Image edge detection method with Canny operator improvement

Abstract

The invention discloses an image edge detection method with Canny operator improvement. The method comprises: S1, carrying out smoothing processing on an image and suppressing noises by using a gaussian filter; S2, calculating an image gradient amplitude and direction after smoothing; S3, carrying out non-maximum-value suppression on a gradient; S4, calculating high and low threshold values by using an iterative algorithm; S5, carrying out edge detection and connection by using a dual-threshold algorithm; and S6, carrying out edge refinement by using a mathematical morphological method. Therefore, noises can be suppressed effectively and an optimal segmentation threshold value can be obtained. The method is suitable for detection of a medical cell image.

Description

An Image Edge Detection Method Based on Improved Canny Operator technical field The invention specifically relates to an image edge detection method of an improved Canny operator. Background technique With the development of computer vision and digital image processing technology, edge detection technology is more and more used in various fields and plays an increasingly important role. Cells are the basic unit of life activities, and all organisms except viruses are composed of cells. The edge of the cell image has the target boundary information, especially the characteristics of the area, roundness and number of the cells. The detection results provide an important basis for the analysis of the cell morphology and the diagnosis of the disease in the future. The traditional edge detection operator is based on the change of the gray value in the neighborhood of each pixel of the image, and uses the change of the first-order or second-order directional derivative in the ...

AI Technical Summary

Problems solved by technology

These operators have a simple structure and fast implementation speed, but they have a great impact on noise. If they are applied to the edge detection of cell images, there will be disadvantages such as discontinuous edges of cell images, interference edges, or loss of cell image details.

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