• 한국임상수의학회지
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• 제38권3호
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• pp.159-162
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• 2021

A 5-year-old, intact male, poodle dog with right external auditory canal obstruction and subaural mass was presented. Physical examination revealed that right external auditory canal opening was absent and right head tilt was identified. Aspiration in right subaural mass revealed a small amount of dark brown exudate. Streptococcus canis and Staphylococcus spp. were identified on the microbial culture. Radiography of the skull was revealed absence of air-filled ear canal at the right external ear canal (EEC) level. Computed tomography (CT) revealed well capsulated, hypoattenuated mass in the right EEC region. On the contrast enhanced CT images, rim enhancement around the mass and ear canal obstruction were identified. Fluid attenuated material filled with right bulla. Mild thickening of the right tympanic bulla wall with mild lytic lesion of the ventral wall were found. Based on the images findings, the case was tentatively diagnosed as right external auditory canal atresia with otitis media. Total ear canal ablation and lateral bulla osteotomy was performed. The entire ear canal was removed, numerous hair in the canal and the thickening wall were founded. Right ear canal was sent for histopathological evaluation and found to otitis externa. The patient was followed up for two weeks and there were no complications. This report described the CT diagnosis of right EEC atresia with otitis media rarely reported in small breed dogs.

• 대한의용생체공학회:의공학회지
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• 제27권4호
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• pp.189-196
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• 2006

Micro computed tomography (micro-CT) has been used for in vivo animal study owing to its noninvasive and high spatial resolution capability. However, the sizes of existing detectors for micro-CT systems are too small to obtain whole-body images of a small animal object with $\sim$10 micron resolution and a part of its bones or other organs should be extracted. So, we have introduced the zoom-in micro-tomography technique which can obtain high-resolution images of a local region of an live animal object without extracting samples. In order to verify our zoom-in technique, we performed in vivo animal bone study. We prepared some SD (Sprague-Dawley) rats for making osteoporosis models. They were divided into control and ovariectomized groups. Again, the ovariectomized group is divided into two groups fed with normal food and with calcium-free food. And we took 3D tomographic images of their femurs with 20 micron resolution using our zoom-in tomography technique and observed the bone changes for 12 weeks. We selected ROI (region of interest) of a femur image and applied 2D FDT (fuzzy distance transform) to measure the trabecular bone thickness. The measured results showed obvious bone changes and big differences between control and ovariectomized groups. However, we found that the reliability of the measurement depended on the selection of ROI in a bone image for thickness calculation. So, we extended the method to 3D FDT technique. We selected 3D VOI (volume of interest) in the obtained 3D tomographic images and applied 3D FDT algorithm. The results showed that the 3D technique could give more accurate and reliable measurement.

• Asian-Australasian Journal of Animal Sciences
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• 제18권8호
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• pp.1194-1198
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• 2005

A computer vision system was designed and validated to recognize an individual Holstein cattle by processing images of their body patterns. This system involves image capture, image pre-processing, algorithm processing, and an artificial neural network recognition algorithm. Optimum management of individuals is one of the most important factors in keeping cattle healthy and productive. In this study, an image-processing system was used to recognize individual Holstein cattle by identifying the body-pattern images captured by a charge-coupled device (CCD). A recognition system was developed and applied to acquire images of 49 cattles. The pixel values of the body images were transformed into input data comprising binary signals for the neural network. Images of the 49 cattle were analyzed to learn input layer elements, and ten cattles were used to verify the output layer elements in the neural network by using an individual recognition program. The system proved to be reliable for the individual recognition of cattles in natural light.

• 한국임상수의학회지
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• 제31권3호
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• pp.237-240
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• 2014

12살령의 암컷 알라스칸 말라뮤트가 후지 파행과 통증으로 내원하였다. 방사선 검사에서 요추 5번 추체 내 방사선 투과성 병변이 확인되었다. MR 영상에서 요추 5번 추체에서 T1 저신호, T2 고신호, GE 저신호 및 조영 후 T1 영상에서 이질적인 조영 증가 병변이 확인되었고, 병변은 척수까지 침입하여 척수 실질을 심하게 압박하고 있었다. 요추 6번 추체 내에서도 국소적인 방사선 투과성 병변이 확인되었으며, MR 영상에서 T1, T2 및 GE 고신호 및 조영 후 국소적인 조영 증가 병변이 관찰되었다. 심한 통증으로 안락사 후, 생검을 통해 형질세포종이 진단되었다. 척수 질환 환자에서 종양은 반드시 고려되어야 하며 영상 검사가 병변의 위치와 범위를 평가하는데 도움이 된다. 이 증례는 개 척추에서 발생한 고립성 형질세포종의 영상에 대한 유용한 정보를 제공할 것이라 생각한다.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2646-2651
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• 2021

A rectangular-shaped PET system with an adjustable gantry (AGPET) has been developed for imaging small animals. The AGPET system employs a new depth of interaction (DOI) method using a depth dependent reflector patterns and a new digital time pickoff method based on the pulse reconstruction method. To evaluate the performance of the AGPET, timing resolution, intrinsic spatial resolution and point source images were acquired. The timing resolution and intrinsic spatial resolution were measured using two detector modules and Na-22 gamma source. The PET images were acquired in two field of view (FOV) sizes, 30 mm and 90 mm, to demonstrate the characteristic of the AGPET. As a result of in the experiment results, the timing resolution was 0.9 ns using the pulse reconstruction method based on the bi-exponential model. The intrinsic spatial resolution was an average of 1.7 mm and the spatial resolution of PET images after DOI correction was 2.08 mm and 2.25 mm at the centers of 30 mm and 90 mm FOV, respectively. The results show that the proposed AGPET system provided higher sensitivity and resolution for small animal imaging.

• 대한의용생체공학회:의공학회지
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• 제29권2호
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• pp.107-113
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• 2008

We introduce a rotating-gantry-based x-ray micro-tomography system to be used for small animal imaging studies. It has the zoom-in imaging capability for high resolution imaging of a local region inside the animal subject without any contrast anomalies arising from truncation of the projection data. With the sliding mechanism mounted on the rotating gantry holding the x-ray source and the x-ray detector, we can control the magnification ratio of the x-ray projection data. By combining the projection data from the large field of view (FOV) scan of the whole animal subject and the projection data from the small FOV scan of the region of interest, we can obtain artifact-free zoomed-in images of the region of interest. For the acquisition of x-ray projection data, we use a $1248{\times}1248$ flat-panel x-ray detector with the pixel pitch of 100 mm. It has been experimentally found that the developed system has the spatial resolution of up to 121p/mm when the highest magnification ratio of 5:1 is applied to the zoom-in imaging. We present some in vivo rat femur images to demonstrate utility of the developed system for small animal imaging.

• 한국축산시설환경학회지
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• 제7권2호
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• pp.103-110
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• 2001

This study conducted to monitor decomposition process of the charomatophore pollution solution of an animal slurry by using a CCD camera. After the solution was put into test tube, the images(R, G, B, H, L, S) values of the solution were measured by the imgae processing system, and those of it\`s optical density were measured for three hours to be decomposed by microscopic organism. The values of measured for three hours to be decomposed by microscopic organism. The values of measured images(R, G, B, H, L, S) were analysed and compared with those of the optical density. Some of the results are as follows. 1. High correlation coefficients, which analyzed by using data on linear equations, were 0.9557 and 0.9672. They were decreased regularly in this R-value experiment of RGB level. The microscopic organism in this experiment was effective for decomposition of the red charomatophore pollution solution. 2. The values of all correlation coefficients from relationship between RGB-value and optical density were more than 0.95 except H-values. RGB-values, which were average values of summed R, G, B values, had correlation coefficients of 0.9863, 0.9937. These results showed so good relationship that decomposition process of charomatophore pollution solution could be monitored by a image processing system.

• 한국축산시설환경학회지
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• 제3권2호
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• pp.105-113
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• 1997

The environment for pig production should be controlled according to a criterion based on the pig's thermoregulartory behavior. Quantifying the pig's thermoregulatroy behavior was needed to prepare a criterion based on the pig's thermoregulatory behavior. Therefore, this study was conducted to quantify the pig's thermoregulatory behavior. The raw images were acquired according to the pig's thermoregulatory behavior and they were processed to binary images. The mean deviations of x and y coordinates of pig's images in a binary image were computed and they were multiplied. The values computed in this manner showed very wide differences according to the pig's thermoregulatory behavior. Therefore, the image processing and mean deviation can be certainly used as a method for classifying the pig's thermoregulatory behavior.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.123-124
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• 2019

This paper proposes an improved deep learning method based on small data sets for animal image classification. Firstly, we use a CNN to build a training model for small data sets, and use data augmentation to expand the data samples of the training set. Secondly, using the pre-trained network on large-scale datasets, such as VGG16, the bottleneck features in the small dataset are extracted and to be stored in two NumPy files as new training datasets and test datasets. Finally, training a fully connected network with the new datasets. In this paper, we use Kaggle famous Dogs vs Cats dataset as the experimental dataset, which is a two-category classification dataset.

• Journal of Biosystems Engineering
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• 제29권4호
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.