Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images
Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images
复制标题
基于迭代四叉树分解的超声乳腺肿瘤图像种子点自动选择
DOI：
10.1007/s11042-016-3761-z
复制
发表时间：
2017
影响因子：
3.6
通讯作者：
["Huaiyu Fan
中科院分区：
文献类型：
--
作者：
["Huaiyu Fan
基于种子区域生长法的超声乳腺肿瘤图像的病灶分割往往需要人工选取种子点，费时费力。为了克服这一限制，本文试图探索一种自动寻找肿瘤内部种子点的方法。两个标准相结合的迭代四叉树分解（QTD）和病变的灰度特征，从而设计定位种子点。通过对110幅超声乳腺肿瘤图像（包括58幅恶性肿瘤图像和52幅良性肿瘤图像）进行实验，验证了该算法的有效性。根据种子区域生长算法的要求，如果种子点在肿瘤内部，则说明该方法是正确的。否则，这意味着该方法失败。定量实验结果表明，本文提出的方法可以自动找到肿瘤内的种子点，准确率为97.27%。
Based on seed region growing method, lesion segmentation for ultrasound breast tumor images often requires manual selection of the seed point, which is both time-consuming and laborious. To overcome this limit, this paper attempts to explore an automatic method for finding the seed point inside the tumor. Two criteria combining iterative quadtree decomposition (QTD) and the gray characteristics of the lesion are thus designed to locate the seed point. One is to seek the biggest homogenous region and the other is to select the seed region where the seed point is found. Furthermore, this study validates the proposed algorithm through 110 ultrasonic breast tumor images (including 58 malignant tumor images and 52 benign tumor images). According to the needs of the seed region growing algorithm, if the seed point is found inside the tumor, it means the proposed method is correct. Otherwise, it means that the method is a failure. As the quantitative experiment results show, the proposed method in this paper can automatically find the seed point inside the tumor with an accuracy rate of 97.27 %.