Quality Of Medical Images Research Articles

A New Approach for Enhancing the Quality of Medical Computerized Tomography Images

Computerized tomography (CT) images contribute immensely to medical research and diagnosis. However, due to degradative factors such as noise, low contrast, and blurring, CT images tend to be a degraded representation of the actual body or part under investigation. To reduce the risk of imprecise diagnosis associated with poor-quality CT images, this paper presents a new technique designed to enhance the quality of medical CT images. The main objective is to improve the appearance of CT images in order to obtain better visual interpretation and analysis, which is expected to ease the diagnosis process. The proposed technique involves applying a median filter to remove noise from the CT images and then using a Laplacian filter to enhance the edges and the contrast in the images. Also, as CT images suffer from low contrast, a Contrast Limited Adaptive Histogram Equalization transform is also applied to solve this problem. The main strength of this transform is its modest computational requirements, ease of application, and excellent results for most images. According to a subjective assessment by a group of radiologists, the proposed technique resulted in excellent enhancement, including that of the contrast and the edges of medical CT images. From a medical perspective, the proposed technique was able to clarify the arteries, tissues, and lung nodules in the CT images. In addition, blurred nodules in chest CT images were enhanced effectively. Therefore the proposed technique can help radiologists to better detect lung nodules and can also assist in diagnosing the presence of tumours and in the detection of abnormal growths.

Series: Practical Evaluation of Clinical Image Quality (3): Subjective Evaluation of Image Quality in Digital Radiography Systems.

In the environment of picture archiving and communication system (PACS), the subjective evaluation of medical image quality is performed using an image display monitor. Commonly, PACS and its image viewer can handle digital imaging and communications in medicine (DICOM) format image files that are displayed as 8-bit grayscale images in the monitor. However, the original grayscale of DICOM image is between 10-bit to 16-bit depth. Therefore, in order for DICOM images to be displayed in the monitor by PACS image viewer, they need to be converted into 8-bit depth grayscale. This conversion indicates that the original characteristics of DICOM images are altered when they are displayed in the monitor by PACS image viewer. Moreover, image data interpolation to match the image matrix for the resolution of the monitor also alters the characteristics of images. For these reasons, it is a must to recognize the influential factors of image display settings for the subjective evaluation of medical image quality.

일반촬영 검사에서 필터 두께 증가에 따른 전방산란율에 관한 연구

Abstract Recently there has been increasing interest in the filter to reduce the proportion of low-energy photons in the polychromatic X-ray, affect the quality of the image quality by X-ray hardening effect is a situation that has been overlooked. In this study, by evaluating the change in FSR based on the filter and it was quantitatively discuss scatter dose affecting the medical image quality. The results of the experiment, as the thickness of the filter is increased, up to 13.9%p, that tends to FSR increases appearance were evaluated. Based on these results, in compliance with the thickness of the filter that has been recommended in KS standard, even while reducing the radiation dose of the patient, in addition to the noise to about 1%p within the FSR only medical image the contribution to it is conceivable. Therefore, even while reducing radiation dose of the patient, in order to improve the quality of the medical image, the use of appropriate filter is considered important. Keywords : General Radiography, Medical Image, Filter, Beam Hardening Effect, Forward Scattering Dose

EVALUASI CIRI CITRA MEDIS MENGGUNAKAN METODE PENINGKATAN KUALITAS CITRA HISTOGRAM EQUALIZATION DAN KARAKTERISASI STATISTIK

This research has an objective to implement histogram equalization technique in the evaluation of characteristics medical images, especially for statistical characterization of medical images. Implementation starting with the pre-processing of the image by converting RGB medical images into grayscale images, followed by determination of the Region of Interest (ROI) and cropping methods. The next process was to increase the quality of medical images by implementing histogram equalization technique. The medical image characteristic was evaluated using characteristic statistical methods with their parameters are mean value, standard deviation, entropy, skewness, and kurtosis. The Medical image was used in this research is a medical image of advanced breast cancer. This research compared the characteristics statistical characterization of medical images obtained with the original medical image which has enhanced use histogram equalization technique. The results of implementation histogram equalization obtain medical image feature extraction results for the value of mean, entropy, skewness, and kurtosis become smaller and standard deviation value was increase.

Current studies and future perspectives of synchrotron radiation imaging trials in human patients

The coherent and monochromatic x-ray beams available at the synchrotron radiation (SR) laboratories are ideal tools for the development and the initial application of new imaging techniques. In the present paper the history of the clinical studies in k-edge subtraction imaging with SR is summarized, including coronary angiography and bronchography. The results of the recent trial in phase-contrast mammography at Elettra (Trieste, Italy) are discussed, in order to assess the clinical impact of the new imaging modality and the potential interest in its translation to clinical practice. The direct measurement of linear attenuation coefficient obtained during the SR mammography trial is also discussed.The new program of phase-contrast breast CT under development at Elettra is presented. Recently, 3D breast imaging (tomosynthesis and cone beam breast CT) has been introduced in clinical practice with significant improvement in diagnostic accuracy. The aim of this research is to study the contribution of the phase-contrast to the image quality of breast CT.Increasing the image quality of the x-ray medical images at the level of the results obtained at the SR laboratories is highly desirable, hence the promising techniques for the translation of the phase-contrast imaging to the hospitals are briefly discussed.

복부/골반 CT 검사 시 팔의 위치에 따른 방사선 선량과 영상화질 비교 연구

The one-year-follow-up test of abdomen/pelvis from 10 patients who were scanned more than twice a years were analyzed the radiation dose and image quality depend on the position of the arm retrospectively from January to December in 2013. There were classified two groups, group A was examined with raising an arm on standard position and group B was performed with lowering an arm, respectively. Group A of an average mAs from the first dose amount was shown 11.4% less compared to Group B. And the value of CTDI from Group B also was investigated 11.3% less. To compare the quality comparison of the second image as histogram value, the value of max from both of two groups was measured similarly. However, a big difference was shown from the value of min and SD, the short dose was appeared depends on the position of arm even though Group A was radiated more than Group B. Less exposure to the medical image quality only by working CT scan when the examiner actively raise the arm before the development and testing of high-end equipment introduction of complex algorithms for obtaining an optimized image will be provided to the patient.

High-contrast and low-computational complexity medical ultrasound imaging using beamspace capon method.

Several adaptive beamforming techniques have been proposed to improve the quality of medical ultrasound images. The beamspace (BS) Capon method is one common method used to depict high-resolution images with low computational complexity. However, the complexity is not low enough for real-time imaging in clinical situations because the conventional BS Capon method employs a time-delay process and a transition process from elementspace signal processing to BS signal processing at all points of interest. Thus, we propose a technique that replaces the time-delay process using a steering vector. In addition, the Capon method employs a spatial averaging (SA) technique to stabilize the estimation in intensity. However, when the averaging size is not adequate, the estimated intensity might be smaller than that given by the delay-and-sum (DAS) method. Because most medical diagnoses are presented based on the estimation of intensity acquired by the DAS beamformer, accurate estimation of intensity is also required. Therefore, we propose a compensation technique that uses both small and large sizes for SA. In an experiment, the -6 dB beam width, sidelobe level, and estimation error in the intensity of the proposed method were 0.17 mm, -27 dB, and 0.92 dB, respectively, where those of the conventional BS Capon method were 0.29 mm, -22 dB, and 8.1 dB. The complexity of the proposed method is one-fourteenth that of the conventional method. Compared with conventional methods, the proposed method succeeded in depicting a higher-contrast image with accurate intensity estimation and lower computational complexity.

Medical Imaging Image Quality Assessment with Monte Carlo Methods

The aim of the present study was to assess image quality of PET scanners through a thin layer chromatography (TLC) plane source. The source was simulated using a previously validated Monte Carlo model. The model was developed by using the GATE MC package and reconstructed images obtained with the STIR software for tomographic image reconstruction, with cluster computing. The PET scanner simulated in this study was the GE DiscoveryST. A plane source consisted of a TLC plate, was simulated by a layer of silica gel on aluminum (Al) foil substrates, immersed in 18F-FDG bath solution (1MBq). Image quality was assessed in terms of the Modulation Transfer Function (MTF). MTF curves were estimated from transverse reconstructed images of the plane source. Images were reconstructed by the maximum likelihood estimation (MLE)-OSMAPOSL algorithm. OSMAPOSL reconstruction was assessed by using various subsets (3 to 21) and iterations (1 to 20), as well as by using various beta (hyper) parameter values. MTF values were found to increase up to the 12th iteration whereas remain almost constant thereafter. MTF improves by using lower beta values. The simulated PET evaluation method based on the TLC plane source can be also useful in research for the further development of PET and SPECT scanners though GATE simulations.

Weighted Capon beamformer combined with coded excitation in ultrasound imaging.

Capon-based beamformers are well-known methods to improve the SNR and quality of medical ultrasound images. Furthermore, they can improve the resolution of the images unexpectedly more than conventional DAS beamformers. Another method used in radar, sonar, and ultrasound imaging to increase the SNR is coded excitation with linear frequency-modulated signal (chirp). In this paper, we illustrate that the combination of coded excitation with a Capon beamformer provides better resolution and higher SNR. However, this combination suffers from high sidelobe levels. We propose a weighted Capon beamformer (WCB) with chirp excitation to suppress the sidelobes and obtain a higher contrast with approximately the same resolution as the standard Capon beamformer (SCB). The weights of the WCB are obtained using element-wise multiplication of the weights of the SCB and a desired window (Hanning) with the same dimension. The results show a 20 dB reduction in the sidelobe levels in simulated point targets and a 6 dB increase in background contrast in simulated cyst phantoms.

A Computational Approach for Image Fusion using Medical Images based on Ripplet Transform

Image fusion is the process of combine different images from one or multiple imaging to raise the imaging quality. The concept is to improve the image content by combing images. Image fusion is important for improving the image quality and clinical application of medical image. Computed Tomography best suited for bone rift and reduction in providing information of the tissues, at the same time Magnetic Resolution Imaging gives soft tissue information and lacks in boundary information. In this paper Ripplet transform is resolving 2 dimensional singularities and show image edges more efficiently. At beginning the medical images are reconstruct by discrete ripplet transform. Fusion rules are applied to low frequency and high frequency subband. Apply inverse discrete ripplet transform to fused coefficient of low frequency and high frequency, for getting fused image. The performance of proposed system is calculated by quantitative approach such as spatial frequency, entropy, mutual information, peak signal to noise ratio, and root mean square error.

An Optimal Method to Improve the Visual Quality of Medical Images

AbstractAs the visual quality of X-ray images is a critical reference for the accuracy of the clinical diagnosis, the methods toimprove the quality of X-ray images have been investigated. Among many existing methods, using frequency domainfilter is a very powerful method to improve the visual quality of images. In this paper, the inherent noises of the inputimages are suppressed by adding the Laplacian image to the subjected image. The medical X-ray images using the optimalhigh pass filter has shown improved edges. Further, the optimal high frequency emphasis filter has shown the improvedcontrast of flat areas by using the result image from the optimal high pass filter. Also the resulting images of the globalcontrast have improved by the histogram equalization. As a result, the proposed methods have shown enhanced contrastand edges of the images with noise canceling effect. Keywords: Laplacian Image, High Pass Filter, High Frequency Emphasis Filter 1. Introduction Even the smallest abnormality should not be over-looked in the correct X-ray diagnosis because the earlytreatment of this abnormality is critical for the recoveryof patients. To identify abnormalities in lung tissue thatare roughly spherical with round opacity and a diameterof up to approximately 30 mm

SU‐E‐J‐185: A Systematic Review of Breathing Guidance in Radiation Oncology and Radiology

Purpose:The advent of image‐guided radiation therapy (IGRT) has led to dramatic improvements in the accuracy of treatment delivery in radiotherapy. Such advancements have highlighted the deleterious impact tumor motion can have on both image quality and radiation treatment delivery. One approach to reducing tumor motion is the use of breathing guidance systems during imaging and treatment. A review of such research had not yet been performed, it was therefore our aim to perform a systematic review of breathing guidance interventions within the fields of radiation oncology and radiology.Methods:Results of online database searches were filtered in accordance to a set of eligibility criteria. The search, filtration, and analysis of articles were conducted in accordance with the PRISMAStatement reporting standard (Preferred Reporting Items for Systematic reviews and Meta‐Analyses) utilizing the PICOS approach (Participants, Intervention, Comparison, Outcome, Study design).Participants:Cancer patients, healthy volunteers.Intervention:Biofeedback breathing guidance systems.Comparison:No breathing guidance of the same breathing type.Outcome:Regularity of breathing signal and anatomic/tumor motion, medical image quality, radiation treatment margins and coverage, medical imaging and radiation treatment times.Study design:Quantitative and controlled prospective or retrospective trials.Results:The systematic search yielded a total of 479 articles, which were filtered down to 27 relevant articles in accordance to the eligibility criteria. The vast majority of investigated outcomes were significantly positively impacted by the use of breathing guidance; however, this was dependent upon the nature of the breathing guidance system and study design. In 25/27 studies significant improvements from the use of breathing guidance were observed.Conclusion:The results found here indicate that further clinical studies are warranted which quantify more comprehensively the clinical impact of The results found here indicate that further clinical studies are warranted which quantify more comprehensively the clinical impact of breathing guidance interventions.

QoE-based optimal resource allocation in wireless healthcare networks: opportunities and challenges

Ubiquitous health monitoring is a mobile health service with the aim of monitoring patients' conditions anytime and anywhere by collecting and transferring biosignal data from patients to health-service providers (e.g., healthcare centers). As a critical issue in ubiquitous health monitoring, wireless resource allocation can influence the performance of health monitoring, and the majority of work in wireless resource allocation for health monitoring has focused on quality-of-service oriented allocation schemes with primary challenges at the physical and MAC layers. Recently, quality-of-experience (QoE) oriented resource allocation schemes in wireless health monitoring have attracted attentions as a promising design to a better service of healthcare monitoring. In this paper, we overview the metrics of assessing the quality of medical images, and discuss the performance of these metrics in QoE-oriented resource allocation for health monitoring. We start with addressing the state-of-the-art QoE metrics by providing a taxonomy of the different metrics employed in assessing medical images. We then present the design of resource allocation schemes for health monitoring. After that, we present a case study to compare the performance of different classes of metrics in designing resource allocation schemes. We end the paper with a few open issues in the design of novel QoE metrics for resource allocation in health monitoring.

Analysis and Estimation of Noise in Embedded Medical Images

Patient information is embedded inside the medical images for the storage or transmission, or healthcare applications. In medical image processing, various types of noises corrupt the image quality. There is a need of measure specific noise for a particular image is required for the evaluation of robustness for embedding techniques used for hiding patient information in medical images. It is very important to obtain precise images to facilitate accurate analysis and estimation of noise in embedded medical image. The current work is focused towards studying the effect of specific noise which affect particular medical image. The strength of the medical image is tested by introducing several attacks to the embedded medical images. The statistical quantity measures like peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and normalized root mean square error (NRMSE) are employed to measure the quality of the output medical image.

Using morphological transforms to enhance the contrast of medical images

Medical imaging plays an important role in monitoring the patient’s health condition and providing an effective treatment. However, the existence of several objects overlapping in an image and the close proximity of adjacent pixels values in medical images make the diagnostic process a difficult task. To cope with such problems, this paper presents a new method based on morphological transforms to enhance the quality of various medical images. In this method, a disk-shaped mask whose size fits that of the original input image is chosen for morphological operations. Afterward, the proposed filter from the Top-Hat transforms is applied to the image, using the chosen mask in a multi-step process. At each step, the size of the mask is increased. Consequently, an enhanced image is provided for each mask size. The number of required steps and the final enhanced image are determined based on the Contrast Improvement Ratio (CIR) measure. Indeed, this approach applies an exfoliation process on the images, in which one or several objects in the image are prominently manifested using morphological filter, hence provide an appropriate image for analysis. The results in this research indicate that the proposed approach makes a better contrast and works much better than the other existing methods in improving the quality of medical images.

Medical Image De-Noising Schemes Using Different Wavelet Threshold Techniques

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.

Region-based medical image compression in teleradiology

Digital radiology has resulted in significant increase in use of digital medical images in the process of diagnosis. Lossless image compression is necessary to preserve the value of diagnostic medical images. This method will be applicable to mainly compressing information without any loss. More particularly, this paper aims to increase compression ratio and quality (PSNR) of medical images. Initially, MRI brain image is segmented using grow cut. This algorithm extracts the region of tumour (abnormal region) and non-region of tumour (normal region) parts. The region of tumour is selected using seed point which is selected by extracting run length features. Morphological processing is used for highlighting the tumour region. The integer wavelet transform is applied on both region of tumour and non-region of tumour parts. The abnormal region is compressed losslessly using arithmetic coding and normal region is compressed using lossy compression (i.e.) EZW coding. Finally, the compressed image of the region of tumour (abnormal region) is fused with compressed image of the normal region. This implementation is found to possess superior reconstruction properties and better compression ratio.

Denoising Ultrasound Medical Images

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques

In this paper, a multi–layer heuristic approach is introduced to segment liver region from other tissues in multi–slice CT images. Image noise is a principal factor which hampers the visual quality of medical images and can therefore lead to misdiagnosis. To address this issue, we first utilise an algorithm based on median filter to remove noise and enhance the contrast of the CT image. This is followed by performing an adaptive threshold algorithm and morphological operators to preserve the liver structure and remove the fragments of other organs. Then, connected component labelling algorithm was applied to remove false positive regions and focused on liver region. To evaluate the performance of the proposed system, we present tests on different liver CT scans images. The experimental results show that the overall accuracy offered by the employed system is high compared with other related works as well as very fast which segment liver from abdominal CT in less than 0.6 s/slice.

An image despeckling approach using quantum-inspired statistics in dual-tree complex wavelet domain

This paper presents a novel despeckling algorithm to enhance image quality of medical ultrasound images. The proposed approach exploits coefficients in the dual-tree complex wavelet transform (DTCWT) domain to develop a new quantum-inspired thresholding function. The inter-scale correlation among the coefficients of different subbands and the intra-scale variance between the coefficient and its neighborhood in the same subband are utilized to develop a new thresholding function, which is further incorporated into a Bayesian framework to perform adaptive image despeckling. Experiments are conducted using both artificially generated and real-world medical images to demonstrate that the proposed approach outperforms the conventional image despeckling approaches.