|
|
|
| Ultrasonic Image Segmentation Based on Variance and Saliency Features |
| QIU Dong-yue1,JI Zhe1,ZHU Teng-fei1,MI Shang-yan1,ZHU Hai-jiang2 |
1. Institute of Metrological Supervision and Measurement of Hebei Provimce Langfang Branch, Langfang, Hebei 065000, China;
2. College of Information Science and Technology, Beijing University of Chemical Techonlogy, Beijing 100029, China |
|
|
|
|
Abstract According to the low contrast of ultrasound images can result the difference of adjacent grayscales is very small, and the human eyes are difficult to distinguish, an ultrasound image segmentation method based on variance and saliency features is proposed. Firstly, the variance and saliency features of the known sample pixels in the image are extracted, then the sample training is performed based on the variance and saliency features of the extracted sample pixel points using a support vector machine to obtain the classification model, finally, the training model is applied to the entire image to achieve effective segmentation of the image. Experimental results show that the proposed method is feasible and effective for the segmentation of ultrasound images.
|
|
Received: 24 May 2018
Published: 05 September 2018
|
|
|
|
|
|
［1］Yan C, Sang N,Zhang T. Local entropy-based transition region extraction and thresholding［J］. Pater Recognition Letters,2003,24(16):2935-2941.
［2］Kiruthika V, Ramya M M. Automatic segmentation of ovarian follicle using K-means clustering［C］//Proceedings of International Conference on Signal and Image Processing. Bangalore, India, 2014:137-141.
［3］Zhang Q, Huang C C, Li C L, et al. Ultrasound image segmentation based on multi-scale fuzzy c-means and particle swarm optimization［C］//IET International Conference on Information Science and Control Engineering. Shenzhen, China, 2012:A10215.
［4］Zhu H J, Zhuang Z H, Zhou J L, et al. Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization［J］.Multimedia Tools & Applications,2017,76(6):8951-8968.
［5］Vapnik V. The nature of statistics learning theory［M］.New York:Springer Verlag,1995.
［6］Guo G, Li S Z, Chna K L. Support vector machines for face recognition［J］.Image and Vision Computing,2001,19(6):631-638.
［7］杨强. 支持向量机的模型及其在图像分割中的应用［D］.重庆:重庆大学,2004.
［8］Yang H Y, Zhang X J, Wang X Y. LS-SVM-based image segmentation using pixel color-texture descriptors［J］. Pattern Analysis and Applications,2014,17(2): 341-359.
［9］程淑红,高许,周斌.基于多特征提取和SVM参数优化的车型识别［J］.计量学报,2018,39(3):348-352.
［10］杨景明,陈伟明,车海军,等.基于粒子群算法优化支持向量机的铝热连轧机轧制力预报［J］.计量学报,2016,37(1):71-74.
［11］Goferman S, Zelnik-Manor L, Tal A.Context-aware saliency detection［J］.IEEE Trans Pattern Anal Mach Intell,2012,34(10):1915-1926.
［12］Cheng M M, Mitra N J, Huang X, et al. Global contrast based salient region detection［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):569-582.
［13］GB 10152—2009 B型超声诊断设备［S］. |
|
|
|
2018, Vol. 39 
