• 대한의용생체공학회:의공학회지
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• 제31권2호
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• pp.94-98
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• 2010

In magnetic resonance electrical impedance tomography (MREIT), a current-injection MR imaging method is adopted to produce a cross-sectional image of an electrical conductivity distribution in addition to MR images. The purpose of this study was to test the feasibility of MREIT for differentiating the canine lung parenchyma without and with pneumonia. Three normal healthy beagles and two mixed breed dogs with pneumonia were used. After attaching electrodes around the chest, we placed the dog inside our MR scanner. We injected as much as 30 mA current in a form of short pulses into the chest region. Reconstructed conductivity images of normal canine lungs exhibit a peculiar pattern of a relatively coarse salt and pepper noise. On the contrary, conductivity images of pneumonic canine lungs show significantly enhanced contrast of the lesions while the corresponding MR images show a little bit of contrast in the middle and caudal lung parenchyma due to the accumulation of pleural fluid. This preliminary study indicates that MREIT imaging of the chest may deliver unique new diagnostic information.

• 농업과학연구
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• 제47권4호
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Reproductive and Developmental Biology
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• 제37권4호
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• pp.247-253
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• 2013

Here, we present an approach of blood plasma proteome profiling and their comparisons between the young and the adult pigs as prerequisite for the identification of bio-markers related to the health conditions, growth performance and meat quality. To profile the proteome in porcine plasma, blood samples were collected from 19 young piglets and 20 adult male barrows and the plasma was retrieved. Then, protein profiling was initiated using one and two-dimensional electrophoresis. Proteins were spotted and then identified by MALDI-TOF-TOF and LC-MS-MS. In the results, more than thirty-six and twenty eight protein spots were selected in young piglets and adult pigs, respectively and twenty three proteins were identified. The proteome profile images were compared between those ones using Image Master Version 7.0. The image of expressed proteome showed that most of proteins from plasma of young piglet separated clearly and concentrated in 2DE display compared to ones from adult. Image analysis in detail was carried out to look for the specific proteins related to age progression. It demonstrated that the characteristics of proteome expression could be distinct to their age stages. Further investigations needed to proceed to understand the age dependent change of protein conformation and biological meaning of those differences in proteome expression between young and mature adult pigs.

• 한국축산식품학회지
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• 제38권2호
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• 한국축산식품학회지
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• 제40권6호
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• pp.896-907
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• 2020

Imaging technique or computer vision (CV) technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of CV technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 h post-mortem. Traits evaluated were color value (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, fat, ash, thiobarbituric acid reactive substance (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total viable count (TVC) and total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determination were done in triplicate and the mean value was reported. Data analysis was carried out using the programme Statgraphics Centurion XVI. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r=0.65) and moisture (r=0.56) with 'a*' value obtained from image analysis and the highest calibration and prediction accuracy was found in lightness (r²_c=0.73, r²_p=0.69) in beef. Results of this work show that CV technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.

• 한국축산식품학회지
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• 제42권1호
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• pp.98-110
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• 2022

Meat jerky is a type of meat snack with a long shelf life, light weight, and unique sensory properties. However, meat jerky requires a long manufacturing time, resulting in high energy consumption. In this study, beef jerky was prepared by injecting different concentrations of brine at different hot-air drying times (0-800 min). When the brine injection levels were increased to 30%, the drying characteristics of beef jerky, such as drying time and effective moisture diffusivity, were significantly improved owing to the relatively high water content and the formation of porous structures. The physicochemical properties (e.g. meat color, porosity, shear force, and volatile basic nitrogen) of the beef jerky injected with 30% brine were improved owing to the shortened drying time. Scanning electron microscopy images showed that the beef jerky structure became porous and irregular during the brine injection process. Our novel processing technique for manufacturing beef jerky leads to improved quality characteristics and shortened drying times.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.167-177
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• 2024

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

• 대한의용생체공학회:의공학회지
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• 제26권5호
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• pp.295-300
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• 2005

We introduce an x-ray micro tomography system capable of high resolution imaging of a local region inside a small animal. By combining two kinds of projection data, one from a full field-of-view (FOV) scan of the whole body and the other from a limited FOV scan of the region of interest, we have obtained zoomed-in images of the region of interest without any contrast a nomalies. We have integrated a micro tomography system using a micro-focus x-ray source, a $1248\times1248$ flat-panel x-ray detector, and a precision scan mechanism. Using the cross-sectional images taken with the zoom-in micro tomography system, we measured trabecular thicknesses of femur bones in postmortem rats. To compensate the limited spatial resolution in the zoom-in micro tomography images, we used the fuzzy distance transform for the calculation of the trabecular thickness. To validate the trabecular thickness measurement with the zoom-in micro tomography images, we compared the measurement results with the ones obtained from the conventional micro tomography images of the extracted bone samples.

• Nuclear Medicine and Molecular Imaging
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• 제42권5호
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• pp.401-409
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• 2008

목적: 최근 들어, 분자 영상에서 다중 영상 기법이 널리 보급되고 있다. 우리는 PET/MR 융합 영상을 얻기 위해 쉽게 이용 가능한 점토와 순간 고형제를 이용하여 동물 특이적 자세 고정 틀을 제작하였다. 동물 특이적 자세 고정 틀은 동물의 고정과 재현성 있는 자세 연출이 가능하도록 한다. 여기에서 우리는 실험 동물의 틀을 제작하는데 있어 점토와 순간 고형제를 비교해 보았다. 재료 및 방법: MicroPET의 받침대와 잘 맞는 바닥이 둥근 아크릴 받침대를 먼저 제작하였다. 실험 동물은 마취 후, 자세 고정을 위해서 틀 위에 올려놓았다. 틀 제작을 위하여 순간 고형제와 점토가 사용되었다. 순간 고형제와 점토를 사용한 두 가지 경우 모두, 실험 동물의 자세 고정을 위하여 부드럽게 실험 동물을 눌러 위치를 잡았다. 위치가 잡혔으면, 쥐를 들어내고, 점토로 뜬 틀은 $60^{\circ}C$ 건조기에 넣어 두어 완전히 경화시켰다. 그리고 $[^{18}F]FDG$용액이 든 밀봉된 4개의 파이펫 팁을 기준 마커로 사용하였다. 실험 동물의 꼬리에 $[^{18}F]FDG$를 정맥 주사하여 microPET 스캔을 실시한 후, 동일한 실험동물을 순차적으로 MRI 스캔하였다. 결과: 다중 영상을 위하여 점토와 순간 고형제로 동물 특이적 자세 고정 틀을 제작하였다. MicroPET과 MRI를 통하여 기능적이고 해부학적인 영상을 얻었다. PET/MR 융합 영상은 무료로 이용 가능한 AMIDE 프로그램을 사용하여 획득할 수 있었다. 결론: 쉽게 이용 가능한 점토, 순간 고형제와 일회용 파이펫 팁을 사용하여 성공적으로 동물 특이적 자세 고정 틀을 제작할 수 있었다. 동물 특이적 자세 고정 틀 덕택으로, 적은 오차 범위 내에서 PET/MR 융합 영상을 얻을 수 있었다.

• Animal Systematics, Evolution and Diversity
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• 제24권2호
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• pp.225-227
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• 2008

Tiphia sulcatus Kim and Han, sp. nov., is described based on specimens collected in Korea. In addition, a diagnosis, and digital images are provided.