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Fractal feature analysis and classification in medical imaging.

Abstract：

: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 7x7 pixel block centered on this pixel.

journal_name

IEEE Trans Med Imaging

journal_title

IEEE transactions on medical imaging

authors

Chen CC,Daponte JS,Fox MD

doi

10.1109/42.24861

subject

Has Abstract

pub_date

1989-01-01 00:00:00

pages

133-42

issue

2

eissn

0278-0062

issn

1558-254X

journal_volume

8

pub_type

杂志文章

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