IEEE J Biomed Health Inform - Local object patterns for the representation and classification of colon tissue images.

Tópicos

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Resumo

This paper presents a new approach for the effective representation and classification of images of histopathological colon tissues stained with hematoxylin and eosin. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. However, as opposed to pixel-level local binary patterns, we define our local object pattern descriptors at the component level to quantify a component. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. Working on microscopic images of colon tissues, our experiments reveal that the use of these component-level texture descriptors results in higher classification accuracies than the previous textural approaches.

Resumo Limpo

paper present new approach effect represent classif imag histopatholog colon tissu stain hematoxylin eosin approach propos decompos tissu imag histolog compon introduc set new textur descriptor call local object pattern compon model composit within tissu defin descriptor use idea local binari pattern quantifi pixel construct binari string base relat intens neighbor howev oppos pixellevel local binari pattern defin local object pattern descriptor compon level quantifi compon end specifi neighborhood differ local rang encod spatial arrang compon within specifi local neighborhood generat string extract textur descriptor string character histolog compon construct bagofword represent imag character compon work microscop imag colon tissu experi reveal use componentlevel textur descriptor result higher classif accuraci previous textur approach

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