Ali Abbasian Ardakani1, Ali Mohammadzadeh2, Nahid Yaghoubi3, Zahra Ghaemmaghami4, Reza Reiazi5, Amir Homayoun Jafari6, Sepideh Hekmat7, Mohammad Bagher Shiran8, Ahmad Bitarafan-Rajabi9. 1. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. 2. Department of Radiology, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. 3. Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. 4. Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. 5. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Medical Image and Signal Processing Research Core, Iran University of Medical Sciences, Tehran, Iran. 6. Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. 7. Department of Nuclear Medicine, School of Medicine, Hasheminejad Hospital, Iran University of Medical Sciences, Tehran, Iran. 8. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address: shiran.m@iums.ac.ir. 9. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. Electronic address: Bitarafan@rhc.ac.ir.
Abstract
PURPOSE: This study investigated the potentiality of ultrasound imaging to classify hot and cold thyroid nodules on the basis of textural and morphological analysis. METHODS: In this research, 42 hypo (hot) and 42 hyper-function (cold) thyroid nodules were evaluated through the proposed method of computer aided diagnosis (CAD) system. To discover the difference between hot and cold nodules, 49 sonographic features (9 morphological, 40 textural) were extracted. A support vector machine classifier was utilized for the classification of LNs based on their extracted features. RESULTS: In the training set data, a combination of morphological and textural features represented the best performance with area under the receiver operating characteristic curve (AUC) of 0.992. Upon testing the data set, the proposed model could classify the hot and cold thyroid nodules with an AUC of 0.948. CONCLUSIONS: CAD method based on textural and morphological features is capable of distinguishing between hot from cold nodules via 2-Dimensional sonography. Therefore, it can be used as a supplementary technique in daily clinical practices to improve the radiologists' understanding of conventional ultrasound imaging for nodules characterization.
PURPOSE: This study investigated the potentiality of ultrasound imaging to classify hot and cold thyroid nodules on the basis of textural and morphological analysis. METHODS: In this research, 42 hypo (hot) and 42 hyper-function (cold) thyroid nodules were evaluated through the proposed method of computer aided diagnosis (CAD) system. To discover the difference between hot and cold nodules, 49 sonographic features (9 morphological, 40 textural) were extracted. A support vector machine classifier was utilized for the classification of LNs based on their extracted features. RESULTS: In the training set data, a combination of morphological and textural features represented the best performance with area under the receiver operating characteristic curve (AUC) of 0.992. Upon testing the data set, the proposed model could classify the hot and cold thyroid nodules with an AUC of 0.948. CONCLUSIONS: CAD method based on textural and morphological features is capable of distinguishing between hot from cold nodules via 2-Dimensional sonography. Therefore, it can be used as a supplementary technique in daily clinical practices to improve the radiologists' understanding of conventional ultrasound imaging for nodules characterization.