A novel quantitative measurement for thyroid cancer detection based on elastography

At present, the widely methods used to evaluate elastograms clinically are color score and strain ratio. The color score is a qualitative measure estimated by radiologists, and its high subjectiveness may lead to error. Although the strain ratio is a quantitative method, the region selected to calculate the value is subjective and its accuracy is still quite low. A new effective, accurate, and quantitative metric using computer aided diagnosis (CAD) techniques is proposed in this paper. The statistical features and texture features are extracted from the lesion region on the elastogram. The important and reliable features are selected by using Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm. The selected features were input to the SVM to classify the thyroid nodules. The experiment results confirm that the method is more accurate and robust than color score and strain ratio.

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