This document summarizes a research paper on computed tomography (CT) dose reduction and view number optimization. It discusses how CT uses X-rays to create images but that radiation exposure is a concern, especially for pediatric patients. The paper explores how iterative reconstruction techniques and compressed sensing theories have aimed to reduce views and dose while maintaining image quality. It presents the goal of investigating the relationship between image quality, view number, and radiation dose level. Numerical tests were performed to determine the optimal view number for a given dose level that achieves the best reconstruction quality.
From filtered back-projection to statistical tomography reconstructionDaniel Pelliccia
An introductory tutorial on tomography reconstruction techniques: Filtered back-projection, algebraic reconstruction techniques, and statistical iterative techniques. It includes some results on neutron tomography.
A Novel Adaptive Denoising Method for Removal of Impulse Noise in Images usin...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The aim of the study was to investigate the relationship between 2D gray scale pixels and 3D gray scale pixels of image reconstructions in computed tomography (CT). The 3D space image reconstruction from data projection was a challenging and difficult research problem. The image was normally reconstructed from the 2D data from CT data projection. In this descriptive study, a synthetics 3D Shepp-Logan phantom was used to simulate the actual data projection from a CT scanner. Real-time data projection of a human abdomen was also included in this study. Additionally, the Graphical User Interface (GUI) for the application was designed using Matlab Graphical User Interface Development Environment (GUIDE). The application was able to reconstruct 2D and 3D images in their respective spaces successfully.The image reconstruction for CT in 3D space was analyzedalong with 2D space in order to show their relationships and shared properties for the purpose of constructing these images.
An Ultrasound Image Despeckling Approach Based on Principle Component AnalysisCSCJournals
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
From filtered back-projection to statistical tomography reconstructionDaniel Pelliccia
An introductory tutorial on tomography reconstruction techniques: Filtered back-projection, algebraic reconstruction techniques, and statistical iterative techniques. It includes some results on neutron tomography.
A Novel Adaptive Denoising Method for Removal of Impulse Noise in Images usin...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The aim of the study was to investigate the relationship between 2D gray scale pixels and 3D gray scale pixels of image reconstructions in computed tomography (CT). The 3D space image reconstruction from data projection was a challenging and difficult research problem. The image was normally reconstructed from the 2D data from CT data projection. In this descriptive study, a synthetics 3D Shepp-Logan phantom was used to simulate the actual data projection from a CT scanner. Real-time data projection of a human abdomen was also included in this study. Additionally, the Graphical User Interface (GUI) for the application was designed using Matlab Graphical User Interface Development Environment (GUIDE). The application was able to reconstruct 2D and 3D images in their respective spaces successfully.The image reconstruction for CT in 3D space was analyzedalong with 2D space in order to show their relationships and shared properties for the purpose of constructing these images.
An Ultrasound Image Despeckling Approach Based on Principle Component AnalysisCSCJournals
An approach based on principle component analysis (PCA) to filter out multiplicative noise from ultrasound images is presented in this paper. An image with speckle noise is segmented into small dyadic lengths, depending on the original size of the image, and the global covariance matrix is found. A projection matrix is then formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is used to filter speckle noise by projecting each segment into the signal subspace. The approach is based on the assumption that the signal and noise are independent and that the signal subspace is spanned by a subset of few principal eigenvectors. When applied on simulated and real ultrasound images, the proposed approach has outperformed some popular nonlinear denoising techniques such as 2D wavelets, 2D total variation filtering, and 2D anisotropic diffusion filtering in terms of edge preservation and maximum cleaning of speckle noise. It has also showed lower sensitivity to outliers resulting from the log transformation of the multiplicative noise.
Image reconstruction in CT is mostly a mathematical process however, this presentation tries to explain the complicated process of image reconstruction in a visual way, mainly focusing om Filtered back projection, Iterative Reconstruction and AI based image reconstruction.
Image Denoising Using Earth Mover's Distance and Local HistogramsCSCJournals
In this paper an adaptive range and domain filtering is presented. In the proposed method local histograms are computed to tune the range and domain extensions of bilateral filter. Noise histogram is estimated to measure the noise level at each pixel in the noisy image. The extensions of range and domain filters are determined based on pixel noise level. Experimental results show that the proposed method effectively removes the noise while preserves the details. The proposed method performs better than bilateral filter and restored test images have higher PSNR than those obtained by applying popular Bayesshrink wavelet denoising method.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human ...CSCJournals
In this paper we present a comparative study on fusion of visual and thermal images using different wavelet transformations. Here, coefficients of discrete wavelet transforms from both visual and thermal images are computed separately and combined. Next, inverse discrete wavelet transformation is taken in order to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms have been used to compare recognition results. For experiments IRIS Thermal/Visual Face Database was used. Experimental results using Haar and Daubechies wavelets show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.
Image fusion can be defined as the process by which several images or some of their features
are combined together to form a fused image. Its aim is to combine maximum information
from multiple images of the same scene such that the obtained new image is more suitable for
human visual and machine perception or further image processing and analysis tasks. The
fusion of images acquired from dissimilar modalities or instrument has been successfully used
for remote sensing images. The biomedical image fusion plays an important role in analysis
towards clinical application which can support more accurate information for physician to
diagnose different diseases.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Smart Noise Cancellation Processing: New Level of Clarity in Digital RadiographyCarestream
Smart Noise Cancellation significantly reduces noise in diagnostic images while retaining fine spatial detail –there is no degradation of anatomical sharpness. When SNC is applied, it produces images that are significantly clearer than with standard processing. It also provides better contrast-to-noise ratio for images acquired at a broad range of exposures.
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...IDES Editor
As the lung cancer is the leading cause of cancer
death in the medical field, Computed Tomography (CT) scan
of the thorax is widely applied in diagnoses for identifying
the lung cancer. In this paper, a technique of rotation invariant
with Local Binary Pattern (LBP) for segmentation of various
lung nodules from the Lung CT cancer data sets is used. This
is tested on various lung data sets from teaching files of
Casimage database and National Cancer Institute (NCI) of
National Biomedical Imaging Archive (NBIA). The results
show the segmented nodules with clear boundaries, which is
helpful in diagnosis of lung cancer. Further, the results are
compared with the watershed segmentation method, which
shows that LBP based method yields better segmentation
accuracy.
A Study of Total-Variation Based Noise-Reduction Algorithms For Low-Dose Cone...CSCJournals
In low-dose cone-beam computed tomography, the reconstructed image is contaminated with
excessive quantum noise. In this work, we examined the performance of two popular noisereduction
algorithms—total-variation based on the split Bregman (TVSB) and total-variation based
on Nesterov’s method (TVN)—on noisy imaging data from a computer-simulated Shepp–Logan
phantom, a physical CATPHAN phantom and head-and-neck patient. Up to 15% Gaussian noise
was added to the Shepp–Logan phantom. The CATPHAN phantom was scanned by a Varian OBI
system with scanning parameters 100 kVp, 4 ms, and 20 mA. Images from the head-and-neck
patient were generated by the same scanner, but with a 20-ms pulse time. The 4-ms low-dose
image of the head-and-neck patient was simulated by adding Poisson noise to the 20-ms image.
The performance of these two algorithms was quantitatively compared by computing the peak
signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR) and the total computational time. For
CATPHAN, PSNR improved by 2.3 dB and 3.1 dB with respect to the low-dose noisy image for the
TVSB and TVN based methods, respectively. The maximum enhancement ratio of CNR for
CATPHAN was 4.6 and 4.8 for TVSB and TVN respectively. For data for head-and-neck patient,
the PSNR improvement was 2.7 dB and 3.4 dB for TVSB and TVN respectively. Convergence
speed for the TVSB-based method was comparatively slower than TVN method. We conclude that
TVN algorithm has more desirable properties than TVSB for image denoising.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The determination of Region-of-Interest has been recognised as an important means by which
unimportant image content can be identified and excluded during image compression or image
modelling, however existing Region-of-Interest detection methods are computationally
expensive thus are mostly unsuitable for managing large number of images and the compression
of images especially for real-time video applications. This paper therefore proposes an
unsupervised algorithm that takes advantage of the high computation speed being offered by
Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to
achieve fast and efficient Region-of-Interest detection.
Image reconstruction in CT is mostly a mathematical process however, this presentation tries to explain the complicated process of image reconstruction in a visual way, mainly focusing om Filtered back projection, Iterative Reconstruction and AI based image reconstruction.
Image Denoising Using Earth Mover's Distance and Local HistogramsCSCJournals
In this paper an adaptive range and domain filtering is presented. In the proposed method local histograms are computed to tune the range and domain extensions of bilateral filter. Noise histogram is estimated to measure the noise level at each pixel in the noisy image. The extensions of range and domain filters are determined based on pixel noise level. Experimental results show that the proposed method effectively removes the noise while preserves the details. The proposed method performs better than bilateral filter and restored test images have higher PSNR than those obtained by applying popular Bayesshrink wavelet denoising method.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human ...CSCJournals
In this paper we present a comparative study on fusion of visual and thermal images using different wavelet transformations. Here, coefficients of discrete wavelet transforms from both visual and thermal images are computed separately and combined. Next, inverse discrete wavelet transformation is taken in order to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms have been used to compare recognition results. For experiments IRIS Thermal/Visual Face Database was used. Experimental results using Haar and Daubechies wavelets show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.
Image fusion can be defined as the process by which several images or some of their features
are combined together to form a fused image. Its aim is to combine maximum information
from multiple images of the same scene such that the obtained new image is more suitable for
human visual and machine perception or further image processing and analysis tasks. The
fusion of images acquired from dissimilar modalities or instrument has been successfully used
for remote sensing images. The biomedical image fusion plays an important role in analysis
towards clinical application which can support more accurate information for physician to
diagnose different diseases.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Smart Noise Cancellation Processing: New Level of Clarity in Digital RadiographyCarestream
Smart Noise Cancellation significantly reduces noise in diagnostic images while retaining fine spatial detail –there is no degradation of anatomical sharpness. When SNC is applied, it produces images that are significantly clearer than with standard processing. It also provides better contrast-to-noise ratio for images acquired at a broad range of exposures.
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...IDES Editor
As the lung cancer is the leading cause of cancer
death in the medical field, Computed Tomography (CT) scan
of the thorax is widely applied in diagnoses for identifying
the lung cancer. In this paper, a technique of rotation invariant
with Local Binary Pattern (LBP) for segmentation of various
lung nodules from the Lung CT cancer data sets is used. This
is tested on various lung data sets from teaching files of
Casimage database and National Cancer Institute (NCI) of
National Biomedical Imaging Archive (NBIA). The results
show the segmented nodules with clear boundaries, which is
helpful in diagnosis of lung cancer. Further, the results are
compared with the watershed segmentation method, which
shows that LBP based method yields better segmentation
accuracy.
A Study of Total-Variation Based Noise-Reduction Algorithms For Low-Dose Cone...CSCJournals
In low-dose cone-beam computed tomography, the reconstructed image is contaminated with
excessive quantum noise. In this work, we examined the performance of two popular noisereduction
algorithms—total-variation based on the split Bregman (TVSB) and total-variation based
on Nesterov’s method (TVN)—on noisy imaging data from a computer-simulated Shepp–Logan
phantom, a physical CATPHAN phantom and head-and-neck patient. Up to 15% Gaussian noise
was added to the Shepp–Logan phantom. The CATPHAN phantom was scanned by a Varian OBI
system with scanning parameters 100 kVp, 4 ms, and 20 mA. Images from the head-and-neck
patient were generated by the same scanner, but with a 20-ms pulse time. The 4-ms low-dose
image of the head-and-neck patient was simulated by adding Poisson noise to the 20-ms image.
The performance of these two algorithms was quantitatively compared by computing the peak
signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR) and the total computational time. For
CATPHAN, PSNR improved by 2.3 dB and 3.1 dB with respect to the low-dose noisy image for the
TVSB and TVN based methods, respectively. The maximum enhancement ratio of CNR for
CATPHAN was 4.6 and 4.8 for TVSB and TVN respectively. For data for head-and-neck patient,
the PSNR improvement was 2.7 dB and 3.4 dB for TVSB and TVN respectively. Convergence
speed for the TVSB-based method was comparatively slower than TVN method. We conclude that
TVN algorithm has more desirable properties than TVSB for image denoising.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The determination of Region-of-Interest has been recognised as an important means by which
unimportant image content can be identified and excluded during image compression or image
modelling, however existing Region-of-Interest detection methods are computationally
expensive thus are mostly unsuitable for managing large number of images and the compression
of images especially for real-time video applications. This paper therefore proposes an
unsupervised algorithm that takes advantage of the high computation speed being offered by
Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to
achieve fast and efficient Region-of-Interest detection.
Is the Needle Really Moving on Enterprise Info Security?CompTIA
As IT innovation storms the enterprise, new technologies allow increased productivity and convenience, but also introduce security risks that must be addressed. In our 11th Annual Information Security Trends Study, we surveyed 500 IT and business professionals and found that, despite an optimistic view of organizations' information security strategies, the prevalence of data-compromising incidents proves there's still work to be done.
En la línea KT con tecnología OFF LINE el KT-1000 permite la Salida de Onda Seoidal Pura; lo que significa una distorsión armónica menor a 5%.
Este equipo diseñado para la mejor confianza en protección de picos de voltaje, corto circuito y sobrecarga, ha superado las expectativas del mercado, al recomendarse para pequeños servidores, sistemas de acceso eléctricos, equipos de laboratorio, etc. Su tecnología de amplia regulación ofrece una potencia de CA limpia y constante.
Este sistema ya estará disponible a través de los mayoristas de DataShield. (DC Mayoristas, CT Internacional, AEM y Azerty) Para obtener mayor información llame al teléfono 53.90.63.05 / 08 o consulte nuestra página en Internet www.datashield.net
Contourlet Transform Based Method For Medical Image DenoisingCSCJournals
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
A summary of recent innovations in radiation oncology focussing on the priniciples of different techniques and their application. An overview of clinical results has also been given
Cancerous lung nodule detection in computed tomography imagesTELKOMNIKA JOURNAL
Diagnosis the computed tomography images (CT-images) is one of the images that may take a lot of time in diagnosis by the radiologist and may miss some of cancerous nodules in these images. Therefore, in this paper a new novel enhancement and detection cancerous nodule algorithm is proposed to diagnose a CT-images. The novel algorithm is divided into three main stages. In first stage, suspicious regions are enhanced using modified LoG algorithm. Then in stage two, a potential cancerous nodule was detected based on visual appearance in lung. Finally, five texture features analysis algorithm is implemented to reduce number of detected FP regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 97% and with FP ratio 25 cluster/image.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
COMPARISON OF CHANNEL ESTIMATION AND EQUALIZATION TECHNIQUES FOR OFDM SYSTEMScsijjournal
In OFDM (Orthogonal frequency division multiplexing) systems, channel estimation and channel equalization play a key role in overcoming distortions caused by phenomena like fading, delay spread and multipath effect. In
this paper, channel estimation and equalization techniques are analyzed to improve the performance of OFDM system. The channel estimation techniques considered here are estimation using wiener filter and frequency
domain approach. Prior Channel estimation leads to simple equalization. The channel equalization techniques employed here are based on LMS algorithm and one tap frequency domain equalization, under different channels;
AWGN, Rayleigh and Rician channels. Eye patterns for different channels are compared in simulation. It is observed from simulation that wiener filter provides better estimation and OFDM performance is better under
AWGN channel than fading channels. SER curves shows 6dB improvement in AWGN performance than fading channels to achieve 0.1 SER. In addition, MSE performance shows fast convergence for AWGN channel.
Embedding Patient Information In Medical Images Using LBP and LTPcsijjournal
In this paper a new efficient interleaving methodology in which patient textual information is embedded in medical images considering Magnetic Resonance Image, Ultrasonic image and CT images of that patient body. The main disadvantages in traditional techniques to Embed patient information in medical images is inability to withstand attacks and Bit Error Rate (BER) when maximum number of characters to be embedded are absent in proposed algorithm. The New robust Embedding Techniques, Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) are used to embed the patient information in medical images. LBP Technique is mainly used to provide the security for the patient information. Along with the security high volume of patient information can be embedded inside the medical image using LTP technique. Statistical parameters such as Normalized Root Mean Square Error, PSNR (Peak Signal to Noise Ratio) are used to measure the reliability of the present technique. Experimental results strongly indicate that the present technique with zero BER (Bit Error Rate), higher PSNR (Peak Signal to Noise Ratio), and high volume of data embedding capacity achieved using LTP as compared with the other data embedding techniques. This technique is found to be robust and watermark information is recoverable without distortion.
Embedding Patient Information In Medical Images Using LBP and LTPcsijjournal
In this paper a new efficient interleaving methodology in which patient textual information is embedded in medical images considering Magnetic Resonance Image, Ultrasonic image and CT images of that patient
body. The main disadvantages in traditional techniques to Embed patient information in medical images is inability to withstand attacks and Bit Error Rate (BER) when maximum number of characters to be
embedded are absent in proposed algorithm. The New robust Embedding Techniques, Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) are used to embed the patient information in medical images. LBP
Technique is mainly used to provide the security for the patient information. Along with the security high volume of patient information can be embedded inside the medical image using LTP technique. Statistical
parameters such as Normalized Root Mean Square Error, PSNR (Peak Signal to Noise Ratio) are used to measure the reliability of the present technique. Experimental results strongly indicate that the present
technique with zero BER (Bit Error Rate), higher PSNR (Peak Signal to Noise Ratio), and high volume of data embedding capacity achieved using LTP as compared with the other data embedding techniques. This
technique is found to be robust and watermark information is recoverable without distortion.
3-D WAVELET CODEC (COMPRESSION/DECOMPRESSION) FOR 3-D MEDICAL IMAGESijitcs
Compression is an important part in image processing in order to save memory space and reduce the
bandwidth while transmitting. The main purpose of this paper is to analyse the performance of 3-D wavelet
encoders using 3-D medical images. Four wavelet transforms, namely, Daubechies 4,Daubechies
6,Cohen-Daubechies-Feauveau 9/7 and Cohen Daubechies-Feauveau5/3 are used in the first stage with
encoders such as 3-D SPIHT,3-D SPECK and 3-D BISK used in the second stage for the compression.
Experiments are performed using medical test image such as magnetic resonance images (MRI) and X-ray
angiograms (XA). The XA and MR image slices are grouped into 4, 8 and 16 slices and the wavelet
transforms and encoding schemes are applied to identify the best wavelet encoder combination. The
performances of the proposed scheme are evaluated in terms of peak signal to noise ratio and bit rate.
3-D WAVELET CODEC (COMPRESSION/DECOMPRESSION) FOR 3-D MEDICAL IMAGES
On Dose Reduction and View Number
1. 第 24 卷 第 2 期 CT 理论与应用研究 Vol.24, No.2
2015 年 3 月(169-176) CT Theory and Applications Mar., 2015
Kaijie Lu, 刘宝东, Hengyong Yu. CT 辐射量降低和投影数目研究[J]. CT 理论与应用研究, 2015, 24(2): 169-176. (英).
doi:10. 15953/j.1004-4140.2015.24.02.03.
Lu KJ, Liu BD, Yu HY. On dose reduction and view number in computed tomography[J]. CT Theory and Applications, 2015,
24(2): 169-176. doi:10.15953/j.1004-4140.2015.24.02.03.
On Dose Reduction and View Number in
Computed Tomography
Kaijie Lu1, 2
, Baodong Liu3, 4
, Hengyong Yu5
1.Department of Biomedical Engineering, Wake Forest University Health Sciences,
Winston-Salem, NC 27157, USA
2.Wallace H. Coulter Department of Biomedical Engineering and Scheller College of
Business, Georgia Institute of Technology, Atlanta, GA 30332, USA
3.Key Laboratory of Nuclear Analytical Techniques, Institute of High Energy Physics,
Chinese Academy of Sciences, Beijing 100049, China
4.Beijing Engineering Research Center of Radiographic Techniques and Equipment,
Beijing 100049, China
5.Department of Electrical and Computer Engineering, University of Massachusetts
Lowell, Lowell, MA 01854, USA
Abstract: The ultimate goal in improving computed tomography (CT) technology is to reconstruct
higher quality images with lower radiation dose, which can help to lower cancer risks to the patients.
Inspired by the compressive sensing (CS) theory, few-view reconstruction has been a hot topic for dose
reduction in recent years. However, when the radiation dose is fixed, fewer view projections does not
always imply higher image quality. Numerical tests are performed in this study to investigate the
relationship between image quality and view numbers under a fixed radiation dose level. It is observed
that for a fixed dose level, as the view number increases, the image quality increases but then
decreases once the view number is sufficiently large. For the dose level and phantom we tested, the
optimal view number for best reconstruction quality is around 300 in an equi-angular full scan
configuration, which provides a useful hint for practical applications.
Key words: Computed tomography; dose reduction; view number; compressive sensing; total variation
minimization; ordered subset simultaneous algebraic reconstruction technique
doi:10.15953/j.1004-4140.2015.24.02.03 中图分类号:TP 391.41 文献标志码:A
Computed tomography (CT) is a technology to create 2-D and 3-D images from the X-ray
attenuation of the body from different angles. These images provide very important information
for physicians to diagnose illnesses. In 2000, the number of CT scans was approximately 46
million, in 2006, 62 million[1]
, and in 2009, the number of scans was above 70 million[2]
. In
particular, use of pediatric CT is rapidly growing. As CT is benefiting clinical diagnosis more and
more often, radiation dose associated with diagnostic CT scans has become a major concern.
X-ray radiation dose may induce genetic, cancerous, and other diseases. Moreover pediatric CT
Date of Receiving: 2014-12-05.
Funding Project: This work was partially supported by the NSF CAREER Award CBET-1540898. The work
of Baodong Liu was partially supported by IHEP-CAS Scientific Research Foundation
2013IHEPYJRC801.
2. CT 理论与应用研究 24 卷170
has a potential of increased risk because developing children are much more sensitive to X-ray
radiation. Reducing the risks of CT scans is one of the main bottlenecks to developing diagnostic
radiology.
The filtered back projection (FBP) algorithm is currently the most widely used
reconstruction technique because it is very fast and robust. The image quality of FBP-type
algorithms is acceptable in most cases. However, these algorithms produce images with a large
amount of noise, substantial streak artifacts, and poor low-contrast detectability in low dose
situation mainly due to the back projection process[3]
. The FBP technique requires a substantial
number of views to reconstruct a decent image for clinical diagnosis. However, complete
projection data usually require higher radiation dose.
Iterative reconstruction (IR) technique is used to overcome data insufficiency. Research has
also been done to develop iterative algorithms based on algebraic reconstruction technique
(ART)[4]
. The simultaneous ART (SART)[5]
works on the complete data and updates all the pixels
to obtain a stable solution. The ordered subset version of SART (OS-SART)[6]
and simultaneous
iterative reconstruction technique (OS-SIRT)[7]
divide the projection data into several groups and
perform the update for each group instead of the complete projection data. Because the IR
algorithms account for noise in the data model and use prior information, they outperform
analytical approaches when the data is sparse, incomplete, and/or noisy and allow for high quality
image reconstructions with reduced radiation dose. The IR works by allowing for multiple
volumes to be obtained at once. This also means that IR algorithms are generally more
computationally intensive[8]
than the analytical counterparts. Therefore the IR methods are
considered to be the best technique in reducing patient dose in practical applications including
pediatric CT imaging. The main concern with IR methods is its demanding for more computation
capacity because of multiple iterations. With the advance in computation facilities in the past
decade, the IR is attracting more and more attention from CT researchers and manufacturers.
Recently the application of compressive sensing (CS) has become popular in signal and
image processing. The CS theory shows that a high-quality signal or image can be reconstructed
from far fewer measurements than what is usually required by the Nyquist sampling theorem[9-11]
.
The main idea of CS is that most signals or images have sparse representations in a transform
domain, thus a very limited amount of samples are taken in a much less correlated basis and then
the signal is recovered with an overwhelming probability from these data via l1 minimization.
Most CT images can be regarded as piece-wise constant function because the X-ray attenuation
coefficient varies slightly within an anatomical component and largely around the borders of
tissue structures. Therefore, the discrete gradient transform of a CT image is sparse. Inspired by
the CS theory, a new CT reconstruction approach, referred to as total variation (TV, the l1 norm of
the discrete gradient transform) minimization was developed[12-15]
. With the CS theory and TV
method, it has attracted researchers to reconstruct higher quality images from fewer view
projections for dose reduction.
In summary, reducing radiation exposure in CT is one of the ultimate goals of scientists
seeking algorithms for high-quality image reconstruction. However, the process of improving the
technology to reduce dose presents a dilemma because with lower dose the image quality declines.
Recently, the CS-based few view reconstruction has become an increasingly popular topic. Thus
some interesting questions arise. Does a smaller view number mean a better image quality for a
fixed dose level? What is the optimal view number to achieve the best image quality? The purpose
of this paper is to try to figure out answers to these questions. More specifically, we try to find
the most optimal view number with best reconstruction quality for a fixed radiation dose level.
The rest of the paper is organized as follow. In Section 2, we describe the model, projection with
3. 2 期 Kaijie Lu et al:On Dose Reduction and View Number in Computed Tomography 171
noise, and reconstruction algorithm. Experimental results are presented in Section 3, and
conclusions and discussions are presented in Section 4.
1 Method
1.1 CT System Model
In the CT reconstruction field, a two-dimensional (2D) image f of size N1-by-N2 usually is
rearranged as a one-dimensional (1D) vector x of length 1 2= N N N with X-ray attenuation
coefficients at each pixel position. The model for IR technique in CT can be represented by
Ax = b (1)
where b represents a projection data as a 1D vector of length M and A is an M-by-N system
matrix determined by the projection model and scanning geometry[16]
. The goal of CT
reconstruction is to solve problem (1) for x.
1.2 Projection Noise Model
The CT transmission data noise arises mainly from X-ray quanta noise and system electronic
noise. The transmission data can be expressed as[17]
,
2
1 poisson( ) Normal( , ) I m (2)
where the parameter λ is the expectation of the numbers of X-ray photons having traversed the
patient. The second term in (2) is the normal distribution of the electronic noise with mean m and
variance σ2
. In practice, m is often calibrated to be zero. According to the Lambert-Beer’s law, the
projection integral p can be approximately defined as the negative logarithm of the ratio between
the outgoing and incoming number of photons,
1
0 1
0
ln ln( ) ln( )
I
p I I
I
(3)
The expectation λ in (2) can be expressed as,
0 exp( )I p (4)
where p is the expectation of p.
In this work, a monochromatic X-ray is assumed. A noise-free linear-integral measurement
p is computed using a given image phantom in an equiangular fan beam geometry. For a given
I0, the transmission data I1 can be simulated using (2) and (4). Finally, the noisy data p is
calculated using (3) and the projection noise
n p p (5)
It should be pointed out that the above process is used to generate one measurement at one
detector cell and one view angle, which can be viewed as one component of b in Eq.(1). The
parameter I0 in (3) is proportional to the X-ray photon intensity prior to arrival at the body.
According to the table III and the relationship
0
1
I
in[17]
, we can establish a relationship
4. CT 理论与应用研究 24 卷172
between I0 and mAs level for a fixed view number (the correspondence between I0 and mAs is
given in Section 3), hence the dose level. Because the dose level is proportional to the product of
I0 and view number for a scanning geometry and detector configuration, this product is used to
represent a measurement of dose level in this work.
1.3 Reconstruction Method
Suppose that xk
is the current approximation to a solution of system (1) after k iterations. The
SART-type solution to system (1) can be written as[6]
,
1
,
1
, ,
1 1
,1
i k
M
lk k
j j k i jM M
i
i j i j
i i
b a x
x x a
a a
(6)
where ,i k
a x is the inner product of the th
i row ai
of A and xk
, ija is the th
j entry of the row
ai
, (0, 2)k is the relaxation parameter, and the index i is cyclically taken as 1, 2, ….
The OS-SART technique divides projection data into several sets. In each iteration, the
SART algorithm is applied to each individual subset, and the intermediate reconstructed image is
used as the input to the next subset reconstruction until passing through all of the subsets. Inspired
by the CS theory, the TV minimization method is adopted to reconstruct the image f by solving
the following optimization problem[18]
,
min TV( f ) subject to Ax = b, (7)
where
,
1 1
TV( )
I J
i j
i j
f d
and
1
2 2 2
, , 1, , , 1 i j i j i j i j i jd f f f f
In this paper, we employ the classical alternative minimization method to minimize Eq.(7).
While the SART or OS-SART is used to minimize the data discrepancy to satisfy the projection
constraint Ax = b in the first step, the steepest descent search method is used to minimize the
TV[13]
.
2 Experimental Results
To investigate the relationship between the image quality and the view numbers for a fixed
radiation dose, numerical tests were performed on different dose levels and view numbers in
MatLab. A 512 × 512 image of a patient’s chest was selected as a digital image phantom. Typical
fan beam geometry with a circular scanning locus was used. The radius of the field of view was
25.0 cm. The detector array contains 888 elements. The distance between the source and object is
53.852 cm and object to detector is 49.83 cm. For each of the selected photon number and view
number over a full scan range, we first acquired the projection dataset with noise as explained in
section 2.2. The parameters for the electronic noise are m = 0 and σ2
= 10[17]
. Then we
5. 2 期 Kaijie Lu et al:On Dose Reduction and View Number in Computed Tomography 173
reconstructed the images using OS-SART with TV minimization algorithm. The parameter λk in
the OS-SART iteration was set to be a constant 1.0, and the maximum number of iterations
performed was calculated by (40 000/view number).
In our experiments, the numbers of views Nvigw were taken 10, 20, 30, 40, 50, 80, 110, 160,
210, 290, 400, 570, 790, 1110, and 1 550; the total number of photons K (the product of the view
number and I0) was taken as 4 different values of 5 × 106
, 107
, 2 × 107
and 5 × 107
. Because K is
proportional to the dose level, here we will use K to indicate the radiation dose level. Based on the
datasets in Table III in[17]
, a linear relationship between the total number of K and the dose level
was fitted as K=1.084 × 106
d, where d is the dose level (mAs). For illustration, the values of
photons, I0 and mAs level for the cases of view numbers = 50, 290, and 1 550 are shown in Table
1. Figure 1 shows the cardiac region of the reconstructed images for different cases. It should be
pointed out that we did not consider the bowtie filter in our simulations.
Table 1 The dose level and I0 for different simulation cases
Nview
K = 5 e6
4.61 mAs
K = 1 e7
9.22 mAs
K = 2 e7
18.45 mAs
K = 5 e7
46.13 mAs
50 1.0 e5 2.0 e5 4.0 e5 1.0 e6
290 1.7 e4 3.4 e4 6.9 e4 1.7 e5
1 550 0.3 e4 0.6 e4 1.3 e4 3.2 e4
Fig.1 The cardiac region of the reconstructed images with different dose and view numbers. From left
to right, the dose levels are 4.61 mAs, 9.22 mAs, 18.45 mAs and 46.13 mAs, respectively.
From top to bottom, the view numbers are 50, 290 and 1 550, respectively. While the maximum
HU number is mapped to white, the minimum HU number is mapped to black
6. CT 理论与应用研究 24 卷174
Each reconstructed image 1 2N NF was compared with the original image 1 2N Nf using the
root mean squared error.
1 2
1
22
1 1
1 2
RMSE
N N
ij ij
i j
f F
N N
Figure 2 shows the RMSE comparison of the reconstructed images with different dose levels
and views. For a fixed dose level, as the number of views increases, RMSE drops to a certain
point but then rises up, which indicates that the image quality changes towards good but then
towards bad again. This is because larger view number brings more information but more noise
while smaller view number brings less noise but less information. Meanwhile, electronic noise
will compromise the image quality when I0 becomes smaller with the increase of the view number.
Hence, there is a trade-off between the view number and noise. We also compared the qualities of
the images using the structural similarity index (SSIM)[19]
, which is a non-unit value between 0
and 1. With the original phantom image as reference, the greater the SSIM, the better the image
quality. Figure 3 shows the SSIM comparison of the same settings as in Figure 2. It shows a
similar trend to RMSE. Note that the log in Figures 2 and 3 is natural logarithm.
Fig.2 RMSE vs. view numbers Fig. 3 SSIM vs. view numbers
From the experimental results, for different dose level K, it is observed that, for a fixed view
number, larger K gives lower RMSE and higher SSIM, thus better image quality. As K increases,
the minimum RMSE and the maximum SSIM increase as well, which implies the capacity of
image quality is better. This is consistent with our common sense in the CT community. More
interestingly and importantly, the best image quality was attained at around 300-400 views for
different dose level we have tested. Views less than this range will result in lack of information
even though the noise is less; on the other hand, views more than that will bring too much noise to
compromise the additional measurements. For the case of 5 × 107
photons, the image quality does
not show decay before 1 550 views. This can be explained by the relationship between higher
photon number and Poisson noise. the higher the photon the number K, the smaller the Poisson
noise will be, which yield a smaller total noise given the fact that the electronic noise is fixed.
3 Discussion and Conclusion
Reducing the number of views is important to lower radiation dose; however, the view numbers
can only be reduced by so much before it begins to affect the image quality and thus undermining
7. 2 期 Kaijie Lu et al:On Dose Reduction and View Number in Computed Tomography 175
the purpose of the CT scan. For a fixed dose level, fewer view number may imply inferior image
quality. Therefore, there is a trade-off between view number and the image quality for a fixed dose
level. General speaking, larger view number brings more information but more noise while smaller
view number brings less noise but less information. It is necessary to investigate the direct
relationship between the image quality and view number for a fixed dose level.
In this work, we have performed several preliminary numerical tests to compare the image
qualities with different dose level and view numbers. It is observed that for a fixed dose level, as
the view number increases, the image quality increases but then decreases. For the cases we tested,
the optimal view number for best image quality is around 300. For a given sparse transform, it
should be pointed out that the optimal view number definitely depend on the image contents (e.g.
unknown variables in the reconstructed images), as well as detector resolution (e.g. the detector
cell number). Therefore, the optimal view number 300 is only suitable for the tested image in this
paper, and it cannot be directly applied to other applications. Nevertheless, the reported results
should provide a useful hint for clinical applications given the fact the image phantom is a
representative cardiac image.
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CT 辐射量降低和投影数目研究
Kaijie Lu
1,2
, 刘宝东
3,4
, Hengyong Yu
5
1.Department of Biomedical Engineering, Wake Forest University Health
Sciences, Winston-Salem, NC 27157, USA
2.Department of Biomedical Engineering, Scheller College of Business,
Georgia Institute of Technology, Atlanta, GA 30332, USA
3.中国科学院高能物理研究所核分析技术重点实验室, 北京 100049
4.北京市射线成像技术与装备工程技术研究中心, 北京 100049
5.Department of Electrical and Computer Engineering, University of
Massachusetts Lowell, Lowell, MA 01854, USA
摘要:改进 CT 技术的最终目标是用较低的辐射剂量重建出更高质量的图像以降低患癌症的风险。近年来
受压缩感知理论的启发,减少投影角度重建一直是减少辐射量的一个热门课题。但是,当辐射剂量固定,
减少投影角度并不总是意味着更好的图像质量。本文研究固定辐射剂量下图像质量和扫描角度数目的关
系。数值实验表明对于固定的辐射剂量,起初图像质量随着扫描角度数目的增加而提高,但是当扫描角度
数目足够大之后图像质量反而下降了。在等角全扫描模式下,对于我们测试的辐射剂量和图像,产生最佳
图像质量的最佳扫描角度数目是 300,这对于实际的应用具有借鉴意义.
关键词:CT;辐射剂量减少;扫描角度数目;压缩感知;全变差最小化;OS-SART
Biography: Kaijie Lu is a Senior at Georgia Tech Scheller College of Business and College
of Engineering. He was Research Assistant with the Department of Biomedical Engineering,
Wake Forest University Health Sciences (2013 Summer), and Math Tutor with Georgia
Southern University (2010~ 2011). Hengyong Yu
(email: hengyong-yu@ieee.org)
received his Bachelor degrees in information science & technology (1998) and
computational mathematics (1998), and his PhD in information & telecommunication
engineering (2003) from Xi’an Jiaotong University. He was Associate Research Scientist
with the University of Iowa (2004 ~ 2006), Research Scientist with Virginia Tech
(2006 ~ 2010), Assistant Professor with Wake Forest University Health Sciences
(2010-2014). Since 2014, he has been an Associate Professor, the Director of Imaging and
Informatics Lab, Department of Electrical and Computer Engineering, University of
Massachusetts Lowell. His interests include computed tomography and medical image
processing. He has authored or coauthored > 110 peer-reviewed journal papers. He is a
senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE
Engineering in Medicine and Biology Society (EMBS), and member of the American
Association of Physicists in Medicine (AAPM) and the Biomedical Engineering Society
(BMES). In 2012, he received an NSF CAREER award for the development of compressive
sensing based interior tomography.