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Thyroid Nodule Classification Using Steerable Pyramid–Based Features From Ultrasound Images

S, Nanda, MTech, BE; M, Sukumar, PhD, MTech, BE

doi: 10.1097/JCE.0000000000000294
Feature Articles

The most common endocrine cancer is thyroid cancer. The incidental rate of thyroid cancer has significantly increased during the past few decades. Timely identification and suitable treatment are essential for better outcome. High-resolution ultrasound is the preferred modality for the detection of thyroid nodules as it has the capability of locating tiny nodules. This article proposes a feature extraction method by integrating steerable pyramid decomposition and cooccurrence matrix features for the characterization of the thyroid nodule. Steerable pyramid decomposition is carried out both in time domain and frequency domain. Textural features are obtained from the pyramid at different levels and with different filters. ReliefF method is used for feature selection. Support vector machine is used to classify the thyroid nodule as benign or malignant, and its performance is evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false alarm rate, and F1_score. The proposed approaches are tested on a dataset containing 110 thyroid ultrasound images (benign, malignant, and borderline cases). A very high overall accuracy of 99.08% with 100% sensitivity (malignant nodule detected as malignant) and 98.16% specificity (benign nodule detected as benign) is obtained for features extracted from steerable pyramid coefficients through convolution using sp1 filter at level 3. Experimental results clearly indicate that steerable pyramid–based cooccurrence matrix features can effectively describe the distinctive nature of the thyroid nodule in ultrasound image.

Corresponding author: Nanda S, MTech, BE, is an assistant professor in the department of Instrumentation Technology, doing research at JSS Research Foundation, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, JSS Technical Institutions Campus, Mysuru 570006, Karnataka, India. Her research interests include biomedical signal processing, image processing, pattern recognition, and machine learning. She can be reached at nanda_prabhu@sjce.ac.in.

Sukumar M, PhD, MTech, BE, is a research scientist at JSS Research Foundation, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, JSS Technical Institutions Campus, Mysuru 570006, Karnataka, India. He received his Bachelor of Engineering degree in Electronics and communication from university of Mysore, Mysuru, Karnataka, India, Master of Technology degree in applied electronics from Madras Institute of Technology, Chennai, India and PhD degree in Bioengineering from IIT Madras, Chennai, India. His research interests include processing and analysis of medical images.

The authors declare no conflicts of interest.

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