Development of new filter bank for detection of nodular patterns and linear patterns in medical images
Development of new filter bank for detection of nodular patterns and linear patterns in medical images
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开发用于检测医学图像中的结节图案和线性图案的新滤波器组
DOI：
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Y. Uchiyama
中科院分区：
文献类型：
--
作者：
R. Nakayama;Y. Uchiyama
医学图像中的异常阴影被描述为孤立的结节结构或线状结构。然而，阴影的大小和形状通常因情况而异。因此，为了高精度地检测异常阴影，我们需要使用能够处理各种大小和形状的过滤器，而不是假设典型的阴影形状的固定过滤器。为了检测出高精度的异常阴影，提出了一种新的基于小波分析的多分辨率重建滤波器组。滤波器组的设计使得子图像在每个分辨率级别上生成海森矩阵的元素。通过计算小本征值和大本征值，新的滤波器组具有以下三个性质。(1)可增强各种大小的结节图案。(2)不同大小的结节状和线状均可增强。(3)去除这些图案后，可以重建原始图像。滤波器组用于增强乳房X光片中的微钙化和增强/去除胸部X光片中的骨组织。验证了该方法的有效性。《威利期刊学报》，36(13)：81-91,2005；在线发表在《威利国际科学》(www.intercience.wiley.com)上。DOI 10.1002/scj.20171
Abnormal shadows in medical images are described as isolated nodular structures or linear structures. However, the size and shape of the shadows usually vary from case to case. Consequently, in order to detect abnormal shadows with high accuracy, we need to use a filter that can deal with various sizes and shapes, rather than a fixed filter assuming a typical shadow shape. The purpose of this study is to detect abnormal shadows with high accuracy, and a new perfect reconstruction filter bank is developed, introducing multiresolution based on wavelet analysis. The filter bank is designed so that the subimages generated the elements of a Hessian matrix at each resolution level. By calculating the small and large eigenvalues, a new filter bank has the following three properties. (1) Nodular patterns of various sizes can be enhanced. (2) Both nodular and linear patterns of various sizes can be enhanced. (3) The original image can be reconstructed with these patterns removed. The filter bank is applied to enhance microcalcifications in mammograms and to enhance/remove bone tissue in chest radiographs. The effectiveness of the method is demonstrated. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(13): 81–91, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20171
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影响因子：
3.8
作者：
GIGER, ML;DOI, K;MACMAHON, H
通讯作者：
MACMAHON, H
胸部X光片中间质性疾病的计算机化分析：几何图案特征分析的改进。
影响因子：
3.8
作者：
Ishida,T;Katsuragawa,S;Kobayashi,T;MacMahon,H;Doi,K
通讯作者：
Doi,K
影响因子：
3.3
作者：
Doi, K;MacMahon, H;Jiang, YL
通讯作者：
Jiang, YL
影响因子：
3.8
作者：
Xu, XW;Doi, K;Giger, ML
通讯作者：
Giger, ML