On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moment-based texture segmentation
Pattern Recognition Letters
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Fast simulation of ultrasound images from a CT volume
Computers in Biology and Medicine
Image indexing using moments and wavelets
IEEE Transactions on Consumer Electronics
Combined Invariants to Similarity Transformation and to Blur Using Orthogonal Zernike Moments
IEEE Transactions on Image Processing
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In ultrasound images, tissues are characterized by their speckle texture. Moment-based techniques have proven their ability to capture texture features. However, in ultrasound images, the speckle size increases with the distance from the probe and in some cases the speckle has a concentric texture arrangement. We propose to use moment invariants with respect to image scale and rotation to capture the texture in such cases. Results on synthetic data show that moment invariants are able to characterize the texture but also that some moment orders are sensitive to regions and, moreover, some are sensitive to the boundaries between two different textures. This behavior seems to be very interesting to be used within some segmentation scheme dealing with a combination of regional and boundary information. In this paper we will try to prove the usability of this complementary information in a min-cut/max-flow graph cut scheme.