Journal of Medical Engineering and Technology 好投吗-刊鹿论文编译

Automated method for detecting and reading seven-segment digits from images of blood glucose metres and blood pressure monitors

Abstract:

Abstract There is an increasing need for fast and accurate transfer of readings from blood glucose metres and blood pressure monitors to a smartphone mHealth application, without a dependency on Bluetooth technology. Most of the medical devices recommended for home monitoring use a seven-segment display to show the recorded measurement to the patient. We aimed to achieve accurate detection and reading of the seven-segment digits displayed on these medical devices using an image taken in a realistic scenario by a smartphone camera. A synthetic dataset of seven-segment digits was developed in order to train and test a digit classifier. A dataset containing realistic images of blood glucose metres and blood pressure monitors using a variety of smartphone cameras was also created. The digit classifier was evaluated on a dataset of seven-segment digits manually extracted from the medical device images. These datasets along with the code for its development have been made public. The developed algorithm first preprocessed the input image using retinex with two bilateral filters and adaptive histogram equalisation. Subsequently, the digit segments were automatically located within the image by two techniques operating in parallel: Maximally Stable Extremal Regions (MSER) and connected components of a binarised image. A filtering and clustering algorithm was then designed to combine digit segments to form seven-segment digits. The resulting digits were classified using a Histogram of Orientated Gradients (HOG) feature set and a neural network trained on the synthetic digits. The model achieved 93% accuracy on digits found on the medical devices. The digit location algorithm achieved a F1 score of 0.87 and 0.80 on images of blood glucose metres and blood pressure monitors respectively. Very few assumptions were made of the locations of the digits on the devices so that the proposed algorithm can be easily implemented on new devices.

Author Listing： E Finnegan;M Villarroel;C Velardo;L Tarassenko

Volume： 43

Pages： 341 - 355

DOI： 10.1080/03091902.2019.1673844

Language： English

Journal： Journal of Medical Engineering & Technology

Journal of Medical Engineering and Technology

影响因子：0.0

是否综述期刊：否

是否OA：否

是否预警：不在预警名单内

发行时间：-

ISSN：0309-1902

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收录数据库：Scopus收录

出版国家/地区：-

出版社：Taylor & Francis

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偏重研究方向	Engineering-Biomedical Engineering
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JCR分区 WOS分区等级：Q0区

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WOS期刊SCI分区（2021-2022年最新版）

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