This study aimed to investigate which areas AI is sensitive when inputting panoramic radiographs with dental area masked and when inputting unmasked ones. Therefore, the null hypothesis of this study was that masking dental area would not make a difference in the sensitive areas of osteoporosis determination of AI. For this study 1165 female(average age 48.4 ± 23.9 years) from whom panoramic radiographs were taken were selected. Either osteoporosis or normal should be clearly defined by oral and maxillofacial radiologists. The panoramic radiographs from the female were classified as either osteoporosis or normal according to the mandibular inferior cortex shape. VGG-16 model was used to get training, validating, and testing to determine between osteoporosis or normal. Two experiments were performed; one using unmasked images of panoramic radiographs, and the other using panoramic radiographs with dental region masked. In two experiments, accuracy of VGG-16 was 97.9% with unmasked images and 98.6% with dental-region-masked images. In the osteoporosis group, the sensitive areas identified with unmasked images included cervical vertebrae, maxillary and mandibular cancellous bone, dental area, zygomatic bone, mandibular inferior cortex, and cranial base. The osteoporosis group shows sensitivity on mandibular cancellous bone, cervical vertebrae, and mandibular inferior cortex with masked images. In the normal group, when unmasked images were input, only dental region was sensitive, while with masked images, only mandibular cancellous bone was sensitive. It is suggestive that when dental influence of panoramic radiographs was excluded, AI determined osteoporosis on the mandibular cancellous bone more sensitively.

본 연구는 360도 파노라마 동영상을 활용하여 해상풍력 경관의 유형을 분류하고 유형별 경관 선호 매 트릭스 요소에 따른 경관 선호도를 분석하고자 한다. 이를 위해 첫째, 연구대상지로 제주 탐라해상풍력 단지로 선정하였으며, 연구대상지를 조망할 수 있는 지점들을 추출하고 방문하여 360도 파노라마 동영 상을 촬영하였다. 둘째, 구축된 360도 파노라마 동영상 자료를 바탕으로 경관의 미학적 특성을 분석하 고 해상풍력 경관의 유형을 분류했다. 셋째, 해상풍력 경관의 유형별로 경관 선호 매트릭스 요소와 경관 선호도를 설문조사했다. 넷째, 수집된 데이터를 활용하여 해상풍력 경관 유형별 선호 매트릭스 요소에 따른 경관 선호도 관계를 분석했다. 분석 결과 해상풍력 경관 유형이 High Continuity(HCN), High Color (HCL), Low Visual Impact(LVI), High Visibility(HVI)로 분류되었다. 또한, HCN경관 선호도에 영향을 주 는 요소로는 응집성이, HCL경관 선호도에 영향을 주는 요소로는 응집성과 신비성이, LVI경관 선호도 에 영향을 주는 요소로는 응집성과 복잡성이, HVI 경관 선호도에 영향을 주는 요소로는 응집성과 가독 성이 도출되었다. 본 연구 결과를 바탕으로 해상풍력 경관 선호도 향상을 위한 전략으로 (1)시각적 영향 을 줄일 수 있는 식재계획 및 동선설계, (2)해상풍력 터빈의 생상 대비 극대화, (3)해상풍력 터빈 설치전 경관 시뮬레이션을 제안하였다.

The purpose of this study was to verify the sensitive areas when the AI determines osteoporosis for the entire area of the panoramic radiograph. Panoramic radiographs of a total of 1,156 female patients(average age of 49.0±24.0 years) were used for this study. The panoramic radiographs were diagnosed as osteoporosis and the normal by Oral and Maxillofacial Radiology specialists. The VGG16 deep learning convolutional neural network(CNN) model was used to determine osteoporosis and the normal from testing 72 osteoporosis(average age of 73.7±8.0 years) and 93 normal(average age of 26.4±5.1 years). VGG16 conducted a gradient-weighted class activation mapping(Grad-CAM) visualization to indicate sensitive areas when determining osteoporosis. The accuracy of CNN in determining osteoporosis was 100%. Heatmap image from 72 panoamic radiographs of osteoporosis revealed that CNN was sensitive to the cervical vertebral in 70.8%(51/72), the cortical bone of the lower mandible in 72.2%(52/72), the cranial base area in 30.6%(22/72), the cancellous bone of the mandible in 33.3%(24/72), the cancellous bone of the maxilla in 20.8%(15/72), the zygoma in 8.3%(6/72), and the dental area in 5.6%(4/72). Consideration: it was found that the cervical vertebral area and the cortical bone of the lower mandible were sensitive areas when CNN determines osteoporosis in the entire area of panoramic radiographs.

The aim of this study was to investigate radiographic features of osteosclerosis on digital panoramic radiographs. Osteoscleosis was diagnosed with its radiographic features of amorphous non- expansible radiopacity with unknown etiology. In diagosing osteosclerosis, differential diagnosis is needed from periapical cemental dysplasia, osteoma, benign cementoblastoma, and anatomic structures. Fifty-eight osteosclerosis on digital panoramic radiopgraphs from 46 patients with osteoscleosis were selected for this study. All of the osteosclerosis were found in the mandible. Among them, 53(91.4%) occurred on the posterior region. The mean diameter was 9.1㎜. The internal structure was radiopaque in 39(67.2%) and mixed radiolucent and radiopaque in 19(35.3%). There was no specific effect on the surrounding structures. In 16(27.6%), partial or complete radiolucent margin was noted which might be digital image artifact by enhancement.

This study was performed as a part of serial experiments of applying convolutional neural network(CNN) in determining osteoporosis on panoramic radiograph. The purpose of this study was to investigate how sensitively CNN determine osteoporosis on cropped panoramic radiograph. Panoramic radiographs from 1268 female patients(mean age 45.2 ± 21.1 yrs) were selected for this study. For the osteoporosis group, 633(mean age 72.2 ± 8.5 yrs) were selected, while for the normal group 635(mean age 28.3 ± 7.0 yrs). AlexNet was utilized as CNN in this study. A multiple-column CNN was designed with two rectangular regions of interest on the mandible inferior area. An occluding method was used to analyze the sensitive area in determining osteoporosis on AlexNet. Testing of AlexNet showed accuracy of 99% in determining osteoporosis on panoramic radiographs. AlexNet was sensitive at the area of cortical and cancellous bone of the mandible inferior area including adjacent soft tissue.

Deep convolutional network is a deep learning approach to optimize image recognition. This study aimed to apply DCNN to the reading of mandibular cortical thinning in digital panoramic radiographs. Digital panoramic radiographs of 1,268 female dental patients (age 45.2 ± 21.1yrs) were used in the reading of the mandibular cortical bone by two maxillofacial radiologists. Among the subjects, 535 normal subject’s panoramic radiographs (age 28.6 ±7.4 yrs) and 533 those of osteoporosis pationts (age 72.1 ± 8.7 yrs) with mandibular cortical thinning were used for training DCNN. In the testing of mandibular cortical thinning, 100 panoramic radiographs of normal subjects (age 26.6 ± 4.5 yrs) and 100 mandibular cortical thinning (age 72.5 ± 7.2 yrs) were used. The sensitive area of DCNN to mandibular cortical thinning was investigated by occluding analysis. The readings of DCNN were compared by two maxillofacial radiologists. DCNN showed 97.5% accuracy, 96% sensitivity, and 99% specificity in reading mandibular cortical thinning. DCNN was sensitively responded on the cancellous and cortical bone of the mandibular inferior area. DCNN was effective in diagnosing mandibular cortical thinning.

Artificial intelligence, has been applied in interpreting osteoporosis on dental panoramic radiograph with high accuracy. The purpose of this study was to investigate the sensitive area of convolutional neural network(CNN), one of artificial intelligence, in interpreting osteoporosis on dental panoramic radiograph. Dental panoramic radiographs taken from 1,170 female (49.19 ±21.91 average age, 21 minimum age, and 84 maximum age) were selected for this study. Two oral maxillofacial radiologists agreed upon interpreting osteoporosis by interpreting mandibular inferior cortical changes. The region of interest included upper and lower jaws for training and testing CNN in interpreting osteoporosis. A filter which was set to look for image characteristics moved through the entire panoramic radiography to identify sensitive areas that distinguish normal groups and osteoporosis patients. In interpreting osteoporosis on panoramic radiograph, CNN responded sensitively at the cancellous bone of the upper and lower jaws while oral maxillofacial radiologists interpreted mandibular inferior cortical change.

This study aimed to measure ramal lengths and angles on panoramic radiography applying a polar coordinate system for analyzing facial asymmetry within normal range. Panoramic radiographs taken from 15 males and 15 females (mean age 31.33±3.7 yrs in males and 28.87±2.72 yrs in females) with symmetric-looking faces were selected. The polar coordinate system, length of condylar and ramal height and angles between the ramus tangent and the connecting line of the most inferior point of bilateral orbital rim were measured from panoramic radiographic images. Bilateral differences in the ramal and condylar heights and angles were determined by asymmetric index. The polar coordinate applied for analyzing facial asymmetry uses length and angle measure. The normal range of facial asymmetry was measured using mean and standard deviation of asymmetry index of length and angle measure. A new analysis method using polar coordinate system on panoramic radiograph may provide more accurate analysis for facial asymmetry.

This study was conducted as part of a series of studies to introduce the Convolutional Neural Network(CNN) into the diagnostic field of osteoporosis. The purpose of this study was to compare the results when testing Digital Radiography(DR) and Computed Radiography(CR) panoramic radiographs by CNN that were trained by DR panoramic radiographs. The digital panoramic radiographs of females who visited for the purpose of diagnosis and treatment at Chonnam National University Dental Hospital were taken. Two Oral and Maxillofacial Radiologists were selected for the study to compare the panoramic radiographs with normal and osteoporosis images. Among them, 1068 panoramic radiographs of females{Mean [± standard deviation] age: 49.19 ± 21.91 years} obtained by DR method were used for training of CNN. 200 panoramic radiographs of females{Mean [± standard deviation] age: 63.95 ± 6.45 years} obtained by DR method and 202 panoramic radiographs of females{Mean [± standard deviation] age: 62.00 ± 6.86 years} obtained by CR method were used for testing of CNN. When the DR panoramic radiographs were tested, the Accuracy was 92.5%. When the CR panoramic radiographs were tested, the Accuracy was 76.2%. It can be seen that the CNN trained by DR panoramic radiographs is suitable to be tested with the same DR panoramic radiographs.

We present the results of our mid-infrared (MIR) observations of distant clusters of galaxies with AKARI. The wide-eld of view of IRC/AKARI (10'X10') is ideally suited for studying dust-obscured star-formation (SF) activity of galaxies along the cosmic web in the distant universe. We performed a deep and wide-field 15 μm (rest-frame  8 μm) imaging observation of the RXJ1716+6708 cluster (z = 0:81) with IRC. We find that 15 m-detected cluster member galaxies (with total infrared luminosities of LIR & 1011L⊙) are most preferentially located in the cluster outskirt regions, whilst such IR-luminous galaxies avoid the cluster centre. Our Hα follow-up study of this field conrmed that a significant fraction of 15 μm-detected cluster galaxies are heavily obscured by dust (with AHα>3 mag in extreme cases). The environment of such dusty star-burst galaxies coincides with the place where we see a sharp "break" of the colour-density relation, suggesting an important link between dust-obscured SF activity and environmental quenching. We also report the discovery of a new cluster candidate around a radio galaxy at z = 1:52 (4C 65.22), where we obtained one of the deepest IRC imaging datasets with all the nine filters at 2-24 μm. This field will provide us with the final, excellent laboratory for studying the dust-enshrouded SF activity in galaxies along the cosmic web at the critical epoch of cluster galaxy evolution with AKARI.

With the rapid growth of the ultra-high-definition (UHD) TV market, immersive technology and applications designed for living room TV environments have become increasingly popular. Therefore, realistic and immersive games are expanding further into next-generation game platforms based on smart TV technology. This paper proposes a technique to integrate 360-degree panoramic video and Internet protocol television (IPTV) systems to create realistic and immersive game services. Three hundred sixty-degree panoramic video is one of the most effective means of providing the immersive perception and reality of first-person free-viewpoints in virtual reality systems. This approach increases immersion into virtual reality space while reducing client loads.

This study aimed to investigate the association between carotid artery calcification (CAC) on panoramic radiograph and intima-media thickeness (IMT) measured on ultrasound. Panoramic radiographs which were taken from dental patients aged 50 years and older who visited for dental treatment were screened for the presence of CAC. The study group was composed of seven patients (four males and three females, average age 74.4±4.2 yrs) with CAC detected on panoramic radiographs, and the control group eleven patients (seven males and four females, average age 64.5±10.1 yrs) without CAC. All the patients underwent carotid ultrasonography to measure carotid IMT. The IMT was compared between the groups by nonparametric analysis of covariance (ANCOVA). The range of IMT of the study group was 1.10~2.0 mm, while that of the control group 0.60~1.10 mm. The mean of IMT was 1.50±0.34 mm in the study group and 0.85±0.14 mm in the control group, and there was statistically significant difference between the two groups (p<.01). In conclusion, CAC detected on panoramic radiograph might have an association with atherosclerosis

This report presents a summary of the Legacy of AKARI: A Panoramic View of the Dusty Universe meeting held between 27-29th February 2012 at Jeju Island, South Korea.

We constructed an unbiased asteroid catalog from the mid-infrared part of the All-Sky Survey with the Infrared Camera (IRC) on board AKARI. About 20% of the point source events recorded in the IRC All-Sky Survey observations were not used for the IRC Point Source Catalog in its production process because of a lack of multiple detection by position. Asteroids, which are moving objects on the celestial sphere, are included in these "residual events" We identified asteroids out of the residual events by matching them with the positions of known asteroids. For the identified asteroids, we calculated the size and albedo based on the Standard Thermal Model. Finally we had a new brand of asteroid catalog, which contains 5,120 objects, about twice as many as the IRAS asteroid catalog.

Taking the great advantage of Subaru's wide field coverage both in the optical and in the near infrared, we have been providing panoramic views of distant clusters and their surrounding environments over the wide redshift range of 0:4 < z < 3. From our unique data sets, a consistent picture has been emerging that the star forming activity is once enhanced and then truncated in galaxy groups in the outskirts of clusters during the course of cluster assembly at z < 1. Such activity is shifted into cluster cores as we go further back in time to z ~ 1.5. At z = 2 - 2.5, we begin to enter the epoch when massive galaxies are actually forming in the cluster core. And by z ~ 3, we eventually go beyond the major epoch of massive galaxy formation. It is likely that the environmental dependence of star forming activity is at least partly due to the external environmental effects such as galaxy-galaxy interaction in medium density regions at z < 1, while the intrinsic effect of galaxy formation bias overtakes the external effect at higher redshifts, resulting in a large star formation activity in the cluster center.

본 연구의 목적은 관광을 위하여 전체적인 지형을 쉽게 이해하고, 전반적인 위치를 암기하기 쉽게 하는 3차원 지도 개발이다. 이를 위하여 파노라마 전경의 블록 유닛 형태 그리고 파노라마 블록 유닛 전경의 색채계획에 관하여 논하였다. 우선 도로 블록을 제작하기 위하여 28 종류를 정의하였고, 이를 기초로 12종류의 주요 그룹을 제작하였다. 그리고 파노라마 블록 유닛을 제작하기 위하여 지도에 표현된 지형 42 종류를 정의하였다. 후, 조합되어지는 방식의 유사도 따라서 5종류의 그룹으로 분류하였고 그리고 표현되어지는 면적의 크기에 따라서 5종류의 그룹으로 분류하였다. 이를 기초로 파노라마 블록 유닛을 3차원 그래픽 구조물의 형태로 디자인하였다. 마지막으로 파노라마 블록 유닛 각각을 5가지의 다른 색깔 클래스로 구분하여 색채를 계획하였다. 색채 계획의 타당성을 조사하기 위하여 문 스팬스의 미적 측정값을 조사하였다. 결과 값은 0.5보다 크므로 제작된 칼라의 조합은 잘 조화된 것으로 평가되었다.

영상 인식 기술은 평면 영상에 대해서 많이 연구되고 그 성능 또한 발전하고 있다. 그러나 평면 영상이 아닌 구면 파노라마 영상과 다양한 환경에서 주어지는 특수한 형태의 영상에 대한 인식은 평면과 다르게 기하학적인 왜곡으로 인해서 많은 어려움이 따른다. 본 논문에서는 평면영상의 인식 기술에서 최근 각광받는 훈련을 통한 신경망 인식 기법이 구면 파노라마 영상의 인식에서도 쓰일 수 있음을 보인다. 또한 구면 영상에 대한 기존 신경망 모델의 인식률을 높이기 위해서 큐브맵 변환을 활용하는 방법을 제시한다.

최근 고령화 사회에 접어들고 있는 의료현장에서 치아건강에 대한 관심이 커지면서 진단을 위한 치아 방사선 검사의 횟수가 증가하고 있다. 이는 국민 전체의 방사선피폭량 또한 증가하고 있다고 볼 수 있다. 또한 치과방사선에 대한 국민들의 방사선 피폭에 대한 관심도 증가하고 있어 치과 파노라마 촬영장치에 대한 기본 데이터 확보와 이에 대한 조사 및 선량의 측정이 필요하다. 본 연구에서는 ALOKA PDM-117 선량계를 이용하여 치과파노라마장치(VATEC Pax-400)에서 발생되는 2차원적인 선량분포도를 측정하고 그 분포에 대한 평가를 환자의 방사선피폭 차원에서 확인하였다. 치과파노라마장치의 선량분포는 치아부분 이외에도 턱과 안면부위에서 높았으며 산란선의 영향까지 고려한다면 방사선에 민감한 수정체에까지 불필요한 방사선의 피폭됨을 알 수 있었다. 본 연구 결과는 다양한 크기의 검사체와 선량 측정위치에서 보다 정확한 선량평가를 하는데 매우 유용하게 이용될 것으로 사료된다.