Chen Dar-Ren, Chang Ruey-Feng, Chen Chii-Jen, Ho Ming-Feng, Kuo Shou-Jen, Chen Shou-Tung, Hung Shin-Jer, Moon Woo Kyung
Department of General Surgery, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 500, Taiwan.
Clin Imaging. 2005 Jul-Aug;29(4):235-45. doi: 10.1016/j.clinimag.2004.11.024.
Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.
分形分析已成功应用于图像压缩、纹理分析和纹理图像分割。分形维数可用于量化纹理信息。在本研究中，利用相邻像素灰度值的差异，通过分形布朗运动估计乳腺病变超声图像的分形维数。此外，还提出了一种基于分形分析的计算机辅助诊断（CAD）系统，将乳腺病变分为良性和恶性两类。为了提高分类性能，对超声图像进行形态学操作和直方图均衡化预处理。最后，采用k均值分类方法对良性肿瘤和恶性肿瘤进行分类。美国乳腺图像数据库仅包括组织学确诊病例：记录了110例恶性肿瘤和140例良性肿瘤。所有数字图像均在活检前使用ATL HDI 3000系统获得。接收者操作特征（ROC）面积指数AZ为0.9218，代表诊断性能。
