FRACTAL FEATURE ANALYSIS AND CLASSIFICATION IN MEDICAL IMAGING
FRACTAL FEATURE ANALYSIS AND CLASSIFICATION IN MEDICAL IMAGING
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DOI：
10.1109/42.24861
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发表时间：
1989-06-01
影响因子：
10.6
通讯作者：
FOX, MD
中科院分区：
文献类型：
--
作者：
CHEN, CC;DAPONTE, JS;FOX, MD
在B. B Mandelbrot的分形理论（1982），发现可以通过分数布朗运动的概念来获得医学图像的分形维数。基于分数布朗运动的概念，讨论了确定分形维数的一种估计方法。发现两个应用：（1）分类;（2）边缘增强和检测。为了分类的目的，归一化分数布朗运动的特征向量定义从这个估计的概念。它表示不同尺度表面上像素对的归一化平均绝对强度差。该特征向量使用相对较少的数据项来表示中间图像表面的统计特性，并且对线性强度变换具有不变性。对于边缘增强和检测应用，通过计算整个医学图像上的每个像素的分形维数来获得变换图像。通过计算以该像素为中心的7*7像素块的分形维数来获得每个像素的分形维数值。< >
Following B.B. Mandelbrot's fractal theory (1982), it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion is discussed. Two applications are found: (1) classification; (2) edge enhancement and detection. For the purpose of classification, a normalized fractional Brownian motion feature vector is defined from this estimation concept. It represented the normalized average absolute intensity difference of pixel pairs on a surface of different scales. The feature vector uses relatively few data items to represent the statistical characteristics of the medial image surface and is invariant to linear intensity transformation. For edge enhancement and detection application, a transformed image is obtained by calculating the fractal dimension of each pixel over the whole medical image. The fractal dimension value of each pixel is obtained by calculating the fractal dimension of 7*7 pixel block centered on this pixel.< >