitatively measure complex biological events with high spatial and temporal resolution. In particular, we will demonstrate the ability of this technique to do single-shot fluorescent lifetime imaging of cells and microspheres.

Multi-Modality Imaging in the Evaluation and Treatment of Mitral Regurgitation.

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Bouchard, Marc-André; Côté-Laroche, Claudia; Beaudoin, Jonathan

2017-10-13

Mitral regurgitation (MR) is frequent and associated with increased mortality and morbidity when severe. It may be caused by intrinsic valvular disease (primary MR) or ventricular deformation (secondary MR). Imaging has a critical role to document the severity, mechanism, and impact of MR on heart function as selected patients with MR may benefit from surgery whereas other will not. In patients planned for a surgical intervention, imaging is also important to select candidates for mitral valve (MV) repair over replacement and to predict surgical success. Although standard transthoracic echocardiography is the first-line modality to evaluate MR, newer imaging modalities like three-dimensional (3D) transesophageal echocardiography, stress echocardiography, cardiac magnetic resonance (CMR), and computed tomography (CT) are emerging and complementary tools for MR assessment. While some of these modalities can provide insight into MR severity, others will help to determine its mechanism. Understanding the advantages and limitations of each imaging modality is important to appreciate their respective role for MR assessment and help to resolve eventual discrepancies between different diagnostic methods. With the increasing use of transcatheter mitral procedures (repair or replacement) for high-surgical-risk patients, multimodality imaging has now become even more important to determine eligibility, preinterventional planning, and periprocedural guidance.

COxSwAIN: Compressive Sensing for Advanced Imaging and Navigation

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Kurwitz, Richard; Pulley, Marina; LaFerney, Nathan; Munoz, Carlos

2015-01-01

The COxSwAIN project focuses on building an image and video compression scheme that can be implemented in a small or low-power satellite. To do this, we used Compressive Sensing, where the compression is performed by matrix multiplications on the satellite and reconstructed on the ground. Our paper explains our methodology and demonstrates the results of the scheme, being able to achieve high quality image compression that is robust to noise and corruption.

New patient-controlled abdominal compression method in radiography: radiation dose and image quality.

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Piippo-Huotari, Oili; Norrman, Eva; Anderzén-Carlsson, Agneta; Geijer, Håkan

2018-05-01

The radiation dose for patients can be reduced with many methods and one way is to use abdominal compression. In this study, the radiation dose and image quality for a new patient-controlled compression device were compared with conventional compression and compression in the prone position . To compare radiation dose and image quality of patient-controlled compression compared with conventional and prone compression in general radiography. An experimental design with quantitative approach. After obtaining the approval of the ethics committee, a consecutive sample of 48 patients was examined with the standard clinical urography protocol. The radiation doses were measured as dose-area product and analyzed with a paired t-test. The image quality was evaluated by visual grading analysis. Four radiologists evaluated each image individually by scoring nine criteria modified from the European quality criteria for diagnostic radiographic images. There was no significant difference in radiation dose or image quality between conventional and patient-controlled compression. Prone position resulted in both higher dose and inferior image quality. Patient-controlled compression gave similar dose levels as conventional compression and lower than prone compression. Image quality was similar with both patient-controlled and conventional compression and was judged to be better than in the prone position.

COMPRESSING BIOMEDICAL IMAGE BY USING INTEGER WAVELET TRANSFORM AND PREDICTIVE ENCODER

OpenAIRE

Anushree Srivastava*, Narendra Kumar Chaurasia

2016-01-01

Image compression has become an important process in today’s world of information exchange. It helps in effective utilization of high speed network resources. Medical image compression has an important role in medical field because they are used for future reference of patients. Medical data is compressed in such a way so that the diagnostics capabilities are not compromised or no medical information is lost. Medical imaging poses the great challenge of having compression algorithms that redu...

Imaging of congenital heart disease in adults: choice of modalities.

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Orwat, Stefan; Diller, Gerhard-Paul; Baumgartner, Helmut

2014-01-01

Major advances in noninvasive imaging of adult congenital heart disease have been accomplished. These tools play now a key role in comprehensive diagnostic work-up, decision for intervention, evaluation for the suitability of specific therapeutic options, monitoring of interventions and regular follow-up. Besides echocardiography, magnetic resonance (CMR) and computed tomography (CT) have gained particular importance. The choice of imaging modality has thus become a critical issue. This review summarizes strengths and limitations of the different imaging modalities and how they may be used in a complementary fashion. Echocardiography obviously remains the workhorse of imaging routinely used in all patients. However, in complex disease and after surgery echocardiography alone frequently remains insufficient. CMR is particularly useful in this setting and allows reproducible and accurate quantification of ventricular function and comprehensive assessment of cardiac anatomy, aorta, pulmonary arteries and venous return including complex flow measurements. CT is preferred when CMR is contraindicated, when superior spatial resolution is required or when "metallic" artefacts limit CMR imaging. In conclusion, the use of currently available imaging modalities in adult congenital heart disease needs to be complementary. Echocardiography remains the basis tool, CMR and CT should be added considering specific open questions and the ability to answer them, availability and economic issues.

Effect of high image compression on the reproducibility of cardiac Sestamibi reporting

International Nuclear Information System (INIS)

Thomas, P.; Allen, L.; Beuzeville, S.

1999-01-01

Full text: Compression algorithms have been mooted to minimize storage space and transmission times of digital images. We assessed the impact of high-level lousy compression using JPEG and wavelet algorithms on image quality and reporting accuracy of cardiac Sestamibi studies. Twenty stress/rest Sestamibi cardiac perfusion studies were reconstructed into horizontal short, vertical long and horizontal long axis slices using conventional methods. Each of these six sets of slices were aligned for reporting and saved (uncompressed) as a bitmap. This bitmap was then compressed using JPEG compression, then decompressed and saved as a bitmap for later viewing. This process was repeated using the original bitmap and wavelet compression. Finally, a second copy of the original bitmap was made. All 80 bitmaps were randomly coded to ensure blind reporting. The bitmaps were read blinded and by consensus of 2 experienced nuclear medicine physicians using a 5-point scale and 25 cardiac segments. Subjective image quality was also reported using a 3-point scale. Samples of the compressed images were also subtracted from the original bitmap for visual comparison of differences. Results showed an average compression ratio of 23:1 for wavelet and 13:1 for JPEG. Image subtraction showed only very minor discordance between the original and compressed images. There was no significant difference in subjective quality between the compressed and uncompressed images. There was no significant difference in reporting reproducibility of the identical bitmap copy, the JPEG image and the wavelet image compared with the original bitmap. Use of the high compression algorithms described had no significant impact on reporting reproducibility and subjective image quality of cardiac Sestamibi perfusion studies

Multiband and Lossless Compression of Hyperspectral Images

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Raffaele Pizzolante

2016-02-01

Full Text Available Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.. We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.

Moving image compression and generalization capability of constructive neural networks

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Ma, Liying; Khorasani, Khashayar

2001-03-01

To date numerous techniques have been proposed to compress digital images to ease their storage and transmission over communication channels. Recently, a number of image compression algorithms using Neural Networks NNs have been developed. Particularly, several constructive feed-forward neural networks FNNs have been proposed by researchers for image compression, and promising results have been reported. At the previous SPIE AeroSense conference 2000, we proposed to use a constructive One-Hidden-Layer Feedforward Neural Network OHL-FNN for compressing digital images. In this paper, we first investigate the generalization capability of the proposed OHL-FNN in the presence of additive noise for network training and/ or generalization. Extensive experimental results for different scenarios are presented. It is revealed that the constructive OHL-FNN is not as robust to additive noise in input image as expected. Next, the constructive OHL-FNN is applied to moving images, video sequences. The first, or other specified frame in a moving image sequence is used to train the network. The remaining moving images that follow are then generalized/compressed by this trained network. Three types of correlation-like criteria measuring the similarity of any two images are introduced. The relationship between the generalization capability of the constructed net and the similarity of images is investigated in some detail. It is shown that the constructive OHL-FNN is promising even for changing images such as those extracted from a football game.

Image Segmentation, Registration, Compression, and Matching

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Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

2011-01-01

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity

Image compression using moving average histogram and RBF network

International Nuclear Information System (INIS)

Khowaja, S.; Ismaili, I.A.

2015-01-01

Modernization and Globalization have made the multimedia technology as one of the fastest growing field in recent times but optimal use of bandwidth and storage has been one of the topics which attract the research community to work on. Considering that images have a lion share in multimedia communication, efficient image compression technique has become the basic need for optimal use of bandwidth and space. This paper proposes a novel method for image compression based on fusion of moving average histogram and RBF (Radial Basis Function). Proposed technique employs the concept of reducing color intensity levels using moving average histogram technique followed by the correction of color intensity levels using RBF networks at reconstruction phase. Existing methods have used low resolution images for the testing purpose but the proposed method has been tested on various image resolutions to have a clear assessment of the said technique. The proposed method have been tested on 35 images with varying resolution and have been compared with the existing algorithms in terms of CR (Compression Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), computational complexity. The outcome shows that the proposed methodology is a better trade off technique in terms of compression ratio, PSNR which determines the quality of the image and computational complexity. (author)

Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method

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Shi, Xiaohui; Huang, Xianwei; Nan, Suqin; Li, Hengxing; Bai, Yanfeng; Fu, Xiquan

2018-04-01

Detector noise has a significantly negative impact on ghost imaging at low light levels, especially for existing recovery algorithm. Based on the characteristics of the additive detector noise, a method named modified compressive sensing ghost imaging is proposed to reduce the background imposed by the randomly distributed detector noise at signal path. Experimental results show that, with an appropriate choice of threshold value, modified compressive sensing ghost imaging algorithm can dramatically enhance the contrast-to-noise ratio of the object reconstruction significantly compared with traditional ghost imaging and compressive sensing ghost imaging methods. The relationship between the contrast-to-noise ratio of the reconstruction image and the intensity ratio (namely, the average signal intensity to average noise intensity ratio) for the three reconstruction algorithms are also discussed. This noise suppression imaging technique will have great applications in remote-sensing and security areas.

ADMultiImg: a novel missing modality transfer learning based CAD system for diagnosis of MCI due to AD using incomplete multi-modality imaging data

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Liu, Xiaonan; Chen, Kewei; Wu, Teresa; Weidman, David; Lure, Fleming; Li, Jing

2018-02-01

Alzheimer's Disease (AD) is the most common cause of dementia and currently has no cure. Treatments targeting early stages of AD such as Mild Cognitive Impairment (MCI) may be most effective to deaccelerate AD, thus attracting increasing attention. However, MCI has substantial heterogeneity in that it can be caused by various underlying conditions, not only AD. To detect MCI due to AD, NIA-AA published updated consensus criteria in 2011, in which the use of multi-modality images was highlighted as one of the most promising methods. It is of great interest to develop a CAD system based on automatic, quantitative analysis of multi-modality images and machine learning algorithms to help physicians more adequately diagnose MCI due to AD. The challenge, however, is that multi-modality images are not universally available for many patients due to cost, access, safety, and lack of consent. We developed a novel Missing Modality Transfer Learning (MMTL) algorithm capable of utilizing whatever imaging modalities are available for an MCI patient to diagnose the patient's likelihood of MCI due to AD. Furthermore, we integrated MMTL with radiomics steps including image processing, feature extraction, and feature screening, and a post-processing for uncertainty quantification (UQ), and developed a CAD system called "ADMultiImg" to assist clinical diagnosis of MCI due to AD using multi-modality images together with patient demographic and genetic information. Tested on ADNI date, our system can generate a diagnosis with high accuracy even for patients with only partially available image modalities (AUC=0.94), and therefore may have broad clinical utility.

Designing sparse sensing matrix for compressive sensing to reconstruct high resolution medical images

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Vibha Tiwari

2015-12-01

Full Text Available Compressive sensing theory enables faithful reconstruction of signals, sparse in domain $ \\Psi $, at sampling rate lesser than Nyquist criterion, while using sampling or sensing matrix $ \\Phi $ which satisfies restricted isometric property. The role played by sensing matrix $ \\Phi $ and sparsity matrix $ \\Psi $ is vital in faithful reconstruction. If the sensing matrix is dense then it takes large storage space and leads to high computational cost. In this paper, effort is made to design sparse sensing matrix with least incurred computational cost while maintaining quality of reconstructed image. The design approach followed is based on sparse block circulant matrix (SBCM with few modifications. The other used sparse sensing matrix consists of 15 ones in each column. The medical images used are acquired from US, MRI and CT modalities. The image quality measurement parameters are used to compare the performance of reconstructed medical images using various sensing matrices. It is observed that, since Gram matrix of dictionary matrix ($ \\Phi \\Psi \\mathrm{} $ is closed to identity matrix in case of proposed modified SBCM, therefore, it helps to reconstruct the medical images of very good quality.

A novel high-frequency encoding algorithm for image compression

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Siddeq, Mohammed M.; Rodrigues, Marcos A.

2017-12-01

In this paper, a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the discrete cosine transform (DCT) together with a high-frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) divide the image into blocks and apply DCT to each block; (2) apply a high-frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a minimized array; (3) build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) apply a delta or differential operator to the list of DC-components; and (5) apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high-frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000.

Reconfigurable Hardware for Compressing Hyperspectral Image Data

Science.gov (United States)

Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua

2010-01-01

High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of

Radiation dose reduction and new image modalities development for interventional C-arm imaging system

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Niu, Kai

Cardiovascular disease and stroke are the leading health problems and causes of death in the US. Due to the minimally invasive nature of the evolution of image guided techniques, interventional radiological procedures are becoming more common and are preferred in treating many cardiovascular diseases and strokes. In addition, with the recent advances in hardware and device technology, the speed and efficacy of interventional treatment has significantly improved. This implies that more image modalities can be developed based on the current C-arm system and patients treated in interventional suites can potentially experience better health outcomes. However, during the treatment patients are irradiated with substantial amounts of ionizing radiation with a high dose rate (digital subtraction angiography (DSA) with 3muGy/frame and 3D cone beam CT image with 0.36muGy/frame for a Siemens Artis Zee biplane system) and/or a long irradiation time (a roadmapping image sequence can be as long as one hour during aneurysm embolization). As a result, the patient entrance dose is extremely high. Despite the fact that the radiation dose is already substantial, image quality is not always satisfactory. By default a temporal average is used in roadmapping images to overcome poor image quality, but this technique can result in motion blurred images. Therefore, reducing radiation dose while maintaining or even improving the image quality is an important area for continued research. This thesis is focused on improving the clinical applications of C-arm cone beam CT systems in two ways: (1) Improve the performance of current image modalities on the C-arm system. (2) Develop new image modalities based on the current system. To be more specific, the objectives are to reduce radiation dose for current modalities (e.g., DSA, fluoroscopy, roadmapping, and cone beam CT) and enable cone beam CT perfusion and time resolved cone beam CT angiography that can be used to diagnose and triage acute

Diffusion tensor imaging in spinal cord compression

International Nuclear Information System (INIS)

Wang, Wei; Qin, Wen; Hao, Nanxin; Wang, Yibin; Zong, Genlin

2012-01-01

Background Although diffusion tensor imaging has been successfully applied in brain research for decades, several main difficulties have hindered its extended utilization in spinal cord imaging. Purpose To assess the feasibility and clinical value of diffusion tensor imaging and tractography for evaluating chronic spinal cord compression. Material and Methods Single-shot spin-echo echo-planar DT sequences were scanned in 42 spinal cord compression patients and 49 healthy volunteers. The mean values of the apparent diffusion coefficient and fractional anisotropy were measured in region of interest at the cervical and lower thoracic spinal cord. The patients were divided into two groups according to the high signal on T2WI (the SCC-HI group and the SCC-nHI group for with or without high signal). A one-way ANOVA was used. Diffusion tensor tractography was used to visualize the morphological features of normal and impaired white matter. Results There were no statistically significant differences in the apparent diffusion coefficient and fractional anisotropy values between the different spinal cord segments of the normal subjects. All of the patients in the SCC-HI group had increased apparent diffusion coefficient values and decreased fractional anisotropy values at the lesion level compared to the normal controls. However, there were no statistically significant diffusion index differences between the SCC-nHI group and the normal controls. In the diffusion tensor imaging maps, the normal spinal cord sections were depicted as fiber tracts that were color-encoded to a cephalocaudal orientation. The diffusion tensor images were compressed to different degrees in all of the patients. Conclusion Diffusion tensor imaging and tractography are promising methods for visualizing spinal cord tracts and can provide additional information in clinical studies in spinal cord compression

HVS scheme for DICOM image compression: Design and comparative performance evaluation

Energy Technology Data Exchange (ETDEWEB)

Prabhakar, B. [Biomedical and Engineering Division, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu (India)]. E-mail: prabhakarb@iitm.ac.in; Reddy, M. Ramasubba [Biomedical and Engineering Division, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu (India)

2007-07-15

Advanced digital imaging technology in medical domain demands efficient and effective DICOM image compression for progressive image transmission and picture archival. Here a compression system, which incorporates sensitivities of HVS coded with SPIHT quantization, is discussed. The weighting factors derived from luminance CSF are used to transform the wavelet subband coefficients to reflect characteristics of HVS in best possible manner. Mannos et al. and Daly HVS models have been used and results are compared. To evaluate the performance, Eskicioglu chart metric is considered. Experiment is done on both Monochrome and Color Dicom images of MRI, CT, OT, and CR, natural and benchmark images. Reconstructed image through our technique showed improvement in visual quality and Eskicioglu chart metric at same compression ratios. Also the Daly HVS model based compression shows better performance perceptually and quantitatively when compared to Mannos et el. model. Further 'bior4.4' wavelet filter provides better results than 'db9' filter for this compression system. Results give strong evidence that under common boundary conditions; our technique achieves competitive visual quality, compression ratio and coding/decoding time, when compared with jpeg2000 (kakadu)

Visual tracking for multi-modality computer-assisted image guidance

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Basafa, Ehsan; Foroughi, Pezhman; Hossbach, Martin; Bhanushali, Jasmine; Stolka, Philipp

2017-03-01

With optical cameras, many interventional navigation tasks previously relying on EM, optical, or mechanical guidance can be performed robustly, quickly, and conveniently. We developed a family of novel guidance systems based on wide-spectrum cameras and vision algorithms for real-time tracking of interventional instruments and multi-modality markers. These navigation systems support the localization of anatomical targets, support placement of imaging probe and instruments, and provide fusion imaging. The unique architecture - low-cost, miniature, in-hand stereo vision cameras fitted directly to imaging probes - allows for an intuitive workflow that fits a wide variety of specialties such as anesthesiology, interventional radiology, interventional oncology, emergency medicine, urology, and others, many of which see increasing pressure to utilize medical imaging and especially ultrasound, but have yet to develop the requisite skills for reliable success. We developed a modular system, consisting of hardware (the Optical Head containing the mini cameras) and software (components for visual instrument tracking with or without specialized visual features, fully automated marker segmentation from a variety of 3D imaging modalities, visual observation of meshes of widely separated markers, instant automatic registration, and target tracking and guidance on real-time multi-modality fusion views). From these components, we implemented a family of distinct clinical and pre-clinical systems (for combinations of ultrasound, CT, CBCT, and MRI), most of which have international regulatory clearance for clinical use. We present technical and clinical results on phantoms, ex- and in-vivo animals, and patients.

Cellular automata codebooks applied to compact image compression

Directory of Open Access Journals (Sweden)

Radu DOGARU

2006-12-01

Full Text Available Emergent computation in semi-totalistic cellular automata (CA is used to generate a set of basis (or codebook. Such codebooks are convenient for simple and circuit efficient compression schemes based on binary vector quantization, applied to the bitplanes of any monochrome or color image. Encryption is also naturally included using these codebooks. Natural images would require less than 0.5 bits per pixel (bpp while the quality of the reconstructed images is comparable with traditional compression schemes. The proposed scheme is attractive for low power, sensor integrated applications.

Multi-Modality Registration And Fusion Of Medical Image Data

International Nuclear Information System (INIS)

Kassak, P.; Vencko, D.; Cerovsky, I.

2008-01-01

Digitalisation of health care providing facilities allows US to maximize the usage of digital data from one patient obtained by various modalities. Complex view on to the problem can be achieved from the site of morphology as well as functionality. Multi-modal registration and fusion of medical image data is one of the examples that provides improved insight and allows more precise approach and treatment. (author)

Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

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Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

A New Algorithm for the On-Board Compression of Hyperspectral Images

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Raúl Guerra

2018-03-01

Full Text Available Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA, is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

Dynamic CT perfusion image data compression for efficient parallel processing.

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Barros, Renan Sales; Olabarriaga, Silvia Delgado; Borst, Jordi; van Walderveen, Marianne A A; Posthuma, Jorrit S; Streekstra, Geert J; van Herk, Marcel; Majoie, Charles B L M; Marquering, Henk A

2016-03-01

The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast results. However, the size of CTP datasets makes transfers to cloud infrastructures time-consuming and therefore not suitable in acute situations. To reduce this transfer time, this work proposes a fast and lossless compression algorithm for CTP data. The algorithm exploits redundancies in the temporal dimension and keeps random read-only access to the image elements directly from the compressed data on the GPU. To the best of our knowledge, this is the first work to present a GPU-ready method for medical image compression with random access to the image elements from the compressed data.

Optical imaging modalities: From design to diagnosis of skin cancer

Science.gov (United States)

Korde, Vrushali Raj

This study investigates three high resolution optical imaging modalities to better detect and diagnose skin cancer. The ideal high resolution optical imaging system can visualize pre-malignant tissue growth non-invasively with resolution comparable to histology. I examined 3 modalities which approached this goal. The first method examined was high magnification microscopy of thin stained tissue sections, together with a statistical analysis of nuclear chromatin patterns termed Karyometry. This method has subcellular resolution, but it necessitates taking a biopsy at the desired tissue site and imaging the tissue ex-vivo. My part of this study was to develop an automated nuclear segmentation algorithm to segment cell nuclei in skin histology images for karyometric analysis. The results of this algorithm were compared to hand segmented cell nuclei in the same images, and it was concluded that the automated segmentations can be used for karyometric analysis. The second optical imaging modality I investigated was Optical Coherence Tomography (OCT). OCT is analogous to ultrasound, in which sound waves are delivered into the body and the echo time and reflected signal magnitude are measured. Due to the fast speed of light and detector temporal integration times, low coherence interferometry is needed to gate the backscattered light. OCT acquires cross sectional images, and has an axial resolution of 1-15 mum (depending on the source bandwidth) and a lateral resolution of 10-20 mum (depending on the sample arm optics). While it is not capable of achieving subcellular resolution, it is a non-invasive imaging modality. OCT was used in this study to evaluate skin along a continuum from normal to sun damaged to precancer. I developed algorithms to detect statistically significant differences between images of sun protected and sun damaged skin, as well as between undiseased and precancerous skin. An Optical Coherence Microscopy (OCM) endoscope was developed in the third

A New Approach for Fingerprint Image Compression

Energy Technology Data Exchange (ETDEWEB)

Mazieres, Bertrand

1997-12-01

The FBI has been collecting fingerprint cards since 1924 and now has over 200 million of them. Digitized with 8 bits of grayscale resolution at 500 dots per inch, it means 2000 terabytes of information. Also, without any compression, transmitting a 10 Mb card over a 9600 baud connection will need 3 hours. Hence we need a compression and a compression as close to lossless as possible: all fingerprint details must be kept. A lossless compression usually do not give a better compression ratio than 2:1, which is not sufficient. Compressing these images with the JPEG standard leads to artefacts which appear even at low compression rates. Therefore the FBI has chosen in 1993 a scheme of compression based on a wavelet transform, followed by a scalar quantization and an entropy coding : the so-called WSQ. This scheme allows to achieve compression ratios of 20:1 without any perceptible loss of quality. The publication of the FBI specifies a decoder, which means that many parameters can be changed in the encoding process: the type of analysis/reconstruction filters, the way the bit allocation is made, the number of Huffman tables used for the entropy coding. The first encoder used 9/7 filters for the wavelet transform and did the bit allocation using a high-rate bit assumption. Since the transform is made into 64 subbands, quite a lot of bands receive only a few bits even at an archival quality compression rate of 0.75 bit/pixel. Thus, after a brief overview of the standard, we will discuss a new approach for the bit-allocation that seems to make more sense where theory is concerned. Then we will talk about some implementation aspects, particularly for the new entropy coder and the features that allow other applications than fingerprint image compression. Finally, we will compare the performances of the new encoder to those of the first encoder.

MR imaging of recurrent hyperparathyroidism in comparison with other imaging modalities

International Nuclear Information System (INIS)

Auffermann, W.; Thurnher, S.; Okerland, M.; Levin, K.; Gooding, G.W.; Clark, O.H.; Higgins, C.B.

1987-01-01

Thirty patients with recurrent hyperparathyroidism were evaluated with MR imaging, performed using a saddle-shaped surface coil producing 5-mm sections with T1 and T2 weighting. Twenty-six and 22 of these patients also underwent T1-201 scintigraphy and high-resolution US, respectively. MR imaging accurately localized abnormal parathyroid glands in 75% evaluated prospectively and 86% retrospectively. Scintigraphy localized 64% prospectively and 72% retrospectively. US demonstrated 57% prospectively and 67% retrospectively. MR imaging showed three of four mediastinal adenomas evaluated prospectively retrospectively. There were two false-positive studies with MR imaging, two with scintigraphy, and one with US. Thus, MR imaging was the most effective imaging modality for parathyroid localization in recurrent hyperparathyroidism

Effect of CT digital image compression on detection of coronary artery calcification

International Nuclear Information System (INIS)

Zheng, L.M.; Sone, S.; Itani, Y.; Wang, Q.; Hanamura, K.; Asakura, K.; Li, F.; Yang, Z.G.; Wang, J.C.; Funasaka, T.

2000-01-01

Purpose: To test the effect of digital compression of CT images on the detection of small linear or spotted high attenuation lesions such as coronary artery calcification (CAC). Material and methods: Fifty cases with and 50 without CAC were randomly selected from a population that had undergone spiral CT of the thorax for screening lung cancer. CT image data were compressed using JPEG (Joint Photographic Experts Group) or wavelet algorithms at ratios of 10:1, 20:1 or 40:1. Five radiologists reviewed the uncompressed and compressed images on a cathode-ray-tube. Observer performance was evaluated with receiver operating characteristic analysis. Results: CT images compressed at a ratio as high as 20:1 were acceptable for primary diagnosis of CAC. There was no significant difference in the detection accuracy for CAC between JPEG and wavelet algorithms at the compression ratios up to 20:1. CT images were more vulnerable to image blurring on the wavelet compression at relatively lower ratios, and 'blocking' artifacts occurred on the JPEG compression at relatively higher ratios. Conclusion: JPEG and wavelet algorithms allow compression of CT images without compromising their diagnostic value at ratios up to 20:1 in detecting small linear or spotted high attenuation lesions such as CAC, and there was no difference between the two algorithms in diagnostic accuracy

Observer detection of image degradation caused by irreversible data compression processes

Science.gov (United States)

Chen, Ji; Flynn, Michael J.; Gross, Barry; Spizarny, David

1991-05-01

Irreversible data compression methods have been proposed to reduce the data storage and communication requirements of digital imaging systems. In general, the error produced by compression increases as an algorithm''s compression ratio is increased. We have studied the relationship between compression ratios and the detection of induced error using radiologic observers. The nature of the errors was characterized by calculating the power spectrum of the difference image. In contrast with studies designed to test whether detected errors alter diagnostic decisions, this study was designed to test whether observers could detect the induced error. A paired-film observer study was designed to test whether induced errors were detected. The study was conducted with chest radiographs selected and ranked for subtle evidence of interstitial disease, pulmonary nodules, or pneumothoraces. Images were digitized at 86 microns (4K X 5K) and 2K X 2K regions were extracted. A full-frame discrete cosine transform method was used to compress images at ratios varying between 6:1 and 60:1. The decompressed images were reprinted next to the original images in a randomized order with a laser film printer. The use of a film digitizer and a film printer which can reproduce all of the contrast and detail in the original radiograph makes the results of this study insensitive to instrument performance and primarily dependent on radiographic image quality. The results of this study define conditions for which errors associated with irreversible compression cannot be detected by radiologic observers. The results indicate that an observer can detect the errors introduced by this compression algorithm for compression ratios of 10:1 (1.2 bits/pixel) or higher.

A Multiresolution Image Completion Algorithm for Compressing Digital Color Images

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R. Gomathi

2014-01-01

Full Text Available This paper introduces a new framework for image coding that uses image inpainting method. In the proposed algorithm, the input image is subjected to image analysis to remove some of the portions purposefully. At the same time, edges are extracted from the input image and they are passed to the decoder in the compressed manner. The edges which are transmitted to decoder act as assistant information and they help inpainting process fill the missing regions at the decoder. Textural synthesis and a new shearlet inpainting scheme based on the theory of p-Laplacian operator are proposed for image restoration at the decoder. Shearlets have been mathematically proven to represent distributed discontinuities such as edges better than traditional wavelets and are a suitable tool for edge characterization. This novel shearlet p-Laplacian inpainting model can effectively reduce the staircase effect in Total Variation (TV inpainting model whereas it can still keep edges as well as TV model. In the proposed scheme, neural network is employed to enhance the value of compression ratio for image coding. Test results are compared with JPEG 2000 and H.264 Intracoding algorithms. The results show that the proposed algorithm works well.

Impact of Medical Therapy on Atheroma Volume Measured by Different Cardiovascular Imaging Modalities

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Mohamad C. N. Sinno

2010-01-01

Full Text Available Atherosclerosis is a systemic disease that affects most vascular beds. The gold standard of atherosclerosis imaging has been invasive intravascular ultrasound (IVUS. Newer noninvasive imaging modalities like B-mode ultrasound, cardiac computed tomography (CT, positron emission tomography (PET, and magnetic resonance imaging (MRI have been used to assess these vascular territories with high accuracy and reproducibility. These imaging modalities have lately been used for the assessment of the atherosclerotic plaque and the response of its volume to several medical therapies used in the treatment of patients with cardiovascular disease. To study the impact of these medications on atheroma volume progression or regression, imaging modalities have been used on a serial basis providing a unique opportunity to monitor the effect these antiatherosclerotic strategies exert on plaque burden. As a result, studies incorporating serial IVUS imaging, quantitative coronary angiography (QCA, B-mode ultrasound, electron beam computed tomography (EBCT, and dynamic contrast-enhanced magnetic resonance imaging have all been used to evaluate the impact of therapeutic strategies that modify cholesterol and blood pressure on the progression/regression of atherosclerotic plaque. In this review, we intend to summarize the impact of different therapies aimed at halting the progression or even result in regression of atherosclerotic cardiovascular disease evaluated by different imaging modalities.

Data compression with applications to digital radiology

International Nuclear Information System (INIS)

Elnahas, S.E.

1985-01-01

The structure of arithmetic codes is defined in terms of source parsing trees. The theoretical derivations of algorithms for the construction of optimal and sub-optimal structures are presented. The software simulation results demonstrate how arithmetic coding out performs variable-length to variable-length coding. Linear predictive coding is presented for the compression of digital diagnostic images from several imaging modalities including computed tomography, nuclear medicine, ultrasound, and magnetic resonance imaging. The problem of designing optimal predictors is formulated and alternative solutions are discussed. The results indicate that noiseless compression factors between 1.7 and 7.4 can be achieved. With nonlinear predictive coding, noisy and noiseless compression techniques are combined in a novel way that may have a potential impact on picture archiving and communication systems in radiology. Adaptive fast discrete cosine transform coding systems are used as nonlinear block predictors, and optimal delta modulation systems are used as nonlinear sequential predictors. The off-line storage requirements for archiving diagnostic images are reasonably reduced by the nonlinear block predictive coding. The online performance, however, seems to be bounded by that of the linear systems. The subjective quality of image imperfect reproductions from the cosine transform coding is promising and prompts future research on the compression of diagnostic images by transform coding systems and the clinical evaluation of these systems

Adaptive compressive ghost imaging based on wavelet trees and sparse representation.

Science.gov (United States)

Yu, Wen-Kai; Li, Ming-Fei; Yao, Xu-Ri; Liu, Xue-Feng; Wu, Ling-An; Zhai, Guang-Jie

2014-03-24

Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.

A Review On Segmentation Based Image Compression Techniques

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S.Thayammal

2013-11-01

Full Text Available Abstract -The storage and transmission of imagery become more challenging task in the current scenario of multimedia applications. Hence, an efficient compression scheme is highly essential for imagery, which reduces the requirement of storage medium and transmission bandwidth. Not only improvement in performance and also the compression techniques must converge quickly in order to apply them for real time applications. There are various algorithms have been done in image compression, but everyone has its own pros and cons. Here, an extensive analysis between existing methods is performed. Also, the use of existing works is highlighted, for developing the novel techniques which face the challenging task of image storage and transmission in multimedia applications.

Development and assessment of compression technique for medical images using neural network. I. Assessment of lossless compression

International Nuclear Information System (INIS)

Fukatsu, Hiroshi

2007-01-01

This paper describes assessment of the lossless compression of a new efficient compression technique (JIS system) using neural network that the author and co-workers have recently developed. At first, theory is explained for encoding and decoding the data. Assessment is done on 55 images each of chest digital roentgenography, digital mammography, 64-row multi-slice CT, 1.5 Tesla MRI, positron emission tomography (PET) and digital subtraction angiography, which are lossless-compressed by the present JIS system to see the compression rate and loss. For comparison, those data are also JPEG lossless-compressed. Personal computer (PC) is an Apple MacBook Pro with configuration of Boot Camp for Windows environment. The present JIS system is found to have a more than 4 times higher efficiency than the usual compressions which compressing the file volume to only 1/11 in average, and thus to be importantly responsible to the increasing medical imaging data. (R.T.)

Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

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Yongjian Nian

2013-01-01

Full Text Available A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.

MO-B-BRC-00: Prostate HDR Treatment Planning - Considering Different Imaging Modalities

International Nuclear Information System (INIS)

2016-01-01

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions

MO-B-BRC-00: Prostate HDR Treatment Planning - Considering Different Imaging Modalities

Energy Technology Data Exchange (ETDEWEB)

NONE

2016-06-15

Brachytherapy has proven to be an effective treatment option for prostate cancer. Initially, prostate brachytherapy was delivered through permanently implanted low dose rate (LDR) radioactive sources; however, high dose rate (HDR) temporary brachytherapy for prostate cancer is gaining popularity. Needle insertion during prostate brachytherapy is most commonly performed under ultrasound (U/S) guidance; however, treatment planning may be performed utilizing several imaging modalities either in an intra- or post-operative setting. During intra-operative prostate HDR, the needles are imaged during implantation, and planning may be performed in real time. At present, the most common imaging modality utilized for intra-operative prostate HDR is U/S. Alternatively, in the post-operative setting, following needle implantation, patients may be simulated with computed tomography (CT) or magnetic resonance imaging (MRI). Each imaging modality and workflow provides its share of benefits and limitations. Prostate HDR has been adopted in a number of cancer centers across the nation. In this educational session, we will explore the role of U/S, CT, and MRI in HDR prostate brachytherapy. Example workflows and operational details will be shared, and we will discuss how to establish a prostate HDR program in a clinical setting. Learning Objectives: Review prostate HDR techniques based on the imaging modality Discuss the challenges and pitfalls introduced by the three imagebased options for prostate HDR brachytherapy Review the QA process and learn about the development of clinical workflows for these imaging options at different institutions.

Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

Science.gov (United States)

Gong, Lihua; Deng, Chengzhi; Pan, Shumin; Zhou, Nanrun

2018-07-01

Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

Fractal Image Compression Based on High Entropy Values Technique

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Douaa Younis Abbaas

2018-04-01

Full Text Available There are many attempts tried to improve the encoding stage of FIC because it consumed time. These attempts worked by reducing size of the search pool for pair range-domain matching but most of them led to get a bad quality, or a lower compression ratio of reconstructed image. This paper aims to present a method to improve performance of the full search algorithm by combining FIC (lossy compression and another lossless technique (in this case entropy coding is used. The entropy technique will reduce size of the domain pool (i. e., number of domain blocks based on the entropy value of each range block and domain block and then comparing the results of full search algorithm and proposed algorithm based on entropy technique to see each of which give best results (such as reduced the encoding time with acceptable values in both compression quali-ty parameters which are C. R (Compression Ratio and PSNR (Image Quality. The experimental results of the proposed algorithm proven that using the proposed entropy technique reduces the encoding time while keeping compression rates and reconstruction image quality good as soon as possible.

On use of image quality metrics for perceptual blur modeling: image/video compression case

Science.gov (United States)

Cha, Jae H.; Olson, Jeffrey T.; Preece, Bradley L.; Espinola, Richard L.; Abbott, A. Lynn

2018-02-01

Linear system theory is employed to make target acquisition performance predictions for electro-optical/infrared imaging systems where the modulation transfer function (MTF) may be imposed from a nonlinear degradation process. Previous research relying on image quality metrics (IQM) methods, which heuristically estimate perceived MTF has supported that an average perceived MTF can be used to model some types of degradation such as image compression. Here, we discuss the validity of the IQM approach by mathematically analyzing the associated heuristics from the perspective of reliability, robustness, and tractability. Experiments with standard images compressed by x.264 encoding suggest that the compression degradation can be estimated by a perceived MTF within boundaries defined by well-behaved curves with marginal error. Our results confirm that the IQM linearizer methodology provides a credible tool for sensor performance modeling.

Image Quality Assessment for Different Wavelet Compression Techniques in a Visual Communication Framework

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Nuha A. S. Alwan

2013-01-01

Full Text Available Images with subband coding and threshold wavelet compression are transmitted over a Rayleigh communication channel with additive white Gaussian noise (AWGN, after quantization and 16-QAM modulation. A comparison is made between these two types of compression using both mean square error (MSE and structural similarity (SSIM image quality assessment (IQA criteria applied to the reconstructed image at the receiver. The two methods yielded comparable SSIM but different MSE measures. In this work, we justify our results which support previous findings in the literature that the MSE between two images is not indicative of structural similarity or the visibility of errors. It is found that it is difficult to reduce the pointwise errors in subband-compressed images (higher MSE. However, the compressed images provide comparable SSIM or perceived quality for both types of compression provided that the retained energy after compression is the same.

Evaluation of registration strategies for multi-modality images of rat brain slices

International Nuclear Information System (INIS)

Palm, Christoph; Vieten, Andrea; Salber, Dagmar; Pietrzyk, Uwe

2009-01-01

In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.

Automatic classification of early Parkinson's disease with multi-modal MR imaging.

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Dan Long

Full Text Available BACKGROUND: In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD. Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals. MATERIALS AND METHODS: Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations, ReHo (regional homogeneity and RFCS (regional functional connectivity strength. For the structural images, we extracted the volume characteristics from the gray matter (GM, the white matter (WM and the cerebrospinal fluid (CSF. A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method. RESULTS: Using the proposed methods to classify the data set, good results (accuracy  = 86.96%, sensitivity  = 78.95%, specificity  = 92.59% were obtained. CONCLUSIONS: This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.

An introduction to video image compression and authentication technology for safeguards applications

International Nuclear Information System (INIS)

Johnson, C.S.

1995-01-01

Verification of a video image has been a major problem for safeguards for several years. Various verification schemes have been tried on analog video signals ever since the mid-1970's. These schemes have provided a measure of protection but have never been widely adopted. The development of reasonably priced complex video processing integrated circuits makes it possible to digitize a video image and then compress the resulting digital file into a smaller file without noticeable loss of resolution. Authentication and/or encryption algorithms can be more easily applied to digital video files that have been compressed. The compressed video files require less time for algorithm processing and image transmission. An important safeguards application for authenticated, compressed, digital video images is in unattended video surveillance systems and remote monitoring systems. The use of digital images in the surveillance system makes it possible to develop remote monitoring systems that send images over narrow bandwidth channels such as the common telephone line. This paper discusses the video compression process, authentication algorithm, and data format selected to transmit and store the authenticated images

Compressive sensing based ptychography image encryption

Science.gov (United States)

Rawat, Nitin

2015-09-01

A compressive sensing (CS) based ptychography combined with an optical image encryption is proposed. The diffraction pattern is recorded through ptychography technique further compressed by non-uniform sampling via CS framework. The system requires much less encrypted data and provides high security. The diffraction pattern as well as the lesser measurements of the encrypted samples serves as a secret key which make the intruder attacks more difficult. Furthermore, CS shows that the linearly projected few random samples have adequate information for decryption with a dramatic volume reduction. Experimental results validate the feasibility and effectiveness of our proposed technique compared with the existing techniques. The retrieved images do not reveal any information with the original information. In addition, the proposed system can be robust even with partial encryption and under brute-force attacks.

A MODIFIED EMBEDDED ZERO-TREE WAVELET METHOD FOR MEDICAL IMAGE COMPRESSION

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T. Celine Therese Jenny

2010-11-01

Full Text Available The Embedded Zero-tree Wavelet (EZW is a lossy compression method that allows for progressive transmission of a compressed image. By exploiting the natural zero-trees found in a wavelet decomposed image, the EZW algorithm is able to encode large portions of insignificant regions of an still image with a minimal number of bits. The upshot of this encoding is an algorithm that is able to achieve relatively high peak signal to noise ratios (PSNR for high compression levels. The EZW algorithm is to encode large portions of insignificant regions of an image with a minimal number of bits. Vector Quantization (VQ method can be performed as a post processing step to reduce the coded file size. Vector Quantization (VQ method can be reduces redundancy of the image data in order to be able to store or transmit data in an efficient form. It is demonstrated by experimental results that the proposed method outperforms several well-known lossless image compression techniques for still images that contain 256 colors or less.

Comparison of imaging modalities for diagnosis of thyroid disorders

International Nuclear Information System (INIS)

Pfannenstiel, P.; Hirsch, H.; Stein, N.; Maier, R.; Meindl, S.; Voges, K.; Willmann, L.

1984-01-01

From 4-1-1980 to 3-30-1983 in more than 10000 patients scanning of the thyroid was performed by a rectiliniear scanner and predominantly by a gamma-camera using a special collimator and a data processing unit for evaluation of global and regional uptake of sup(99m)TcO 4 or 123 I. The functional scans were compared with the structural information provided by real-time ultrasonography of the thyroid, employing in questionable cases specially developed computer aided pattern recognition methods. Other modalities as a multiwire camera, X-ray fluorescence, CT-imaging, NMR-tomography were also evaluated in selected cases. From the data a diagnostic strategy for the use of imaging modalities to diagnose thyroid diseases was derived. (orig.) [de

Multi-dimensional medical images compressed and filtered with wavelets

International Nuclear Information System (INIS)

Boyen, H.; Reeth, F. van; Flerackers, E.

2002-01-01

Full text: Using the standard wavelet decomposition methods, multi-dimensional medical images can be compressed and filtered by repeating the wavelet-algorithm on 1D-signals in an extra loop per extra dimension. In the non-standard decomposition for multi-dimensional images the areas that must be zero-filled in case of band- or notch-filters are more complex than geometric areas such as rectangles or cubes. Adding an additional dimension in this algorithm until 4D (e.g. a 3D beating heart) increases the geometric complexity of those areas even more. The aim of our study was to calculate the boundaries of the formed complex geometric areas, so we can use the faster non-standard decomposition to compress and filter multi-dimensional medical images. Because a lot of 3D medical images taken by PET- or SPECT-cameras have only a few layers in the Z-dimension and compressing images in a dimension with a few voxels is usually not worthwhile, we provided a solution in which one can choose which dimensions will be compressed or filtered. With the proposal of non-standard decomposition on Daubechies' wavelets D2 to D20 by Steven Gollmer in 1992, 1D data can be compressed and filtered. Each additional level works only on the smoothed data, so the transformation-time halves per extra level. Zero-filling a well-defined area alter the wavelet-transform and then performing the inverse transform will do the filtering. To be capable to compress and filter up to 4D-Images with the faster non-standard wavelet decomposition method, we have investigated a new method for calculating the boundaries of the areas which must be zero-filled in case of filtering. This is especially true for band- and notch filtering. Contrary to the standard decomposition method, the areas are no longer rectangles in 2D or cubes in 3D or a row of cubes in 4D: they are rectangles expanded with a half-sized rectangle in the other direction for 2D, cubes expanded with half cubes in one and quarter cubes in the

The wavelet/scalar quantization compression standard for digital fingerprint images

Energy Technology Data Exchange (ETDEWEB)

Bradley, J.N.; Brislawn, C.M.

1994-04-01

A new digital image compression standard has been adopted by the US Federal Bureau of Investigation for use on digitized gray-scale fingerprint images. The algorithm is based on adaptive uniform scalar quantization of a discrete wavelet transform image decomposition and is referred to as the wavelet/scalar quantization standard. The standard produces archival quality images at compression ratios of around 20:1 and will allow the FBI to replace their current database of paper fingerprint cards with digital imagery.

Subsurface Profile Mapping using 3-D Compressive Wave Imaging

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Hazreek Z A M

2017-01-01

Full Text Available Geotechnical site investigation related to subsurface profile mapping was commonly performed to provide valuable data for design and construction stage based on conventional drilling techniques. From past experience, drilling techniques particularly using borehole method suffer from limitations related to expensive, time consuming and limited data coverage. Hence, this study performs subsurface profile mapping using 3-D compressive wave imaging in order to minimize those conventional method constraints. Field measurement and data analysis of compressive wave (p-wave, vp was performed using seismic refraction survey (ABEM Terraloc MK 8, 7 kg of sledgehammer and 24 units of vertical geophone and OPTIM (SeisOpt@Picker & SeisOpt@2D software respectively. Then, 3-D compressive wave distribution of subsurface studied was obtained using analysis of SURFER software. Based on 3-D compressive wave image analyzed, it was found that subsurface profile studied consist of three main layers representing top soil (vp = 376 – 600 m/s, weathered material (vp = 900 – 2600 m/s and bedrock (vp > 3000 m/s. Thickness of each layer was varied from 0 – 2 m (first layer, 2 – 20 m (second layer and 20 m and over (third layer. Moreover, groundwater (vp = 1400 – 1600 m/s starts to be detected at 2.0 m depth from ground surface. This study has demonstrated that geotechnical site investigation data related to subsurface profiling was applicable to be obtained using 3-D compressive wave imaging. Furthermore, 3-D compressive wave imaging was performed based on non destructive principle in ground exploration thus consider economic, less time, large data coverage and sustainable to our environment.

Optimization of wavelet decomposition for image compression and feature preservation.

Science.gov (United States)

Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

2003-09-01

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

Enhancement of Satellite Image Compression Using a Hybrid (DWT-DCT) Algorithm

Science.gov (United States)

Shihab, Halah Saadoon; Shafie, Suhaidi; Ramli, Abdul Rahman; Ahmad, Fauzan

2017-12-01

Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) image compression techniques have been utilized in most of the earth observation satellites launched during the last few decades. However, these techniques have some issues that should be addressed. The DWT method has proven to be more efficient than DCT for several reasons. Nevertheless, the DCT can be exploited to improve the high-resolution satellite image compression when combined with the DWT technique. Hence, a proposed hybrid (DWT-DCT) method was developed and implemented in the current work, simulating an image compression system on-board on a small remote sensing satellite, with the aim of achieving a higher compression ratio to decrease the onboard data storage and the downlink bandwidth, while avoiding further complex levels of DWT. This method also succeeded in maintaining the reconstructed satellite image quality through replacing the standard forward DWT thresholding and quantization processes with an alternative process that employed the zero-padding technique, which also helped to reduce the processing time of DWT compression. The DCT, DWT and the proposed hybrid methods were implemented individually, for comparison, on three LANDSAT 8 images, using the MATLAB software package. A comparison was also made between the proposed method and three other previously published hybrid methods. The evaluation of all the objective and subjective results indicated the feasibility of using the proposed hybrid (DWT-DCT) method to enhance the image compression process on-board satellites.

Compression and information recovery in ptychography

Science.gov (United States)

Loetgering, L.; Treffer, D.; Wilhein, T.

2018-04-01

Ptychographic coherent diffraction imaging (PCDI) is a scanning microscopy modality that allows for simultaneous recovery of object and illumination information. This ability renders PCDI a suitable technique for x-ray lensless imaging and optics characterization. Its potential for information recovery typically relies on large amounts of data redundancy. However, the field of view in ptychography is practically limited by the memory and the computational facilities available. We describe techniques that achieve robust ptychographic information recovery at high compression rates. The techniques are compared and tested with experimental data.

Dual-modality imaging with a ultrasound-gamma device for oncology

Science.gov (United States)

Polito, C.; Pellegrini, R.; Cinti, M. N.; De Vincentis, G.; Lo Meo, S.; Fabbri, A.; Bennati, P.; Cencelli, V. Orsolini; Pani, R.

2018-06-01

Recently, dual-modality systems have been developed, aimed to correlate anatomical and functional information, improving disease localization and helping oncological or surgical treatments. Moreover, due to the growing interest in handheld detectors for preclinical trials or small animal imaging, in this work a new dual modality integrated device, based on a Ultrasounds probe and a small Field of View Single Photon Emission gamma camera, is proposed.

Lossless compression of multispectral images using spectral information

Science.gov (United States)

Ma, Long; Shi, Zelin; Tang, Xusheng

2009-10-01

Multispectral images are available for different purposes due to developments in spectral imaging systems. The sizes of multispectral images are enormous. Thus transmission and storage of these volumes of data require huge time and memory resources. That is why compression algorithms must be developed. A salient property of multispectral images is that strong spectral correlation exists throughout almost all bands. This fact is successfully used to predict each band based on the previous bands. We propose to use spectral linear prediction and entropy coding with context modeling for encoding multispectral images. Linear prediction predicts the value for the next sample and computes the difference between predicted value and the original value. This difference is usually small, so it can be encoded with less its than the original value. The technique implies prediction of each image band by involving number of bands along the image spectra. Each pixel is predicted using information provided by pixels in the previous bands in the same spatial position. As done in the JPEG-LS, the proposed coder also represents the mapped residuals by using an adaptive Golomb-Rice code with context modeling. This residual coding is context adaptive, where the context used for the current sample is identified by a context quantization function of the three gradients. Then, context-dependent Golomb-Rice code and bias parameters are estimated sample by sample. The proposed scheme was compared with three algorithms applied to the lossless compression of multispectral images, namely JPEG-LS, Rice coding, and JPEG2000. Simulation tests performed on AVIRIS images have demonstrated that the proposed compression scheme is suitable for multispectral images.

Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

Science.gov (United States)

Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

2017-04-01

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

Compressed Sensing and Low-Rank Matrix Decomposition in Multisource Images Fusion

Directory of Open Access Journals (Sweden)

Kan Ren

2014-01-01

Full Text Available We propose a novel super-resolution multisource images fusion scheme via compressive sensing and dictionary learning theory. Under the sparsity prior of images patches and the framework of the compressive sensing theory, the multisource images fusion is reduced to a signal recovery problem from the compressive measurements. Then, a set of multiscale dictionaries are learned from several groups of high-resolution sample image’s patches via a nonlinear optimization algorithm. Moreover, a new linear weights fusion rule is proposed to obtain the high-resolution image. Some experiments are taken to investigate the performance of our proposed method, and the results prove its superiority to its counterparts.

Image compression software for the SOHO LASCO and EIT experiments

Science.gov (United States)

Grunes, Mitchell R.; Howard, Russell A.; Hoppel, Karl; Mango, Stephen A.; Wang, Dennis

1994-01-01

This paper describes the lossless and lossy image compression algorithms to be used on board the Solar Heliospheric Observatory (SOHO) in conjunction with the Large Angle Spectrometric Coronograph and Extreme Ultraviolet Imaging Telescope experiments. It also shows preliminary results obtained using similar prior imagery and discusses the lossy compression artifacts which will result. This paper is in part intended for the use of SOHO investigators who need to understand the results of SOHO compression in order to better allocate the transmission bits which they have been allocated.

Effect of Image Linearization on Normalized Compression Distance

Science.gov (United States)

Mortensen, Jonathan; Wu, Jia Jie; Furst, Jacob; Rogers, John; Raicu, Daniela

Normalized Information Distance, based on Kolmogorov complexity, is an emerging metric for image similarity. It is approximated by the Normalized Compression Distance (NCD) which generates the relative distance between two strings by using standard compression algorithms to compare linear strings of information. This relative distance quantifies the degree of similarity between the two objects. NCD has been shown to measure similarity effectively on information which is already a string: genomic string comparisons have created accurate phylogeny trees and NCD has also been used to classify music. Currently, to find a similarity measure using NCD for images, the images must first be linearized into a string, and then compared. To understand how linearization of a 2D image affects the similarity measure, we perform four types of linearization on a subset of the Corel image database and compare each for a variety of image transformations. Our experiment shows that different linearization techniques produce statistically significant differences in NCD for identical spatial transformations.

Continuous monitoring of arthritis in animal models using optical imaging modalities

Science.gov (United States)

Son, Taeyoon; Yoon, Hyung-Ju; Lee, Saseong; Jang, Won Seuk; Jung, Byungjo; Kim, Wan-Uk

2014-10-01

Given the several difficulties associated with histology, including difficulty in continuous monitoring, this study aimed to investigate the feasibility of optical imaging modalities-cross-polarization color (CPC) imaging, erythema index (EI) imaging, and laser speckle contrast (LSC) imaging-for continuous evaluation and monitoring of arthritis in animal models. C57BL/6 mice, used for the evaluation of arthritis, were divided into three groups: arthritic mice group (AMG), positive control mice group (PCMG), and negative control mice group (NCMG). Complete Freund's adjuvant, mineral oil, and saline were injected into the footpad for AMG, PCMG, and NCMG, respectively. LSC and CPC images were acquired from 0 through 144 h after injection for all groups. EI images were calculated from CPC images. Variations in feet area, EI, and speckle index for each mice group over time were calculated for quantitative evaluation of arthritis. Histological examinations were performed, and the results were found to be consistent with those from optical imaging analysis. Thus, optical imaging modalities may be successfully applied for continuous evaluation and monitoring of arthritis in animal models.

Information theoretic bounds for compressed sensing in SAR imaging

International Nuclear Information System (INIS)

Jingxiong, Zhang; Ke, Yang; Jianzhong, Guo

2014-01-01

Compressed sensing (CS) is a new framework for sampling and reconstructing sparse signals from measurements significantly fewer than those prescribed by Nyquist rate in the Shannon sampling theorem. This new strategy, applied in various application areas including synthetic aperture radar (SAR), relies on two principles: sparsity, which is related to the signals of interest, and incoherence, which refers to the sensing modality. An important question in CS-based SAR system design concerns sampling rate necessary and sufficient for exact or approximate recovery of sparse signals. In the literature, bounds of measurements (or sampling rate) in CS have been proposed from the perspective of information theory. However, these information-theoretic bounds need to be reviewed and, if necessary, validated for CS-based SAR imaging, as there are various assumptions made in the derivations of lower and upper bounds on sub-Nyquist sampling rates, which may not hold true in CS-based SAR imaging. In this paper, information-theoretic bounds of sampling rate will be analyzed. For this, the SAR measurement system is modeled as an information channel, with channel capacity and rate-distortion characteristics evaluated to enable the determination of sampling rates required for recovery of sparse scenes. Experiments based on simulated data will be undertaken to test the theoretic bounds against empirical results about sampling rates required to achieve certain detection error probabilities

High-speed reconstruction of compressed images

Science.gov (United States)

Cox, Jerome R., Jr.; Moore, Stephen M.

1990-07-01

A compression scheme is described that allows high-definition radiological images with greater than 8-bit intensity resolution to be represented by 8-bit pixels. Reconstruction of the images with their original intensity resolution can be carried out by means of a pipeline architecture suitable for compact, high-speed implementation. A reconstruction system is described that can be fabricated according to this approach and placed between an 8-bit display buffer and the display's video system thereby allowing contrast control of images at video rates. Results for 50 CR chest images are described showing that error-free reconstruction of the original 10-bit CR images can be achieved.

Context-dependent JPEG backward-compatible high-dynamic range image compression

Science.gov (United States)

Korshunov, Pavel; Ebrahimi, Touradj

2013-10-01

High-dynamic range (HDR) imaging is expected, together with ultrahigh definition and high-frame rate video, to become a technology that may change photo, TV, and film industries. Many cameras and displays capable of capturing and rendering both HDR images and video are already available in the market. The popularity and full-public adoption of HDR content is, however, hindered by the lack of standards in evaluation of quality, file formats, and compression, as well as large legacy base of low-dynamic range (LDR) displays that are unable to render HDR. To facilitate the wide spread of HDR usage, the backward compatibility of HDR with commonly used legacy technologies for storage, rendering, and compression of video and images are necessary. Although many tone-mapping algorithms are developed for generating viewable LDR content from HDR, there is no consensus of which algorithm to use and under which conditions. We, via a series of subjective evaluations, demonstrate the dependency of the perceptual quality of the tone-mapped LDR images on the context: environmental factors, display parameters, and image content itself. Based on the results of subjective tests, it proposes to extend JPEG file format, the most popular image format, in a backward compatible manner to deal with HDR images also. An architecture to achieve such backward compatibility with JPEG is proposed. A simple implementation of lossy compression demonstrates the efficiency of the proposed architecture compared with the state-of-the-art HDR image compression.

Statistical Analysis of Compression Methods for Storing Binary Image for Low-Memory Systems

Directory of Open Access Journals (Sweden)

Roman Slaby

2013-01-01

Full Text Available The paper is focused on the statistical comparison of the selected compression methods which are used for compression of the binary images. The aim is to asses, which of presented compression method for low-memory system requires less number of bytes of memory. For assessment of the success rates of the input image to binary image the correlation functions are used. Correlation function is one of the methods of OCR algorithm used for the digitization of printed symbols. Using of compression methods is necessary for systems based on low-power micro-controllers. The data stream saving is very important for such systems with limited memory as well as the time required for decoding the compressed data. The success rate of the selected compression algorithms is evaluated using the basic characteristics of the exploratory analysis. The searched samples represent the amount of bytes needed to compress the test images, representing alphanumeric characters.

Magnetic resonance imaging of vascular compression in trigeminal neuralgia and hemifacial spasms

International Nuclear Information System (INIS)

Nagaseki, Yoshishige; Horikoshi, Tohru; Omata, Tomohiro; Sugita, Masao; Nukui, Hideaki; Sakamoto, Hajime; Kumagai, Hiroshi; Sasaki, Hideo; Tsuji, Reizou.

1991-01-01

We show how neurosurgical planning can benefit from the better visualization of the precise vascular compression of the nerve provided by the oblique-sagittal and gradient-echo method (OS-GR image) using magnetic resonance images (MRI). The scans of 3 patients with trigeminal neuralgia (TN) and of 15 with hemifacial spasm (HFS) were analyzed for the presence and appearance of the vascular compression of the nerves. Imaging sequences consisted of an OS-GR image (TR/TE: 200/20, 3-mm-thick slice) cut along each nerve shown by the axial view, which was scanned at the angle of 105 degrees taken between the dorsal line of the brain stem and the line corresponding to the pontomedullary junction. In the OS-GR images of the TN's, the vascular compressions of the root entry zone (REZ) of the trigeminal nerve were well visualized as high-intensity lines in the 2 cases whose vessels were confirmed intraoperatively. In the other case, with atypical facial pain, vascular compression was confirmed at the rostral distal site on the fifth nerve, apart from the REZ. In the 15 cases of HFS, twelve OS-GR images (80%) demonstrated vascular compressions at the REZ of the facial nerves from the direction of the caudoventral side. During the surgery for these 12 cases, in 11 cases (excepting the 1 case whose facial nerve was not compressed by any vessels), vascular compressions were confirmed corresponding to the findings of the OS-GR images. Among the 10 OS-GR images on the non-affected side, two false-positive findings were visualized. It is concluded that OS-GR images obtained by means of MRI may serve as a useful planning aid prior to microvascular decompression for cases of TN and HFS. (author)

Compression and Processing of Space Image Sequences of Northern Lights and Sprites

DEFF Research Database (Denmark)

Forchhammer, Søren Otto; Martins, Bo; Jensen, Ole Riis

1999-01-01

Compression of image sequences of auroral activity as northern lights and thunderstorms with sprites is investigated.......Compression of image sequences of auroral activity as northern lights and thunderstorms with sprites is investigated....

Application of a Noise Adaptive Contrast Sensitivity Function to Image Data Compression

Science.gov (United States)

Daly, Scott J.

1989-08-01

The visual contrast sensitivity function (CSF) has found increasing use in image compression as new algorithms optimize the display-observer interface in order to reduce the bit rate and increase the perceived image quality. In most compression algorithms, increasing the quantization intervals reduces the bit rate at the expense of introducing more quantization error, a potential image quality degradation. The CSF can be used to distribute this error as a function of spatial frequency such that it is undetectable by the human observer. Thus, instead of being mathematically lossless, the compression algorithm can be designed to be visually lossless, with the advantage of a significantly reduced bit rate. However, the CSF is strongly affected by image noise, changing in both shape and peak sensitivity. This work describes a model of the CSF that includes these changes as a function of image noise level by using the concepts of internal visual noise, and tests this model in the context of image compression with an observer study.

Facial Image Compression Based on Structured Codebooks in Overcomplete Domain

Directory of Open Access Journals (Sweden)

Vila-Forcén JE

2006-01-01

Full Text Available We advocate facial image compression technique in the scope of distributed source coding framework. The novelty of the proposed approach is twofold: image compression is considered from the position of source coding with side information and, contrarily to the existing scenarios where the side information is given explicitly; the side information is created based on a deterministic approximation of the local image features. We consider an image in the overcomplete transform domain as a realization of a random source with a structured codebook of symbols where each symbol represents a particular edge shape. Due to the partial availability of the side information at both encoder and decoder, we treat our problem as a modification of the Berger-Flynn-Gray problem and investigate a possible gain over the solutions when side information is either unavailable or available at the decoder. Finally, the paper presents a practical image compression algorithm for facial images based on our concept that demonstrates the superior performance in the very-low-bit-rate regime.

In Situ Imaging during Compression of Plastic Bonded Explosives for Damage Modeling

Directory of Open Access Journals (Sweden)

Virginia W. Manner

2017-06-01

Full Text Available The microstructure of plastic bonded explosives (PBXs is known to influence behavior during mechanical deformation, but characterizing the microstructure can be challenging. For example, the explosive crystals and binder in formulations such as PBX 9501 do not have sufficient X-ray contrast to obtain three-dimensional data by in situ, absorption contrast imaging. To address this difficulty, we have formulated a series of PBXs using octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX crystals and low-density binder systems. The binders were hydroxyl-terminated polybutadiene (HTPB or glycidyl azide polymer (GAP cured with a commercial blend of acrylic monomers/oligomers. The binder density is approximately half of the HMX, allowing for excellent contrast using in situ X-ray computed tomography (CT imaging. The samples were imaged during unaxial compression using micro-scale CT in an interrupted in situ modality. The rigidity of the binder was observed to significantly influence fracture, crystal-binder delamination, and flow. Additionally, 2D slices from the segmented 3D images were meshed for finite element simulation of the mesoscale response. At low stiffness, the binder and crystal do not delaminate and the crystals move with the material flow; at high stiffness, marked delamination is noted between the crystals and the binder, leading to very different mechanical properties. Initial model results exhibit qualitatively similar delamination.

Telemedicine + OCT: toward design of optimized algorithms for high-quality compressed images

Science.gov (United States)

Mousavi, Mahta; Lurie, Kristen; Land, Julian; Javidi, Tara; Ellerbee, Audrey K.

2014-03-01

Telemedicine is an emerging technology that aims to provide clinical healthcare at a distance. Among its goals, the transfer of diagnostic images over telecommunication channels has been quite appealing to the medical community. When viewed as an adjunct to biomedical device hardware, one highly important consideration aside from the transfer rate and speed is the accuracy of the reconstructed image at the receiver end. Although optical coherence tomography (OCT) is an established imaging technique that is ripe for telemedicine, the effects of OCT data compression, which may be necessary on certain telemedicine platforms, have not received much attention in the literature. We investigate the performance and efficiency of several lossless and lossy compression techniques for OCT data and characterize their effectiveness with respect to achievable compression ratio, compression rate and preservation of image quality. We examine the effects of compression in the interferogram vs. A-scan domain as assessed with various objective and subjective metrics.

Comparing Image Perception of Bladder Tumors in Four Different Storz Professional Image Enhancement System Modalities Using the íSPIES App

NARCIS (Netherlands)

Kamphuis, Guido M.; de Bruin, D. Martijn; Brandt, Martin J.; Knoll, Thomas; Conort, Pierre; Lapini, Alberto; Dominguez-Escrig, Jose L.; de La Rosette, Jean J. M. C. H.

2016-01-01

To evaluate the variation of interpretation of the same bladder urothelium image in different Storz Professional Image Enhancement System (SPIES) modalities. SPIES contains a White light (WL), Spectra A (SA), Spectra B (SB), and Clara and Chroma combined (CC) modality. An App for the iPAD retina was

Congenitally short trachea with compression of the left mainstem bronchus: MRI findings

International Nuclear Information System (INIS)

De Priester, J.A.; Oei Toen-Khiam; Vos, G.D.; Van Waardenburg, D.A.

1998-01-01

The MRI features of congenitally short trachea in a patient presenting with respiratory distress are presented. Compression of the left mainstem bronchus by the aortic arch, a recognised complication of this anomaly, could be demonstrated by MRI, obviating the need for other imaging modalities. (orig.)

Two-level image authentication by two-step phase-shifting interferometry and compressive sensing

Science.gov (United States)

Zhang, Xue; Meng, Xiangfeng; Yin, Yongkai; Yang, Xiulun; Wang, Yurong; Li, Xianye; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

2018-01-01

A two-level image authentication method is proposed; the method is based on two-step phase-shifting interferometry, double random phase encoding, and compressive sensing (CS) theory, by which the certification image can be encoded into two interferograms. Through discrete wavelet transform (DWT), sparseness processing, Arnold transform, and data compression, two compressed signals can be generated and delivered to two different participants of the authentication system. Only the participant who possesses the first compressed signal attempts to pass the low-level authentication. The application of Orthogonal Match Pursuit CS algorithm reconstruction, inverse Arnold transform, inverse DWT, two-step phase-shifting wavefront reconstruction, and inverse Fresnel transform can result in the output of a remarkable peak in the central location of the nonlinear correlation coefficient distributions of the recovered image and the standard certification image. Then, the other participant, who possesses the second compressed signal, is authorized to carry out the high-level authentication. Therefore, both compressed signals are collected to reconstruct the original meaningful certification image with a high correlation coefficient. Theoretical analysis and numerical simulations verify the feasibility of the proposed method.

Image and video compression for multimedia engineering fundamentals, algorithms, and standards

CERN Document Server

Shi, Yun Q

2008-01-01

Part I: Fundamentals Introduction Quantization Differential Coding Transform Coding Variable-Length Coding: Information Theory Results (II) Run-Length and Dictionary Coding: Information Theory Results (III) Part II: Still Image Compression Still Image Coding: Standard JPEG Wavelet Transform for Image Coding: JPEG2000 Nonstandard Still Image Coding Part III: Motion Estimation and Compensation Motion Analysis and Motion Compensation Block Matching Pel-Recursive Technique Optical Flow Further Discussion and Summary on 2-D Motion Estimation Part IV: Video Compression Fundam

Fast hybrid fractal image compression using an image feature and neural network

International Nuclear Information System (INIS)

Zhou Yiming; Zhang Chao; Zhang Zengke

2008-01-01

Since fractal image compression could maintain high-resolution reconstructed images at very high compression ratio, it has great potential to improve the efficiency of image storage and image transmission. On the other hand, fractal image encoding is time consuming for the best matching search between range blocks and domain blocks, which limits the algorithm to practical application greatly. In order to solve this problem, two strategies are adopted to improve the fractal image encoding algorithm in this paper. Firstly, based on the definition of an image feature, a necessary condition of the best matching search and FFC algorithm are proposed, and it could reduce the search space observably and exclude most inappropriate domain blocks according to each range block before the best matching search. Secondly, on the basis of FFC algorithm, in order to reduce the mapping error during the best matching search, a special neural network is constructed to modify the mapping scheme for the subblocks, in which the pixel values fluctuate greatly (FNFC algorithm). Experimental results show that the proposed algorithms could obtain good quality of the reconstructed images and need much less time than the baseline encoding algorithm

High bit depth infrared image compression via low bit depth codecs

DEFF Research Database (Denmark)

Belyaev, Evgeny; Mantel, Claire; Forchhammer, Søren

2017-01-01

images via 8 bit depth codecs in the following way. First, an input 16 bit depth image is mapped into 8 bit depth images, e.g., the first image contains only the most significant bytes (MSB image) and the second one contains only the least significant bytes (LSB image). Then each image is compressed.......264/AVC codecs, which are usually available in efficient implementations, and compare their rate-distortion performance with JPEG2000, JPEG-XT and H.265/HEVC codecs supporting direct compression of infrared images in 16 bit depth format. A preliminary result shows that two 8 bit H.264/AVC codecs can...

Parallelization of one image compression method. Wavelet, Transform, Vector Quantization and Huffman Coding

International Nuclear Information System (INIS)

Moravie, Philippe

1997-01-01

Today, in the digitized satellite image domain, the needs for high dimension increase considerably. To transmit or to stock such images (more than 6000 by 6000 pixels), we need to reduce their data volume and so we have to use real-time image compression techniques. The large amount of computations required by image compression algorithms prohibits the use of common sequential processors, for the benefits of parallel computers. The study presented here deals with parallelization of a very efficient image compression scheme, based on three techniques: Wavelets Transform (WT), Vector Quantization (VQ) and Entropic Coding (EC). First, we studied and implemented the parallelism of each algorithm, in order to determine the architectural characteristics needed for real-time image compression. Then, we defined eight parallel architectures: 3 for Mallat algorithm (WT), 3 for Tree-Structured Vector Quantization (VQ) and 2 for Huffman Coding (EC). As our system has to be multi-purpose, we chose 3 global architectures between all of the 3x3x2 systems available. Because, for technological reasons, real-time is not reached at anytime (for all the compression parameter combinations), we also defined and evaluated two algorithmic optimizations: fix point precision and merging entropic coding in vector quantization. As a result, we defined a new multi-purpose multi-SMIMD parallel machine, able to compress digitized satellite image in real-time. The definition of the best suited architecture for real-time image compression was answered by presenting 3 parallel machines among which one multi-purpose, embedded and which might be used for other applications on board. (author) [fr

A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

International Nuclear Information System (INIS)

Xiao Di; Cai Hong-Kun; Zheng Hong-Ying

2015-01-01

In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc. (paper)

Multi-modal brain imaging software for guiding invasive treatment of epilepsy

NARCIS (Netherlands)

Ossenblok, P.P.W.; Marien, S.; Meesters, S.P.L.; Florack, L.M.J.; Hofman, P.; Schijns, O.E.M.G.; Colon, A.

2017-01-01

Purpose: The surgical treatment of patients with complex epilepsies is changing more and more from open, invasive surgery towards minimally invasive, image guided treatment. Multi-modal brain imaging procedures are developed to delineate preoperatively the region of the brain which is responsible

A Near-Lossless Image Compression Algorithm Suitable for Hardware Design in Wireless Endoscopy System

Directory of Open Access Journals (Sweden)

Xie Xiang

2007-01-01

Full Text Available In order to decrease the communication bandwidth and save the transmitting power in the wireless endoscopy capsule, this paper presents a new near-lossless image compression algorithm based on the Bayer format image suitable for hardware design. This algorithm can provide low average compression rate ( bits/pixel with high image quality (larger than dB for endoscopic images. Especially, it has low complexity hardware overhead (only two line buffers and supports real-time compressing. In addition, the algorithm can provide lossless compression for the region of interest (ROI and high-quality compression for other regions. The ROI can be selected arbitrarily by varying ROI parameters. In addition, the VLSI architecture of this compression algorithm is also given out. Its hardware design has been implemented in m CMOS process.

Contributions to HEVC Prediction for Medical Image Compression

OpenAIRE

Guarda, André Filipe Rodrigues

2016-01-01

Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compressi...

Spinal cord compression at C1-C2 level due to tophaceous gout (magnetic resonance imaging and computed tomography cisternographic findings)

International Nuclear Information System (INIS)

Vries, C. de; Slegte, R.G.M. de; Valk, J.

1987-01-01

The authors report a case of spinal cord compression at the level of the foramen magnum due to tophaceous gout in a patient with no clinical history of gout. The presence of a foramen magnum mass due to urate crystal deposition in a patient without clinical history of gout or additional bone abnormalities has, to the best of the authors' knowledge, never been described before. In the case presented here, no bone changes were encountered with CT or MRI. Neither the presence of small high-density punctuations on the CT examination nor the signal intensities of the mass on T1- and T2-weighted images led to the radiological diagnois of tophaceous gout. The foramen magnum mass and the spinal cord compression were, however, beautifully depicted by both modalities. 14 refs.; 2 figs

Magnetic resonance image compression using scalar-vector quantization

Science.gov (United States)

Mohsenian, Nader; Shahri, Homayoun

1995-12-01

A new coding scheme based on the scalar-vector quantizer (SVQ) is developed for compression of medical images. SVQ is a fixed-rate encoder and its rate-distortion performance is close to that of optimal entropy-constrained scalar quantizers (ECSQs) for memoryless sources. The use of a fixed-rate quantizer is expected to eliminate some of the complexity issues of using variable-length scalar quantizers. When transmission of images over noisy channels is considered, our coding scheme does not suffer from error propagation which is typical of coding schemes which use variable-length codes. For a set of magnetic resonance (MR) images, coding results obtained from SVQ and ECSQ at low bit-rates are indistinguishable. Furthermore, our encoded images are perceptually indistinguishable from the original, when displayed on a monitor. This makes our SVQ based coder an attractive compression scheme for picture archiving and communication systems (PACS), currently under consideration for an all digital radiology environment in hospitals, where reliable transmission, storage, and high fidelity reconstruction of images are desired.

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

Science.gov (United States)

She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie

2014-02-01

To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.

CMOS Compressed Imaging by Random Convolution

OpenAIRE

Jacques, Laurent; Vandergheynst, Pierre; Bibet, Alexandre; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf

2009-01-01

We present a CMOS imager with built-in capability to perform Compressed Sensing. The adopted sensing strategy is the random Convolution due to J. Romberg. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current issued from each CMOS pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. A pseudo-random triggering of the ADC reading is finally applied to comp...

Expandable image compression system: A modular approach

International Nuclear Information System (INIS)

Ho, B.K.T.; Chan, K.K.; Ishimitsu, Y.; Lo, S.C.; Huang, H.K.

1987-01-01

The full-frame bit allocation algorithm for radiological image compression developed in the authors' laboratory can achieve compression ratios as high as 30:1. The software development and clinical evaluation of this algorithm has been completed. It involves two stages of operations: a two-dimensional discrete cosine transform and pixel quantization in the transform space with pixel depth kept accountable by a bit allocation table. Their design took an expandable modular approach based on the VME bus system which has a maximum data transfer rate of 48 Mbytes per second and a Motorola 68020 microprocessor as the master controller. The transform modules are based on advanced digital signal processor (DSP) chips microprogrammed to perform fast cosine transforms. Four DSP's built into a single-board transform module can process an 1K x 1K image in 1.7 seconds. Additional transform modules working in parallel can be added if even greater speeds are desired. The flexibility inherent in the microcode extends the capabilities of the system to incorporate images of variable sizes. Their design allows for a maximum image size of 2K x 2K

A Novel Medical Image Watermarking in Three-dimensional Fourier Compressed Domain

Directory of Open Access Journals (Sweden)

Baoru Han

2015-09-01

Full Text Available Digital watermarking is a research hotspot in the field of image security, which is protected digital image copyright. In order to ensure medical image information security, a novel medical image digital watermarking algorithm in three-dimensional Fourier compressed domain is proposed. The novel medical image digital watermarking algorithm takes advantage of three-dimensional Fourier compressed domain characteristics, Legendre chaotic neural network encryption features and robust characteristics of differences hashing, which is a robust zero-watermarking algorithm. On one hand, the original watermarking image is encrypted in order to enhance security. It makes use of Legendre chaotic neural network implementation. On the other hand, the construction of zero-watermarking adopts differences hashing in three-dimensional Fourier compressed domain. The novel watermarking algorithm does not need to select a region of interest, can solve the problem of medical image content affected. The specific implementation of the algorithm and the experimental results are given in the paper. The simulation results testify that the novel algorithm possesses a desirable robustness to common attack and geometric attack.

Dual photon excitation microscopy and image threshold segmentation in live cell imaging during compression testing.

Science.gov (United States)

Moo, Eng Kuan; Abusara, Ziad; Abu Osman, Noor Azuan; Pingguan-Murphy, Belinda; Herzog, Walter

2013-08-09

Morphological studies of live connective tissue cells are imperative to helping understand cellular responses to mechanical stimuli. However, photobleaching is a constant problem to accurate and reliable live cell fluorescent imaging, and various image thresholding methods have been adopted to account for photobleaching effects. Previous studies showed that dual photon excitation (DPE) techniques are superior over conventional one photon excitation (OPE) confocal techniques in minimizing photobleaching. In this study, we investigated the effects of photobleaching resulting from OPE and DPE on morphology of in situ articular cartilage chondrocytes across repeat laser exposures. Additionally, we compared the effectiveness of three commonly-used image thresholding methods in accounting for photobleaching effects, with and without tissue loading through compression. In general, photobleaching leads to an apparent volume reduction for subsequent image scans. Performing seven consecutive scans of chondrocytes in unloaded cartilage, we found that the apparent cell volume loss caused by DPE microscopy is much smaller than that observed using OPE microscopy. Applying scan-specific image thresholds did not prevent the photobleaching-induced volume loss, and volume reductions were non-uniform over the seven repeat scans. During cartilage loading through compression, cell fluorescence increased and, depending on the thresholding method used, led to different volume changes. Therefore, different conclusions on cell volume changes may be drawn during tissue compression, depending on the image thresholding methods used. In conclusion, our findings confirm that photobleaching directly affects cell morphology measurements, and that DPE causes less photobleaching artifacts than OPE for uncompressed cells. When cells are compressed during tissue loading, a complicated interplay between photobleaching effects and compression-induced fluorescence increase may lead to interpretations in

Diagnosis and Characterization of Patellofemoral Instability: Review of Available Imaging Modalities.

Science.gov (United States)

Haj-Mirzaian, Arya; Thawait, Gaurav K; Tanaka, Miho J; Demehri, Shadpour

2017-06-01

Patellofemoral instability (PI) is defined as single or multiple episodes of patellar dislocation. Imaging modalities are useful for characterization of patellar malalignment, maltracking, underlying morphologic abnormalities, and stabilizing soft-tissue injuries. Using these findings, orthopedic surgeons can decide when to operate, determine the best operation, and measure degree of correction postoperatively in PI patients. Also, these methods assist with PI diagnosis in some suspicious cases. Magnetic resonance imaging is the preferred method especially in the setting of acute dislocations. Multidetector computed tomography allows a more accurate assessment for malalignment such as patellar tilt and lateral subluxation and secondary osteoarthritis. Dynamic magnetic resonance imaging and 4-dimensional computed tomography have been introduced for better kinematic assessment of the patellofemoral maltracking during extension-flexion motions. In this review article, we will discuss the currently available evidence regarding both the conventional and the novel imaging modalities that can be used for diagnosis and characterization of PI.

WE-H-206-02: Recent Advances in Multi-Modality Molecular Imaging of Small Animals

Energy Technology Data Exchange (ETDEWEB)

Tsui, B. [Johns Hopkins University (United States)

2016-06-15

Lihong V. Wang: Photoacoustic tomography (PAT), combining non-ionizing optical and ultrasonic waves via the photoacoustic effect, provides in vivo multiscale functional, metabolic, and molecular imaging. Broad applications include imaging of the breast, brain, skin, esophagus, colon, vascular system, and lymphatic system in humans or animals. Light offers rich contrast but does not penetrate biological tissue in straight paths as x-rays do. Consequently, high-resolution pure optical imaging (e.g., confocal microscopy, two-photon microscopy, and optical coherence tomography) is limited to penetration within the optical diffusion limit (∼1 mm in the skin). Ultrasonic imaging, on the contrary, provides fine spatial resolution but suffers from both poor contrast in early-stage tumors and strong speckle artifacts. In PAT, pulsed laser light penetrates tissue and generates a small but rapid temperature rise, which induces emission of ultrasonic waves due to thermoelastic expansion. The ultrasonic waves, orders of magnitude less scattering than optical waves, are then detected to form high-resolution images of optical absorption at depths up to 7 cm, conquering the optical diffusion limit. PAT is the only modality capable of imaging across the length scales of organelles, cells, tissues, and organs (up to whole-body small animals) with consistent contrast. This rapidly growing technology promises to enable multiscale biological research and accelerate translation from microscopic laboratory discoveries to macroscopic clinical practice. PAT may also hold the key to label-free early detection of cancer by in vivo quantification of hypermetabolism, the quintessential hallmark of malignancy. Learning Objectives: To understand the contrast mechanism of PAT To understand the multiscale applications of PAT Benjamin M. W. Tsui: Multi-modality molecular imaging instrumentation and techniques have been major developments in small animal imaging that has contributed significantly

WE-H-206-02: Recent Advances in Multi-Modality Molecular Imaging of Small Animals

International Nuclear Information System (INIS)

Tsui, B.

2016-01-01

Lihong V. Wang: Photoacoustic tomography (PAT), combining non-ionizing optical and ultrasonic waves via the photoacoustic effect, provides in vivo multiscale functional, metabolic, and molecular imaging. Broad applications include imaging of the breast, brain, skin, esophagus, colon, vascular system, and lymphatic system in humans or animals. Light offers rich contrast but does not penetrate biological tissue in straight paths as x-rays do. Consequently, high-resolution pure optical imaging (e.g., confocal microscopy, two-photon microscopy, and optical coherence tomography) is limited to penetration within the optical diffusion limit (∼1 mm in the skin). Ultrasonic imaging, on the contrary, provides fine spatial resolution but suffers from both poor contrast in early-stage tumors and strong speckle artifacts. In PAT, pulsed laser light penetrates tissue and generates a small but rapid temperature rise, which induces emission of ultrasonic waves due to thermoelastic expansion. The ultrasonic waves, orders of magnitude less scattering than optical waves, are then detected to form high-resolution images of optical absorption at depths up to 7 cm, conquering the optical diffusion limit. PAT is the only modality capable of imaging across the length scales of organelles, cells, tissues, and organs (up to whole-body small animals) with consistent contrast. This rapidly growing technology promises to enable multiscale biological research and accelerate translation from microscopic laboratory discoveries to macroscopic clinical practice. PAT may also hold the key to label-free early detection of cancer by in vivo quantification of hypermetabolism, the quintessential hallmark of malignancy. Learning Objectives: To understand the contrast mechanism of PAT To understand the multiscale applications of PAT Benjamin M. W. Tsui: Multi-modality molecular imaging instrumentation and techniques have been major developments in small animal imaging that has contributed significantly

NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator

Science.gov (United States)

Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Stern, Adrian

2018-04-01

The acquisition of hyperspectral (HS) image datacubes with available 2D sensor arrays involves a time consuming scanning process. In the last decade, several compressive sensing (CS) techniques were proposed to reduce the HS acquisition time. In this paper, we present a method for near-infrared (NIR) HS imaging which relies on our rapid CS resonator spectroscopy technique. Within the framework of CS, and by using a modified Fabry–Perot resonator, a sequence of spectrally modulated images is used to recover NIR HS datacubes. Owing to the innovative CS design, we demonstrate the ability to reconstruct NIR HS images with hundreds of spectral bands from an order of magnitude fewer measurements, i.e. with a compression ratio of about 10:1. This high compression ratio, together with the high optical throughput of the system, facilitates fast acquisition of large HS datacubes.

Interleaved EPI diffusion imaging using SPIRiT-based reconstruction with virtual coil compression.

Science.gov (United States)

Dong, Zijing; Wang, Fuyixue; Ma, Xiaodong; Zhang, Zhe; Dai, Erpeng; Yuan, Chun; Guo, Hua

2018-03-01

To develop a novel diffusion imaging reconstruction framework based on iterative self-consistent parallel imaging reconstruction (SPIRiT) for multishot interleaved echo planar imaging (iEPI), with computation acceleration by virtual coil compression. As a general approach for autocalibrating parallel imaging, SPIRiT improves the performance of traditional generalized autocalibrating partially parallel acquisitions (GRAPPA) methods in that the formulation with self-consistency is better conditioned, suggesting SPIRiT to be a better candidate in k-space-based reconstruction. In this study, a general SPIRiT framework is adopted to incorporate both coil sensitivity and phase variation information as virtual coils and then is applied to 2D navigated iEPI diffusion imaging. To reduce the reconstruction time when using a large number of coils and shots, a novel shot-coil compression method is proposed for computation acceleration in Cartesian sampling. Simulations and in vivo experiments were conducted to evaluate the performance of the proposed method. Compared with the conventional coil compression, the shot-coil compression achieved higher compression rates with reduced errors. The simulation and in vivo experiments demonstrate that the SPIRiT-based reconstruction outperformed the existing method, realigned GRAPPA, and provided superior images with reduced artifacts. The SPIRiT-based reconstruction with virtual coil compression is a reliable method for high-resolution iEPI diffusion imaging. Magn Reson Med 79:1525-1531, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Single-event transient imaging with an ultra-high-speed temporally compressive multi-aperture CMOS image sensor.

Science.gov (United States)

Mochizuki, Futa; Kagawa, Keiichiro; Okihara, Shin-ichiro; Seo, Min-Woong; Zhang, Bo; Takasawa, Taishi; Yasutomi, Keita; Kawahito, Shoji

2016-02-22

In the work described in this paper, an image reproduction scheme with an ultra-high-speed temporally compressive multi-aperture CMOS image sensor was demonstrated. The sensor captures an object by compressing a sequence of images with focal-plane temporally random-coded shutters, followed by reconstruction of time-resolved images. Because signals are modulated pixel-by-pixel during capturing, the maximum frame rate is defined only by the charge transfer speed and can thus be higher than those of conventional ultra-high-speed cameras. The frame rate and optical efficiency of the multi-aperture scheme are discussed. To demonstrate the proposed imaging method, a 5×3 multi-aperture image sensor was fabricated. The average rising and falling times of the shutters were 1.53 ns and 1.69 ns, respectively. The maximum skew among the shutters was 3 ns. The sensor observed plasma emission by compressing it to 15 frames, and a series of 32 images at 200 Mfps was reconstructed. In the experiment, by correcting disparities and considering temporal pixel responses, artifacts in the reconstructed images were reduced. An improvement in PSNR from 25.8 dB to 30.8 dB was confirmed in simulations.

Evaluation of the distortions of the digital chest image caused by the data compression

International Nuclear Information System (INIS)

Ando, Yutaka; Kunieda, Etsuo; Ogawa, Koichi; Tukamoto, Nobuhiro; Hashimoto, Shozo; Aoki, Makoto; Kurotani, Kenichi.

1988-01-01

The image data compression methods using orthogonal transforms (Discrete cosine transform, Discrete fourier transform, Hadamard transform, Haar transform, Slant transform) were analyzed. From the points of the error and the speed of the data conversion, the discrete cosine transform method (DCT) is superior to the other methods. The block quantization by the DCT for the digital chest image was used. The quality of data compressed and reconstructed images by the score analysis and the ROC curve analysis was examined. The chest image with the esophageal cancer and metastatic lung tumors was evaluated at the 17 checkpoints (the tumor, the vascular markings, the border of the heart and ribs, the mediastinal structures and et al). By our score analysis, the satisfactory ratio of the data compression is 1/5 and 1/10. The ROC analysis using normal chest images superimposed by the artificial coin lesions was made. The ROC curve of the 1/5 compressed ratio is almost as same as the original one. To summarize our study, the image data compression method using the DCT is thought to be useful for the clinical use and the 1/5 compression ratio is a tolerable ratio. (author)

Evaluation of the distortions of the digital chest image caused by the data compression

Energy Technology Data Exchange (ETDEWEB)

Ando, Yutaka; Kunieda, Etsuo; Ogawa, Koichi; Tukamoto, Nobuhiro; Hashimoto, Shozo; Aoki, Makoto; Kurotani, Kenichi

1988-08-01

The image data compression methods using orthogonal transforms (Discrete cosine transform, Discrete fourier transform, Hadamard transform, Haar transform, Slant transform) were analyzed. From the points of the error and the speed of the data conversion, the discrete cosine transform method (DCT) is superior to the other methods. The block quantization by the DCT for the digital chest image was used. The quality of data compressed and reconstructed images by the score analysis and the ROC curve analysis was examined. The chest image with the esophageal cancer and metastatic lung tumors was evaluated at the 17 checkpoints (the tumor, the vascular markings, the border of the heart and ribs, the mediastinal structures and et al). By our score analysis, the satisfactory ratio of the data compression is 1/5 and 1/10. The ROC analysis using normal chest images superimposed by the artificial coin lesions was made. The ROC curve of the 1/5 compressed ratio is almost as same as the original one. To summarize our study, the image data compression method using the DCT is thought to be useful for the clinical use and the 1/5 compression ratio is a tolerable ratio.

Multiband CCD Image Compression for Space Camera with Large Field of View

Directory of Open Access Journals (Sweden)

Jin Li

2014-01-01

Full Text Available Space multiband CCD camera compression encoder requires low-complexity, high-robustness, and high-performance because of its captured images information being very precious and also because it is usually working on the satellite where the resources, such as power, memory, and processing capacity, are limited. However, the traditional compression approaches, such as JPEG2000, 3D transforms, and PCA, have the high-complexity. The Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC algorithm decreases the average PSNR by 2 dB compared with JPEG2000. In this paper, we proposed a low-complexity compression algorithm based on deep coupling algorithm among posttransform in wavelet domain, compressive sensing, and distributed source coding. In our algorithm, we integrate three low-complexity and high-performance approaches in a deeply coupled manner to remove the spatial redundant, spectral redundant, and bit information redundancy. Experimental results on multiband CCD images show that the proposed algorithm significantly outperforms the traditional approaches.

An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT).

Science.gov (United States)

Li, Ran; Duan, Xiaomeng; Li, Xu; He, Wei; Li, Yanling

2018-04-17

Aimed at a low-energy consumption of Green Internet of Things (IoT), this paper presents an energy-efficient compressive image coding scheme, which provides compressive encoder and real-time decoder according to Compressive Sensing (CS) theory. The compressive encoder adaptively measures each image block based on the block-based gradient field, which models the distribution of block sparse degree, and the real-time decoder linearly reconstructs each image block through a projection matrix, which is learned by Minimum Mean Square Error (MMSE) criterion. Both the encoder and decoder have a low computational complexity, so that they only consume a small amount of energy. Experimental results show that the proposed scheme not only has a low encoding and decoding complexity when compared with traditional methods, but it also provides good objective and subjective reconstruction qualities. In particular, it presents better time-distortion performance than JPEG. Therefore, the proposed compressive image coding is a potential energy-efficient scheme for Green IoT.

An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT

Directory of Open Access Journals (Sweden)

Ran Li

2018-04-01

Full Text Available Aimed at a low-energy consumption of Green Internet of Things (IoT, this paper presents an energy-efficient compressive image coding scheme, which provides compressive encoder and real-time decoder according to Compressive Sensing (CS theory. The compressive encoder adaptively measures each image block based on the block-based gradient field, which models the distribution of block sparse degree, and the real-time decoder linearly reconstructs each image block through a projection matrix, which is learned by Minimum Mean Square Error (MMSE criterion. Both the encoder and decoder have a low computational complexity, so that they only consume a small amount of energy. Experimental results show that the proposed scheme not only has a low encoding and decoding complexity when compared with traditional methods, but it also provides good objective and subjective reconstruction qualities. In particular, it presents better time-distortion performance than JPEG. Therefore, the proposed compressive image coding is a potential energy-efficient scheme for Green IoT.

Congenitally short trachea with compression of the left mainstem bronchus: MRI findings

Energy Technology Data Exchange (ETDEWEB)

De Priester, J.A.; Oei Toen-Khiam [Department of Radiology, University Hospital Maastricht (Netherlands); Vos, G.D.; Van Waardenburg, D.A. [Department of Pediatrics, Division of Pediatric Intensive Care, University Hospital Maastricht, Maastricht (Netherlands)

1998-05-01

The MRI features of congenitally short trachea in a patient presenting with respiratory distress are presented. Compression of the left mainstem bronchus by the aortic arch, a recognised complication of this anomaly, could be demonstrated by MRI, obviating the need for other imaging modalities. (orig.) With 2 figs., 5 refs.

Sparse representations and compressive sensing for imaging and vision

CERN Document Server

Patel, Vishal M

2013-01-01

Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.

Robotic 3D scanner as an alternative to standard modalities of medical imaging.

Science.gov (United States)

Chromy, Adam; Zalud, Ludek

2014-01-01

There are special medical cases, where standard medical imaging modalities are able to offer sufficient results, but not in the optimal way. It means, that desired results are produced with unnecessarily high expenses, with redundant informations or with needless demands on patient. This paper deals with one special case, where information useful for examination is the body surface only, inner sight into the body is needless. New specialized medical imaging device is developed for this situation. In the Introduction section, analysis of presently used medical imaging modalities is presented, which declares, that no available imaging device is best fitting for mentioned purposes. In the next section, development of the new specialized medical imaging device is presented, and its principles and functions are described. Then, the parameters of new device are compared with present ones. It brings significant advantages comparing to present imaging systems.

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

Directory of Open Access Journals (Sweden)

Barni Mauro

2007-01-01

Full Text Available This paper deals with the application of distributed source coding (DSC theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.

DSP accelerator for the wavelet compression/decompression of high- resolution images

Energy Technology Data Exchange (ETDEWEB)

Hunt, M.A.; Gleason, S.S.; Jatko, W.B.

1993-07-23

A Texas Instruments (TI) TMS320C30-based S-Bus digital signal processing (DSP) module was used to accelerate a wavelet-based compression and decompression algorithm applied to high-resolution fingerprint images. The law enforcement community, together with the National Institute of Standards and Technology (NISI), is adopting a standard based on the wavelet transform for the compression, transmission, and decompression of scanned fingerprint images. A two-dimensional wavelet transform of the input image is computed. Then spatial/frequency regions are automatically analyzed for information content and quantized for subsequent Huffman encoding. Compression ratios range from 10:1 to 30:1 while maintaining the level of image quality necessary for identification. Several prototype systems were developed using SUN SPARCstation 2 with a 1280 {times} 1024 8-bit display, 64-Mbyte random access memory (RAM), Tiber distributed data interface (FDDI), and Spirit-30 S-Bus DSP-accelerators from Sonitech. The final implementation of the DSP-accelerated algorithm performed the compression or decompression operation in 3.5 s per print. Further increases in system throughput were obtained by adding several DSP accelerators operating in parallel.

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

KAUST Repository

Wang, Jingbin

2015-10-09

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., An image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.

Adaptive bit plane quadtree-based block truncation coding for image compression

Science.gov (United States)

Li, Shenda; Wang, Jin; Zhu, Qing

2018-04-01

Block truncation coding (BTC) is a fast image compression technique applied in spatial domain. Traditional BTC and its variants mainly focus on reducing computational complexity for low bit rate compression, at the cost of lower quality of decoded images, especially for images with rich texture. To solve this problem, in this paper, a quadtree-based block truncation coding algorithm combined with adaptive bit plane transmission is proposed. First, the direction of edge in each block is detected using Sobel operator. For the block with minimal size, adaptive bit plane is utilized to optimize the BTC, which depends on its MSE loss encoded by absolute moment block truncation coding (AMBTC). Extensive experimental results show that our method gains 0.85 dB PSNR on average compare to some other state-of-the-art BTC variants. So it is desirable for real time image compression applications.

Alternatives to the discrete cosine transform for irreversible tomographic image compression

International Nuclear Information System (INIS)

Villasenor, J.D.

1993-01-01

Full-frame irreversible compression of medical images is currently being performed using the discrete cosine transform (DCT). Although the DCT is the optimum fast transform for video compression applications, the authors show here that it is out-performed by the discrete Fourier transform (DFT) and discrete Hartley transform (DHT) for images obtained using positron emission tomography (PET) and magnetic resonance imaging (MRI), and possibly for certain types of digitized radiographs. The difference occurs because PET and MRI images are characterized by a roughly circular region D of non-zero intensity bounded by a region R in which the Image intensity is essentially zero. Clipping R to its minimum extent can reduce the number of low-intensity pixels but the practical requirement that images be stored on a rectangular grid means that a significant region of zero intensity must remain an integral part of the image to be compressed. With this constraint imposed, the DCT loses its advantage over the DFT because neither transform introduces significant artificial discontinuities. The DFT and DHT have the further important advantage of requiring less computation time than the DCT

Accelerated Air-coupled Ultrasound Imaging of Wood Using Compressed Sensing

Directory of Open Access Journals (Sweden)

Yiming Fang

2015-12-01

Full Text Available Air-coupled ultrasound has shown excellent sensitivity and specificity for the nondestructive imaging of wood-based material. However, it is time-consuming, due to the high scanning density limited by the Nyquist law. This study investigated the feasibility of applying compressed sensing techniques to air-coupled ultrasound imaging, aiming to reduce the number of scanning lines and then accelerate the imaging. Firstly, an undersampled scanning strategy specified by a random binary matrix was proposed to address the limitation of the compressed sensing framework. The undersampled scanning can be easily implemented, while only minor modification was required for the existing imaging system. Then, discrete cosine transform was selected experimentally as the representation basis. Finally, orthogonal matching pursuit algorithm was utilized to reconstruct the wood images. Experiments on three real air-coupled ultrasound images indicated the potential of the present method to accelerate air-coupled ultrasound imaging of wood. The same quality of ACU images can be obtained with scanning time cut in half.

MR imaging of spinal factors and compression of the spinal cord in cervical myelopathy

International Nuclear Information System (INIS)

Kokubun, Shoichi; Ozawa, Hiroshi; Sakurai, Minoru; Ishii, Sukenobu; Tani, Shotaro; Sato, Tetsuaki.

1992-01-01

Magnetic resonance (MR) images of surgical 109 patients with cervical spondylotic myelopathy were retrospectively reviewed to examine whether MR imaging would replace conventional radiological procedures in determining spinal factors and spinal cord compression in this disease. MR imaging was useful in determining spondylotic herniation, continuous type of ossification of posterior longitudinal ligament, and calcification of yellow ligament, probably replacing CT myelography, discography, and CT discography. When total defect of the subarachnoid space on T2-weighted images and block on myelograms were compared in determining spinal cord compression, the spinal cord was affected more extensively by 1.3 intervertebral distance (IVD) on T2-weighted images. When indentation of one third or more in anterior and posterior diameter of the spinal cord was used as spinal cord compression, the difference in the affected extension between myelography and MR imaging was 0.2 IVD on T1-weighted images and 0.6 IVD on T2-weighted images. However, when block was seen in 3 or more IVD on myelograms, the range of spinal cord compression tended to be larger on T1-weighted images. For a small range of spinal cord compression, T1-weighted imaging seems to be helpful in determining the range of decompression. When using T2-weighted imaging, the range of decompression becomes large, frequently including posterior decompression. (N.K.)

Bi-level image compression with tree coding

DEFF Research Database (Denmark)

Martins, Bo; Forchhammer, Søren

1996-01-01

Presently, tree coders are the best bi-level image coders. The current ISO standard, JBIG, is a good example. By organising code length calculations properly a vast number of possible models (trees) can be investigated within reasonable time prior to generating code. Three general-purpose coders...... are constructed by this principle. A multi-pass free tree coding scheme produces superior compression results for all test images. A multi-pass fast free template coding scheme produces much better results than JBIG for difficult images, such as halftonings. Rissanen's algorithm `Context' is presented in a new...

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

Directory of Open Access Journals (Sweden)

Konsti Juho

2012-03-01

Full Text Available Abstract Background Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC stainings and automated tumor segmentation. Methods Two tissue microarray (TMA slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images. Results Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and

Optimization of compressive 4D-spatio-spectral snapshot imaging

Science.gov (United States)

Zhao, Xia; Feng, Weiyi; Lin, Lihua; Su, Wu; Xu, Guoqing

2017-10-01

In this paper, a modified 3D computational reconstruction method in the compressive 4D-spectro-volumetric snapshot imaging system is proposed for better sensing spectral information of 3D objects. In the design of the imaging system, a microlens array (MLA) is used to obtain a set of multi-view elemental images (EIs) of the 3D scenes. Then, these elemental images with one dimensional spectral information and different perspectives are captured by the coded aperture snapshot spectral imager (CASSI) which can sense the spectral data cube onto a compressive 2D measurement image. Finally, the depth images of 3D objects at arbitrary depths, like a focal stack, are computed by inversely mapping the elemental images according to geometrical optics. With the spectral estimation algorithm, the spectral information of 3D objects is also reconstructed. Using a shifted translation matrix, the contrast of the reconstruction result is further enhanced. Numerical simulation results verify the performance of the proposed method. The system can obtain both 3D spatial information and spectral data on 3D objects using only one single snapshot, which is valuable in the agricultural harvesting robots and other 3D dynamic scenes.

Spatial correlation genetic algorithm for fractal image compression

International Nuclear Information System (INIS)

Wu, M.-S.; Teng, W.-C.; Jeng, J.-H.; Hsieh, J.-G.

2006-01-01

Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In this paper, a spatial correlation genetic algorithm (SC-GA) is proposed to speed up the encoder. There are two stages for the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfied. With the aid of spatial correlation in images, the encoding time is 1.5 times faster than that of traditional genetic algorithm method, while the quality of the retrieved image is almost the same. Moreover, about half of the matched blocks come from the correlated space, so fewer bits are required to represent the fractal transform and therefore the compression ratio is also improved

ITERATION FREE FRACTAL COMPRESSION USING GENETIC ALGORITHM FOR STILL COLOUR IMAGES

Directory of Open Access Journals (Sweden)

A.R. Nadira Banu Kamal

2014-02-01

Full Text Available The storage requirements for images can be excessive, if true color and a high-perceived image quality are desired. An RGB image may be viewed as a stack of three gray-scale images that when fed into the red, green and blue inputs of a color monitor, produce a color image on the screen. The abnormal size of many images leads to long, costly, transmission times. Hence, an iteration free fractal algorithm is proposed in this research paper to design an efficient search of the domain pools for colour image compression using Genetic Algorithm (GA. The proposed methodology reduces the coding process time and intensive computation tasks. Parameters such as image quality, compression ratio and coding time are analyzed. It is observed that the proposed method achieves excellent performance in image quality with reduction in storage space.

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

OpenAIRE

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with vari...

Digitized hand-wrist radiographs: comparison of subjective and software-derived image quality at various compression ratios.

Science.gov (United States)

McCord, Layne K; Scarfe, William C; Naylor, Rachel H; Scheetz, James P; Silveira, Anibal; Gillespie, Kevin R

2007-05-01

The objectives of this study were to compare the effect of JPEG 2000 compression of hand-wrist radiographs on observer image quality qualitative assessment and to compare with a software-derived quantitative image quality index. Fifteen hand-wrist radiographs were digitized and saved as TIFF and JPEG 2000 images at 4 levels of compression (20:1, 40:1, 60:1, and 80:1). The images, including rereads, were viewed by 13 orthodontic residents who determined the image quality rating on a scale of 1 to 5. A quantitative analysis was also performed by using a readily available software based on the human visual system (Image Quality Measure Computer Program, version 6.2, Mitre, Bedford, Mass). ANOVA was used to determine the optimal compression level (P quality. When we used quantitative indexes, the JPEG 2000 images had lower quality at all compression ratios compared with the original TIFF images. There was excellent correlation (R2 >0.92) between qualitative and quantitative indexes. Image Quality Measure indexes are more sensitive than subjective image quality assessments in quantifying image degradation with compression. There is potential for this software-based quantitative method in determining the optimal compression ratio for any image without the use of subjective raters.

A Coded Aperture Compressive Imaging Array and Its Visual Detection and Tracking Algorithms for Surveillance Systems

Directory of Open Access Journals (Sweden)

Hanxiao Wu

2012-10-01

Full Text Available In this paper, we propose an application of a compressive imaging system to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system is proposed to reduce the needed high resolution coded mask requirements and facilitate the storage of the projection matrix. Random Gaussian, Toeplitz and binary phase coded masks are utilized to obtain the compressive sensing images. The corresponding motion targets detection and tracking algorithms directly using the compressive sampling images are developed. A mixture of Gaussian distribution is applied in the compressive image space to model the background image and for foreground detection. For each motion target in the compressive sampling domain, a compressive feature dictionary spanned by target templates and noises templates is sparsely represented. An l1 optimization algorithm is used to solve the sparse coefficient of templates. Experimental results demonstrate that low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz phase mask, motion detection algorithms using a random binary phase mask can yield better detection results. However using random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed image. Our tracking algorithm can achieve a real time speed that is up to 10 times faster than that of the l1 tracker without any optimization.

Color image lossy compression based on blind evaluation and prediction of noise characteristics

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Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Egiazarian, Karen O.; Lepisto, Leena

2011-03-01

The paper deals with JPEG adaptive lossy compression of color images formed by digital cameras. Adaptation to noise characteristics and blur estimated for each given image is carried out. The dominant factor degrading image quality is determined in a blind manner. Characteristics of this dominant factor are then estimated. Finally, a scaling factor that determines quantization steps for default JPEG table is adaptively set (selected). Within this general framework, two possible strategies are considered. A first one presumes blind estimation for an image after all operations in digital image processing chain just before compressing a given raster image. A second strategy is based on prediction of noise and blur parameters from analysis of RAW image under quite general assumptions concerning characteristics parameters of transformations an image will be subject to at further processing stages. The advantages of both strategies are discussed. The first strategy provides more accurate estimation and larger benefit in image compression ratio (CR) compared to super-high quality (SHQ) mode. However, it is more complicated and requires more resources. The second strategy is simpler but less beneficial. The proposed approaches are tested for quite many real life color images acquired by digital cameras and shown to provide more than two time increase of average CR compared to SHQ mode without introducing visible distortions with respect to SHQ compressed images.

Assessment of the impact of modeling axial compression on PET image reconstruction.

Science.gov (United States)

Belzunce, Martin A; Reader, Andrew J

2017-10-01

To comprehensively evaluate both the acceleration and image-quality impacts of axial compression and its degree of modeling in fully 3D PET image reconstruction. Despite being used since the very dawn of 3D PET reconstruction, there are still no extensive studies on the impact of axial compression and its degree of modeling during reconstruction on the end-point reconstructed image quality. In this work, an evaluation of the impact of axial compression on the image quality is performed by extensively simulating data with span values from 1 to 121. In addition, two methods for modeling the axial compression in the reconstruction were evaluated. The first method models the axial compression in the system matrix, while the second method uses an unmatched projector/backprojector, where the axial compression is modeled only in the forward projector. The different system matrices were analyzed by computing their singular values and the point response functions for small subregions of the FOV. The two methods were evaluated with simulated and real data for the Biograph mMR scanner. For the simulated data, the axial compression with span values lower than 7 did not show a decrease in the contrast of the reconstructed images. For span 11, the standard sinogram size of the mMR scanner, losses of contrast in the range of 5-10 percentage points were observed when measured for a hot lesion. For higher span values, the spatial resolution was degraded considerably. However, impressively, for all span values of 21 and lower, modeling the axial compression in the system matrix compensated for the spatial resolution degradation and obtained similar contrast values as the span 1 reconstructions. Such approaches have the same processing times as span 1 reconstructions, but they permit significant reduction in storage requirements for the fully 3D sinograms. For higher span values, the system has a large condition number and it is therefore difficult to recover accurately the higher

Improved medical image modality classification using a combination of visual and textual features.

Science.gov (United States)

Dimitrovski, Ivica; Kocev, Dragi; Kitanovski, Ivan; Loskovska, Suzana; Džeroski, Sašo

2015-01-01

In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariant feature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results from the extensive experimental evaluation identify the SIFT and opponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far. Copyright © 2014 Elsevier Ltd. All rights reserved.

View compensated compression of volume rendered images for remote visualization.

Science.gov (United States)

Lalgudi, Hariharan G; Marcellin, Michael W; Bilgin, Ali; Oh, Han; Nadar, Mariappan S

2009-07-01

Remote visualization of volumetric images has gained importance over the past few years in medical and industrial applications. Volume visualization is a computationally intensive process, often requiring hardware acceleration to achieve a real time viewing experience. One remote visualization model that can accomplish this would transmit rendered images from a server, based on viewpoint requests from a client. For constrained server-client bandwidth, an efficient compression scheme is vital for transmitting high quality rendered images. In this paper, we present a new view compensation scheme that utilizes the geometric relationship between viewpoints to exploit the correlation between successive rendered images. The proposed method obviates motion estimation between rendered images, enabling significant reduction to the complexity of a compressor. Additionally, the view compensation scheme, in conjunction with JPEG2000 performs better than AVC, the state of the art video compression standard.

Development of information preserving data compression algorithm for CT images

International Nuclear Information System (INIS)

Kobayashi, Yoshio

1989-01-01

Although digital imaging techniques in radiology develop rapidly, problems arise in archival storage and communication of image data. This paper reports on a new information preserving data compression algorithm for computed tomographic (CT) images. This algorithm consists of the following five processes: 1. Pixels surrounding the human body showing CT values smaller than -900 H.U. are eliminated. 2. Each pixel is encoded by its numerical difference from its neighboring pixel along a matrix line. 3. Difference values are encoded by a newly designed code rather than the natural binary code. 4. Image data, obtained with the above process, are decomposed into bit planes. 5. The bit state transitions in each bit plane are encoded by run length coding. Using this new algorithm, the compression ratios of brain, chest, and abdomen CT images are 4.49, 4.34. and 4.40 respectively. (author)

Chronic granulomatous disease: Value of the newer imaging modalities

International Nuclear Information System (INIS)

Stricof, D.D.; Glazer, M.; Amendola, A.

1984-01-01

The contribution of computed tomography (CT), ultrasound (US), and nuclear medicine studies in the evaluation and management of seven patients with chronic granulatous disease was retrospectively reviewed. These modalities proved valuable in detecting sites of infection, particularly in the abdomen. Three patients had liver abscesses, two had suppurative retroperitoneal lymphadenopathy, one had empyema, and one hand a scrotal abscess. Furthermore, CT or US-guided percutaneous aspiration and/or drainage of infected material was successfully performed on three separate occasions in a single patient, obviating the need for surgery. The newer imaging modalities are useful in the prompt diagnosis and in some instances non-operative therapy of complications of chronic granulomatous disease. (orig.)

Virtual endoscopic images by 3D FASE cisternography for neurovascular compression

International Nuclear Information System (INIS)

Ishimori, Takashi; Nakano, Satoru; Kagawa, Masahiro

2003-01-01

Three-dimensional fast asymmetric spin echo (3D FASE) cisternography provides high spatial resolution and excellent contrast as a water image acquisition technique. It is also useful for the evaluation of various anatomical regions. This study investigated the usefulness and limitations of virtual endoscopic images obtained by 3D FASE MR cisternography in the preoperative evaluation of patients with neurovascular compression. The study included 12 patients with neurovascular compression: 10 with hemifacial spasm and two with trigeminal neuralgia. The diagnosis was surgically confirmed in all patients. The virtual endoscopic images obtained were judged to be of acceptable quality for interpretation in all cases. The areas of compression identified in preoperative diagnosis with virtual endoscopic images showed good agreement with those observed from surgery, except in one case in which the common trunk of the anterior inferior cerebellar artery and posterior inferior cerebellar artery (AICA-PICA) bifurcated near the root exit zone of the facial nerve. The veins are displayed in some cases but not in others. The main advantage of generating virtual endoscopic images is that such images can be used for surgical simulation, allowing the neurosurgeon to perform surgical procedures with greater confidence. (author)

Establishing physical criteria to stop the losing compression of digital medical imaging

International Nuclear Information System (INIS)

Perez Diaz, M

2008-01-01

Full text: A key to store and/or transmit digital medical images obtained from modern technologies is the size in bytes they occupy difficulty. One way to solve the above is the implementation of compression algorithms (codecs) with or without losses. Particularly the latter do allow significant reductions in the size of the images, but if not applied on solid scientific criteria can lead to useful diagnostic information is lost. This talk takes a description and assessment of the quality of image obtained after the application of current compression codecs from analysis of physical parameters such as: Spatial resolution, random noise , contrast and image generation devices. Open for Medical Physics and Image Processing, directed toward establishing objective criteria to stop losing compression, based on the implementation of Univariate and bivariate traditional metrics such as mean square error introduced by each issue focuses rate compression, Signal to Noise peak to peak noise and contrast ratio , and other metrics, more modern, such as Structural Similarity Index and, Measures Distance , singular value decomposition of the image matrix and Correlation and Spectral Measurements. It also makes a review of physical approaches for predicting image quality from use mathematical observers as the Hotelling and Hotelling Pipeline with Gabor functions or Laguerre - Gauss polynomials . Finally the correlation of these objective methods with subjective assessment of image quality made ​​from ROC analysis based on Diagnostic Performance Curves is analyzed. (author)

SU-E-I-83: Error Analysis of Multi-Modality Image-Based Volumes of Rodent Solid Tumors Using a Preclinical Multi-Modality QA Phantom

Energy Technology Data Exchange (ETDEWEB)

Lee, Y [University of Kansas Hospital, Kansas City, KS (United States); Fullerton, G; Goins, B [University of Texas Health Science Center at San Antonio, San Antonio, TX (United States)

2015-06-15

Purpose: In our previous study a preclinical multi-modality quality assurance (QA) phantom that contains five tumor-simulating test objects with 2, 4, 7, 10 and 14 mm diameters was developed for accurate tumor size measurement by researchers during cancer drug development and testing. This study analyzed the errors during tumor volume measurement from preclinical magnetic resonance (MR), micro-computed tomography (micro- CT) and ultrasound (US) images acquired in a rodent tumor model using the preclinical multi-modality QA phantom. Methods: Using preclinical 7-Tesla MR, US and micro-CT scanners, images were acquired of subcutaneous SCC4 tumor xenografts in nude rats (3–4 rats per group; 5 groups) along with the QA phantom using the same imaging protocols. After tumors were excised, in-air micro-CT imaging was performed to determine reference tumor volume. Volumes measured for the rat tumors and phantom test objects were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three imaging modalities. Then linear regression analysis was performed to compare image-based tumor volumes with the reference tumor volume and known test object volume for the rats and the phantom respectively. Results: The slopes of regression lines for in-vivo tumor volumes measured by three imaging modalities were 1.021, 1.101 and 0.862 for MRI, micro-CT and US respectively. For phantom, the slopes were 0.9485, 0.9971 and 0.9734 for MRI, micro-CT and US respectively. Conclusion: For both animal and phantom studies, random and systematic errors were observed. Random errors were observer-dependent and systematic errors were mainly due to selected imaging protocols and/or measurement method. In the animal study, there were additional systematic errors attributed to ellipsoidal assumption for tumor shape. The systematic errors measured using the QA phantom need to be taken into account to reduce measurement

Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager

Science.gov (United States)

Keymeulen, Didlier; Aranki, Nazeeh I.; Klimesh, Matthew A.; Bakhshi, Alireza

2012-01-01

Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx

Inter frame motion estimation and its application to image sequence compression: an introduction

International Nuclear Information System (INIS)

Cremy, C.

1996-01-01

With the constant development of new communication technologies like, digital TV, teleconference, and the development of image analysis applications, there is a growing volume of data to manage. Compression techniques are required for the transmission and storage of these data. Dealing with original images would require the use of expansive high bandwidth communication devices and huge storage media. Image sequence compression can be achieved by means of interframe estimation that consists in retrieving redundant information relative to zones where there is little motion between two frames. This paper is an introduction to some motion estimation techniques like gradient techniques, pel-recursive, block-matching, and its application to image sequence compression. (Author) 17 refs

Endoscopic tri-modal imaging for detection of early neoplasia in Barrett's oesophagus: a multi-centre feasibility study using high-resolution endoscopy, autofluorescence imaging and narrow band imaging incorporated in one endoscopy system

NARCIS (Netherlands)

Curvers, W. L.; Singh, R.; Song, L.-M. Wong-Kee; Wolfsen, H. C.; Ragunath, K.; Wang, K.; Wallace, M. B.; Fockens, P.; Bergman, J. J. G. H. M.

2008-01-01

OBJECTIVE: To investigate the diagnostic potential of endoscopic tri-modal imaging and the relative contribution of each imaging modality (i.e. high-resolution endoscopy (HRE), autofluorescence imaging (AFI) and narrow-band imaging (NBI)) for the detection of early neoplasia in Barrett's oesophagus.

Differential diagnosis of benign and malignant vertebral compression fractures with MR imaging

International Nuclear Information System (INIS)

Staebler, A.; Krimmel, K.; Seiderer, M.; Gaertner, C.; Fritsch, S.; Raum, W.

1992-01-01

42 patients with known malignancy and vertebral compressions underwent MRI. Sagittal T 1 -weighted spin-echo images pre and post Gd-DTPA, out of phase long TR gradient-echo images (GE) and short T 1 inversion recovery images (STIR) were obtained at 1.0 T. In 39 of 42 cases a correct differentiation between osteoporotic and tumorous vertebral compression fractures was possible by quantification and correlation of SE and GE signal intensities. Gd-DTPA did not improve differential diagnosis, since both tumour infiltration and bone marrow oedema in acute compression fracture showed comparable enhancement. STIR-sequences were most sensitive for pathology but unspecific due to a comparable amount of water in tumour tissue and bone marrow oedema. Susceptibility-induced signal reduction in GE images and morphologic criteria proved to be most reliable for differentiation of benign and tumour-related fractures. (orig./GDG) [de

A new approach of objective quality evaluation on JPEG2000 lossy-compressed lung cancer CT images

Science.gov (United States)

Cai, Weihua; Tan, Yongqiang; Zhang, Jianguo

2007-03-01

Image compression has been used to increase the communication efficiency and storage capacity. JPEG 2000 compression, based on the wavelet transformation, has its advantages comparing to other compression methods, such as ROI coding, error resilience, adaptive binary arithmetic coding and embedded bit-stream. However it is still difficult to find an objective method to evaluate the image quality of lossy-compressed medical images so far. In this paper, we present an approach to evaluate the image quality by using a computer aided diagnosis (CAD) system. We selected 77 cases of CT images, bearing benign and malignant lung nodules with confirmed pathology, from our clinical Picture Archiving and Communication System (PACS). We have developed a prototype of CAD system to classify these images into benign ones and malignant ones, the performance of which was evaluated by the receiver operator characteristics (ROC) curves. We first used JPEG 2000 to compress these cases of images with different compression ratio from lossless to lossy, and used the CAD system to classify the cases with different compressed ratio, then compared the ROC curves from the CAD classification results. Support vector machine (SVM) and neural networks (NN) were used to classify the malignancy of input nodules. In each approach, we found that the area under ROC (AUC) decreases with the increment of compression ratio with small fluctuations.

Correspondence normalized ghost imaging on compressive sensing

International Nuclear Information System (INIS)

Zhao Sheng-Mei; Zhuang Peng

2014-01-01

Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale ''double-slit'' image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale ''lena'' object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale “double-slit'' object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

Dual-Modality PET/Ultrasound imaging of the Prostate

Energy Technology Data Exchange (ETDEWEB)

Huber, Jennifer S.; Moses, William W.; Pouliot, Jean; Hsu, I.C.

2005-11-11

Functional imaging with positron emission tomography (PET)will detect malignant tumors in the prostate and/or prostate bed, as well as possibly help determine tumor ''aggressiveness''. However, the relative uptake in a prostate tumor can be so great that few other anatomical landmarks are visible in a PET image. Ultrasound imaging with a transrectal probe provides anatomical detail in the prostate region that can be co-registered with the sensitive functional information from the PET imaging. Imaging the prostate with both PET and transrectal ultrasound (TRUS) will help determine the location of any cancer within the prostate region. This dual-modality imaging should help provide better detection and treatment of prostate cancer. LBNL has built a high performance positron emission tomograph optimized to image the prostate.Compared to a standard whole-body PET camera, our prostate-optimized PET camera has the same sensitivity and resolution, less backgrounds and lower cost. We plan to develop the hardware and software tools needed for a validated dual PET/TRUS prostate imaging system. We also plan to develop dual prostate imaging with PET and external transabdominal ultrasound, in case the TRUS system is too uncomfortable for some patients. We present the design and intended clinical uses for these dual imaging systems.

Dual-Modality PET/Ultrasound imaging of the Prostate

International Nuclear Information System (INIS)

Huber, Jennifer S.; Moses, William W.; Pouliot, Jean; Hsu, I.C.

2005-01-01

Functional imaging with positron emission tomography (PET)will detect malignant tumors in the prostate and/or prostate bed, as well as possibly help determine tumor ''aggressiveness''. However, the relative uptake in a prostate tumor can be so great that few other anatomical landmarks are visible in a PET image. Ultrasound imaging with a transrectal probe provides anatomical detail in the prostate region that can be co-registered with the sensitive functional information from the PET imaging. Imaging the prostate with both PET and transrectal ultrasound (TRUS) will help determine the location of any cancer within the prostate region. This dual-modality imaging should help provide better detection and treatment of prostate cancer. LBNL has built a high performance positron emission tomograph optimized to image the prostate.Compared to a standard whole-body PET camera, our prostate-optimized PET camera has the same sensitivity and resolution, less backgrounds and lower cost. We plan to develop the hardware and software tools needed for a validated dual PET/TRUS prostate imaging system. We also plan to develop dual prostate imaging with PET and external transabdominal ultrasound, in case the TRUS system is too uncomfortable for some patients. We present the design and intended clinical uses for these dual imaging systems

VLSI ARCHITECTURE FOR IMAGE COMPRESSION THROUGH ADDER MINIMIZATION TECHNIQUE AT DCT STRUCTURE

Directory of Open Access Journals (Sweden)

N.R. Divya

2014-08-01

Full Text Available Data compression plays a vital role in multimedia devices to present the information in a succinct frame. Initially, the DCT structure is used for Image compression, which has lesser complexity and area efficient. Similarly, 2D DCT also has provided reasonable data compression, but implementation concern, it calls more multipliers and adders thus its lead to acquire more area and high power consumption. To contain an account of all, this paper has been dealt with VLSI architecture for image compression using Rom free DA based DCT (Discrete Cosine Transform structure. This technique provides high-throughput and most suitable for real-time implementation. In order to achieve this image matrix is subdivided into odd and even terms then the multiplication functions are removed by shift and add approach. Kogge_Stone_Adder techniques are proposed for obtaining a bit-wise image quality which determines the new trade-off levels as compared to the previous techniques. Overall the proposed architecture produces reduced memory, low power consumption and high throughput. MATLAB is used as a funding tool for receiving an input pixel and obtaining output image. Verilog HDL is used for implementing the design, Model Sim for simulation, Quatres II is used to synthesize and obtain details about power and area.

Simultaneous optical image compression and encryption using error-reduction phase retrieval algorithm

International Nuclear Information System (INIS)

Liu, Wei; Liu, Shutian; Liu, Zhengjun

2015-01-01

We report a simultaneous image compression and encryption scheme based on solving a typical optical inverse problem. The secret images to be processed are multiplexed as the input intensities of a cascaded diffractive optical system. At the output plane, a compressed complex-valued data with a lot fewer measurements can be obtained by utilizing error-reduction phase retrieval algorithm. The magnitude of the output image can serve as the final ciphertext while its phase serves as the decryption key. Therefore the compression and encryption are simultaneously completed without additional encoding and filtering operations. The proposed strategy can be straightforwardly applied to the existing optical security systems that involve diffraction and interference. Numerical simulations are performed to demonstrate the validity and security of the proposal. (paper)

Modality prediction of biomedical literature images using multimodal feature representation

Directory of Open Access Journals (Sweden)

Pelka, Obioma

2016-08-01

Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

Integration of vibro-acoustography imaging modality with the traditional mammography.

Science.gov (United States)

Hosseini, H Gholam; Alizad, A; Fatemi, M

2007-01-01

Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Integrating unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we establish correspondence between the VA images and traditional X-ray mammogram by adopting a flexible control-point selection technique for image registration. A modified second-order polynomial, which simply leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the X-ray mammography with a registration error of less than 1.65 mm. The fused image is defined as a linear integration of the VA and X-ray images. Moreover, a color-based fusion technique was employed to integrate the images for better visualization of structural information.

Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application

Science.gov (United States)

Ouyang, Bing; Hou, Weilin; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.; Gong, Cuiling

2017-07-01

The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.

Multi-scale simulations of field ion microscopy images—Image compression with and without the tip shank

International Nuclear Information System (INIS)

NiewieczerzaŁ, Daniel; Oleksy, CzesŁaw; Szczepkowicz, Andrzej

2012-01-01

Multi-scale simulations of field ion microscopy images of faceted and hemispherical samples are performed using a 3D model. It is shown that faceted crystals have compressed images even in cases with no shank. The presence of the shank increases the compression of images of faceted crystals quantitatively in the same way as for hemispherical samples. It is hereby proven that the shank does not influence significantly the local, relative variations of the magnification caused by the atomic-scale structure of the sample. -- Highlights: ► Multi-scale simulations of field ion microscopy images. ► Faceted and hemispherical samples with and without shank. ► Shank causes overall compression, but does not influence local magnification effects. ► Image compression linearly increases with the shank angle. ► Shank changes compression of image of faceted tip in the same way as for smooth sample.

Local System Matrix Compression for Efficient Reconstruction in Magnetic Particle Imaging

Directory of Open Access Journals (Sweden)

T. Knopp

2015-01-01

Full Text Available Magnetic particle imaging (MPI is a quantitative method for determining the spatial distribution of magnetic nanoparticles, which can be used as tracers for cardiovascular imaging. For reconstructing a spatial map of the particle distribution, the system matrix describing the magnetic particle imaging equation has to be known. Due to the complex dynamic behavior of the magnetic particles, the system matrix is commonly measured in a calibration procedure. In order to speed up the reconstruction process, recently, a matrix compression technique has been proposed that makes use of a basis transformation in order to compress the MPI system matrix. By thresholding the resulting matrix and storing the remaining entries in compressed row storage format, only a fraction of the data has to be processed when reconstructing the particle distribution. In the present work, it is shown that the image quality of the algorithm can be considerably improved by using a local threshold for each matrix row instead of a global threshold for the entire system matrix.

Extended feature-fusion guidelines to improve image-based multi-modal biometrics

CSIR Research Space (South Africa)

Brown, Dane

2016-09-01

Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images

Directory of Open Access Journals (Sweden)

Christian Schou Oxvig

2014-10-01

Full Text Available Magni is an open source Python package that embraces compressed sensing and Atomic Force Microscopy (AFM imaging techniques. It provides AFM-specific functionality for undersampling and reconstructing images from AFM equipment and thereby accelerating the acquisition of AFM images. Magni also provides researchers in compressed sensing with a selection of algorithms for reconstructing undersampled general images, and offers a consistent and rigorous way to efficiently evaluate the researchers own developed reconstruction algorithms in terms of phase transitions. The package also serves as a convenient platform for researchers in compressed sensing aiming at obtaining a high degree of reproducibility of their research.

A review of imaging modalities in pulmonary hypertension

Directory of Open Access Journals (Sweden)

Mona Ascha

2017-01-01

Full Text Available Pulmonary hypertension (PH is defined as resting mean pulmonary artery pressure ≥25 mmHg measured by right heart catheterization. PH is a progressive, life-threatening disease with a variety of etiologies. Swift and accurate diagnosis of PH and appropriate classification in etiologic group will allow for earlier treatment and improved outcomes. A number of imaging tools are utilized in the evaluation of PH, such as chest X-ray, computed tomography (CT, ventilation/perfusion (V/Q scan, and cardiac magnetic resonance imaging. Newer imaging tools such as dual-energy CT and single-photon emission computed tomography/computed tomography V/Q scanning have also emerged; however, their place in the diagnostic evaluation of PH remains to be determined. In general, each imaging technique provides incremental information, with varying degrees of sensitivity and specificity, which helps suspect the presence and identify the etiology of PH. The present study aims to provide a comprehensive review of the utility, advantages, and shortcomings of the imaging modalities that may be used to evaluate patients with PH.

Improved proton computed tomography by dual modality image reconstruction

Energy Technology Data Exchange (ETDEWEB)

Hansen, David C., E-mail: dch@ki.au.dk; Bassler, Niels [Experimental Clinical Oncology, Aarhus University, 8000 Aarhus C (Denmark); Petersen, Jørgen Breede Baltzer [Medical Physics, Aarhus University Hospital, 8000 Aarhus C (Denmark); Sørensen, Thomas Sangild [Computer Science, Aarhus University, 8000 Aarhus C, Denmark and Clinical Medicine, Aarhus University, 8200 Aarhus N (Denmark)

2014-03-15

Purpose: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full 360° rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. Methods: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was created in the Monte Carlo code Geant4 and a 360° proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained nonlinear conjugate gradient algorithm, minimizing total variation and the x-ray CT prior while remaining consistent with the proton projection data. The proton histories were reconstructed along curved cubic-spline paths. Results: The spatial resolution of the cone beam CT prior was retained for the fully sampled case and the 90° interval case, with the MTF = 0.5 (modulation transfer function) ranging from 5.22 to 5.65 linepairs/cm. In the 45° interval case, the MTF = 0.5 dropped to 3.91 linepairs/cm For the fully sampled DMR, the maximal root mean square (RMS) error was 0.006 in units of relative stopping power. For the limited angle cases the maximal RMS error was 0.18, an almost five-fold improvement over the cone beam CT estimate. Conclusions: Dual modality reconstruction yields the high spatial resolution of cone beam x-ray CT while maintaining the improved stopping power estimation of proton CT. In the case of limited angles, the use of prior image proton CT greatly improves the resolution and stopping power estimate, but does not fully achieve the quality of a 360�

Improved proton computed tomography by dual modality image reconstruction

International Nuclear Information System (INIS)

Hansen, David C.; Bassler, Niels; Petersen, Jørgen Breede Baltzer; Sørensen, Thomas Sangild

2014-01-01

Purpose: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full 360° rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. Methods: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was created in the Monte Carlo code Geant4 and a 360° proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained nonlinear conjugate gradient algorithm, minimizing total variation and the x-ray CT prior while remaining consistent with the proton projection data. The proton histories were reconstructed along curved cubic-spline paths. Results: The spatial resolution of the cone beam CT prior was retained for the fully sampled case and the 90° interval case, with the MTF = 0.5 (modulation transfer function) ranging from 5.22 to 5.65 linepairs/cm. In the 45° interval case, the MTF = 0.5 dropped to 3.91 linepairs/cm For the fully sampled DMR, the maximal root mean square (RMS) error was 0.006 in units of relative stopping power. For the limited angle cases the maximal RMS error was 0.18, an almost five-fold improvement over the cone beam CT estimate. Conclusions: Dual modality reconstruction yields the high spatial resolution of cone beam x-ray CT while maintaining the improved stopping power estimation of proton CT. In the case of limited angles, the use of prior image proton CT greatly improves the resolution and stopping power estimate, but does not fully achieve the quality of a 360�

Faster tissue interface analysis from Raman microscopy images using compressed factorisation

Science.gov (United States)

Palmer, Andrew D.; Bannerman, Alistair; Grover, Liam; Styles, Iain B.

2013-06-01

The structure of an artificial ligament was examined using Raman microscopy in combination with novel data analysis. Basis approximation and compressed principal component analysis are shown to provide efficient compression of confocal Raman microscopy images, alongside powerful methods for unsupervised analysis. This scheme allows the acceleration of data mining, such as principal component analysis, as they can be performed on the compressed data representation, providing a decrease in the factorisation time of a single image from five minutes to under a second. Using this workflow the interface region between a chemically engineered ligament construct and a bone-mimic anchor was examined. Natural ligament contains a striated interface between the bone and tissue that provides improved mechanical load tolerance, a similar interface was found in the ligament construct.

Efficient JPEG 2000 Image Compression Scheme for Multihop Wireless Networks

Directory of Open Access Journals (Sweden)

Halim Sghaier

2011-08-01

Full Text Available When using wireless sensor networks for real-time data transmission, some critical points should be considered. Restricted computational power, reduced memory, narrow bandwidth and energy supplied present strong limits in sensor nodes. Therefore, maximizing network lifetime and minimizing energy consumption are always optimization goals. To overcome the computation and energy limitation of individual sensor nodes during image transmission, an energy efficient image transport scheme is proposed, taking advantage of JPEG2000 still image compression standard using MATLAB and C from Jasper. JPEG2000 provides a practical set of features, not necessarily available in the previous standards. These features were achieved using techniques: the discrete wavelet transform (DWT, and embedded block coding with optimized truncation (EBCOT. Performance of the proposed image transport scheme is investigated with respect to image quality and energy consumption. Simulation results are presented and show that the proposed scheme optimizes network lifetime and reduces significantly the amount of required memory by analyzing the functional influence of each parameter of this distributed image compression algorithm.

The guarantee of the consistency of patient information through modality worklist among PACS, RIS and imaging equipments

International Nuclear Information System (INIS)

Luo Min; Peng Chenglin; Wang Xiaolin; Luo Song; Lei Wenyong; Wang Kang; Wang Xuejian; Wen Hongyu; Wu Hongxing

2003-01-01

Objective: To realize the consistency of patient information through modality worklist among picture archiving and communication system (PACS), radiology information systems (RIS), and imaging equipments. Methods: Many digital modalities including GE Signa 1.5 T MR system, digital mammography, Agfa digital radiography, computed radiography, GE CT, and etc were installed in our hospital. Since the GE PACS system was in English version, the images were saved and the information was managed to PACS without a truly worklist. Two months later, while still using the previous PACS, the consistency of patient information was resolved through modality worklist by adopting the international advanced approach to realize thread worklist of digital imaging and communication in medicine. The information in Chinese will be transformed into information in English in RIS system and saved as the English information to worklist. Results: After the implementation and integration of PACS and RIS was achieved in my hospital, the consistency of patient information was guaranteed through modality worklist between RIS and imaging equipments and the first-rate effect was acquired. The patient information could be checked, edited, and created by using the Chinese RIS system on all diagnostic workstations. Conclusion: The consistency of patient information through modality worklist was realized among PACS, RIS, and imaging equipments, yet at present, the question of guarantee the consistency of patient information must be thought over in PACS building

Detection of relationships among multi-modal brain imaging meta-features via information flow.

Science.gov (United States)

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is

PET image reconstruction with rotationally symmetric polygonal pixel grid based highly compressible system matrix

International Nuclear Information System (INIS)

Yu Yunhan; Xia Yan; Liu Yaqiang; Wang Shi; Ma Tianyu; Chen Jing; Hong Baoyu

2013-01-01

To achieve a maximum compression of system matrix in positron emission tomography (PET) image reconstruction, we proposed a polygonal image pixel division strategy in accordance with rotationally symmetric PET geometry. Geometrical definition and indexing rule for polygonal pixels were established. Image conversion from polygonal pixel structure to conventional rectangular pixel structure was implemented using a conversion matrix. A set of test images were analytically defined in polygonal pixel structure, converted to conventional rectangular pixel based images, and correctly displayed which verified the correctness of the image definition, conversion description and conversion of polygonal pixel structure. A compressed system matrix for PET image recon was generated by tap model and tested by forward-projecting three different distributions of radioactive sources to the sinogram domain and comparing them with theoretical predictions. On a practical small animal PET scanner, a compress ratio of 12.6:1 of the system matrix size was achieved with the polygonal pixel structure, comparing with the conventional rectangular pixel based tap-mode one. OS-EM iterative image reconstruction algorithms with the polygonal and conventional Cartesian pixel grid were developed. A hot rod phantom was detected and reconstructed based on these two grids with reasonable time cost. Image resolution of reconstructed images was both 1.35 mm. We conclude that it is feasible to reconstruct and display images in a polygonal image pixel structure based on a compressed system matrix in PET image reconstruction. (authors)

Image Compression using Haar and Modified Haar Wavelet Transform

Directory of Open Access Journals (Sweden)

Mohannad Abid Shehab Ahmed

2013-04-01

Full Text Available Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and horizontal Haar filters are composed to construct four 2-dimensional filters, such filters applied directly to the image to speed up the implementation of the Haar wavelet transform. Modified Haar technique is studied and implemented for odd based numbers i.e. (N=3 & N=5 to generate many solution sets, these sets are tested using the energy function or numerical method to get the optimum one.The Haar transform is simple, efficient in memory usage due to high zero value spread (it can use sparse principle, and exactly reversible without the edge effects as compared to DCT (Discrete Cosine Transform. The implemented Matlab simulation results prove the effectiveness of DWT (Discrete Wave Transform algorithms based on Haar and Modified Haar techniques in attaining an efficient compression ratio (C.R, achieving higher peak signal to noise ratio (PSNR, and the resulting images are of much smoother as compared to standard JPEG especially for high C.R. A comparison between standard JPEG, Haar, and Modified Haar techniques is done finally which approves the highest capability of Modified Haar between others.

PROMISE: parallel-imaging and compressed-sensing reconstruction of multicontrast imaging using SharablE information.

Science.gov (United States)

Gong, Enhao; Huang, Feng; Ying, Kui; Wu, Wenchuan; Wang, Shi; Yuan, Chun

2015-02-01

A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS-PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. The target of this work is to improve existing CS-PPI methods for multicontrast imaging, especially for two-dimensional imaging. If the same field of view is scanned in multicontrast imaging, there is significant amount of sharable information. It is proposed in this study to use manifold sharable information among multicontrast images to enhance CS-PPI in a sequential way. Coil sensitivity information and structure based adaptive regularization, which were extracted from previously reconstructed images, were applied to enhance the following reconstructions. The proposed method is called Parallel-imaging and compressed-sensing Reconstruction Of Multicontrast Imaging using SharablE information (PROMISE). Using L1 -SPIRiT as a CS-PPI example, results on multicontrast brain and carotid scans demonstrated that lower error level and better detail preservation can be achieved by exploiting manifold sharable information. Besides, the privilege of PROMISE still exists while there is interscan motion. Using the sharable information among multicontrast images can enhance CS-PPI with tolerance to motions. © 2014 Wiley Periodicals, Inc.

Compressive Sampling for Non-Imaging Remote Classification

Science.gov (United States)

2013-10-22

spectro -­‐polarization  imager,   a  compressive  coherence  imager  to  resolve  objects  through  turbulence...2    The  relay  lens  for   UV -­‐CASSI,  which  focuses  the  aperture  code  onto  the  monochrome  detector...below  in  Fig.  3,  with  a  silicon   UV  sensitive  detector  on  the  left,  and   a   UV

An compression algorithm for medical images and a display with the decoding function

International Nuclear Information System (INIS)

Gotoh, Toshiyuki; Nakagawa, Yukihiro; Shiohara, Morito; Yoshida, Masumi

1990-01-01

This paper describes and efficient image compression method for medical images, a high-speed display with the decoding function. In our method, an input image is divided into blocks, and either of Discrete Cosine Transform coding (DCT) or Block Truncation Coding (BTC) is adaptively applied on each block to improve image quality. The display, we developed, receives the compressed data from the host computer and reconstruct images of good quality at high speed using four decoding microprocessors on which our algorithm is implemented in pipeline. By the experiments, our method and display were verified to be effective. (author)

Magni: A Python Package for Compressive Sampling and Reconstruction of Atomic Force Microscopy Images

DEFF Research Database (Denmark)

Oxvig, Christian Schou; Pedersen, Patrick Steffen; Arildsen, Thomas

2014-01-01

Magni is an open source Python package that embraces compressed sensing and Atomic Force Microscopy (AFM) imaging techniques. It provides AFM-specific functionality for undersampling and reconstructing images from AFM equipment and thereby accelerating the acquisition of AFM images. Magni also pr...... as a convenient platform for researchers in compressed sensing aiming at obtaining a high degree of reproducibility of their research....

Clinical evaluation of the JPEG2000 compression rate of CT and MR images for long term archiving in PACS

International Nuclear Information System (INIS)

Cha, Soon Joo; Kim, Sung Hwan; Kim, Yong Hoon

2006-01-01

We wanted to evaluate an acceptable compression rate of JPEG2000 for long term archiving of CT and MR images in PACS. Nine CT images and 9 MR images that had small or minimal lesions were randomly selected from the PACS at our institute. All the images are compressed with rates of 5:1, 10:1, 20:1, 40:1 and 80:1 by the JPEG2000 compression protocol. Pairs of original and compressed images were compared by 9 radiologists who were working independently. We designed a JPEG2000 viewing program for comparing two images on one monitor system for performing easy and quick evaluation. All the observers performed the comparison study twice on 5 mega pixel grey scale LCD monitors and 2 mega pixel color LCD monitors, respectively. The PSNR (Peak Signal to Noise Ratio) values were calculated for making quantitative comparisions. On MR and CT, all the images with 5:1 compression images showed no difference from the original images by all 9 observers and only one observer could detect a image difference on one CT image for 10:1 compression on only the 5 mega pixel monitor. For the 20:1 compression rate, clinically significant image deterioration was found in 50% of the images on the 5M pixel monitor study, and in 30% of the images on the 2M pixel monitor. PSNR values larger than 44 dB were calculated for all the compressed images. The clinically acceptable image compression rate for long term archiving by the JPEG2000 compression protocol is 10:1 for MR and CT, and if this is applied to PACS, it would reduce the cost and responsibility of the system

Edge-based compression of cartoon-like images with homogeneous diffusion

DEFF Research Database (Denmark)

Mainberger, Markus; Bruhn, Andrés; Weickert, Joachim

2011-01-01

Edges provide semantically important image features. In this paper a lossy compression method for cartoon-like images is presented, which is based on edge information. Edges together with some adjacent grey/colour values are extracted and encoded using a classical edge detector, binary compressio...

Multiview Depth-Image Compression Using an Extended H.264 Encoder

NARCIS (Netherlands)

Morvan, Y.; Farin, D.S.; With, de P.H.N.; Blanc-Talon, J.; Philips, W.

2007-01-01

This paper presents a predictive-coding algorithm for the compression of multiple depth-sequences obtained from a multi-camera acquisition setup. The proposed depth-prediction algorithm works by synthesizing a virtual depth-image that matches the depth-image (of the predicted camera). To generate

IMPROVED COMPRESSION OF XML FILES FOR FAST IMAGE TRANSMISSION

Directory of Open Access Journals (Sweden)

S. Manimurugan

2011-02-01

Full Text Available The eXtensible Markup Language (XML is a format that is widely used as a tool for data exchange and storage. It is being increasingly used in secure transmission of image data over wireless network and World Wide Web. Verbose in nature, XML files can be tens of megabytes long. Thus, to reduce their size and to allow faster transmission, compression becomes vital. Several general purpose compression tools have been proposed without satisfactory results. This paper proposes a novel technique using modified BWT for compressing XML files in a lossless fashion. The experimental results show that the performance of the proposed technique outperforms both general purpose and XML-specific compressors.

Efficient Imaging and Real-Time Display of Scanning Ion Conductance Microscopy Based on Block Compressive Sensing

Science.gov (United States)

Li, Gongxin; Li, Peng; Wang, Yuechao; Wang, Wenxue; Xi, Ning; Liu, Lianqing

2014-07-01

Scanning Ion Conductance Microscopy (SICM) is one kind of Scanning Probe Microscopies (SPMs), and it is widely used in imaging soft samples for many distinctive advantages. However, the scanning speed of SICM is much slower than other SPMs. Compressive sensing (CS) could improve scanning speed tremendously by breaking through the Shannon sampling theorem, but it still requires too much time in image reconstruction. Block compressive sensing can be applied to SICM imaging to further reduce the reconstruction time of sparse signals, and it has another unique application that it can achieve the function of image real-time display in SICM imaging. In this article, a new method of dividing blocks and a new matrix arithmetic operation were proposed to build the block compressive sensing model, and several experiments were carried out to verify the superiority of block compressive sensing in reducing imaging time and real-time display in SICM imaging.

USING H.264/AVC-INTRA FOR DCT BASED SEGMENTATION DRIVEN COMPOUND IMAGE COMPRESSION

Directory of Open Access Journals (Sweden)

S. Ebenezer Juliet

2011-08-01

Full Text Available This paper presents a one pass block classification algorithm for efficient coding of compound images which consists of multimedia elements like text, graphics and natural images. The objective is to minimize the loss of visual quality of text during compression by separating text information which needs high special resolution than the pictures and background. It segments computer screen images into text/graphics and picture/background classes based on DCT energy in each 4x4 block, and then compresses both text/graphics pixels and picture/background blocks by H.264/AVC with variable quantization parameter. Experimental results show that the single H.264/AVC-INTRA coder with variable quantization outperforms single coders such as JPEG, JPEG-2000 for compound images. Also the proposed method improves the PSNR value significantly than standard JPEG, JPEG-2000 and while keeping competitive compression ratios.

REMOTELY SENSEDC IMAGE COMPRESSION BASED ON WAVELET TRANSFORM

Directory of Open Access Journals (Sweden)

Heung K. Lee

1996-06-01

Full Text Available In this paper, we present an image compression algorithm that is capable of significantly reducing the vast mount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet trans-form to remove the spatial redundancy. The transformed images are than encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with KITSAT-1 image as well as the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by peak signal to noise ratio (PSNR and classification capability.

Compressed sensing in imaging mass spectrometry

International Nuclear Information System (INIS)

Bartels, Andreas; Dülk, Patrick; Trede, Dennis; Alexandrov, Theodore; Maaß, Peter

2013-01-01

Imaging mass spectrometry (IMS) is a technique of analytical chemistry for spatially resolved, label-free and multipurpose analysis of biological samples that is able to detect the spatial distribution of hundreds of molecules in one experiment. The hyperspectral IMS data is typically generated by a mass spectrometer analyzing the surface of the sample. In this paper, we propose a compressed sensing approach to IMS which potentially allows for faster data acquisition by collecting only a part of the pixels in the hyperspectral image and reconstructing the full image from this data. We present an integrative approach to perform both peak-picking spectra and denoising m/z-images simultaneously, whereas the state of the art data analysis methods solve these problems separately. We provide a proof of the robustness of the recovery of both the spectra and individual channels of the hyperspectral image and propose an algorithm to solve our optimization problem which is based on proximal mappings. The paper concludes with the numerical reconstruction results for an IMS dataset of a rat brain coronal section. (paper)

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

Science.gov (United States)

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

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Efficient reconfigurable architectures for 3D medical image compression

OpenAIRE

Afandi, Ahmad

2010-01-01

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University. Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In thes...

Medical Students’ Knowledge of Indications for Imaging Modalities and Cost Analysis of Incorrect Requests, Shiraz, Iran 2011-2012

Directory of Open Access Journals (Sweden)

Parisa Islami Parkoohi

2015-05-01

Full Text Available Medical imaging has a remarkable role in the practice of clinical medicine. This study intends to evaluate the knowledge of indications of five common medical imaging modalities and estimation of the imposed cost of their non-indicated requests among medical students who attend Shiraz University of Medical Sciences, Shiraz, Iran. We conducted across-sectional survey using a self-administered questionnaire to assess the knowledge of indications of a number of medical imaging modalities among 270 medical students during their externship or internship periods. Knowledge scoring was performed according to a descriptive international grade conversion (fail to excellent using Iranian academic grading (0 to 20. In addition, we estimated the cost for incorrect selection of those modalities according to public and private tariffs in US dollars. The participation and response rate was 200/270 (74%. The mean knowledge score was fair for all modalities. Similar scores were excellent for X-ray, acceptable for Doppler ultrasonography, and fair for ultrasonography, CT scan and MRI. The total cost for non-indicated requests of those modalities equaled $104303 (public tariff and $205581 (private tariff. Medical students at Shiraz University of Medical Sciences lacked favorable knowledge about indications for common medical imaging modalities. The results of this study have shown a significant cost for non-indicated requests of medical imaging. Of note, the present radiology curriculum is in need of a major revision with regards to evidence-based radiology and health economy concerns.

Medical students' knowledge of indications for imaging modalities and cost analysis of incorrect requests, shiraz, iran 2011-2012.

Science.gov (United States)

Islami Parkoohi, Parisa; Jalli, Reza; Danaei, Mina; Khajavian, Shiva; Askarian, Mehrdad

2014-05-01

Medical imaging has a remarkable role in the practice of clinical medicine. This study intends to evaluate the knowledge of indications of five common medical imaging modalities and estimation of the imposed cost of their non-indicated requests among medical students who attend Shiraz University of Medical Sciences, Shiraz, Iran. We conducted across-sectional survey using a self-administered questionnaire to assess the knowledge of indications of a number of medical imaging modalities among 270 medical students during their externship or internship periods. Knowledge scoring was performed according to a descriptive international grade conversion (fail to excellent) using Iranian academic grading (0 to 20). In addition, we estimated the cost for incorrect selection of those modalities according to public and private tariffs in US dollars. The participation and response rate was 200/270 (74%). The mean knowledge score was fair for all modalities. Similar scores were excellent for X-ray, acceptable for Doppler ultrasonography, and fair for ultrasonography, CT scan and MRI. The total cost for non-indicated requests of those modalities equaled $104303 (public tariff) and $205581 (private tariff). Medical students at Shiraz University of Medical Sciences lacked favorable knowledge about indications for common medical imaging modalities. The results of this study have shown a significant cost for non-indicated requests of medical imaging. Of note, the present radiology curriculum is in need of a major revision with regards to evidence-based radiology and health economy concerns.

Single-photon compressive imaging with some performance benefits over raster scanning

International Nuclear Information System (INIS)

Yu, Wen-Kai; Liu, Xue-Feng; Yao, Xu-Ri; Wang, Chao; Zhai, Guang-Jie; Zhao, Qing

2014-01-01

A single-photon imaging system based on compressed sensing has been developed to image objects under ultra-low illumination. With this system, we have successfully realized imaging at the single-photon level with a single-pixel avalanche photodiode without point-by-point raster scanning. From analysis of the signal-to-noise ratio in the measurement we find that our system has much higher sensitivity than conventional ones based on point-by-point raster scanning, while the measurement time is also reduced. - Highlights: • We design a single photon imaging system with compressed sensing. • A single point avalanche photodiode is used without raster scanning. • The Poisson shot noise in the measurement is analyzed. • The sensitivity of our system is proved to be higher than that of raster scanning

Edge-Based Image Compression with Homogeneous Diffusion

Science.gov (United States)

Mainberger, Markus; Weickert, Joachim

It is well-known that edges contain semantically important image information. In this paper we present a lossy compression method for cartoon-like images that exploits information at image edges. These edges are extracted with the Marr-Hildreth operator followed by hysteresis thresholding. Their locations are stored in a lossless way using JBIG. Moreover, we encode the grey or colour values at both sides of each edge by applying quantisation, subsampling and PAQ coding. In the decoding step, information outside these encoded data is recovered by solving the Laplace equation, i.e. we inpaint with the steady state of a homogeneous diffusion process. Our experiments show that the suggested method outperforms the widely-used JPEG standard and can even beat the advanced JPEG2000 standard for cartoon-like images.

Medical image compression based on vector quantization with variable block sizes in wavelet domain.

Science.gov (United States)

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

A highly sensitive x-ray imaging modality for hepatocellular carcinoma detection in vitro

Science.gov (United States)

Rand, Danielle; Walsh, Edward G.; Derdak, Zoltan; Wands, Jack R.; Rose-Petruck, Christoph

2015-01-01

Innovations that improve sensitivity and reduce cost are of paramount importance in diagnostic imaging. The novel x-ray imaging modality called spatial frequency heterodyne imaging (SFHI) is based on a linear arrangement of x-ray source, tissue, and x-ray detector, much like that of a conventional x-ray imaging apparatus. However, SFHI rests on a complete paradigm reversal compared to conventional x-ray absorption-based radiology: while scattered x-rays are carefully rejected in absorption-based x-ray radiology to enhance the image contrast, SFHI forms images exclusively from x-rays scattered by the tissue. In this study we use numerical processing to produce x-ray scatter images of hepatocellular carcinoma labeled with a nanoparticle contrast agent. We subsequently compare the sensitivity of SFHI in this application to that of both conventional x-ray imaging and magnetic resonance imaging (MRI). Although SFHI is still in the early stages of its development, our results show that the sensitivity of SFHI is an order of magnitude greater than that of absorption-based x-ray imaging and approximately equal to that of MRI. As x-ray imaging modalities typically have lower installation and service costs compared to MRI, SFHI could become a cost effective alternative to MRI, particularly in areas of the world with inadequate availability of MRI facilities.

vECTlab-A fully integrated multi-modality Monte Carlo simulation framework for the radiological imaging sciences

International Nuclear Information System (INIS)

Peter, Joerg; Semmler, Wolfhard

2007-01-01

Alongside and in part motivated by recent advances in molecular diagnostics, the development of dual-modality instruments for patient and dedicated small animal imaging has gained attention by diverse research groups. The desire for such systems is high not only to link molecular or functional information with the anatomical structures, but also for detecting multiple molecular events simultaneously at shorter total acquisition times. While PET and SPECT have been integrated successfully with X-ray CT, the advance of optical imaging approaches (OT) and the integration thereof into existing modalities carry a high application potential, particularly for imaging small animals. A multi-modality Monte Carlo (MC) simulation approach at present has been developed that is able to trace high-energy (keV) as well as optical (eV) photons concurrently within identical phantom representation models. We show that the involved two approaches for ray-tracing keV and eV photons can be integrated into a unique simulation framework which enables both photon classes to be propagated through various geometry models representing both phantoms and scanners. The main advantage of such integrated framework for our specific application is the investigation of novel tomographic multi-modality instrumentation intended for in vivo small animal imaging through time-resolved MC simulation upon identical phantom geometries. Design examples are provided for recently proposed SPECT-OT and PET-OT imaging systems

A new modified fast fractal image compression algorithm

DEFF Research Database (Denmark)

Salarian, Mehdi; Nadernejad, Ehsan; MiarNaimi, Hossein

2013-01-01

In this paper, a new fractal image compression algorithm is proposed, in which the time of the encoding process is considerably reduced. The algorithm exploits a domain pool reduction approach, along with the use of innovative predefined values for contrast scaling factor, S, instead of searching...

Efficient burst image compression using H.265/HEVC

Science.gov (United States)

Roodaki-Lavasani, Hoda; Lainema, Jani

2014-02-01

New imaging use cases are emerging as more powerful camera hardware is entering consumer markets. One family of such use cases is based on capturing multiple pictures instead of just one when taking a photograph. That kind of a camera operation allows e.g. selecting the most successful shot from a sequence of images, showing what happened right before or after the shot was taken or combining the shots by computational means to improve either visible characteristics of the picture (such as dynamic range or focus) or the artistic aspects of the photo (e.g. by superimposing pictures on top of each other). Considering that photographic images are typically of high resolution and quality and the fact that these kind of image bursts can consist of at least tens of individual pictures, an efficient compression algorithm is desired. However, traditional video coding approaches fail to provide the random access properties these use cases require to achieve near-instantaneous access to the pictures in the coded sequence. That feature is critical to allow users to browse the pictures in an arbitrary order or imaging algorithms to extract desired pictures from the sequence quickly. This paper proposes coding structures that provide such random access properties while achieving coding efficiency superior to existing image coders. The results indicate that using HEVC video codec with a single reference picture fixed for the whole sequence can achieve nearly as good compression as traditional IPPP coding structures. It is also shown that the selection of the reference frame can further improve the coding efficiency.

Least median of squares filtering of locally optimal point matches for compressible flow image registration

International Nuclear Information System (INIS)

Castillo, Edward; Guerrero, Thomas; Castillo, Richard; White, Benjamin; Rojo, Javier

2012-01-01

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration. (paper)

Dual-modal photoacoustic and ultrasound imaging of dental implants

Science.gov (United States)

Lee, Donghyun; Park, Sungjo; Kim, Chulhong

2018-02-01

Dental implants are common method to replace decayed or broken tooth. As the implant treatment procedures varies according to the patients' jawbone, bone ridge, and sinus structure, appropriate examinations are necessary for successful treatment. Currently, radiographic examinations including periapical radiology, panoramic X-ray, and computed tomography are commonly used for diagnosing and monitoring. However, these radiographic examinations have limitations in that patients and operators are exposed to radioactivity and multiple examinations are performed during the treatment. In this study, we demonstrated photoacoustic (PA) and ultrasound (US) combined imaging of dental implant that can lower the total amount of absorbed radiation dose in dental implant treatment. An acoustic resolution PA macroscopy and a clinical PA/US system was used for dental implant imaging. The acquired dual modal PA/US imaging results support that the proposed photoacoustic imaging strategy can reduce the radiation dose rate during dental implant treatment.

Diffusion-Weighted Imaging for Predicting New Compression Fractures Following Percutaneous Vertebroplasty

International Nuclear Information System (INIS)

Sugimoto, T.

2008-01-01

Background: Percutaneous vertebroplasty (PVP) is a technique that structurally stabilizes a fractured vertebral body. However, some patients return to the hospital due to recurrent back pain following PVP, and such pain is sometimes caused by new compression fractures. Purpose: To investigate whether the apparent diffusion coefficient (ADC) of adjacent vertebral bodies as assessed by diffusion-weighted imaging before PVP could predict the onset of new compression fractures following PVP. Material and Methods: 25 patients with osteoporotic compression fractures who underwent PVP were enrolled in this study. ADC was measured for 49 vertebral bodies immediately above and below each vertebral body injected with bone cement before and after PVP. By measuring ADC for each adjacent vertebral body, ADC was compared between vertebral bodies with a new compression fracture within 1 month and those without new compression fractures. In addition, the mean ADC of adjacent vertebral bodies per patient was calculated. Results: Mean preoperative ADC for the six adjacent vertebral bodies with new compression fractures was 0.55x10 -3 mm 2 /s (range 0.36-1.01x10 -3 mm 2 /s), and for the 43 adjacent vertebral bodies without new compression fractures 0.20x10 -3 mm 2 /s (range 0-0.98x10 -3 mm 2 /s) (P -3 mm 2 /s (range 0.21-1.01x10 -3 mm 2 /s), and that for the 19 patients without new compression fractures 0.17x10 -3 mm 2 /s (range 0.01-0.43x10 -3 mm 2 /s) (P<0.001). Conclusion: The ADC of adjacent vertebral bodies as assessed by diffusion-weighted imaging before PVP might be one of the predictors for new compression fractures following PVP

Adaptive Binary Arithmetic Coder-Based Image Feature and Segmentation in the Compressed Domain

Directory of Open Access Journals (Sweden)

Hsi-Chin Hsin

2012-01-01

Full Text Available Image compression is necessary in various applications, especially for efficient transmission over a band-limited channel. It is thus desirable to be able to segment an image in the compressed domain directly such that the burden of decompressing computation can be avoided. Motivated by the adaptive binary arithmetic coder (MQ coder of JPEG2000, we propose an efficient scheme to segment the feature vectors that are extracted from the code stream of an image. We modify the Compression-based Texture Merging (CTM algorithm to alleviate the influence of overmerging problem by making use of the rate distortion information. Experimental results show that the MQ coder-based image segmentation is preferable in terms of the boundary displacement error (BDE measure. It has the advantage of saving computational cost as the segmentation results even at low rates of bits per pixel (bpp are satisfactory.

Fusion of different modalities of imaging the fist

International Nuclear Information System (INIS)

Verdenet, J.; Garbuio, P.; Runge, M.; Cardot, J.C.

1997-01-01

The standard radiographical pictures are not able always to bring out the fracture of one of the fist bones. In an early study it was shown that 40% of patients presenting a suspicion of fracture and in which the radio- image was normal, have had a fracture confirmed with quantification by MRI and scintigraphy. The last one does not allow to specify the localization and consequently we developed a code to fusion entirely automatically the radiologic image and the scintigraphic image using no external marker. The code has been installed on a PC and uses the Matlab environment. Starting from the histogram processing the contours are individualized on the interpolated radio- and scinti-images. For matching there are 3 freedom degrees: one of rotation and 2 of translation (in x and y axes). The internal axes of the forearm was chosen to effect the rotation and translation. The forehand thickness, identical for each modality, allows to match properly the images. We have obtained an anatomic image on which the contour and the hyper-fixating zones of the scintigraphy are added. On a set of 100 examinations we observed 38 fractures while the difference between a fracture of the scaphoid and of another fist bone is confirmed in 93% of cases

Video on the Internet: An introduction to the digital encoding, compression, and transmission of moving image data.

Science.gov (United States)

Boudier, T; Shotton, D M

1999-01-01

In this paper, we seek to provide an introduction to the fast-moving field of digital video on the Internet, from the viewpoint of the biological microscopist who might wish to store or access videos, for instance in image databases such as the BioImage Database (http://www.bioimage.org). We describe and evaluate the principal methods used for encoding and compressing moving image data for digital storage and transmission over the Internet, which involve compromises between compression efficiency and retention of image fidelity, and describe the existing alternate software technologies for downloading or streaming compressed digitized videos using a Web browser. We report the results of experiments on video microscopy recordings and three-dimensional confocal animations of biological specimens to evaluate the compression efficiencies of the principal video compression-decompression algorithms (codecs) and to document the artefacts associated with each of them. Because MPEG-1 gives very high compression while yet retaining reasonable image quality, these studies lead us to recommend that video databases should store both a high-resolution original version of each video, ideally either uncompressed or losslessly compressed, and a separate edited and highly compressed MPEG-1 preview version that can be rapidly downloaded for interactive viewing by the database user. Copyright 1999 Academic Press.

Epi-detected quadruple-modal nonlinear optical microscopy for label-free imaging of the tooth

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Wang, Zi; Zheng, Wei; Huang, Zhiwei, E-mail: biehzw@nus.edu.sg [Optical Bioimaging Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117576 (Singapore); Stephen Hsu, Chin-Ying [Department of Dentistry, Faculty of Dentistry, National University of Singapore and National University Health System, Singapore 119083 (Singapore)

2015-01-19

We present an epi-detected quadruple-modal nonlinear optical microscopic imaging technique (i.e., coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), third-harmonic generation (THG), and two-photon excited fluorescence (TPEF)) based on a picosecond (ps) laser-pumped optical parametric oscillator system for label-free imaging of the tooth. We demonstrate that high contrast ps-CARS images covering both the fingerprint (500–1800 cm{sup −1}) and high-wavenumber (2500–3800 cm{sup −1}) regions can be acquired to uncover the distributions of mineral and organic biomaterials in the tooth, while high quality TPEF, SHG, and THG images of the tooth can also be acquired under ps laser excitation without damaging the samples. The quadruple-modal nonlinear microscopic images (CARS/SHG/THG/TPEF) acquired provide better understanding of morphological structures and biochemical/biomolecular distributions in the dentin, enamel, and the dentin-enamel junction of the tooth without labeling, facilitating optical diagnosis and characterization of the tooth in dentistry.

Algorithm of imaging modalities in cases of mandibular fractures

International Nuclear Information System (INIS)

Mihailova, H.

2009-01-01

Mandibular fracture is the most common bone fracture of maxillo-facial trauma. Up to now the main method for examination of the mandible is radiography. The aim of the issue is to present an algorithm of imaging modalities for investigation of patients in cases of mandibular trauma. It consists of series of X ray techniques and views of the facial skull named mandibulo-facial. This standardizes mandibulo-facial series includes exactly determined four projections done by conventional X ray techniques: posterior-anterior view of skull (PA or AP), oblique view of the left mandible; oblique view of the right mandible; occipito-mental view. Using these four planned radiograms is obligatory for each mandibular trauma. Panoramic X-ray is obligatory in cases of apparatus availability; this abolish only oblique views (left and right). Occipito-mental view of the skull gives anatomically better the coronoid process of the mandible, the zygoma complex, the orbital edges and maxillar sinus than Waters projection. So mandibulo-facial series of four planned radiograms is not only for diagnostic of mandibular fractures, but as a screening of mandibulo-facial trauma too. Thus using algorithm of imaging modalities in cases of mandibular fracture leads to optimization of diagnostic process in patients with mandibular trauma. (author)

MO-AB-BRA-02: A Novel Scatter Imaging Modality for Real-Time Image Guidance During Lung SBRT

International Nuclear Information System (INIS)

Redler, G; Bernard, D; Templeton, A; Chu, J; Nair, C Kumaran; Turian, J

2015-01-01

Purpose: A novel scatter imaging modality is developed and its feasibility for image-guided radiation therapy (IGRT) during stereotactic body radiation therapy (SBRT) for lung cancer patients is assessed using analytic and Monte Carlo models as well as experimental testing. Methods: During treatment, incident radiation interacts and scatters from within the patient. The presented methodology forms an image of patient anatomy from the scattered radiation for real-time localization of the treatment target. A radiographic flat panel-based pinhole camera provides spatial information regarding the origin of detected scattered radiation. An analytical model is developed, which provides a mathematical formalism for describing the scatter imaging system. Experimental scatter images are acquired by irradiating an object using a Varian TrueBeam accelerator. The differentiation between tissue types is investigated by imaging simple objects of known compositions (water, lung, and cortical bone equivalent). A lung tumor phantom, simulating materials and geometry encountered during lung SBRT treatments, is fabricated and imaged to investigate image quality for various quantities of delivered radiation. Monte Carlo N-Particle (MCNP) code is used for validation and testing by simulating scatter image formation using the experimental pinhole camera setup. Results: Analytical calculations, MCNP simulations, and experimental results when imaging the water, lung, and cortical bone equivalent objects show close agreement, thus validating the proposed models and demonstrating that scatter imaging differentiates these materials well. Lung tumor phantom images have sufficient contrast-to-noise ratio (CNR) to clearly distinguish tumor from surrounding lung tissue. CNR=4.1 and CNR=29.1 for 10MU and 5000MU images (equivalent to 0.5 and 250 second images), respectively. Conclusion: Lung SBRT provides favorable treatment outcomes, but depends on accurate target localization. A comprehensive

MO-AB-BRA-02: A Novel Scatter Imaging Modality for Real-Time Image Guidance During Lung SBRT

Energy Technology Data Exchange (ETDEWEB)

Redler, G; Bernard, D; Templeton, A; Chu, J [Rush University Medical Center, Chicago, IL (United States); Nair, C Kumaran [University of Chicago, Chicago, IL (United States); Turian, J [Rush University Medical Center, Chicago, IL (United States); Rush Radiosurgery LLC, Chicago, IL (United States)

2015-06-15

Purpose: A novel scatter imaging modality is developed and its feasibility for image-guided radiation therapy (IGRT) during stereotactic body radiation therapy (SBRT) for lung cancer patients is assessed using analytic and Monte Carlo models as well as experimental testing. Methods: During treatment, incident radiation interacts and scatters from within the patient. The presented methodology forms an image of patient anatomy from the scattered radiation for real-time localization of the treatment target. A radiographic flat panel-based pinhole camera provides spatial information regarding the origin of detected scattered radiation. An analytical model is developed, which provides a mathematical formalism for describing the scatter imaging system. Experimental scatter images are acquired by irradiating an object using a Varian TrueBeam accelerator. The differentiation between tissue types is investigated by imaging simple objects of known compositions (water, lung, and cortical bone equivalent). A lung tumor phantom, simulating materials and geometry encountered during lung SBRT treatments, is fabricated and imaged to investigate image quality for various quantities of delivered radiation. Monte Carlo N-Particle (MCNP) code is used for validation and testing by simulating scatter image formation using the experimental pinhole camera setup. Results: Analytical calculations, MCNP simulations, and experimental results when imaging the water, lung, and cortical bone equivalent objects show close agreement, thus validating the proposed models and demonstrating that scatter imaging differentiates these materials well. Lung tumor phantom images have sufficient contrast-to-noise ratio (CNR) to clearly distinguish tumor from surrounding lung tissue. CNR=4.1 and CNR=29.1 for 10MU and 5000MU images (equivalent to 0.5 and 250 second images), respectively. Conclusion: Lung SBRT provides favorable treatment outcomes, but depends on accurate target localization. A comprehensive

Development of a compressive sampling hyperspectral imager prototype

Science.gov (United States)

Barducci, Alessandro; Guzzi, Donatella; Lastri, Cinzia; Nardino, Vanni; Marcoionni, Paolo; Pippi, Ivan

2013-10-01

Compressive sensing (CS) is a new technology that investigates the chance to sample signals at a lower rate than the traditional sampling theory. The main advantage of CS is that compression takes place during the sampling phase, making possible significant savings in terms of the ADC, data storage memory, down-link bandwidth, and electrical power absorption. The CS technology could have primary importance for spaceborne missions and technology, paving the way to noteworthy reductions of payload mass, volume, and cost. On the contrary, the main CS disadvantage is made by the intensive off-line data processing necessary to obtain the desired source estimation. In this paper we summarize the CS architecture and its possible implementations for Earth observation, giving evidence of possible bottlenecks hindering this technology. CS necessarily employs a multiplexing scheme, which should produce some SNR disadvantage. Moreover, this approach would necessitate optical light modulators and 2-dim detector arrays of high frame rate. This paper describes the development of a sensor prototype at laboratory level that will be utilized for the experimental assessment of CS performance and the related reconstruction errors. The experimental test-bed adopts a push-broom imaging spectrometer, a liquid crystal plate, a standard CCD camera and a Silicon PhotoMultiplier (SiPM) matrix. The prototype is being developed within the framework of the ESA ITI-B Project titled "Hyperspectral Passive Satellite Imaging via Compressive Sensing".

On the Use of Normalized Compression Distances for Image Similarity Detection

Directory of Open Access Journals (Sweden)

Dinu Coltuc

2018-01-01

Full Text Available This paper investigates the usefulness of the normalized compression distance (NCD for image similarity detection. Instead of the direct NCD between images, the paper considers the correlation between NCD based feature vectors extracted for each image. The vectors are derived by computing the NCD between the original image and sequences of translated (rotated versions. Feature vectors for simple transforms (circular translations on horizontal, vertical, diagonal directions and rotations around image center and several standard compressors are generated and tested in a very simple experiment of similarity detection between the original image and two filtered versions (median and moving average. The promising vector configurations (geometric transform, lossless compressor are further tested for similarity detection on the 24 images of the Kodak set subject to some common image processing. While the direct computation of NCD fails to detect image similarity even in the case of simple median and moving average filtering in 3 × 3 windows, for certain transforms and compressors, the proposed approach appears to provide robustness at similarity detection against smoothing, lossy compression, contrast enhancement, noise addition and some robustness against geometrical transforms (scaling, cropping and rotation.

Optical image transformation and encryption by phase-retrieval-based double random-phase encoding and compressive ghost imaging

Science.gov (United States)

Yuan, Sheng; Yang, Yangrui; Liu, Xuemei; Zhou, Xin; Wei, Zhenzhuo

2018-01-01

An optical image transformation and encryption scheme is proposed based on double random-phase encoding (DRPE) and compressive ghost imaging (CGI) techniques. In this scheme, a secret image is first transformed into a binary image with the phase-retrieval-based DRPE technique, and then encoded by a series of random amplitude patterns according to the ghost imaging (GI) principle. Compressive sensing, corrosion and expansion operations are implemented to retrieve the secret image in the decryption process. This encryption scheme takes the advantage of complementary capabilities offered by the phase-retrieval-based DRPE and GI-based encryption techniques. That is the phase-retrieval-based DRPE is used to overcome the blurring defect of the decrypted image in the GI-based encryption, and the CGI not only reduces the data amount of the ciphertext, but also enhances the security of DRPE. Computer simulation results are presented to verify the performance of the proposed encryption scheme.

Visual servoing in medical robotics: a survey. Part II: tomographic imaging modalities--techniques and applications.

Science.gov (United States)

Azizian, Mahdi; Najmaei, Nima; Khoshnam, Mahta; Patel, Rajni

2015-03-01

Intraoperative application of tomographic imaging techniques provides a means of visual servoing for objects beneath the surface of organs. The focus of this survey is on therapeutic and diagnostic medical applications where tomographic imaging is used in visual servoing. To this end, a comprehensive search of the electronic databases was completed for the period 2000-2013. Existing techniques and products are categorized and studied, based on the imaging modality and their medical applications. This part complements Part I of the survey, which covers visual servoing techniques using endoscopic imaging and direct vision. The main challenges in using visual servoing based on tomographic images have been identified. 'Supervised automation of medical robotics' is found to be a major trend in this field and ultrasound is the most commonly used tomographic modality for visual servoing. Copyright © 2014 John Wiley & Sons, Ltd.

Astronomical Image Compression Techniques Based on ACC and KLT Coder

Directory of Open Access Journals (Sweden)

J. Schindler

2011-01-01

Full Text Available This paper deals with a compression of image data in applications in astronomy. Astronomical images have typical specific properties — high grayscale bit depth, size, noise occurrence and special processing algorithms. They belong to the class of scientific images. Their processing and compression is quite different from the classical approach of multimedia image processing. The database of images from BOOTES (Burst Observer and Optical Transient Exploring System has been chosen as a source of the testing signal. BOOTES is a Czech-Spanish robotic telescope for observing AGN (active galactic nuclei and the optical transient of GRB (gamma ray bursts searching. This paper discusses an approach based on an analysis of statistical properties of image data. A comparison of two irrelevancy reduction methods is presented from a scientific (astrometric and photometric point of view. The first method is based on a statistical approach, using the Karhunen-Loeve transform (KLT with uniform quantization in the spectral domain. The second technique is derived from wavelet decomposition with adaptive selection of used prediction coefficients. Finally, the comparison of three redundancy reduction methods is discussed. Multimedia format JPEG2000 and HCOMPRESS, designed especially for astronomical images, are compared with the new Astronomical Context Coder (ACC coder based on adaptive median regression.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

Science.gov (United States)

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

Directory of Open Access Journals (Sweden)

Huiyan Jiang

2012-01-01

Full Text Available An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

Ultra high-speed x-ray imaging of laser-driven shock compression using synchrotron light

Science.gov (United States)

Olbinado, Margie P.; Cantelli, Valentina; Mathon, Olivier; Pascarelli, Sakura; Grenzer, Joerg; Pelka, Alexander; Roedel, Melanie; Prencipe, Irene; Laso Garcia, Alejandro; Helbig, Uwe; Kraus, Dominik; Schramm, Ulrich; Cowan, Tom; Scheel, Mario; Pradel, Pierre; De Resseguier, Thibaut; Rack, Alexander

2018-02-01

A high-power, nanosecond pulsed laser impacting the surface of a material can generate an ablation plasma that drives a shock wave into it; while in situ x-ray imaging can provide a time-resolved probe of the shock-induced material behaviour on macroscopic length scales. Here, we report on an investigation into laser-driven shock compression of a polyurethane foam and a graphite rod by means of single-pulse synchrotron x-ray phase-contrast imaging with MHz frame rate. A 6 J, 10 ns pulsed laser was used to generate shock compression. Physical processes governing the laser-induced dynamic response such as elastic compression, compaction, pore collapse, fracture, and fragmentation have been imaged; and the advantage of exploiting the partial spatial coherence of a synchrotron source for studying low-density, carbon-based materials is emphasized. The successful combination of a high-energy laser and ultra high-speed x-ray imaging using synchrotron light demonstrates the potentiality of accessing complementary information from scientific studies of laser-driven shock compression.

Evaluation of onboard hyperspectral-image compression techniques for a parallel push-broom sensor

Energy Technology Data Exchange (ETDEWEB)

Briles, S.

1996-04-01

A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation coefficient error. Implementation issues are considered in algorithm development.

Real-time Image Generation for Compressive Light Field Displays

International Nuclear Information System (INIS)

Wetzstein, G; Lanman, D; Hirsch, M; Raskar, R

2013-01-01

With the invention of integral imaging and parallax barriers in the beginning of the 20th century, glasses-free 3D displays have become feasible. Only today—more than a century later—glasses-free 3D displays are finally emerging in the consumer market. The technologies being employed in current-generation devices, however, are fundamentally the same as what was invented 100 years ago. With rapid advances in optical fabrication, digital processing power, and computational perception, a new generation of display technology is emerging: compressive displays exploring the co-design of optical elements and computational processing while taking particular characteristics of the human visual system into account. In this paper, we discuss real-time implementation strategies for emerging compressive light field displays. We consider displays composed of multiple stacked layers of light-attenuating or polarization-rotating layers, such as LCDs. The involved image generation requires iterative tomographic image synthesis. We demonstrate that, for the case of light field display, computed tomographic light field synthesis maps well to operations included in the standard graphics pipeline, facilitating efficient GPU-based implementations with real-time framerates.

Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information

International Nuclear Information System (INIS)

Kim, Kil Joong; Kim, Bohyoung; Lee, Hyunna; Choi, Hosik; Jeon, Jong-June; Ahn, Jeong-Hwan; Lee, Kyoung Ho

2011-01-01

Purpose: To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images. Methods: Our institutional review board approved this study and waived informed patient consent. Using a JPEG2000 algorithm, 360 abdomen CT images were compressed reversibly (n = 48, as negative control) or irreversibly (n = 312) to one of different compression ratios (CRs) ranging from 4:1 to 10:1. Five radiologists independently determined whether the original and compressed images were distinguishable or indistinguishable. The 312 irreversibly compressed images were divided randomly into training (n = 156) and testing (n = 156) sets. The MLR and ANN models were constructed regarding the DICOM header information as independent variables and the pooled radiologists' responses as dependent variable. As independent variables, we selected the CR (DICOM tag number: 0028, 2112), effective tube current-time product (0018, 9332), section thickness (0018, 0050), and field of view (0018, 0090) among the DICOM tags. Using the training set, an optimal subset of independent variables was determined by backward stepwise selection in a four-fold cross-validation scheme. The MLR and ANN models were constructed with the determined independent variables using the training set. The models were then evaluated on the testing set by using receiver-operating-characteristic (ROC) analysis regarding the radiologists' pooled responses as the reference standard and by measuring Spearman rank correlation between the model prediction and the number of radiologists who rated the two images as distinguishable. Results: The CR and section thickness were determined as the optimal independent variables. The areas under the ROC curve for the MLR and ANN predictions were 0.91 (95% CI; 0

Acoustic Angiography: A New Imaging Modality for Assessing Microvasculature Architecture

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Ryan C. Gessner

2013-01-01

Full Text Available The purpose of this paper is to provide the biomedical imaging community with details of a new high resolution contrast imaging approach referred to as “acoustic angiography.” Through the use of dual-frequency ultrasound transducer technology, images acquired with this approach possess both high resolution and a high contrast-to-tissue ratio, which enables the visualization of microvascular architecture without significant contribution from background tissues. Additionally, volumetric vessel-tissue integration can be visualized by using b-mode overlays acquired with the same probe. We present a brief technical overview of how the images are acquired, followed by several examples of images of both healthy and diseased tissue volumes. 3D images from alternate modalities often used in preclinical imaging, contrast-enhanced micro-CT and photoacoustics, are also included to provide a perspective on how acoustic angiography has qualitatively similar capabilities to these other techniques. These preliminary images provide visually compelling evidence to suggest that acoustic angiography may serve as a powerful new tool in preclinical and future clinical imaging.

Regional variance of visually lossless threshold in compressed chest CT images: Lung versus mediastinum and chest wall

International Nuclear Information System (INIS)

Kim, Tae Jung; Lee, Kyoung Ho; Kim, Bohyoung; Kim, Kil Joong; Chun, Eun Ju; Bajpai, Vasundhara; Kim, Young Hoon; Hahn, Seokyung; Lee, Kyung Won

2009-01-01

Objective: To estimate the visually lossless threshold (VLT) for the Joint Photographic Experts Group (JPEG) 2000 compression of chest CT images and to demonstrate the variance of the VLT between the lung and mediastinum/chest wall. Subjects and methods: Eighty images were compressed reversibly (as negative control) and irreversibly to 5:1, 10:1, 15:1 and 20:1. Five radiologists determined if the compressed images were distinguishable from their originals in the lung and mediastinum/chest wall. Exact tests for paired proportions were used to compare the readers' responses between the reversible and irreversible compressions and between the lung and mediastinum/chest wall. Results: At reversible, 5:1, 10:1, 15:1, and 20:1 compressions, 0%, 0%, 3-49% (p < .004, for three readers), 69-99% (p < .001, for all readers), and 100% of the 80 image pairs were distinguishable in the lung, respectively; and 0%, 0%, 74-100% (p < .001, for all readers), 100%, and 100% were distinguishable in the mediastinum/chest wall, respectively. The image pairs were less frequently distinguishable in the lung than in the mediastinum/chest wall at 10:1 (p < .001, for all readers) and 15:1 (p < .001, for two readers). In 321 image comparisons, the image pairs were indistinguishable in the lung but distinguishable in the mediastinum/chest wall, whereas there was no instance of the opposite. Conclusion: For JPEG2000 compression of chest CT images, the VLT is between 5:1 and 10:1. The lung is more tolerant to the compression than the mediastinum/chest wall.

Regional variance of visually lossless threshold in compressed chest CT images: Lung versus mediastinum and chest wall

Energy Technology Data Exchange (ETDEWEB)

Kim, Tae Jung [Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 (Korea, Republic of); Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of); Lee, Kyoung Ho [Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 (Korea, Republic of); Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of)], E-mail: kholee@snubhrad.snu.ac.kr; Kim, Bohyoung; Kim, Kil Joong; Chun, Eun Ju; Bajpai, Vasundhara; Kim, Young Hoon [Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 (Korea, Republic of); Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of); Hahn, Seokyung [Medical Research Collaborating Center, Seoul National University Hospital, 28 Yongon-dong, Chongno-gu, Seoul 110-744 (Korea, Republic of); Seoul National University College of Medicine (Korea, Republic of); Lee, Kyung Won [Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 (Korea, Republic of); Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center (Korea, Republic of)

2009-03-15

Objective: To estimate the visually lossless threshold (VLT) for the Joint Photographic Experts Group (JPEG) 2000 compression of chest CT images and to demonstrate the variance of the VLT between the lung and mediastinum/chest wall. Subjects and methods: Eighty images were compressed reversibly (as negative control) and irreversibly to 5:1, 10:1, 15:1 and 20:1. Five radiologists determined if the compressed images were distinguishable from their originals in the lung and mediastinum/chest wall. Exact tests for paired proportions were used to compare the readers' responses between the reversible and irreversible compressions and between the lung and mediastinum/chest wall. Results: At reversible, 5:1, 10:1, 15:1, and 20:1 compressions, 0%, 0%, 3-49% (p < .004, for three readers), 69-99% (p < .001, for all readers), and 100% of the 80 image pairs were distinguishable in the lung, respectively; and 0%, 0%, 74-100% (p < .001, for all readers), 100%, and 100% were distinguishable in the mediastinum/chest wall, respectively. The image pairs were less frequently distinguishable in the lung than in the mediastinum/chest wall at 10:1 (p < .001, for all readers) and 15:1 (p < .001, for two readers). In 321 image comparisons, the image pairs were indistinguishable in the lung but distinguishable in the mediastinum/chest wall, whereas there was no instance of the opposite. Conclusion: For JPEG2000 compression of chest CT images, the VLT is between 5:1 and 10:1. The lung is more tolerant to the compression than the mediastinum/chest wall.

Compressive Sampling based Image Coding for Resource-deficient Visual Communication.

Science.gov (United States)

Liu, Xianming; Zhai, Deming; Zhou, Jiantao; Zhang, Xinfeng; Zhao, Debin; Gao, Wen

2016-04-14

In this paper, a new compressive sampling based image coding scheme is developed to achieve competitive coding efficiency at lower encoder computational complexity, while supporting error resilience. This technique is particularly suitable for visual communication with resource-deficient devices. At the encoder, compact image representation is produced, which is a polyphase down-sampled version of the input image; but the conventional low-pass filter prior to down-sampling is replaced by a local random binary convolution kernel. The pixels of the resulting down-sampled pre-filtered image are local random measurements and placed in the original spatial configuration. The advantages of local random measurements are two folds: 1) preserve high-frequency image features that are otherwise discarded by low-pass filtering; 2) remain a conventional image and can therefore be coded by any standardized codec to remove statistical redundancy of larger scales. Moreover, measurements generated by different kernels can be considered as multiple descriptions of the original image and therefore the proposed scheme has the advantage of multiple description coding. At the decoder, a unified sparsity-based soft-decoding technique is developed to recover the original image from received measurements in a framework of compressive sensing. Experimental results demonstrate that the proposed scheme is competitive compared with existing methods, with a unique strength of recovering fine details and sharp edges at low bit-rates.

Performance evaluation of objective quality metrics for HDR image compression

Science.gov (United States)

Valenzise, Giuseppe; De Simone, Francesca; Lauga, Paul; Dufaux, Frederic

2014-09-01

Due to the much larger luminance and contrast characteristics of high dynamic range (HDR) images, well-known objective quality metrics, widely used for the assessment of low dynamic range (LDR) content, cannot be directly applied to HDR images in order to predict their perceptual fidelity. To overcome this limitation, advanced fidelity metrics, such as the HDR-VDP, have been proposed to accurately predict visually significant differences. However, their complex calibration may make them difficult to use in practice. A simpler approach consists in computing arithmetic or structural fidelity metrics, such as PSNR and SSIM, on perceptually encoded luminance values but the performance of quality prediction in this case has not been clearly studied. In this paper, we aim at providing a better comprehension of the limits and the potentialities of this approach, by means of a subjective study. We compare the performance of HDR-VDP to that of PSNR and SSIM computed on perceptually encoded luminance values, when considering compressed HDR images. Our results show that these simpler metrics can be effectively employed to assess image fidelity for applications such as HDR image compression.

Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager

Science.gov (United States)