Combined index-based image segmentation result quantitative evaluation method

Abstract

The invention discloses a combined index-based image segmentation result quantitative evaluation method which comprises the steps of selecting multiple pictures of which the types are the same as the type of an image to be subjected to segmentation result evaluation, and segmenting the pictures to obtain a segmentation result set; performing manual subjective evaluation on the segmentation result set, and acquiring subjective evaluation results of all segmentation results to obtain a subjective evaluation set; performing multi-index objective evaluation on the segmentation result set, and calculating objective evaluation index values of all the segmentation results to obtain an objective evaluation set; training to obtain a trained classifier by taking the objective evaluation set as the input of the classifier and taking the subjective evaluation set as the output; calculating objective evaluation index values of segmentation results of the image to be evaluated; inputting the objective evaluation index values into the trained classifier to obtain an evaluation result. By adopting the method, the image segmentation evaluation result representing a worker evaluation criteria of a field can be obtained, the cost is reduced, the evaluation cycle is shortened, and more convenience, universality and easiness in implementation are realized.

Description

technical field [0001] The invention belongs to the cross-technical field of computer technology and image processing, and more specifically relates to a quantitative evaluation method for image segmentation results based on combined indexes. Background technique [0002] In the research and application of image segmentation technology, it is necessary to compare the segmentation quality of different segmentation algorithms, or compare the segmentation quality of the same algorithm with different parameter settings, when considering different types of images (such as medical images, natural images, SAR images, etc.) When , whether the segmentation effect of the same segmentation algorithm with the same parameter setting is the same also needs to be investigated. Solving the above problems involves research on the evaluation of segmentation results. [0003] The current evaluation methods of image segmentation results are divided into subjective evaluation and objective eval...

AI Technical Summary

Problems solved by technology

On the one hand, in the process of model design, artificial reference factors will inevitably be introduced, making the method itself unable to be completely objective; on the other hand, due to different requirements for image segmentation results in different fields, even in the same field , when faced with different types of pictures (such as carotid artery cross-section pictures and carotid artery longitudinal section pictures in the medical field), the requirements for segmentation results will also be different, so in these cases, the models must be designed separately, reducing the The applicab

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