基于神经网络的乳腺X光片中肿块检测法  

Neural Network-based Detection of Masses in Mammograms

作　　者：史盛君[1] 宋立新[1] 赵阳[1] 

机构地区：[1]哈尔滨理工大学电气与电子工程学院,黑龙江哈尔滨150040

出　　处：《哈尔滨理工大学学报》2008年第6期101-104,共4页Journal of Harbin University of Science and Technology

摘　　要：针对乳腺X光片中肿块与背景的对比度较低,边界不清晰等问题,提出了利用小波变换与直方图均衡化结合的方法来增强图像,使肿块部分更加突出.再对每个像素点进行特征提取,依据神经网络可以通过训练来进行分类的特点,利用BP神经网络,将每个点分类为肿块区域像素点和非肿块区域像素点,从而实现肿块部分的检测.经过M IAS资料库中的30幅乳腺X光片的测试,其有效性达到80.6%,且方法简单易行.To solve the pvoblem that the masses on the and have the fuzzy boundaries, the paper presents a method gram equalization theory firstly, and extract four features of mammograms exhibit poor contrast to the background to enhance the image by wavelet transform and histoevery pixel. According to the principle that artificial neural network can implement classification through training, the paper presents the method to classify every pixel to the masses or not by BP neural network to achieve the detection of the masses. The method has simple operation, and is evaluated by 30 mammograms images in the MIAS, and the effectiveness is 83%.

关 键 词：乳腺X光片 肿块 检测 小波变换 直方图均衡化 BP神经网络 

分 类 号：R445.9[医药卫生—影像医学与核医学]