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.

Key words: Support Vector Machine (SVM), image segmentation, image set segmentation, machine learning, feature extraction

摘要： 针对传统的支持向量机（SVM）模型对连续超声图像集进行分割时需要为图像集中每张图片提取样本点来建立分割模型的问题，提出了一个对整个连续超声图像集的统一的SVM分割模型。首先，从图像的灰度直方图中提取灰度特征作为表征图像集中图像连续性的特征；其次，从图像集中选取部分图像作为样本，并从中提取像素点的灰度特征；最后，将各像素点的灰度特征与各像素点所在图像中表征图像集连续性的特征相结合，用SVM的方法训练出分割模型对整个图像集进行分割。实验结果表明，与传统SVM分割方法相比，新模型在面对大量的有连续变化的图像集的分割问题上，大幅地减少了人工选取样本点的工作量，并且在分割的准确率上也有保证。

关键词: 支持向量机, 图像分割, 图像集分割, 机器学习, 特征提取