This document describes a proposed system to automatically segment and register CT scan images for regional anesthesia of abnormal nerves. The main objective is to minimize the time required for segmentation and registration. It involves detecting the femoral artery in ultrasound images using a novel algorithm based on a modified Kalman filter, and registering a mesh model of surrounding anatomy by landmark registration of the artery center points. This could help make ultrasound-guided regional anesthesia safer and more successful. The system will be implemented in MATLAB to achieve minimum average time for artery detection and registration.

1. ARTERY SEGMENTATION AND
REGISTRATION FOR REGIONAL
ANAESTHESIA
Guided by
Submitted by

2. ABSTRACT
• This project is to create an assistant for ultrasound guided femoral nerve
block. By segmenting and visualizing the important structures such as the
femoral artery.
• The first step towards this goal and presents a novel method for identifying
and registering a model of the surrounding anatomy to the ultrasound
images.
• The femoral artery is modelled as an ellipse. The artery is first detected by
a novel algorithm which initializes the artery tracking.
• This algorithm is completely automatic and requires no user interaction.
Artery tracking is achieved with a modified Kalman filter. A mesh model of
the surrounding anatomy was created from a CT dataset.
• Registration of this model is achieved by landmark registration using the
center points from the artery tracking and the femoral artery centerline of
the model. The artery detection method was able to automatically detect the
femoral artery.
• This project can be implemented using MATLAB. This algorithm will
achieve minimum average time for the artery detection and registration
methods.

3. OBJECTIVE
• The main objective of this project is to automatically segment
and register the CT scan image for regional anaesthesia
abnormal nerve.
• This algorithm is to mmnimize the time required for
segmentation and registration process.

4. INTRODUCTION
• The use of regional anaesthesia (RA) is increasing due to the
benefits over general anaesthesia (GA) such as reduced morbidity
and mortality, reduced postoperative pain, earlier mobility, shorter
hospital stay, and lower costs.
• Despite these clinical benefits, RA remains less popular than GA.
One reason for this is that GA is far more successful and reliable
than RA.
• Ultrasound has been employed to increase the success rate of RA .
However, ultrasound- guided RA can be a challenging technique,
especially for inexperienced physicians and in difficult cases.
• Good theoretical, practical and non-cognitive skills are needed in
order to achieve confidence in performing RA and to keep
complications to a minimum.
• This Project aims to design a system to reduces such risk in RA.

5. EXISTING SYSTEM
• Fuzzy c-mean clusters.
• Kalman filter.

6. DISADVANTAGES OF EXISTING SYSTEM
• More time required .

7. • Modified kalman filter.
Advantages:
• More accurate.
• Faster.
• Simple to implement.
PROPOSED SYSTEM

8. BLOCK DIAGRAM OF PROPOSED SYSTEM
Input
Image
Output
Image
Noise removal
using Gaussian
filter
Reading
Image
Registration

9. TOOLS USED
• MATLAB 2009a or above.
• Image processing tool box.

10. REFERENCES
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Lab Windows/CVI', International Conference on Industrial Mechatronics and
Automation, ICIMA, 2009.
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Images into Simple Generic Patterns using Mathematical Morphology and
Windowing Technique', CoGRAMM Melaka, Malaysia 2002
3. N.K. Khalid. 'An Image Processing Approach Towards Classification of Defects on
Printed Circuit Board', Projek Sarjana Muda, Universiti Teknologi Malaysia, 2007.
4. T. Taniguchi, D. Kacprzak, S. Yamada, M. Iwahara, and T. Miyogashi, 'Defect
Detection on Printed Circuit Board by using Eddy-Current Technique and Image
Processing', 101 Press, 2000
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Printed Circuit Board Through Machine Vision', Computers in Industry, 28 (1996)
pp.I03-III.
6. K. Chomsuwan, S. Yamada and M. Iwahara,' Improvement on Defect Detection
Performance of PCB Inspection Based on ECT Technique With Multi-SV-GMR
Sensor', IEEE Transaction on Magnetics vol. 43, no. 6, June 2007

11. Thank you