Classifying Uterine Myoma and Adenomyosis Based on Ultrasound Image Fractal and Texture Features

Wu K, Kaikai Wu, Yuanyuan Wang, Wang Y, Pan Y, Ying Pan, Junhua Zhang, Zhang J, Qian B, Bin Qian, Chang C, Cai Chang
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference · 2005 · vol. 2005 , pp. 1790–1793 · doi:10.1109/iembs.2005.1616795 · PMID:17282564 · W2162327890
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This study extracted multiresolution texture and orientational fractal features from ultrasound images and used them with a support vector machine to classify normal, myoma, and adenomyosis cases.

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Abstract

The classification of the uterine myoma and adenomyosis from their ultrasound images mainly depends on doctors' experience and lacks objective criterions. Here a novel classification method is proposed using the multiresolution analysis and the orientational fractal analysis. Firstly, texture features under various resolutions and orientational fractal features are obtained from ultrasound images. Then the feature selection (FS) process is implemented using the sequential forward selection algorithm (SFS). Finally a classifier based on the support vector machine (SVM) is set up to classify the images into normal (Nor), myoma (Myo) and adenomyosis (Ad) cases. From the application of 27 Nor, 45 Ad and 74 Myo cases, it is showed that orientational fractal features and some multiresolution texture features are sensitive to the uterine Myo and Ad classification. The SVM classifier with the selected features may be useful in the practical classification.

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adenomyosis

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