Effect of Complex Wavelet Transform Filter on Thyroid Tumor Classification in 3D Ultrasound

(2013). Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound. Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters. Abstract Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by inter-observer variability, and further, the speckle distribution poses a challenge during the classification process. This paper thus presents an automated system for tumor classification in 3D Contrast-Enhanced Ultrasonography (CEUS) data sets. The system first processes the CEUS images using Complex Wavelet Transform (CWT) based filter to mitigate the effect of speckle noise. The Higher Order Spectra (HOS) features are then extracted and used as input for training and testing a Fuzzy classifier. In the off-line training system, HOS features are extracted from a set of images known as the training images. These HOS features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the on-line testing phase, the estimated classifier parameters are applied on the HOS features which are extracted from the testing images, to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the CWT filter, the Fuzzy classifier demonstrated an accuracy of 91.6%, while the accuracy significantly boosted to 99.1% by utilizing the CWT filter.