Park Sangyun, Kong Hyoun-Joong, Moon Woo Kyoung, Kim Hee Chan
Interdisciplinary Program, Biomedical Engineering Major, Graduate School, Seoul National University, Seoul, Korea.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5650-3. doi: 10.1109/IEMBS.2007.4353628.
An accurate segmentation of solid nodules in ultrasonographic (US) breast image is presented. 1-level 2-dimensional Discrete Wavelet Transform (DWT) is used to create features reflecting the texture information of the original image. Using these features, the texture classification is achieved. Finally, solid nodule region is segmented from the classified texture region. Proper threshold for texture classification is automatically decided. Empirically acquired information about the relationship between the texture characteristic of the original image and the optimal threshold is examined and used. Presented algorithm is applied to 284 malignant solid nodules and 300 benign solid nodules and the resulting images are presented.
本文提出了一种对超声(US)乳腺图像中实性结节进行准确分割的方法。采用一级二维离散小波变换(DWT)来创建反映原始图像纹理信息的特征。利用这些特征实现纹理分类。最后,从分类后的纹理区域中分割出实性结节区域。自动确定纹理分类的合适阈值。研究并使用了通过经验获取的关于原始图像纹理特征与最佳阈值之间关系的信息。将所提出的算法应用于284个恶性实性结节和300个良性实性结节,并展示了所得图像。