This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour with...This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.展开更多
基金This work was supported in part by National Natural Science Foundation of China under Grant Nos.61572063 and 61401308Natural Science Foundation of Hebei Province under Grant Nos.F2016201142,F2018210148,F2019201151 and F2020201025+3 种基金Science Research Project of Hebei Province under Grant Nos.BJ2020030,QN2016085 and QN2017306Foundation of President of Hebei University under Grant No.XZJJ201909Opening Foundation of Machine Vision Technology Innovation Center of Hebei Province under Grant Nos.2018HBMV01 and 2018HBMV02Natural Science Foundation of Hebei University under Grant Nos.2014-303 and 8012605.
文摘This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.
