• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.37-44
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• 2013

In this paper, we propose the pulmonary nodule detection system based on voxel classification. The proposed system consists of three main steps. In the first step, we segment lung volume. In the second step, the lung structures are initially segmented. In the last step, we classify the nodules using voxel classification. To describe characteristics of each voxel, we extract the log-polar sampling based features. Support Vector Machine is applied to the extracted features to classify into nodules and non-nodules.

• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.201-207
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• 2019

This study has evaluated whether the method of using the combination of different risk group, according to K-TIRADS classification and K-TIRADS classification in thyroid ultrasonography is useful in a differential diagnosis of benign and malignant nodules. The subject was patients underwent thyroid ultrasonography and retrospective analysis were performed based on the results of fine needle aspiration cytology. A chi-square test was performed for the difference analysis of the score system in K-TIRADS and different risk group according to the benign and malignant of thyroid nodule. The optimized cut off value was determined by the K-TIRADS score and different risk group to predict malignant nodule through ROC curve analysis. In the differential verification result of K-TIRADS and different risk group, according to the classification of benign and malignant nodule group each showed significant difference statistically(p=.001). In the point classification according to K-TIRADS for the prediction of benign and malignant in ROC curve analysis showed AUC 0.786, Cut-off value>2(p=.001), and in the different risk group, it was decided as AUC 0.640, Cut-off value>2(p=.001). When discovering the nodule in thyroid ultrasound, it is considered that the K-TIRADAS which helps in identifying benign and malignant thyroid nodules, it is considered to be helpful in the differential diagnosis of thyroid nodules, than the classification system according to Different risk group, and when applying the classification system according to K-TIRADS, it is considered that it can reduce unnecessary fine needle aspiration cytology and could be helpful in finding the malignant nodules early.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.317-325
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• 2024

This study aimed to classify normal and nodule images in thyroid ultrasound images using GLCM and machine learning. The research was conducted on 600 patients who visited S Hospital in Busan and were diagnosed with thyroid nodules using thyroid ultrasound. In the thyroid ultrasound images, the ROI was set to a size of 50x50 pixels, and 21 parameters and 4 angles were used with GLCM to analyze the normal thyroid patterns and thyroid nodule patterns. The analyzed data was used to distinguish between normal and nodule diagnostic results using the SVM model and KNN model in MATLAB. As a result, the accuracy of the thyroid nodule classification rate was 94% for SVM model and 91% for the KNN model. Both models showed an accuracy of over 90%, indicating that the classification rate is excellent when using machine learning for the classification of normal thyroid and thyroid nodules. In the ROC curve, the ROC curve for the SVM model was generally higher compared to the KNN model, indicating that the SVM model has higher within-sample performance than the KNN model. Based on these results, the SVM model showed high accuracy in diagnosing thyroid nodules. This result can be used as basic data for future research as an auxiliary tool for medical diagnosis and is expected to contribute to the qualitative improvement of medical services through machine learning technology.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.35-43
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• 2009

In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.3
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• pp.710-714
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• 2009

Several studies showed that the survival rate of stage IIIB disease with malignant pleural effusion is worse than stage IIIB disease without malignant effusion. But, malignant pleural effusion was considered T4. To analyze changes the survival time for malignant pleural effusion, in the seventh revision of TNM classification for lung cancer. The records of all patients had to have either a histological or cytological diagnosis of non-small cell lung cancer (NSCLC), who were admitted to Wonkwang university hospital between January 2004 and December 2006 were reviewed retrospectively. We evaluated the survival time of 187 patients with advanced lung cancer with and without malignant pleural effusion. This included the pleural effusion or nodule M1 a (pleural dissemination, currently classified as T4), nodule(s) in the other lung M1 a (contralateral lung nodule, currently classified as M1), nodule(s) with the same lobe as the primary tumor T3 (currently classified as T4), other T4 factors T4 (T4 MO anyN), and extrathoracic sites of disease M1b (distant metastasis, currently classified M1). Among the 187 patients, T4anyNMO was 57 patients in the current TNM classification. In the next edition of the TNM classification, T4MOanyN-T4 (excluding same lobe nodules) was 12 patients, pleural dissemiantion-M1a was 45 patients, contralateral lung nodule(s)-M1a was 7 patients, and metastatic disease-M1b was 55 patients. We compared the survival time for these groups. Survival time was 11 months, 8 months, 11 months, and 4 months. The survival time of malignant pleural effusion was shorter than other T4 factors without pleural effusion. But, there was no remarkable difference in statistics due to small cases (p=0.23). We strongly suggest that malignant pleural effusion in advanced NSCLC will be categorized with metastatic disease.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.994-1003
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• 2017

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.31-39
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• 2018

In this paper, we propose an automated method for the ground-glass nodule(GGN) classification using GGN-Net based on intensity, texture, and shape-enhanced images in chest CT images. First, we propose the utilization of image that enhances the intensity, texture, and shape information so that the input image includes the presence and size information of the solid component in GGN. Second, we propose GGN-Net which integrates and trains feature maps obtained from various input images through multiple convolution modules on the internal network. To evaluate the classification accuracy of the proposed method, we used 90 pure GGNs, 38 part-solid GGNs less than 5mm with solid component, and 23 part-solid GGNs larger than 5mm with solid component. To evaluate the effect of input image, various input image set is composed and classification results were compared. The results showed that the proposed method using the composition of intensity, texture and shape-enhanced images showed the best result with 82.75% accuracy.

• Journal of radiological science and technology
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• v.42 no.3
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• pp.201-207
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• 2019

The purpose of this study was to evaluate the prevalence of thyroid nodules and malignant findings of thyroid nodules in 1,954 patients (654 males and 1,300 females) aged 20 years or older who underwent thyroid ultrasound from January 2018 to December 2018. Examination of the thyroid gland was performed, and fine needle aspiration cytology was performed on the thyroid nodule. As a result, 108 (16.5%) out of 654 males and 368 (28.3%) out of 1,300 females showed higher prevalence than males. The prevalence of single nodules and multiple nodules in gender and age groups was significantly higher for women and for ages (male p=.001, female p=.001). There was a significant difference in males in the nodule size (p=.001) and no significant difference in females (p=.069). Fine - needle aspiration cytology of 476 patients with nodules was diagnosed as malignant in 46 patients (9.6%). Based on pathologic results, 383 benign and 93 malignant groups were analyzed. Ultrasonographic findings were as follows single nodule (p=.000), solid(p=.004), hypoechoic (p=.000), ill-defined peripheral boundary (p=.000), and calcification (p=.000), respectively. In the diagnosis of thyroid nodule, primary ultrasonographic findings through morphological classification of the nodules may reduce indiscriminate fine needle aspiration cytology in benign and malignant nodules.

• Journal of radiological science and technology
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• v.45 no.4
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• pp.341-346
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• 2022

The color overlay pattern of thyroid shear wave elastography applied in this study distinguishes benign and malignant nodules based on the optimal cut-off value of 74.2 kPa. From august 2021 to september 2021, thyroid ultrasound and elastography were performed on 57 patients with thyroid lesions using an ultrasound device RS85 prestige (Samsung Medison, Korea) and a 2-14 MHz linear transducer. In addition, the results of classification by K-TIRADS for each thyroid nodule and the results of classification by color overlay pattern according to the kPa value of acoustic ultrasound were compared and analyzed. In the color overlay pattern, the results classified as 40 people from dark blue to light blue and 17 people from green to red were similar to the K-TIRADS category results, which were classified as 42 benign and 15 malignant. Between blue and light blue, benign, and between green and red, malignant. If the shear wave elastography method is applied before the fine-needle aspiration cytology of the thyroid nodule is performed, the differential diagnosis of thyroid tissue from benign and malignant can be predicted in advance, and it will help to reduce unnecessary invasive tests.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.12 no.1
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• pp.313-337
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• 1999

In oriental medicine, 'chi(痔)' is 'the prolapsed nodule' in 'Ku-gyu(九竅)', but in this paper, I will write about prolapsed nodule only in anus or around it, chi-jil(痔疾), it called hemorrhoid in western medicine. So in the literatual studies on chi(痔) in anus or around it, the results are as follows. 1. The etiology and pathogenesis of ch.i-jil(痔疾) is wind, wetness, dryness, and heat caused by inrregular diet habit, severe drinking and sexual action, deficiency of ki(氣) and hyeol(血). 2. Characteristic symptoms of chi-jil(痔疾) is the prolapsed nodule in the anus or around it, and general symptoms are hematochezia, pain, hernia, swelling, abcess, and mucosal secretion. 3. Chi-jil(痔疾) is classified eight types by characteristic symptom, shape, etiology and pathogenesis. They are mac-chi(脈痔), jang-chi(腸痔), ki-chi(氣痔), hyeol-chi(血痔), joo-chi(酒痔), mo-chi(牡痔), bin-chi(牡痔), and loo-chi(屢痔)(or young-chi(영痔), choong-chi(蟲痔)). Additionally, they are divided into two parts, internal and external chi-jil(痔疾), as likely as classification of internal and external hemorrhoid in western medicine. 4. Treatment of chi-jil(痔疾) is two methods, internal treatment and external treatment. Internal treatment is per os herb-pharmacotheraphy, external treatment is surgical or the other external pharmacotheraphy. There are several external treatment, these are fumigation-theraphy(熏痔法). irrigation-theraphy(洗痔法), paint-theraphy(塗痔法). withering-theraphy(枯痔法), bending-therphy(結紮法) and incisal -theraphy(切開法).