• Journal of Veterinary Clinics
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• v.16 no.1
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• pp.75-79
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• 1999

Magnetic resonance imaging (MRI) is an imaging technique used to produce high quality images of the inside of the animal body. MRI is based on the principles of nuclear magnetic resonance (NMR) and started out as a tomographic imaging technique, that is it produced an image of the NMR signal in a thin slice through the animal body. The animal body is primarily fat and water, Fat and water have many hydrogen atoms. Hydrogen nuclei have an NMR signal. For these reasons magnetic resonance imaging primarily images the NMR signal from the hydrogen nuclei. Hydrogen protons, within the body align with the magnetic field. By applying short radio frequency (RF) pulses to a specific anatomical slice, the protons in the slice absorb energy at this resonant frequency causing them to spin perpendicular to the magnetic field. As the protons relax back into alignment with the magnetic field, a signal is received by an RF coil that acts as an antennae. This signal is processed by a computer to produce diagnostic images of the anatomical area of interest.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1708-1718
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• 2021

Objective: The purpose of this study was to evaluate the magnetic resonance (MR) characteristics and applicability of new, uniform, extremely small iron-based nanoparticles (ESIONs) with 3-4-nm iron cores using contrast-enhanced magnetic resonance angiography (MRA). Materials and Methods: Seven types of ESIONs were used in phantom and animal experiments with 1.5T, 3T, and 4.7T scanners. The MR characteristics of the ESIONs were evaluated via phantom experiments. With the ESIONs selected by the phantom experiments, animal experiments were performed on eight rabbits. In the animal experiments, the in vivo kinetics and enhancement effect of the ESIONs were evaluated using half-diluted and non-diluted ESIONs. The between-group differences were assessed using a linear mixed model. A commercially available gadolinium-based contrast agent (GBCA) was used as a control. Results: All ESIONs showed a good T1 shortening effect and were applicable for MRA at 1.5T and 3T. The relaxivity ratio of the ESIONs increased with increasing magnetic field strength. In the animal experiments, the ESIONs showed peak signal intensity on the first-pass images and persistent vascular enhancement until 90 minutes. On the 1-week follow-up images, the ESIONs were nearly washed out from the vascular structures and organs. The peak signal intensity on the first-pass images showed no significant difference between the non-diluted ESIONs with 3-mm iron cores and GBCA (p = 1.000). On the 10-minutes post-contrast images, the non-diluted ESIONs showed a significantly higher signal intensity than did the GBCA (p < 0.001). Conclusion: In the phantom experiments, the ESIONs with 3-4-nm iron oxide cores showed a good T1 shortening effect at 1.5T and 3T. In the animal experiments, the ESIONs with 3-nm iron cores showed comparable enhancement on the first-pass images and superior enhancement effect on the delayed images compared to the commercially available GBCA at 3T.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.125-135
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• 2010

This study analyzed the influences of human personalities and physiognomies on anthropomorphized animal characters and the relationship between the personalities and images of anthropomorphized animal characters and the associated images of real animals when animations expressed the animals. At first, the study classified animals who often appeared in animations, selected the representative characters whose personalities were well expressed, visualized them and rated them with words expressing personalities. Then, it rated personalities which were associated with the name of each animal(word) with words expressing personalities and analyzed the relationship the images and word association. The study results indicates that the similarity of two tests is very high and the images of animals which are perceived by human are anthropomorphized and expressed with familiar images for human. The study suggests expressing appropriate personalities when expressing anthropomorphized animal characters in animations.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• Korean Journal of Veterinary Research
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• v.62 no.1
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• pp.8.1-8.6
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• 2022

The aim of this prospective study was to investigate the effects of focal spot size of X-ray tube on sharpness of clinical radiographic images of dogs and cats. Radiographic images of 24 stifle joints, 15 carpi, 18 lumbar spines, 61 thoraxes, and 47 abdomens of 102 dogs and 4 cats were obtained in the present study, using 2 X-ray tubes with nominal focal spots of 2.0 mm and 0.6 mm, respectively. The sharpness of specific anatomical structures in all the images of 5 projections was assessed. The radiographic sharpness of various anatomical structures of lumbar spine and cortex of stifle with fine focal spot was increased significantly compared with broad focal spot images. In addition, the blurred motion was significantly higher in the fine focal spot images of thorax. In conclusion, our study suggests that a selective use of fine foci for imaging of lumbar spine or cortex of stifle enhanced radiographic sharpness.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.638-651
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• 2023

The objective of this study was to quantitatively estimate the level of grazing area damage in outdoor free-range pig production using a Unmanned Aerial Vehicles (UAV) with an RGB image sensor. Ten corn field images were captured by a UAV over approximately two weeks, during which gestating sows were allowed to graze freely on the corn field measuring 100 × 50 m². The images were corrected to a bird's-eye view, and then divided into 32 segments and sequentially inputted into the YOLOv4 detector to detect the corn images according to their condition. The 43 raw training images selected randomly out of 320 segmented images were flipped to create 86 images, and then these images were further augmented by rotating them in 5-degree increments to create a total of 6,192 images. The increased 6,192 images are further augmented by applying three random color transformations to each image, resulting in 24,768 datasets. The occupancy rate of corn in the field was estimated efficiently using You Only Look Once (YOLO). As of the first day of observation (day 2), it was evident that almost all the corn had disappeared by the ninth day. When grazing 20 sows in a 50 × 100 m² cornfield (250 m²/sow), it appears that the animals should be rotated to other grazing areas to protect the cover crop after at least five days. In agricultural technology, most of the research using machine and deep learning is related to the detection of fruits and pests, and research on other application fields is needed. In addition, large-scale image data collected by experts in the field are required as training data to apply deep learning. If the data required for deep learning is insufficient, a large number of data augmentation is required.

• Journal of Veterinary Clinics
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• v.34 no.5
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• pp.377-380
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• 2017

An 8-year-old, shih-tzu female dog was referred due to neurological signs including paraparesis and back pain. On the complete blood count, hematologic analysis showed elevated leukocytosis. Serum biochemical analysis revealed elevated serum alkaline phosphatase concentration and C-reactive protein concentration. On the neurologic exam, the dog was suspected to have thoracolumbar myelopathy. On magnetic resonance imaging, there were masses within the spinal canal at L1-3 intervertebral disc space that were located dorsal to spinal cord. It was hyperintense on T1-, T2-weighted magnetic resonance images, Fluid-attenuated inversion recovery, and fat suppression images. The contrast-enhanced T1-weighted images showed no enhancement. The lesions were well circumscribed. The spinal cord was compressed and displaced ventrally by the mass. After removal of the masses via L1-L3 dorsal laminectomy, pyogranulomatous inflammation was confirmed by histopathological examination. Six months after surgery, the dog recovered uneventfully and remained fully ambulatory with no neurological deficits. This case demonstrates the utility of magnetic resonance imaging for the diagnosis of spinal canal pyogranulomatous inflammation.

• Journal of Veterinary Clinics
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• v.31 no.4
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• pp.337-340
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• 2014

An 11-year-old male Maltese dog was presented with sudden onset of convulsion and right sided circling. On neurological examination, left side proprioception and menace reflexes were delayed. Blood examinations indicated severe thrombocytopenia and increased hepatic enzymes. On brain magnetic resonance imaging, lesions were founded on the left lateral subarachnoid space area. Those lesions showed hyperintense on T1-weighted images, hyperintense on T2-weighted images and hyperintense on fluid attenuated inversion recovery images. Cerebrospinal fluid analysis revealed xanthochromia and erythrophagocytosis. Coagulation test results demonstrated that fibrin degradation product and D-dimer concentrations were higher than normal range. The patient expired few hours after presentation. This case report demonstrates intracranial hemorrhage with disseminated intravascular coagulation in a dog.

• Animal Bioscience
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• v.36 no.6
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• pp.980-989
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• 2023

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.536-537
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• 2018

A goal of this paper is doing fine grained recognition of breed of animal from pet images. Research about fine grained recognition from images is continuously developing, but it is not for animal object recognition because they have polymorphism. This paper proposes method of higher animal object recognition using Grab-cut algorithm for object segmentation and Fisher Vector for image encoding.