B Gopinath1, N Shanthi. 1. Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore, India. gopiphd@yahoo.com
Abstract
OBJECTIVE: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). MATERIALS AND METHODS: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. RESULTS: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. CONCLUSION: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.
OBJECTIVE: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). MATERIALS AND METHODS: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. RESULTS: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. CONCLUSION: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.