15 March 2006 Effect of quantization on co-occurrence matrix based texture features: An example study in mammography
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Abstract
A co-occurrence matrix is a joint probability distribution of the pixel values of two pixels in an image separated by a distance d in the direction θ. It is one of the texture analysis tools favored by the medical image processing community. The size of a co-occurrence matrix depends on gray levels re-quantization Q. Hence, when dealing with high depth resolution images, gray levels re-quantization is routinely performed to reduce the size of the co-occurrence matrix. The gray levels re-quantization may play a role in the display of spatial relationships in co-occurrence matrix but is usually dealt with lightly. In this paper, we use an example to study the effect of gray-level re-quantization in high depth resolution medical images. Digitized film-screen mammograms have a typical depth resolution of 4096 gray levels. In a study classifying masses on mammograms as benign or malignant, 260 texture features are measured on 43 regions-of-interest (ROIs) containing malignant masses and 28 ROIs containing benign masses. Of the 260 texture features, 240 are texture features measured on co-occurrence matrices with parameters θ = 0, π/2; d = 11, 15, 21, 25, 31; and Q = 50, 100, 400. A genetic algorithm is used to select a subset of features (out of 260) that has discriminative power. Results show that top performing feature combinations selected by the genetic algorithm are not restricted to a single value of Q. This indicates that instead of searching for a correct Q, it may be more appropriate to explore a range of Q values.
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Gobert N. Lee, Murk J. Bottema, Takeshi Hara, Hiroshi Fujita, "Effect of quantization on co-occurrence matrix based texture features: An example study in mammography", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445C (15 March 2006); doi: 10.1117/12.653256; https://doi.org/10.1117/12.653256
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