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Texture Segmentation in Magnetic Resonance Images Using Discrete Wavelet Transform Combined with Gabor Wavelets

Z Huang

Z Huang1*, S Lo2, N Mayr3, W Yuh3, (1) East Carolina University, Greenville, NC, (2) Case Comprehensive Cancer Center, Cleveland, OH, (3) Ohio State University, Columbus, OH

SU-E-J-108 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Edge detection improves image readability and plays an important role in images preprocessing aimed to their segmentation and automatic recognition of their contents. The purpose of this study was to describe methods of edge detection in magnetic resonance images, with the emphasis on the use of discrete wavelet transform (DWT) combined with Gabor wavelets.

Modulus maxima method by Mallat S (A Wavelet Tour of Signal Processing. Academic Press, 1998), provides the method for edge detection using wavelet transform. This method is based on finding local maxima of horizontal and vertical wavelet coefficients in the first level of wavelet decomposition because it is supposed that this level represents edges. This method was tested with various wavelet functions both on simulated and real medical images. A multiresolution approach using undecimated wavelet transform is also employed which allows the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands to remain at full size. A simple peak finding algorithm is used to determine the peaks out of array of these texture features.

Using wavelet transform method, the decomposition was performed up to two levels. Gabor filters are then applied to the wavelet approximations at all levels to obtain the characteristic texture features such as entropy, second to fourth central moments and coefficient of variation. High values of the second central variance and fourth central variance signify images in which regions can be clearly differentiated. The corresponding filter outputs are compared to obtain an image containing minimum pixel values.

A complex wavelet function could help to improve results of edge detection in real images. A comparison of basic edge detection methods including simple gradient operators and Gabor wavelets, and their combination with wavelet transform was presented. Mathematical principals were studied, as well as application of these methods.

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