Clinical image quality evaluation for panoramic radiography in Korean dental clinics

최보람,최다혜,허경회,이원진,허민석,최순철,배광학,이삼선 대한영상치의학회 2012 Imaging Science in Dentistry Vol.42 No.3

Purpose: The purpose of this study was to investigate the level of clinical image quality of panoramic radiographs and to analyze the parameters that influence the overall image quality. Materials and Methods: Korean dental clinics were asked to provide three randomly selected panoramic radiographs. An oral and maxillofacial radiology specialist evaluated those images using our self-developed Clinical Image Quality Evaluation Chart. Three evaluators classified the overall image quality of the panoramic radiographs and evaluated the causes of imaging errors. Results: A total of 297 panoramic radiographs were collected from 99 dental hospitals and clinics. The mean of the scores according to the Clinical Image Quality Evaluation Chart was 79.9. In the classification of the overall image quality, 17 images were deemed ‘optimal for obtaining diagnostic information,’ 153 were ‘adequate for diagnosis,’ 107 were ‘poor but diagnosable,’ and nine were ‘unrecognizable and too poor for diagnosis’. The results of the analysis of the causes of the errors in all the images are as follows: 139 errors in the positioning, 135 in the processing, 50 from the radiographic unit, and 13 due to anatomic abnormality. Conclusion: Panoramic radiographs taken at local dental clinics generally have a normal or higher-level image quality. Principal factors affecting image quality were positioning of the patient and image density, sharpness, and contrast. Therefore, when images are taken, the patient position should be adjusted with great care. Also, standardizing objective criteria of image density, sharpness, and contrast is required to evaluate image quality effectively. Purpose: The purpose of this study was to investigate the level of clinical image quality of panoramic radiographs and to analyze the parameters that influence the overall image quality. Materials and Methods: Korean dental clinics were asked to provide three randomly selected panoramic radiographs. An oral and maxillofacial radiology specialist evaluated those images using our self-developed Clinical Image Quality Evaluation Chart. Three evaluators classified the overall image quality of the panoramic radiographs and evaluated the causes of imaging errors. Results: A total of 297 panoramic radiographs were collected from 99 dental hospitals and clinics. The mean of the scores according to the Clinical Image Quality Evaluation Chart was 79.9. In the classification of the overall image quality, 17 images were deemed ‘optimal for obtaining diagnostic information,’ 153 were ‘adequate for diagnosis,’ 107 were ‘poor but diagnosable,’ and nine were ‘unrecognizable and too poor for diagnosis’. The results of the analysis of the causes of the errors in all the images are as follows: 139 errors in the positioning, 135 in the processing, 50 from the radiographic unit, and 13 due to anatomic abnormality. Conclusion: Panoramic radiographs taken at local dental clinics generally have a normal or higher-level image quality. Principal factors affecting image quality were positioning of the patient and image density, sharpness, and contrast. Therefore, when images are taken, the patient position should be adjusted with great care. Also, standardizing objective criteria of image density, sharpness, and contrast is required to evaluate image quality effectively.

A new image-quality evaluating and enhancing methodology for bridge inspection using an unmanned aerial vehicle

Jin Hwan Lee,Sungsik Yoon,Byunghyun Kim,Gi-Hun Gwon,In-Ho Kim,Hyung-Jo Jung 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.2

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).

Clinical image quality evaluation for panoramic radiography in Korean dental clinics

Choi, Bo-Ram,Choi, Da-Hye,Huh, Kyung-Hoe,Yi, Won-Jin,Heo, Min-Suk,Choi, Soon-Chul,Bae, Kwang-Hak,Lee, Sam-Sun Korean Academy of Oral and Maxillofacial Radiology 2012 Imaging Science in Dentistry Vol.42 No.3

Purpose: The purpose of this study was to investigate the level of clinical image quality of panoramic radiographs and to analyze the parameters that influence the overall image quality. Materials and Methods: Korean dental clinics were asked to provide three randomly selected panoramic radiographs. An oral and maxillofacial radiology specialist evaluated those images using our self-developed Clinical Image Quality Evaluation Chart. Three evaluators classified the overall image quality of the panoramic radiographs and evaluated the causes of imaging errors. Results: A total of 297 panoramic radiographs were collected from 99 dental hospitals and clinics. The mean of the scores according to the Clinical Image Quality Evaluation Chart was 79.9. In the classification of the overall image quality, 17 images were deemed 'optimal for obtaining diagnostic information,' 153 were 'adequate for diagnosis,' 109 were 'poor but diagnosable,' and nine were 'unrecognizable and too poor for diagnosis'. The results of the analysis of the causes of the errors in all the images are as follows: 139 errors in the positioning, 135 in the processing, 50 from the radiographic unit, and 13 due to anatomic abnormality. Conclusion: Panoramic radiographs taken at local dental clinics generally have a normal or higher-level image quality. Principal factors affecting image quality were positioning of the patient and image density, sharpness, and contrast. Therefore, when images are taken, the patient position should be adjusted with great care. Also, standardizing objective criteria of image density, sharpness, and contrast is required to evaluate image quality effectively.

Clinical image quality evaluation for panoramic radiography in Korean dental clinics

Bo-Ram Choi,Da-Hye Choi,Kyung Hoe Huh,Won-Jin Yi,Min-Suk Heo,Soon-Chul Choi,Kwang-Hak Bae,Sam-Sun Lee 대한구강악안면방사선학회 2012 Imaging Science in Dentistry Vol.42 No.3

Purpose: The purpose of this study was to investigate the level of clinical image quality of panoramic radiographs and to analyze the parameters that influence the overall image quality. Materials and Methods: Korean dental clinics were asked to provide three randomly selected panoramic radiographs. An oral and maxillofacial radiology specialist evaluated those images using our self-developed Clinical Image Quality Evaluation Chart. Three evaluators classified the overall image quality of the panoramic radiographs and evaluated the causes of imaging errors. Results: A total of 297 panoramic radiographs were collected from 99 dental hospitals and clinics. The mean of the scores according to the Clinical Image Quality Evaluation Chart was 79.9. In the classification of the overall image quality, 17 images were deemed ‘optimal for obtaining diagnostic information,’ 153 were ‘adequate for diagnosis,’ 107 were ‘poor but diagnosable,’ and nine were ‘unrecognizable and too poor for diagnosis’. The results of the analysis of the causes of the errors in all the images are as follows: 139 errors in the positioning, 135 in the processing, 50 from the radiographic unit, and 13 due to anatomic abnormality. Conclusion: Panoramic radiographs taken at local dental clinics generally have a normal or higher-level image quality. Principal factors affecting image quality were positioning of the patient and image density, sharpness, and contrast. Therefore, when images are taken, the patient position should be adjusted with great care. Also, standardizing objective criteria of image density, sharpness, and contrast is required to evaluate image quality effectively.

Adaptive fingerprint image enhancement with fingerprint image quality analysis

Yun, Eun-Kyung,Cho, Sung-Bae Butterworths 2006 Image and vision computing Vol.24 No.1

<P><B>Abstract</B></P><P>Accurate minutiae extraction from fingerprint images is heavily dependent on the quality of the fingerprint images. In order to improve the performance of the system, much effort has been made on the image enhancement algorithms. If the preprocessing is adaptive to the fingerprint image characteristics in the image enhancement step, the performance gets to be more robust. In this paper, we propose an adaptive preprocessing method, which extracts five features from the fingerprint images, analyzes image quality with clustering method, and enhances the images according to their characteristics. Experimental results indicate that the proposed method improves the performance of the fingerprint identification significantly.</P>

수중 환경 특성 분석 기반의 가시성 왜곡 보정을 통한 수중 영상 복원 기법

신도경,김영대 융복합지식학회 2023 융복합지식학회논문지 Vol.11 No.2

Recently, the utilization of unmanned submersibles is increasing in the civil and national defense fields to perform underwater resource exploration, disaster prediction, underwater terrain survey, anti-submarine surveillance and reconnaissance, and mine removal. The use of underwater optical imaging is also increasing in order to perform various missions using unmanned submersibles. However, images acquired underwater have problems with low visibility due to distortions such as color distortion, low contrast ratio, underwater fog, and blur due to attenuation of light propagating underwater. Therefore, in this paper, the quality of the image is improved by removing and correcting the distortion phenomenon through the analysis of the characteristics of the underwater environment. To perform visual evaluation and quantitative evaluation of the proposed algorithm, UIEB underwater image enhancement benchmark data set was used, and quality scores were compared with 12 existing methods. In addition, to measure the quality score of the corrected image, we used UIQM, UCIQE, FDUM, CCF, and FADE underwater image quality evaluation metrics based on non-reference, which measure a single image without a reference image. As a result of the experiment, it was confirmed that the method proposed in this paper showed better performance than the existing underwater image improvement method. 최근 민수 및 국방 분야에서 해저 자원 탐사, 재난 예측, 해저 지형 조사, 대잠 감시정찰, 기뢰 제거 등을 수행하기 위한 무인잠수정의 활용도가 높아지고 있다. 무인잠수정을 활용한 다양한 임무를 수행하기 위해서 수중 광학 영상의 활용도 또한 높아지고 있다. 하지만 수중에서 획득된 영상은 수중으로 전파되는 빛의 감쇠로 인해 색상 왜곡, 낮은 대조비, 수중 안개 현상 및 블러 등의 왜곡이 발생함에 따라 가시성 낮은 문제점이 존재한다. 따라서 본 논문에서는 수중 환경 특성 분석을 통해 왜곡 현상을 제거하고 보정함으로써 영상의 품질을 향상시킨다. 제안한 알고리즘의 시각적 평가 및 정량적 평가를 수행하기 위해서 UIEB 수중 영상 향상 벤치마크 데이터 셋을 사용하였으며, 12개의 기존 방법과의 품질 점수를 비교를 수행하였다. 또한 보정된 영상의 품질 점수를 측정하기 위해서 참조 영상 없이 단일 영상으로 측정하는 무참조 기반의 UIQM, UCIQE, FDUM, CCF, FADE 수중 영상 품질 평가 메트릭을 사용하였다. 실험 결과, 본 논문에서 제안하는 방법이 기존의 수중 영상 개선 방법보다 우수한 성능을 보임을 확인하였다.

Reduced-Reference 3D Image Quality Measurement via Spatial to Gradient Domain Feature Aggregation

Ma Jian,Xu Guoming,Han Xiyu 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2

Objective quality measurement of a three-dimensional (3D) image is a challenging issue in various 3D visual applications since it is infl uenced by multiple aspects such as binocular fusion, binocular rivalry and visual comfort, etc. Existing studies show that classic 2D and some 3D image quality measurement (IQM) are only perform well for symmetric distorted 3D images, but not able to evaluate the quality of asymmetrical distorted 3D images accurately. In this paper, based on the statistical characteristics of natural images and perceptual properties of human visual system (HVS), we propose a novel reduced-reference (RR) 3D quality assessment evaluator (R3DQAE) to deal with the characteristics of 3D images. Two key technical steps are involved in R3DQAE: the statistical characteristics of 3D images and the perceptual properties of HVS. Specifi cally, in spatial domain, the generalized Gaussian density fi ts of luminance wavelet coeffi cients and correlations of luminance and disparity wavelet coeffi cients are used to represent the statistical characteristics of 3D image. Furthermore, in gradient domain, the enhanced gradient magnitudes are computed by using neighborhood phase congruency information to weight the gradient magnitudes in a locally adaptive manner. Afterward, the entropy diff erencing of discrete wavelet transform coeffi cients of enhanced gradient magnitudes are extracted as the perceptual features of HVS. Finally, the qualities index of both the statistical characteristics of 3D image and the perceptual properties of HVS are combined to yield 3D image quality index. Experiments are performed on published 3D image quality assessment database show that the proposed model achieves highly competitive performance as compared with the state-of-the art some typical full-reference and RR 3DIQM models.

디지털 방사선시스템에서 영상증강 파라미터의 영상특성 평가

김창수(Changsoo Kim),강세식(Se-Sik Kang),고성진(Seong-Jin Ko) 한국콘텐츠학회 2010 한국콘텐츠학회논문지 Vol.10 No.6

디지털 방사선시스템에서의 의료영상 획득의 방법은 X선을 조사하고, 반도체 디텍터(Detector)를 이용하여 직접 및 간접으로 변환하여 기존 업체마다 여러 가지 알고리즘을 적용하여 적절한 이미지 프로세싱을 거쳐서 임상의 적정한 영상을 획득한다. 방사선과에서 적절한 의료 영상 형성을 위하여 적용하는 이미지 프로세싱 파라미터(Image Processing Parameters)는 Edge, Frequency, Contrast, Latitude, LUT, Noise 등의 영상 증강의 과정은 기술력 및 업체 알고리즘에 따라 다르게 적용되고 있다. 따라서 본 논문에서는 디지털 방사선 환경에서의 최종의 임상 영상을 위한 이미지 증강의 파라미터들의 적정 세팅 값의 기준을 제시하고자 한다. 그리고 각 병원들의 의료 영상을 바탕으로 이미지 프로세싱 파라미터들을 변화하여 각 파라미터들의 세부적인 기준 세팅값을 연구하며, 실제적인 파라미터 변화에 대한 적합한 의료영상을 디지털방사선시스템의 영상 평가 방법을 도식화하여 결과를 제시하고, 향후 임상에서 적응 및 활용 가능한 객관적인 영상 파라미터에 대한 특성 평가의 응용을 정립하고자 한다. 또한 다양한 표본 병원의 디지털 방사선 환경에서 적정 파라미터 값들을 조사하여 임상에서 영상의 화질에 미치는 영향으로 특성평가의 객관적인 기준의 변조전달함수(MTF)의 공간해상력을 제시하고 한다. Digital imaging detectors can use a variety of detection materials to convert X-ray radiation either to light or directly to electron charge. Many detectors such as amorphous silicon flat panels, CCDs, and CMOS photodiode arrays incorporate a scintillator screen to convert x-ray to light. The digital radiography systems based on semiconductor detectors, commonly referred to as flat panel detectors, are gaining popularity in the clinical & hospital. The X-ray detectors are described between a-Silicon based indirect type and a-Selenium based direct type. The DRS of detectors is used to convert the x-ray to electron hole pairs. Image processing is described by specific image features: Latitude compression, Contrast enhancement, Edge enhancement, Look up table, Noise suppression. The image features are tuned independently. The final enhancement result is a combination of all image features. The parameters are altered by using specific image features in the different several hospitals. The image in a radiological report consists of two image evaluation processes: Clinical image parameters and MTF is a descriptor of the spatial resolution of a digital imaging system. We used the edge test phantom and exposure procedure described in the IEC 61267 to obtain an edge spread function from which the MTF is calculated. We can compare image in the processing parameters to change between original and processed image data. The angle of the edge with respect to the axes of detector was varied in order to determine the MTF as a function of direction. Each MTF is integrated within the spatial resolution interval of 1.35- 11.70 cycles/㎜ at the 50 % MTF point. Each image enhancement parameters consists of edge, frequency, contrast, LUT, noise, sensitometry curve, threshold level, windows. The digital device is also shown to have good uniformity of MTF and image parameters across its modality. The measurements reported here represent a comprehensive evaluation of digital radiography system designed for use in the DRS. The results indicate that the parameter enables very good image quality in the digital radiography. Of course, the quality of image from a parameter is determined by other digital devices in addition to the proper clinical image.

적응적인 인자 설정을 통한 저조도 영역 개선

장석우 한국산학기술학회 2023 한국산학기술학회논문지 Vol.24 No.12

Difficulties may occur in image processing if an image obtained in an outdoor environment includes a low-light area, depending on the space or time in which the image is captured. In this paper, we propose a method for improving a low-illuminance area included in an image through parameter settings that consider the characteristics of the image. The input image is first inverted, and a dark channel prior algorithm is applied by setting parameters adaptively. Then, the resultant image is inverted again to improve low-illuminance areas. Experimental results suggest that the suggested method robustly improves low-light areas by adaptively adjusting parameters from various input images. The method is expected to be used effectively in many applications related to computer vision, such as target object detection, image quality restoration, compression, restoration, big data storage, and indexing.

Adaptive Image Rescaling for Weakly Contrast-Enhanced Lesions in Dedicated Breast CT: A Phantom Study

Kim Bitbyeol,Kim Ho Kyung,Kim Jinsung,Ki Yongkan,Joo Ji Hyeon,전호상,Park Dahl,Kim Wontaek,Nam Jiho,Kim Dong Hyeon 대한영상의학회 2021 대한영상의학회지 Vol.82 No.6

Purpose Dedicated breast CT is an emerging volumetric X-ray imaging modality for diagnosis that does not require any painful breast compression. To improve the detection rate of weakly enhanced lesions, an adaptive image rescaling (AIR) technique was proposed. Materials and Methods Two disks containing five identical holes and five holes of different diameters were scanned using 60/100 kVp to obtain single-energy CT (SECT), dual-energy CT (DECT), and AIR images. A piece of pork was also scanned as a subclinical trial. The image quality was evaluated using image contrast and contrast-to-noise ratio (CNR). The difference of imaging performances was confirmed using student’s t test. Results Total mean image contrast of AIR (0.70) reached 74.5% of that of DECT (0.94) and was higher than that of SECT (0.22) by 318.2%. Total mean CNR of AIR (5.08) was 35.5% of that of SECT (14.30) and was higher than that of DECT (2.28) by 222.8%. A similar trend was observed in the subclinical study. Conclusion The results demonstrated superior image contrast of AIR over SECT, and its higher overall image quality compared to DECT with half the exposure. Therefore, AIR seems to have the potential to improve the detectability of lesions with dedicated breast CT.