Image Processing Method Based on Sliding Window Fusion for Twice Sparse Representation

Abstract

The invention discloses a twice-sparse representation image processing method based on sliding window fusion and mainly aims at solving the problem of low breast mass detection accuracy in the prior art. The twice-sparse representation image processing method based on sliding window fusion comprises the implementation steps of (1) reading in an image, (2) preprocessing, (3) extracting the gray-scale characteristics of a training set image and a target image, (4) performing primary sparse representation, (5) performing sliding window fusion, (6) performing region growing, (7) extracting the gray-scale characteristics of an ROI, and (8) performing secondary sparse representation. The twice-sparse representation image processing method based on sliding window fusion is capable of improving the detection rate of the breast mass, accurately representing the location information of the breast mass and reducing the false positive rate of breast mass detection, and as a result, the accuracy of detection can be improved; the twice-sparse representation image processing method is capable of quickly detecting a suspicious mass region from a molybdenum-target mammographic image and marking the suspicious mass region.

Description

technical field [0001] The invention belongs to the field of image processing. It further relates to an image processing method of mammary gland mass based on twice sparse representation based on sliding window fusion. The present invention can be used to quickly detect and mark suspicious mass areas from mammogram X-ray images. Background technique [0002] At present, many scholars have proposed mammogram mass detection methods, but the existing methods cannot obtain good detection results, and the reliability of the detection system is low. With the rapid development of computer vision information processing, pattern recognition, machine learning and other disciplines, the integration of various new ideas into the detection of tumor areas has gradually become a hot direction to improve the performance of the detection system. [0003] Commonly used image detection tools include support vector machine (Support Vector Machine, SVM), Bayesian, neural network and K nearest ...

AI Technical Summary

Problems solved by technology

This method can detect breast masses more accurately, but there are still shortcomings: the construction of the classifier uses a layered method, and each classifier needs to be trained, the process is more complicated, and the detection efficiency is reduced. In addition, the method The method has relatively high requirements for training samples

However, the shortcomings of this method are: the SVM classifier algorithm is relatively complex, the selection of kernel function is very difficult, and the SVM classifier is difficult to implement for large-scale training samples, it takes a long time and the detection efficiency is low

The shortcomings of this method are: only the texture features are used for analysis, the spatial information is ignored, and the false positive rate is high

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