Breast molybdenum target image breast area segmentation and calcification point detection method

Abstract

The invention provides a breast molybdenum target image breast area segmentation and calcification point detection method. According to the method, artificial interfering substances in a breast molybdenum target image is rapidly removed by the aid of an image gradient weight calculation method based on neighborhood pixels, a breast and chest muscles are initially segmented by the aid of an image segmentation method based on pixel aggregation, and breast boundaries are accurately detected and fitted by the aid of a straight line detection algorithm based on Hough transformation and a curve fitting algorithm based on a polynomial. By the method, marking precision of boundaries among the breast, a background and the chest muscles in the breast molybdenum target image is remarkably improved, calcification points in the breast are detected by the aid of texture filtering, so that breast area calibration and calcification point detection accuracy is improved, the breast area can be automatically segmented and calibrated, and the calcification points can be automatically detected, marked and measured.

Description

technical field [0001] The invention relates to the fields of biomedical imaging and biomedical detection, in particular to a method for segmenting breast regions and detecting calcification points in mammography images based on pixel clustering and texture filtering. Background technique [0002] The full name of mammography mammography mammography, also known as molybdenum palladium examination, is the first choice, the simplest and most reliable non-invasive detection method for the diagnosis of breast diseases. It is relatively painless, easy to implement, and has high resolution. , good reproducibility, the retained images can be compared before and after, and are not limited by age and body shape. It has been used as a routine inspection method at present. As a relatively non-invasive examination method, mammography can comprehensively and correctly reflect the general anatomical structure of the whole breast, observe the influence of various physiological factors such...

AI Technical Summary

Problems solved by technology

[0004] For the application of mammography image processing and analysis, manual recognition can only qualitatively estimate the boundary between the breast and the background and the clearer boundary between the breast and chest muscles, which has been difficult to meet the accuracy and speed requirements of breast morphology analysis. Traditional mammography image processing and analysis methods also have shortcomings that seriously affect the analysis results: the breast itself has various shapes, and it is difficult to use traditional methods based on morphological models to segment various tissues; the nucleus, cytoplasm and extracellular matrix The distribution of calcification is uneven, it is difficult to use traditional texture features to generalize and analyze the image, and the calculation of local texture features is large, and the efficiency is not high; the projected area of ​​calcification is small, and the contrast with the surrounding tissue is not high

The above shortcomings lead to inaccurate division of boundaries between breast and image background and chest muscles, as well as inaccurate detection of breast calcification points, resulting in errors in the statistical analysis of different types of tissues in mammography images, which seriously affect the discrimination of mammography image processing Accuracy and processing speed

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