An automatic segmentation method for thin section microscopic image of sandstone

Abstract

The invention discloses an automatic segmentation method of a sandstone thin-section microscopic image, which comprises the following steps: 1) adopting a super pixel segmentation technology, pre-segmenting an orthogonal polarized light microscopic image of the sandstone into image blocks; 2) extracting color features and texture feature of image blocks and constructing feature vectors base on orthogonal polarizing microscopic images; 3) extracting image boundary features by adopt boundary detection technology based on that single polarized light microscopic sheet image; 4) trainning a supportvector machine classifier base on a sandstone particle sample data set; 5) using the trained classifier to predict the probability that each image block belongs to quartz, feldspar and debris, and marking the image block type through preset conditions; 6) predicting a type of an image block of an unlabeled type by using a label propagation algorithm; 7) merging the adjacent image blocks with thesame type and lower boundary characteristic intensity. This method utilizes image processing technology, machine learning method and data mining method, and combines the orthogonal polarizing microscope image and mono-polarizing microscope image obtained from the same sandstone slice to automatically segment the mineral particles contained in the sandstone slice, so as to reduce the time and economic cost of manual division of labor and improve the segmentation accuracy.

Description

technical field [0001] The present invention relates to the problem of automatic segmentation of microscopic images of sandstone slices. Combining orthogonal polarized light images and single polarized light images, image processing technology and machine learning methods are used to classify image blocks to realize the segmentation of sandstone thin slice microscopic images. The mineral grains are segmented into separate regions one by one. Background technique [0002] Sandstone is widely distributed in nature and is the main reservoir of oil, natural gas and groundwater. Sandstone thin section identification can analyze sandstone mineral components and mineral content, obtain sandstone type and structural characteristics, and has important application value in the detection and evaluation of oil and gas reservoirs. The basis of sandstone thin section identification is image segmentation of sandstone thin section microscopic images. [0003] The existing microscopic imag...

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Problems solved by technology

[0003] The existing microscopic image segmentation of sandstone thin sections is mainly manual segmentation, which is time-con

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