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21 May 1999 Is there texture information in standard brain MRI?
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We have developed a texture feature extraction method for MRI utilizing the recently developed multiwavelet theory. Texture based features are used in Eigenimage Filtering to enhance analysis results of tumor patient MRI studies. The steps of the proposed method are as follows: (1) Each original image is convolved with a Gaussian filter. This step suppresses the image noise. (2) Each of the resulting images is convolved with eight multiwavelet coefficient matrices. (3) The output of each filter is stored in a separate image (feature plane). This step generates features (images) in which texture information is enhanced. (4) Local energy of each feature is calculated by squaring the feature values. This step converts variance disparities into mean value differences and transforms large values of local pass- band energy into large image gray levels. (5) Eigenimage filter is applied to different sets of MRI images and the results are compared. First, it is applied to the conventional MRI images (T1-weighted, T2-weighted, and proton density weighted). Then, it is applied to the set consisting of these images and the texture feature images generated in the previous step for them. Finally, it is applied to four original images (three conventional and a non-conventional). (6) The eigenimages obtained in the previous step are compared. This step illustrates presence and significance of the texture information present in MRI and role of the proposed method in extracting these features. Applications of the proposed method to MRI studies of brain tumor patients illustrate that the method successfully extracts texture features which are useful in tumor segmentation and characterization.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hamid Soltanian-Zadeh, Reza Nezafat, and Joe P. Windham "Is there texture information in standard brain MRI?", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999);

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