• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.473-479
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• 2021

The diagnostic criteria for diffuse thyroid disease are ambiguous and there are many errors due to the subjective diagnosis of experts. Also, studies on ultrasound imaging of thyroid nodules have been actively conducted, but studies on diffuse thyroid disease are insufficient. In this study, features were extracted by applying the GLCM algorithm to ultrasound images of normal and diffuse thyroid disease, and quantitative analysis was performed using the extracted feature values. Using the GLCM algorithm for thyroid ultrasound images of patients diagnosed at W hospital, 199 normal cases, 132 mild cases, and 99 moderate cases, a region of interest (50×50 pixel) was set for a total of 430 images, and Autocorrelation, Sum of squares, sum average, sum variance, cluster prominence, and energy were analyzed using six parameters. As a result, in autocorrelation, sum of squares, sum average, and sum variance four parameters, Normal, Mild, and Moderate were distinguished with a high recognition rate of over 90%. This study is valuable as a criterion for classifying the severity of diffuse thyroid disease in ultrasound images using the GLCM algorithm. By applying these parameters, it is expected that errors due to visual reading can be reduced in the diagnosis of thyroid disease and can be utilized as a secondary means of diagnosing diffuse thyroid disease.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.956-962
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• 2022

Diffuse thyroid disease has ambiguous diagnostic criteria and many errors occur according to the subjective diagnosis of skilled practitioners. If image processing technology is applied to ultrasound images, quantitative data is extracted, and applied to a computer auxiliary diagnostic system, more accurate and political diagnosis is possible. In this paper, 19 parameters were extracted by applying the Gray level co-occurrence matrix (GLCM) algorithm to ultrasound images classified as normal, mild, and moderate in patients with thyroid disease. Using these parameters, an artificial neural network (ANN) was applied to analyze diffuse thyroid ultrasound images. The final classification rate using ANN was 96.9%. Using the results of the study, it is expected that errors caused by visual reading in the diagnosis of thyroid diseases can be reduced and used as a secondary means of diagnosing diffuse thyroid diseases.

• 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 radiological science and technology
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• v.40 no.2
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• pp.237-243
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• 2017

This study evaluated the applicability of computer-aided diagnosis by retrospective analysis of GLCM algorithm based on cytopathological diagnosis of normal and malignant nodules in thyroid ultrasound images. In the experiment, the recognition rate and ROC curve of thyroid malignant nodule were analyzed using 6 parameters of GLCM algorithm. Experimental results showed 97% energy, 93% contrast, 92% correlation, 92% homogeneity, 100% entropy and 100% variance. Statistical analysis showed that the area under the curve of each parameter was more than 0.947 (p = 0.001) in the ROC curve, which was significant in the recognition of thyroid malignant nodules. In the GLCM, the cut-off value of each parameter can be used to predict the disease through analysis of quantitative computer-aided diagnosis.

• 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 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.