Breast lump image feature extraction method based on edge neighborhood weighing

Abstract

The invention discloses a breast lump image feature extraction method based on edge neighborhood weighing. The breast lump image feature extraction method mainly solves the problem that in the prior art, extracted features do not contain the edge neighborhood local features of a breast lump. The breast lump image feature extraction method comprises the steps of (1) inputting an image, (2) adjusting the size of the breast lump image which is input, (3) extracting a lump edge, (4) determining the number of inner indentation pixel points and the number of outer extension pixel points, (5) determining the inner region of a lump which undergoes inner indentation, (6) determining the inner region of the lump which undergoes outer extension, (7) obtaining an edge neighborhood image of the breast lump, (8) obtaining weighing values, (9) extracting scale invariant features, (10) extracting word bag features and (11) obtaining features of the breast lump image which undergoes edge neighborhood weighing. By means of the breast lump image feature extraction method, expression of the features of the breast lump image are more robust, the image features are expressed more effectively, the benign and malignant classification accuracy of lumps is improved, and therefore doctors in the radiology department can be assisted in conducting medical diagnosis.

Description

technical field [0001] The invention belongs to the field of image processing. It further relates to a feature extraction method of breast mass images based on edge neighborhood weighting in the field of clinical medical diagnosis. According to the feature information contained in the edge neighborhood of the mammary gland mass image is richer, the weight of the region feature is increased, thereby improving the benign and malignant classification accuracy of the mammary gland mass image. It can be applied to clinical medical diagnostic image classification, improve classification accuracy, and assist radiologists in diagnosis. Background technique [0002] At present, image features used in clinical medical diagnosis include global features such as color, texture, and shape, and local features such as scale-invariant feature transform (SIFT) and histogram of oriented gradient features (Histogram of Oriented Gradient, HOG). feature. Compared with the global feature, the l...

AI Technical Summary

Problems solved by technology

The shortcomings of this method are: the method is relatively complicated, the effect on grayscale images is not significantly improved, and the spatial information of the image is not included, resulting in low accuracy in the classification of breast mass images.

The shortcomings of this method are: the local features of the image are not acquired, and it is sensitive to rotation, scaling, and brightness changes, resulting in low accuracy in the classification of breast mass images.

The shortcomings of this method are: the importance of the edge of the tumor is not highlighted, the spatial information of the image is not included, and the classification accuracy is not high

The disadvantages of this method are: the local neighborhood is not clearly defined, and the algorithm is relatively complicated, which cannot be fully used in the feature extraction of breast images.

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