analytical approach for intelligent vision system using image processing
Murugamani,S. Saravanakumar
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:3 Issue:1 Year: 31 July,2014 Pages:375-382
Abstract
The vision-based automated needle wear monitoring systems are very important and efficient for unmanned surgical systems. This research is to use the needle vision inspection technique to automate the surgical monitoring of needles. A new method based on computer vision using image processing technique is proposed to estimate the wear and tear of surgical needles in order to identify the time for their replacement. This is possible by using a supervised approach, such that the replacement of needle is carried out before the wear reaches the highest level thereby causing harm. The perimeter of the wear region was described by means of a shape signature, which was normalized and resized to a set of values. These vectors have been classified using SNNalgorithm. This algorithm focuses on the appropriate estimation technique in order to replace the surgical needle based on its thickness and depth
Kewords
vision, image processing, surgical needles, replacement, SNN algorithm
Reference
1. Kurada, S. & Bradley, C. A review of machine vision sensors for tool condition monitoring.Computers in Industry, 1997, 34, 55-72. 2. Teti, R. Machining of composite materials. Annals of the CIRP, 2002, 51 (2), 611-634. 3. Weckenmann, A. &Nalbantic, K. Precision measurement of cutting tools with two matched optical 3d-sensors. Annals of CIRP, 2003, 52(1), 443-446. 4. Kopac, J. &Sali, S. Tool wear monitoring during the turning process. Journal of Materials Processing Technology, 2001, 113, 312-316. 5. Wang, W.; Hong, G. & Wong, Y. Flank wear measurement by a threshold independent method with sub-pixel accuracy. International Journal of Machine Tools and Manufacture, 205, 46(2), 199- 207. 6. Street, W.N., Wolberg, W.H. and Mangasarian, O.L. (1993) nuclear feature extraction for breast tumor diagnosis. Proceedings IS&T/SPIE International Symposium on Electronic Imaging, 1905, 861-870. 7. Martynenko A.I. (2008). Computer Vision System for Ginseng Drying: Remote Sensing, Control and Optimization of Quality in Food Thermal Processing. ADM Verlag, 200 p. 8. Martynenko A.I.,Satanina V. 2010. Multi-spectral imaging of quality degradation of wild blueberry paste (submitted to Drying Technology). 9. MacDonald M.T., Lada R.R., Martynenko A.I., Dorais M., Pepin S., Desjardins Y. Ethylene triggers needle abscission of root-detached balsam fir. Trees, 2010, DOI 10.1007/s00468-010-0457-2. 10. Martynenko A.I.The system of correlations between moisture, shrinkage, density and porosity.Drying Technology, 2008, 26(12): 1496-1499. 11. Automatic Needle Segmentation in 3D Ultrasound Images Using 3D Improved Hough Transform Proc. of SPIE Vol. 6918 691821-1 2008 SPIE Digital Library
