• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.118-124
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• 2023

Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.343-344
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• 2022

간암에는 크게 두 종류가 있는데 하나는 간에서 생긴 종양이 악성종양으로 진행된 것이고 다른 하나는 다른 장기에서 생긴 암이 간으로 전이되는 것이다. 본 논문에서는 간에서 생긴 종양이 악성종양으로 진행되는 것을 조기 발견하고 막고자 Object Detect 모델인 YOLO v5의 다섯 가지 모델을 비교하여 악성 종양으로의 발전 가능성이 있는 간의 lesion을 찾아보았다.

• The Journal of the Korean dental association
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• v.49 no.3
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• pp.153-158
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• 2011

Oral precancerous lesion is a morphologically altered tissue in which oral cancer is more likely to occur than is apparently normal counterpart. As dentists always do oral examination and dental treatment, with fundamental knowledge and attention of this lesion, it is relatively easy to find one. If followed by proper treatment and management, it is possible to minimize its oral cancer progression, or at least delay it. Even if it were to progress to oral cancer, very early detection is possible. However, no specific biomarkers are present at the moment that could reveal oral precnacerous lesion that is high risk of oral cancer progression. Since early detection of oral cancer followed by treatment could show good prognosis with just a simple ablative surgery. Dentists should also instruct people to avoid risk factor related oral cancer progression and take natural compound having anticancer effect. Hereby, As a primary care givers, dentists play an important role in prevention of oral cancer.

• Korean Journal of Veterinary Research
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• v.42 no.2
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• pp.219-224
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• 2002

We report the seasonal prevalence of the mycoplasmal pneumoniae of swine (MPS) in slaughter pigs from July of 1999 to June of 2000. Gross finding of lung lesion observed and examined by dot-ELISA. In gross finding of lung lesion from 750 pig samples, 465 (62.0%) was MPS, and 129 (17.2%) was single or double infection with actinobacillosis and pasturellosis. However, 156 (20.8%) had no lesion. In seasonal detection, the prevalence was found to be winter (69.5%), autumn (63.5%), summer (60.0%) and spring (54.7%) in orderly frequency. In dot-ELISA, the result was showed the positive reaction (x16>titre) with 58.0% and negative (x4

• The Journal of the Korean bone and joint tumor society
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• v.11 no.1
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• pp.32-39
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• 2005

Introduction: Currently, F-18 fluorodeoxyglucose positron emission tomography scans (FDG-PET) has been investigated in soft tissue tumor especially for tumor detection and noninvasive grading. However, the validity and the efficacy of FDG-PET are still unclear in clinical evaluation. The purpose of this study is to determine the efficacy of FDG-PET in compared to conventional diagnostic imaging studies currently used in the soft tissue tumor. Methods: Between March 2001 and March 2002, 29 patients (sixteen males, thirteen females, mean age, 47 years; a range from 4 to 73) diagnosed with soft tissue tumor were evaluated by both conventional diagnostic imaging and FDG-PET. Valid reference test of the local lesion was the histopathologic diagnosis, which was measured in all patients. The suspecting metastasis in the imaging studies was validated by pathology or follow up imaging for at least 6 months. Each imaging diagnosis was made independently. The accuracy of each diagnostic method was evaluated. The incremental cost accuracy ratio was determined in each diagnostic method. Results: For detection of local lesion, sensitivity, specificity, and accuracy for MRI and FDGPET scans were 91%, 57%, 83% and 95%, 43%, 83% respectively. For detection of distant lesion, sensitivity, specificity, accuracy for conventional diagnostic methods and FDG-PET scans were 77%, 89%, 87% and 92%, 94%, 93% respectively. The incremental cost accuracy ratio (ICAR) of FDG-PET for detection of distant lesion was 145,000won/%. According to ICAR for each tumor grade, PET strategy is most cost-effective at high grade tumors. Conclusions: For detection of local lesion such as recurrence or remnant tumor, FDG-PET scan was not more accurate than MRI. However, It was more accurate for detection of metastatic lesion than conventional methods. For detection of high grade tumor, PET was most costeffective than for detection of lower grade tumor.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.207-214
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• 2005

Breast cancer detection using electrical impedance techniques is based on numerous experimental findings that cancerous tissues have higher electrical conductivity values than normal tissues. Lately, by taking advantage of the structure of current flows underneath a planar probe of array electrodes, a mathematical formula to find lesions from a measured trans­admittance map has been derived. In order to experimentally validate its mathematical analysis and the suggested lesion estimation algorithm, we developed a 256-channel trans-admittance scanner (TAS) for probing anomalies underneath a planar array of electrodes. In this paper, we describe the design and implementation of the TAS. Its performance together with the lesion estimation algorithm was evaluated using saline phantoms. Further studies are proposed to validate the system on human subjects.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.436-442
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• 2017

A novel method of Skin lesion segmentation based on the combination of Texture and Neural Network is proposed in this paper. This paper combines the textures of different pixels in the skin images in order to increase the performance of lesion segmentation. For segmenting skin lesions, a two-step process is done. First, automatic border detection is performed to separate the lesion from the background skin. This begins by identifying the features that represent the lesion border clearly by the process of Texture analysis. In the second step, the obtained features are given as input towards the Recurrent Echo state neural networks in order to obtain the segmented skin lesion region. The proposed algorithm is trained and tested for 862 skin lesion images in order to evaluate the accuracy of segmentation. Overall accuracy of the proposed method is compared with existing algorithms. An average accuracy of 98.8% for segmenting skin lesion images has been obtained.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.77-85
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• 2014

Usually, the skin pigmentation detection and diagnosis are made by clinicians. In this process it is subjective and non-quantitative. We develop an approach to detect and measure the different pigmentation lesions base on computer vision technology. In the paper we study several usually used skin-detecting color space like HSV, YCbCr and normalized RGB. We compare their performance with illumination influence for detecting the pigmentation lesions better. Base on a relatively stable color space, we propose an approach which is RGB channels vector difference characteristic for the detection. After the object region detection, we also use the difference to measure the difference between the lesion and the surrounding normal skin. From the experiment results, our approach can effectively detect the pigmentation lesion, and perform robustness with different illumination.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.979-987
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• 2021

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder have been recently proposed for automated detection of MS lesions. However, these autoencoder-based methods were developed only for 2D images (e.g. 2D cross-sectional slices) of MRI, so do not utilize the full 3D information of MRI. In this paper, therefore, we propose a novel 3D autoencoder-based framework for detection of the lesion volume of MS in MRI. We first define a 3D convolutional neural network (CNN) for full MRI volumes, and build each encoder and decoder layer of the 3D autoencoder based on 3D CNN. We also add a skip connection between the encoder and decoder layer for effective data reconstruction. In the experimental results, we compare the 3D autoencoder-based method with the 2D autoencoder models using the training datasets of 80 healthy subjects from the Human Connectome Project (HCP) and the testing datasets of 25 MS patients from the Longitudinal multiple sclerosis lesion segmentation challenge, and show that the proposed method achieves superior performance in prediction of MS lesion by up to 15%.

• Journal of Preventive Medicine and Public Health
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• v.7 no.2
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• pp.367-372
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• 1974

Authors reviewed 2,362 cases of consecutive vaginal and cervical smears submitted to the Department of Pathology, Pusan Gospel Hospital during one year period from Jan. 1, 1974 to Dec. 31, 1974. Prevalence of dysplasia, carcinoma in situ and invasive carcinoma of the uterine cervix was analyzed, and cost per a lesion was calculated. The followings are conclusions: 1. Prevalence of dysplasia, carcinoma in situ and invasive carcinoma was 2.88%, 0.34% and 2.58% restectively. 2. Cost per a lesion for dysplasia was calculated as 34,735 Won, for carcinoma in situ, as 295,250 Won and for invasive carcinoma as 38,721 Won. Cost per a lesion for dysplasia and carcinoma in situ was calculated as 31,079 Won and for dysplasia and for all the lesions as 17,248 Won. 3. The results obtained suggested that mass cytologic screeiding for detection of dysplasia, carcinoma in situ and invasive carcinoma was reasonable in the present status of economy.