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  • 學位論文

自動化手術器材辨識與定位之研究

Research on Automatic Recognition and Localization of Surgical Equipment

指導教授 : 張元翔

摘要


外科手術在醫學中對患者的治療是很重要的醫療過程。儘管機器人或機械手臂已廣泛的運用於工業上,但目前並沒有看到被應用在外科手術。這是因為用於醫用機器人在視覺上的問題,其技術是項挑戰。在本研究目標是開發一個視覺基礎的系統,可以自動識別與定位手術器材。研究的方法包括:(1) 影像前處理;(2) 區塊分割;及(3) 物件辨識。在研究中使用一組手術器材(例如:手術刀、持針器、止血鉗),研究結果顯示我們的系統可以達到超過83.8%的辨識率。總結而言,本系統可與機器人或機械手臂結合,發揮手術助手的作用,雖然這超出了本研究的範圍。

並列摘要


In medicine, surgical operations are important clinical procedure for patient treatment. Although robots and/or robot arms have been widely used in industry, they are not yet seen in surgical operations at present. This is partly because the technical challenge of implementing such a medical robot remains in resolving the vision problems. In this study, our objective is to develop a vision-based system for the automatic recognition and localization of surgical equipment. The technical approaches include: (1) image preprocessing; (2) region segmentation; and (3) object recognition. Using a set of surgical equipment, i.e., scalpels, acutenaculums, and haemostats, preliminary results showed that our system could achieve the recognition rate of over 83.8%. In conclusion, our system has the potentials to be incorporated in a robot and/or a robot arm to play the role of a surgical assistant, despite that this is beyond the scope of this study.

參考文獻


[1]. Serge Belongie, Jitendra Malik, and Jan Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 509-522, 2002.
[3]. Salve, S.G. and Jondhale, K.C, "Shape Matching and Object Recognition Using Shape Contexts," International Conference onComputer Science and Information Technology,pp. 471-474, 2010.
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[7]. Pulkit Agrawal, Ross Girshick and Jitendra Malik, "Analyzing the Performance of Multilayer Neural Networks for Object Recognition," Computer Vision - ECCV, pp. 329-334, 2014.
[8]. Pedro F. Felzenszwalb and Daniel P. Huttenlocher, "Pictorial Structures for Object Recognition," International Journal of Computer Vision, vol. 61, pp.55-79, 2005.

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