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.

의사 참조 이미지를 활용한 무참조 이미지 화질 평가

용윤정,오희석 대한전자공학회 2024 전자공학회논문지 Vol.61 No.2

No-reference image quality assessment (NR-IQA) aims to objectively quantify the level of image quality degradation by reflecting the human visual system in the absence of information about the pristine image. Existing NR-IQA techniques have high sensitivity to specific distortion types, but have limitations in determining semantic quality information or global image quality degradation. In this paper, to resolve the limitations of existing NR-IQA approaches and improve predictive power, we propose a multi-scale pseudo image quality assessor (MPIQ). MPIQ is an NR-IQA model that follows the framework of full-reference IQA (FR-IQA), which is more proficient in extracting local distortion patterns and aggregating higher-level perceptual quality information. The proposed MPIQ employs a hybrid scheme that seeks to understand local distortion patterns through the convolutional neural networks and global level of image quality based on transformers, and consists of two modules: a pseudo-reference image reconstructor and a quasi FR-IQA regressor. Similar to the FR-IQA approach, the pseudo-reference image reconstructor utilizes an encoder-decoder structure to reconstruct the pseudo-reference image and learn image degradation information compared to a distorted one. Here, a multi-scale structure is reflected to extract scale-invariant distortion patterns. Quasi FR-IQA regressor predicts the image quality score by deriving the global distortion level through the difference between the features extracted from the distorted and the pseudo-reference images. MPIQ was supervised onto the mean opinion score obtained through subjective evaluation in an end-to-end manner, and experimental results showed a 20% performance improvement compared to the existing NR-IQA. 무참조 이미지 화질 평가(no-reference image quality assessment; NR-IQA)는 참조 영상에 대한 정보가 주어지지 않은 상태에서 인간 시각 체계를 반영하여 이미지 화질 저하 수준을 객관적으로 정량화 하는 것을 목표로 한다. 기존 NR-IQA 기술은 특정 왜곡 특성에 대해 높은 민감성을 갖고 있으며, 의미론적 화질 인지 및 화질 저하 수준을 판단하기에는 한계를 지니고 있다. 본 논문에서는 기존 NR-IQA 접근 방식의 한계를 극복하고 예측 정확도를 향상시키기 위해 지역적 왜곡 패턴 추출과 고수준 의미 정보 추출에 보다 능숙한 완전 참조 (full-reference IQA; FR-IQA) 방식의 프레임워크를 따르는 NR-IQA 모델인 multi-scale pseudo image quality assessor(MPIQ)를 제안한다. 제안하는 MPIQ는 하이브리드 구조에 기반해 합성곱 네트워크를 통한 지역적 왜곡 패턴 추출과 트랜스포머 기반의 전역적 화질 수준 이해를 도모하며, 의사 참조 이미지 reconstructor와 quasi FR-IQA regressor라는 두 가지 모듈로 구성된다. 의사 참조 이미지 reconstructor는 FR-IQA 방식과 유사하게 화질 추론 모델이 동작할 수 있도록 인코더-디코더 구조를 차용하여 의사 참조 이미지를 재구성하고 이미지 왜곡 정보를 학습한다. 이 과정에서 크기 불변 왜곡 패턴의 추출을 위해 다중 스케일 구조를 반영하였다. Quasi FR-IQA regressor는 왜곡 이미지에서 추출된 특징과 의사 참조 이미지 특징의 차이를 통해 전역적 왜곡 수준을 도출함으로써 이미지 화질 수준을 최종적으로 예측한다. MPIQ는 주관적 평가를 통해 획득된 mean opinion score에 end-to-end로 학습되었으며, 실험 결과 기존 NR-IQA 기술 대비 20%의 성능 향상을 달성하였다.

패밀리 레스토랑 이미지와 LOHAS 이미지가 고객만족, 관계품질 및 고객충성도에 미치는 영향

김영자,김동진 한국외식경영학회 2010 외식경영연구 Vol.13 No.4

This study expanded previous studies of image by exploring the effects of family restaurant and LOHAS images on customer satisfaction, relationship quality, and customer loyalty with the application of existing family restaurant image and LOHAS image which is recently emerging. It was found that both restaurant and LOHAS images have significant effects on customer satisfaction and relationship quality. Also, the results showed that customer satisfaction has a direct and indirect influence on relationship quality and customer loyalty, while relationship quality has a direct influence on customer loyalty. Finally, it has been found that a restaurant and LOHAS images have indirect influences on customer loyalty through customer satisfaction and relationship quality. Overall, it can be concluded that customers recognize not only existing restaurant image, but also LOHAS image, which focuses on the whole society, as corporate image. This study expanded previous studies of image by exploring the effects of family restaurant and LOHAS images on customer satisfaction, relationship quality, and customer loyalty with the application of existing family restaurant image and LOHAS image which is recently emerging. It was found that both restaurant and LOHAS images have significant effects on customer satisfaction and relationship quality. Also, the results showed that customer satisfaction has a direct and indirect influence on relationship quality and customer loyalty, while relationship quality has a direct influence on customer loyalty. Finally, it has been found that a restaurant and LOHAS images have indirect influences on customer loyalty through customer satisfaction and relationship quality. Overall, it can be concluded that customers recognize not only existing restaurant image, but also LOHAS image, which focuses on the whole society, as corporate image.

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).

일반 장면의 정규분포 분석을 기반으로 한 화질 측정 모형

박형주 ( Hyung Ju Park ),하동환 ( Dong Hwan Har ) 한국감성과학회 2013 감성과학 Vol.16 No.3

본 연구에서는 이미지 감상자가 선호하는 화질의 객관적 평가 항목들의 범위를 구체화하고 실제 이미지를 기반으로 화질의 선호도를 측정하는 방식을 따랐다. 즉, 무기준법(No-Reference)을 기반으로 하고 화질 평가 요소를 다이내믹 레인지, 컬러, 콘트라스트로 규정하였다. 샘플 사진 수집은 인터넷 갤러리에서 추천수 30회 이상을 기준으로 감상자들이 선호하는 풍경사진 200장의 이미지를 선정하였다. 그리고 세 가지 객관적 화질 평가 항목에 의한 정규분포분석을 통하여 총점을 백점 기준으로 환산하여 최종적으로 예상되는 화질의 선호도를 측정하였다. 본 실험에서 적용한 실제 사진 샘플의 다이내믹 레인지 측정값은 10 stop, LAB 평균은 L:54.7, A:2.96, B:-15.84, RSC 콘트라스트는 376.9로 나타났다. 총 200장 샘플 사진의 정규분포 z값은 다이내믹 레인지가 0.21, LAB 평균이 LAB 평균이 L:0.15, A:0.38, B:0.13, RSC 콘트라스트가 0.08을 나타냈다. 표준정규분포 표에서 위의 z값이 나타날 확률을 상위 백분율로 나타내면 실제 사진 샘플의 다이내믹 레인지는 8.32%, LAB 평균은 LAB 평균은 L:5.96%, A:14.8%, B:5.17%, RSC 콘트라스트는 3.19%를 나타낸다. 즉 화질 평가 모형에 사용된 실제 사진 샘플의 상위 백분율을 100점으로 환산한 다이내믹 레인지는 91.68점, LAB 평균은 91.36점, RSC 콘트라스트는 96.81점이다. 따라서 세 가지 객관적 화질 평가 항목에 의한 총점의 평균은 94.99점으로 나타낼 수 있었다. 즉 본 연구에서 제안한 화질 측정 모형을 통하여 실제 사진 샘플에 대한 선호도를 수치적으로 측정할 수 있었다. 이와 같은 연구를 통하여 이미지 감상자가 선호하는 화질의 재현 성능 범위를 구체화하고 예상되는 화질의 선호도를 수치화하는 실용적인 연구 결과를 제안하였다. In this research, we specify the image consumers` preferred image quality ranges based on objective image quality evaluation factors and follow a method which measures preference of the natural image scenes. In other words, according to No-Reference, we select dynamic range, color, and contrast as factors of image quality measurements. For collecting sample images, we choose the preferred 200 landscapes which have over 30 recommendations by image consumers on the internet photo gallery. According to the scores of three objective factors of image quality measurements, the final expected score which means the image quality preference is measured and its total score is 100 points. In the main test, the actual image sample shows dynamic range 10 stop, LAB mean value L:54.7, A:2.96, B:-15.84, and RSC contrast 376.9. Total 200 image samples` normal distribution z value represents in dynamic range 0.21, LAB mean value L:0.15, A:0.38, B:0.13, and RSC contrast 0.08. In the standard normal distribution table, we can convert the z value as a percentage; dynamic range is 8.32%, LAB mean value is L:5.96%, A:14.8%, B:5.17%, and RSC contrast is 3.19%. And then, we convert the percentage values into the scores of 100; dynamic range is 91.68, LAB mean value is 91.36, and RSC contrast is 96.81. Therefore, we can conclude that the sample image`s total mean score is 94.99 based on three objective image quality factors. Throughout our proposed image quality assessment model, we can measure the preference value of natural scenes. Also, we can specify the preferred image quality representation ranges and measure the expected image quality preference.

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.

Image Analysis Fuzzy System

Abdelwahed Motwakel,Adnan Shaout,Anwer Mustafa Hilal,Manar Ahmed Hamza International Journal of Computer ScienceNetwork S 2024 International journal of computer science and netw Vol.24 No.1

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

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.

통계적 영상 품질 측정

배경율(Kyoung Yul Bae) 한국지능정보시스템학회 2007 지능정보연구 Vol.13 No.4

The image quality measure is an important issue in the image processing. Several methods which measure the image quality have been proposed and these are based on the mathematical point of view. However, there is difference between the mathematicalmeasure and the measure based on the human visual system and a new measure has to be proposed because the final target of the image is a human visual system In this paper, a statistical image quality measure which is considered the human visual feature was suggested. The human visual system is using the global quality of the image and the local quality of the image and the local quality is more important to human visual system. In this paper, the image divided into several segments and the image qualities were calculated respectively. After then, the statistical method using scoring was applied to the image qualities. The result of the image quality measure was similar to the result of measure based on the human visual system.

서비스품질과 브랜드이미지 및 행동의도 관계 연구: 특급호텔 뷔페레스토랑을 중심으로

양윤의 ( Jun-yi Yang ),염수길 ( Soo-kil Yeum ),이수호 ( Su-ho Lee ) 대한관광경영학회 2016 觀光硏究 Vol.31 No.4

본 연구는 호텔산업 발전을 위하여 뷔페레스토랑의 역할과 긍정적인 호텔 브랜드이미지형성에 대하여 연구하고, 호텔 뷔페레스토랑의 성장을 통한 긍정적인 호텔 브랜드이미지 형성과 고객유치 확대 방안을 제시하고자 하였다. 연구의 목적을 달성하기 위하여 본 연구에서는 호텔 뷔페레스토랑 서비스품질을 3차원 서비스품질 모델을 채택하고 이에 대한 지각과 호텔 브랜드이미지, 행동의도 간의 인과관계를 분석하였다. 실증분석을 위한 설문조사는 2015년 1월20일부터 2월20일 까지 서울시내에 위치한 특급호텔의 뷔페레스토랑을 이용하는 내국인 고객을 대상으로 400부의 설문지를 배포하였으며 이가운데 유효한 설문지 352부가 실증분석에 사용되었다. 분석결과를 종합해 보면 첫째, 본 연구에서 채택한 3차원 서비스품질인 상호작용품질, 결과품질, 물리적 환경품질 가운데 상호작용품질은 브랜드이미지의 매력적인 이미지에 영향이 있는 것으로 나타났고 결과품질은 독특한 이미지를 제외한 브랜드이미지 구성요인에 모두 영향이 있는 것으로 나타났다. 한편, 물리적 환경품질은 심미적 환경과 기능적 환경으로 구분되었는데, 이 가운데 기능적 환경은 모든 브랜드이미지 요인에 영향이 있는 것으로 나타난 반면에 심미적 환경은 가치있는 이미지와 매력적인 이미지에 영향이 있는 것으로 나타났다. 둘째, 서비스품질과 브랜드이미지의 모든 구성요인은 고객의 긍정적인 행동의도에 영향을 미치는 것으로 나타났다. 본 연구의 결과에서 나타났듯이 호텔의 브랜드이미지는 호텔 뷔페레스토랑 서비스품질에 의해 형성되어 질수 있으며 긍정적인 행동의도에 영향을 미치는 변수이다. 따라서 호텔 관광자의 긍정적인 구전과 재방문을 위하여 브랜드이미지에 영향을 미치는 호텔 뷔페레스토랑의 서비스품질에 대하여 더욱 집중적인 경영관리를 해야 할 것이다. The purpose of this study is to investigate the role of Hotel buffet restaurant as an alternative for the development of the Hotel industry and for the formation of a positive brand image of the Hotel. It also suggests some ways to attract customers and form a positive brand image of Hotel through the growth of Hotel buffet restaurant. For the purpose of the study, this study adopted the three dimensional model of service quality for take the measure of hotel buffet restaurant service quality, and then analyzed the effect of perceived service quality of Hotel buffet restaurant on Hotel brand image and behavioral intention. The empirical survey was conducted between January 20 and February 20, 2015 on 400 Hotel buffet restaurant customers at five-star Hotels in Seoul city. The subjects were domestic tourists, and 352 valid questionnaires were analyzed. The consequences of this research is presented as follows : First, the perceived interaction quality has effect on attractive image and outcome quality has effect on all the component of brand image except unique image. Meanwhile, physical environment quality was divided into functional environment and esthetic environment. Functional environment has effect on all of the brand image. On the other hand, esthetic environment has only effect on worthful Image and attractive Image. Second, all the component of service quality and brand image has effect on behavioral intention. The research shows that the service quality of hotel buffet restaurant has effect on hotel brand image and positive behavioral intention. Therefore, efforts should be focused on the improvement of the service quality of hotel buffet restaurant.