Research on image segmentation algorithm based on features of venous gray value

Wang Dinghan; Feng Guilan; Wang Xiong; Wu Yufeng; Deng Maohua

doi:10.12086/oee.2018.180066

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Wang Dinghan, Feng Guilan, Wang Xiong, et al. Research on image segmentation algorithm based on features of venous gray value[J]. Opto-Electronic Engineering, 2018, 45(12): 180066. doi: 10.12086/oee.2018.180066

Citation:

Wang Dinghan, Feng Guilan, Wang Xiong, et al. Research on image segmentation algorithm based on features of venous gray value[J]. Opto-Electronic Engineering, 2018, 45(12): 180066. doi: 10.12086/oee.2018.180066

Research on image segmentation algorithm based on features of venous gray value

College of Optical and Electronic Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China

Fund Project: Supported by National Natural Science Foundation of China (61505192) and Natural Science Foundation of Zhejiang Province (LQ15F050004)

More Information

Corresponding author: Feng Guilan, E-mail：fengguilan@cjlu.edu

Received Date 02 February 2018

Revised Date 02 July 2018

Published Date 01 December 2018

Abstract

Abstract

In the process of collecting hand vein images, due to the influence of image acquisition equipment, illumination and subcutaneous fat thickness, the contrast of hand vein images is relatively low. Meanwhile, vein extraction is seriously affected by image noise. To solve this problem, an algorithm of image segmentation and contrast enhancement based on features of venous gray value is proposed in this paper. Firstly, effective region of interest (ROI) is extracted and filtered through Wiener filtering. Secondly, a new image segmentation algorithm is obtained to extract vein image. The venous binary image is denoised by an 8-adjacent inner boundary tracking method and morphological processing. Finally, contrast-enhanced venous images are obtained by weight stack of the ROI and denoised images. The experiments results show that intravenous veins can be obtained perfectly by using the image segmentation algorithm based on features of venous gray value. Moreover, the high contrast venous images can be obtained.
- hand vein image /
- image segmentation /
- 8-adjacent inner boundary tracking /
- image weighted superposition

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References

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Overview

Overview

Overview: In the process of collecting hand vein images, due to the influence of image acquisition equipment, illumination and subcutaneous fat thickness, the contrast of hand vein images is relatively low. Meanwhile, vein extraction was seriously affected by image noise. To solve this problem, an algorithm of image segmentation and contrast enhancement based on features of venous gray value is proposed in this paper. The algorithm is divided into the following six steps. 1) The OTSU is used to determine the threshold value of gray level between hand and background. The threshold is 75. According to the threshold of hand vein images, the gray value of the hand is set as 255, and the gray value of the background is set as zero. 2) The gray value distribution of hand is used to determine the centroid coordinates of hand, and the area of region of interest (ROI) is set as one-third of the hand area. ROI is extracted from the hand vein images based on center of mass coordinates and area. 3) Before the image segmentation, Wiener filtering is performed on the ROI by using a 3×3 template. 4) According to the gray value distribution of the ROI images, the ROI image is segmented using a threshold of one and a step size of eight. 5) After segmentation of the ROI images, in addition to containing veins, these noises of spots, holes and burrs are included. The boundary coordinates of the spots and holes and the numbers of pixels on the boundary are obtained by using the 8-adjacent inner boundary tracking method. When the number of pixels is less than 300, the area can be judged as a spot or hole. According to the boundary coordinates, the gray value of all the pixels in the spot are changed as 0, and the gray value of all the pixels in the hole are changed as 250. After removal of spots and holes, morphological treatments are used to remove burrs. 6) After the noise is removed, contrast enhanced venous images are obtained by weight stack of the venous binary and ROI images. The coefficient of the venous binary image is set as -0.04, and the coefficient of the ROI image is set as 1.2. The experiments results show that intravenous veins can be obtained perfectly by using the image segmentation algorithm based on features of the venous gray value. In the end, the high contrast venous images can be obtained by weighted stack of the images.