Image segmentation quality evaluating method based on ROC Graph

Abstract

The invention provides an image segmentation quality evaluating method based on an ROC Graph. The image segmentation quality evaluating method includes the following steps: firstly, dividing pixels in a to-be-evaluated segmentation image into four types by comparing with a reference segmentation image; secondly, leading in pixel space information, performing distance transform on pixels in the reference image, and calculating distance from each pixel to a target boundary; thirdly, giving a weight to a pixel at a corresponding position in the segmentation image according to distance coefficient of each pixel in the reference image; fourthly, utilizing the weights to calculate a weighted true positive rate wTPR and a weighted false positive rate wFPR, and depicting a point (wFPR, wTPR) in the ROC Graph; fifthly, according to distance from the point (wFPR, wTPR) to a point (0,1), scoring the segmentation image, wherein the higher the score is, the better the segmentation effect of the to-be-evaluated image is. By the image segmentation quality evaluating method, quality of the segmentation image can be evaluated effectively, and the evaluation result is more reasonable and more visual and consistent with subjective evaluation result of human vision.

Description

technical field [0001] The invention belongs to the technical field of image segmentation, and in particular relates to an image segmentation quality evaluation method based on ROC Graph. Background technique [0002] Image segmentation is a key technology in the field of image processing and the basis of image understanding and recognition. The quality of the image segmentation results has a very important impact on the subsequent processing and analysis of the image. Usually, the quality of an image segmentation result is based on human subjective judgment as the evaluation standard, which leads to inconsistencies in the evaluation of image segmentation due to human visual differences. Therefore, it is necessary and meaningful to make a quantitative and qualitative evaluation of the segmentation results. An effective segmentation evaluation method can help researchers in this field choose segmentation algorithms that meet specific applications, and can further optimize s...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art, solve the problem of distortion of the evaluation index for skewed data, and introduce the spatial information of pixels, adopt the weighting idea, ...

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