Directional features for automatic tumor classification of mammogram images
Directional features for automatic tumor classification of mammogram images
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DOI：
10.1016/j.bspc.2010.10.003
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发表时间：
2011-10-01
影响因子：
5.1
通讯作者：
Gacsadi, Alexandru
中科院分区：
文献类型：
--
作者：
Buciu, Ioan;Gacsadi, Alexandru
乳腺癌诊断的一种方法是通过为可疑患者拍摄放射线照相（X射线）图像（称为乳房X线照片）来提供，医生进一步使用这些图像来通过视觉检查识别潜在的异常区域。当数字乳房X线照片可用时，基于计算机辅助的诊断可以帮助医生做出更准确的决定。这意味着使用分割自动检测异常区域，然后进行肿瘤分类。这项工作的目的是描述一种方法来处理数字乳腺X线照片的分类。手动提取肿瘤周围的斑块，以将异常区域从被视为背景的图像的剩余部分中分割出来。利用Gabor小波对乳腺图像进行滤波，提取不同方向和频率的方向特征。主成分分析被用来降低过滤和未过滤的高维数据的维数。最后采用邻近支持向量机对数据进行分类。当提取Gabor特征而不是使用原始乳腺X线图像时，获得了上级乳腺X线图像分类性能。还讨论了Gabor特征对不同强度泊松噪声引起的数字乳腺X线图像失真的鲁棒性。(C)2010爱思唯尔有限公司版权所有。
One way for breast cancer diagnosis is provided by taking radiographic (X-ray) images (termed mammograms) for suspect patients, images further used by physicians to identify potential abnormal areas thorough visual inspection. When digital mammograms are available, computer-aided based diagnostic may help the physician in having a more accurate decision. This implies automatic abnormal areas detection using segmentation, followed by tumor classification. This work aims at describing an approach to deal with the classification of digital mammograms. Patches around tumors are manually extracted to segment the abnormal areas from the remaining of the image, considered as background. The mammogram images are filtered using Gabor wavelets and directional features are extracted at different orientation and frequencies. Principal Component Analysis is employed to reduce the dimension of filtered and unfiltered high-dimensional data. Proximal Support Vector Machines are used to final classify the data. Superior mammogram image classification performance is attained when Gabor features are extracted instead of using original mammogram images. The robustness of Gabor features for digital mammogram images distorted by Poisson noise with different intensity levels is also addressed. (C) 2010 Elsevier Ltd. All rights reserved.