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Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review. [PDF]
Quant Imaging Med SurgMing W, Liu T, Hu R, He W, Yang Y.
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Advances in Artificial Intelligence-Based Liver-Related Semantic Segmentation Techniques and Applications Using CT Imaging. [PDF]
Cancer MedPu J, Wang X, Zhu L, Pan J.
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Texture segmentation benchmark
2008 19th International Conference on Pattern Recognition, 2008The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods.
Stanislav Mikes, Michal Haindl
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[1988 Proceedings] 9th International Conference on Pattern Recognition, 2003
The technique presented solves the problem of texture segmentation in two steps. In the first, a textured image is divided into small squares (20*20 in this case) and a hierarchical clustering algorithm related to a choice of ultrametric distances is used to obtain an initial segmentation. In the second step, the texture boundaries are improved using a
André Gagalowicz, Christine Graffigne
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The technique presented solves the problem of texture segmentation in two steps. In the first, a textured image is divided into small squares (20*20 in this case) and a hierarchical clustering algorithm related to a choice of ultrametric distances is used to obtain an initial segmentation. In the second step, the texture boundaries are improved using a
André Gagalowicz, Christine Graffigne
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Segmentation of textured images
Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003The authors present a method for texture segmentation that does not assume any prior knowledge about either the type of textures or the number of textured regions present in the image. Local orientation and spatial frequencies are used as the key parameters for classifying texture.
Adi Perry, David G. Lowe
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Segmentation of color textures
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000This paper describes an approach to perceptual segmentation of color image textures. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input.
M, Mirmehdi, M, Petrou
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Texture Segmentation by Genetic Programming
Evolutionary Computation, 2008This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features.
Andy Song, Victor Ciesielski
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Picture Segmentation by Texture Discrimination
IEEE Transactions on Computers, 1975This correspondence describes a method of dividing a picture into differently textured regions by thresholding the values of a suitable local picture property. The approach used is a generalization to natural textures of a technique recently proposed by Tsuji. The examples given involve textures that differ in coarseness; a method of estimating texture
Steven W. Zucker +2 more
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