Vision-based patient identification recognition based on image content analysis and support vector machine for medical information system

EURASIP journal on advances in signal processing

article

Lin, Guo-Shiang¹  Chai, Sin-Kuo²  Li, Hsiang-Min²  Lin, Jen-Yung³ 

[1] Department of Computer Science and Information Engineering, National Chin-Yi University of Technology;Department of Health Services Administration, China Medical University;Department of Computer Science and Information Engineering, Da-Yeh University

关键词: Patient identification recognition;    Digit recognition;    Support vector machine;   

DOI  :  10.1186/s13634-020-00686-3

来源: SpringerOpen

【 摘 要 】

In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This proposed system is composed of three parts: pre-processing, candidate region detection, and digit recognition. To consider the efficiency of the proposed scheme, image normalization is performed. The color information is used to identify camera-captured screen images. In the pre-processing part, the effect of noise in captured screen images is reduced by a bilateral filter. The color and spatial information is used to initially and roughly locate the candidate region. To reduce the skew effect, a skew correction algorithm based on the Hough transform is developed. A template matching algorithm is used to find special symbols for locating the region of interest (ROI). For digit segmentation, digits are segmented in the ROI based on the vertical projection and adaptive thresholding. For the digit recognition, some features are measured from each digit segment and a classifier based on the support vector machine is applied to recognize digits. The experiment’s results show that the proposed system could effectively not only use color information to distinguish the captured screen images from the skin images but also detect the ROIs. After the digit segmentation, the accuracy rates of digit recognition are 98.4% and 94.2% for the proposed system and the Tesseract Optical Character Recognition (OCR) software, respectively. These results demonstrate that the proposed system outperforms the Tesseract OCR software in terms of the accuracy rate of digit recognition.

【 授权许可】

CC BY