• Journal of Animal Science and Technology
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• v.66 no.1
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.533-540
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• 2022

Specialists diagnose skin cancer using a dermatoscopy to detect skin cancer as early as possible, but it is difficult to determine accurate skin lesions because skin lesions have various shapes. Recently, the skin lesion segmentation method using deep learning, which has shown high performance, has a problem in segmenting skin lesions because the boundary between healthy skin and skin lesions is not clear. To solve these issues, the proposed method constructs a transformer block to effectively segment the skin lesion, and constructs an edge decoder for each layer of the network to segment the skin lesion in detail. Experiment results have shown that the proposed method achieves a performance improvement of 0.041 ~ 0.071 for Dic Coefficient and 0.062 ~ 0.112 for Jaccard Index, compared with the previous method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.1-12
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• 2009

Mobbing is going the rounds through a society rapidly and Military is not exception. Because mobbing of military is expressed not only psychology exclusion that is mobbing pattern of adult society but also sometimes psychologic and physical mobbing, is possible to join serious military discipline like a suicide and outrageous behavior. Specially military try to protect occurrence of victims that is public service through various rules and management plan but victims is going on happen. It means importance of grasp not only current mobbing victims but also potential mobbing victims better than preparation of various rules and management plans. Therefore this paper extracts seven factors and fifty attributes that are related to this matter mobbing. Next, by using Gunwoo's Social Network Service that is made for oneself and expressing extracting factors as '1' if they are related me or not '0'. And apply similarity function(Dice's coefficient) to attributes summation included in factors to calculate similarity between the users. Third, calculate optimizing weight choosing factors included attributes by applying neural network algorithm of SPSS Clementine and propose Mobbing Value(MV) Algorithm through this total summation. Finally through this algorithm which will contribute to efficient personnel management, we can grasp mobbing victims and tentative mobbing victims.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• Journal of Korean Academy of Nursing
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• v.36 no.7
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• pp.1204-1214
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• 2006

Purpose: This retrospective study was done to evaluate the status of nosocomial urinary tract infections and to determine the risk factors and transmission route of causal IRPA through molecular epidemiology. Method: Two hundred ninety-nine of 423 patients admitted to the internal medicine and surgery ICU at a university hospital incity B had a positiveurine culture. Twelve of the 299 patients who had a urinary tract infection had IRPA strains. The data was collected from November 1, 2004 to January 31, 2005. The following results were obtained after the data was analyzed using percentile and UPGMA. Result: The rate of nosocomial urinary tract infections in the ICU was 10.8%. Therewere 16.8 cases of infection based on the period of hospitalization. There were 16.9 cases of infection based on the use of a foley catheter. The rate of nosocomial urinary tract infection in the ICU and urinary tract infections related to IRPA were higher in patients with the following characteristics: men, old age, admission through the emergency room, longer than seven days admission, severity of admitting causes, disturbance of consciousness, hydration less than 300cc in 24hours, a long course of antibiotics, a long period of foley catheterization and perineal care. Most of the microorganisms that caused the urinary tract infection were gram negative bacilli, among which P. aeruginosa was found in 70 patients (18.5%) and IRPA in 12 (4.0%). Among the 12 IRPA strains that were tested with PFGE, eight showed a dice coefficient higher than 80%, suggesting a genetic relationship. They were related with the period of hospitalization in the same ICU. These patients all received direct care for a urinary tract infection. Conclusion: Through these results, IRPA can be consideredas a contributing factors to urinary tract infections thus, active preventative measures are needed by the medical staff.

• Journal of Microbiology and Biotechnology
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• v.17 no.11
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• pp.1772-1781
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• 2007

Activated sludge was sequentially adapted to benzene, toluene, and o-xylene (BTX) to study the effects on the change of microbial community. Sludge adapted to BTX separately degraded each by various rates in the following order; toluene>o-xylene>benzene. Degradation rates were increased after exposure to repeated spikes of substrates. Eleven different kinds of sludge were prepared by the combination of BTX sequential adaptations. Clustering analyses (Jaccard, Dice, Pearson, and cosine product coefficient and dimensional analysis of MDS and PCA for DGGE patterns) revealed that acclimated sludge had different features from nonacclimated sludge and could be grouped together according to their prior treatment. Benzene- and xylene-adapted sludge communities showed similar profiles. The sludge profile was affected from the point of the final adaptation substrate regardless of the adaptation sequence followed. In the sludge adapted to 50 ppm toluene, Nitrosomonas sp. and bacterium were dominant, but these bands were not dominant in benzene and benzene after toluene adaptations. Instead, Flexibacter sp. was dominant in these cultures. Dechloromonas sp. was dominant in the culture adapted to 50 ppm benzene. Thauera sp. was the main band in the sludge adapted to 50 ppm xylene, but became vaguer as the xylene concentration was increased. Rather, Flexibacter sp. dominated in the sludge adapted to 100 ppm xylene, although not in the culture adapted to 250 ppm xylene. Two bacterial species dominated in the sludge adapted to 250 ppm xylene, and they also existed in the sludge adapted to 250 ppm xylene after toluene and benzene.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1376-1384
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• 2013

In this paper, we propose an automaticdetection method of foreign bodies through template matching in industrial CT volume data. Our method is composed of three main steps. First,Indown-sampling data, the product region is separated from background after noise reduction and initial foreign-body candidates are extracted using mean and standard deviation of the product region. Then foreign-body candidates are extracted using K-means clustering. Second, the foreign body with different intensity of product region is detected using template matching. At this time, the template matching is performed by evaluating SSD orjoint entropy according to the size of detected foreign-body candidates. Third, to improve thedetection rate of foreign body in original volume data, final foreign bodiesare detected using percolation method. For the performance evaluation of our method, industrial CT volume data and simulation data are used. Then visual inspection and accuracy assessment are performed and processing time is measured. For accuracy assessment, density-based detection method is used as comparative method and Dice's coefficient is measured.

• Investigative Magnetic Resonance Imaging
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• v.24 no.3
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• pp.123-131
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• 2020

Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (r_pxy, r_pz, r_sxy, r_sz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of r_pxy = 10, r_pz = 1, r_sxy = 3, r_sz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

• Journal of Biomedical Engineering Research
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• v.41 no.5
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• pp.195-202
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• 2020

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.