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Application of wavelets and principal component analysis in image query and mammography.

机译：小波和主成分分析在图像查询和乳腺摄影中的应用。

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Breast cancer is currently one of the major causes of death for women in the U.S. Mammography is currently the most effective method for detection of breast cancer and early detection has proven to be an efficient tool to reduce the number of deaths. Mammography is the most demanding of all clinical imaging applications as it requires high contrast, high signal to noise ratio and resolution with minimal x-radiation. The success rate in the diagnosis depends heavily on the skills of the radiologist during a visual inspection of the mammogram. According to studies [36], 10% to 30% of women having breast cancer and undergoing mammography, have negative mammograms, i.e. are misdiagnosed. Furthermore, only 20%--40% of the women who undergo biopsy, have cancer. Biopsies are expensive, invasive and traumatic to the patient. The high rate of false positives is partly because of the difficulties in the diagnosis process and partly due to the fear of missing a cancer. These facts motivate research aimed to enhance the mammogram images (e.g. by enhancement of features such as clustered calcification regions which were found to be associated with breast cancer), to provide CAD (Computer Aided Diagnostics) tools that can alert the radiologist to potentially malignant regions in the mammograms and to develop tools for automated classification of mammograms into benign and malignant classes. In this paper we apply wavelet and Principal Component Analysis, including the Approximate Karhunen Loeve Transform to mammographic images, to derive feature vectors used for classification.; Another area where wavelet analysis was found useful, is the area of image query. Image query of large data bases must provide a fast and efficient search of the query image. Lately, a group of researchers developed an algorithm based on wavelet analysis that was found to provide fast and efficient search in large data bases. Their method overcomes some of the difficulties associated with previous approaches, but the search algorithm is sensitive to displacement and rotation of the query image due to the fact that wavelet analysis is not invariant under displacement and rotation. In this study we propose the integration of the Hotelling transform to improve on this sensitivity and provide some experimental results in the context of the standard alphabetic characters.
机译：乳腺癌目前是美国女性死亡的主要原因之一。乳房X线照相术是目前最有效的乳腺癌检测方法，早期检测已被证明是减少死亡人数的有效工具。乳腺X射线摄影术是所有临床成像应用中最苛刻的要求，因为它需要高对比度，高信噪比和最小X射线分辨率。诊断的成功率在很大程度上取决于X光检查中放射科医生的技能。根据研究[36]，有10％到30％的患有乳腺癌且接受乳房X线摄影的妇女的乳房X线照片阴性，即被误诊。此外，接受活检的女性中只有20％-40％患有癌症。活检对患者而言是昂贵的，侵入性的和创伤性的。假阳性率很高，部分是由于诊断过程中的困难，另一部分是由于担心错过癌症。这些事实激发了旨在增强乳房X线照片的研究（例如，通过增强诸如与乳腺癌相关的成簇钙化区域的特征），以提供可以提醒放射线医师注意潜在恶性区域的CAD（计算机辅助诊断）工具。并在乳房X线照片上开发工具，以将乳房X线照片自动分类为良性和恶性类。在本文中，我们将小波和主成分分析（包括近似Karhunen Loeve变换）应用于乳腺X线照片，以导出用于分类的特征向量。发现小波分析有用的另一个领域是图像查询领域。大型数据库的图像查询必须提供对查询图像的快速有效搜索。最近，一组研究人员开发了一种基于小波分析的算法，该算法可在大型数据库中提供快速有效的搜索。他们的方法克服了与先前方法相关的一些困难，但是由于小波分析在位移和旋转下不是不变的，因此搜索算法对查询图像的位移和旋转敏感。在这项研究中，我们提出了Hotelling变换的集成，以改善这种敏感性，并在标准字母字符的情况下提供一些实验结果。

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