• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.237-241
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• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.803-808
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• 2002

This paper describes automatic methods for detection of kidney and kidney tumor on abdominal CT scans. The abdominal CT images were digitalized using a film digitizer and a gray-level threshold method was used to segment the kidney. Based on texture analysis results, which were perform on sample images of kidney tumors, SEED region of kidney tumor was selected as result of homogeneity test. The average and standard deviation, which are representative statistical moments, were used to as an acceptance criteria for homogeneous test. Region growing method was used to segment the kidney tumor from the center pixel of selected SEED region using a gray-level value as an acceptance criteria for homogeneity test. These method were applied to 113 images of 9 cases, which were scanned by GE Hispeed Advantage CT scanner and digitalized by Lumisvs LS-40 film digitizer. The sensitivity was 85% and there was no false-positive results.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.348-355
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• 2011

In this paper, we propose an automatic prostate segmentation method from dynamic magnetic resonance (MR) images. Our method detects contrast-enhanced images among the dynamic MR images using an average intensity analysis. Then, the candidate regions of prostate are detected by the B-spline non-rigid registration and subtraction between the pre-contrast and contrast-enhanced MR images. Finally, the prostate is segmented by performing a dilation operation outward, and sequential shape propagation inward. Our method was validated by ten data sets and the results were compared with the manually segmented results. The average volumetric overlap error was 6.8%, and average absolute volumetric measurement error was 2.5%. Our method could be used for the computer-aided prostate diagnosis, which requires an accurate prostate segmentation.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.702-712
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• 2013

In this paper, we proposed an algorithm that extracts the airway and lung without loss of information in spite of the pulmonary vessel and nodules of the chest wall in the chest CT images. We use a mask image in order to improve the performance and to save processing time of airway and lung segmentation. In the second step, by converting left and right lungs to binary image using the morphological information, we have removed the solitary pulmonary nodule to identify the value of the threshold lung and the chest wall. The last step is to connect the outer shell of the lung with cubic Spline interpolation by adding the perfect pixel and computing the distance of the removed part. Experimental results using Matlab verified that the proposed method could overcome the drawbacks of the conventional methods.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.80-85
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• 2021

We developed a model to classify the absence of cervical cancer using deep learning from the cervical image to which the histogram equalization algorithm was applied, and to compare the performance of each model. A total of 4259 images were used for this study, of which 1852 images were normal and 2407 were abnormal. And this paper applied Image Sharpening(IS), Histogram Equalization(HE), and Contrast Limited Adaptive Histogram Equalization(CLAHE) to the original image. Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity index for Measuring image quality(SSIM) were used to assess the quality of images objectively. As a result of assessment, IS showed 81.75dB of PSNR and 0.96 of SSIM, showing the best image quality. CLAHE and HE showed the PSNR of 62.67dB and 62.60dB respectively, while SSIM of CLAHE was shown as 0.86, which is closer to 1 than HE of 0.75. Using ResNet-50 model with transfer learning, digital image-processed images are classified into normal and abnormal each. In conclusion, the classification accuracy of each model is as follows. 90.77% for IS, which shows the highest, 90.26% for CLAHE and 87.60% for HE. As this study shows, applying proper digital image processing which is for cervical images to Computer Aided Diagnosis(CAD) can help both screening and diagnosing.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.211-217
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• 2023

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.4
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• pp.328-343
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• 2023

Recently, a method of fabricating an esthetic anterior fixed prosthesis by integrating data such as three-dimensional facial scan and jaw motion to form a virtual patient with dynamic occlusion has been introduced. This enables smooth communication with patients during the diagnosis process, improves the predictability of esthetic prosthetic treatment, and lowers the possibility of occlusal adjustment. In this case report, a virtual patient with dynamic occlusion was created in which the results of the treatment were simulated, and esthetic maxillary anterior fixed prosthesis was fabricated. With the aid of the virtual patient, the final restorations were satisfactory both in terms of esthetic and function.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.303-310
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• 2013

Thyroid nodular disease is the most frequently appeared in thyroid disease. Thyroid ultrasonography offers location of nodules, size, the number, information of internal echo characteristic. Thus, it makes possible to sort high-risk nodule containing high possibility about thyroid cancer and to induct precisely when take a Fine Needle Biopsy Aspiration. On thyroid nodule, the case which is diagnosed as malignant is less than 5% but screening test is very important on ultrasound and also must be reduced unnecessary procedure. Therefore, in this study an approach for describing a region is to quantity its texture content. We applied TFA algorithm on case which has been pathologically diagnosed as papillary thyroid cancer. we obtained experiment image which set the ROI on ultrasound and cut the $50{\times}50$ pixel size, histogram equalization. Consequently, Disease recognition detection efficiency of GLavg, SKEW, UN, ENT parameter were high as 91~100%. It is suggestion about possibility on CAD which distinguishes thyroid nodule. In addition, it will be helpful to differential diagnosis of thyroid nodule. If the study on additional parameter algorithm is continuously progressed from now on, it is able to arrange practical base on CAD and it is possible to apply various disease in the thyroid US.

• Annals of Clinical Neurophysiology
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• v.12 no.1
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• pp.21-26
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• 2010

Background: Although quantitative sensory test (QST) is being used with increasing frequency for measuring sensory thresholds in clinical practice and epidemiologic studies, there has been no age-matched normative data in Korean adults. The objective of this study is to evaluate the value of QST in diabetic polyneuropathy with normal range in Korean adults. Methods: The Computer Aided Sensory Examination IV 4,2 (WR Medical Electronics Co., Stillwater, Minnesota, U.S.A.), with 4,2,1 stepping algorithm was used to determine vibration and cold perception threshold in 70 normal controls and 19 patients with diabetic polyneuropathy aged from 21 to 79 years. The data were used to define age-matched upper and lower normal limits and normal range of side to side difference. We also evaluated the duration of diabetes, serum HbA1C level, and findings of nerve conduction study (NCS) and QST in patients with diabetic polyneuropathy. Results: In normal adults, sensory thresholds slightly increased with age, and a slight side-to-side difference was observed. The diagnostic sensitivity of QST was not higher than NCS in patients with diabetic polyneuropathy (36.8% vs. 42.1%, p=0.716), especially among elderly patients. Conclusions: QST might be used as a complementary test for NCS in the diagnosis of diabetic polyneuropathy. Although the QST is a simple method for the evaluation of peripheral nerve function, there are some limitations. Most of all, because the QST measuring is dependent on the subjective response of patients, the degree of concentration and cooperation of the patients can significantly affect the result. And thus, attention should be paid during the interpretation of QST results in patients with peripheral neuropathy.