Abstract:A novel Support Vector Machine (SVM)-based unified segmentation model was proposed for segmenting a continuous ultrasound image set, because the traditional SVM-based segmenting method needed to extract sample points for each image to create a segmentation model. Firstly, the gray feature was extracted from the gray histogram of the image as the characteristic representing the continuity of the image in the image set. Secondly, part images were selected as the samples and the gray feature of each pixel was extracted. Finally, the gray feature of the pixel was combined with the feature of image sequence continuity in the image where each pixel was located. The SVM was used to train the segmentation model to segment the whole image set. The experimental results show that compared with the traditional SVM-based segmentation method, the new model can greatly reduce the workload of manually selecting the sample points when segmenting the image set with large quantity and continuous variation and guarantees the segmentation accuracy simultaneously.
刘俊, 李鹏飞. 基于支持向量机的连续超声图像集分割算法[J]. 计算机应用, 2017, 37(7): 2089-2094.
LIU Jun, LI Pengfei. Continuous ultrasound image set segmentation method based on support vector machine. Journal of Computer Applications, 2017, 37(7): 2089-2094.
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