An improved method of microscopic image segmentation

Zhang Feng Shou,Meng Xin,Liu Jian Ting,Dong Fang,Changde Lu,Xuesheng Pei,Jianning Su

doi:10.1051/matecconf/201817601041

Zhang Feng Shou, Meng Xin + Show 5 more

Open Access

https://doi.org/10.1051/matecconf/201817601041

Copy DOI

Abstract

In order to improve the effectiveness and accuracy of image processing in modern medical inspection, a segmentation image optimization algorithm of improved two-dimensional maximum entropy threshold based on genetic algorithm combined with mathematical morphology is proposed, in view of the microscopic cell images characteristic and the shortcomings of the traditional segmentation algorithm. Through theoretical analysis and contrast test, the segmentation method proposed is superior to the traditional threshold segmentation method in microscopic cell images, and the average segmentation time of the improved algorithm is 73% and 44% higher than the traditional two-dimensional maximum entropy threshold and the improved two-dimensional maximum entropy threshold.

Highlights

Image segmentation is the key step of the process from image processing to image analysis[1,2,3]
The coarse segmentation of microscopic images are presented.,the object contoury extracted after filtering out residual noise and filling small holes in the target by mathematical morphology methods
After segmenting the microscopic images with animprovedtwo-dimensional maximum entropy threshold based on genetic algorithm, most of the noises are removed, but there is some small holes that goes on in target area and some undesired impurity appeared, so mathematical morphology methods are used to fill the small holes in the target area by multiple close calculation, and remove thin impurity by multiple open calculation

Summary

INTRODUCTION

Image segmentation is the key step of the process from image processing to image analysis[1,2,3]. The background of high noise microscopic images presents great complexity, ,it is difficult to simultaneously extract the object contours possible during filtering image noise by a single image segmentation method[4,5]. We would analyze the traditional segmentation algorithm from experiment sand put forward an improved genetic algorithm of threshold segment method by using two-dimensional maximum entropy. The coarse segmentation of microscopic images are presented.,the object contoury extracted after filtering out residual noise and filling small holes in the target by mathematical morphology methods

IMPROVED METHOD

EXPERIMENT

Findings

CONCLUSION