COMPUTER ASSISTED
MINIMAL INVASIVE SURGERY
TOWARDS GUIDED MOTOR
CONTROL
Vinay B Gavirangaswamy
Canny edge detection algorithm
Gaussian
Convolution to
smooth the
image
Sobel Filtering
to find
• gradient
magnitude
• and gradient
direction
Non-maximum
suppression
Hysteresis and
connected
components
analysis
Output
Original
Single Threaded
Output (contd.)
Original
Multi-Threaded (OpenMP)
Output (contd.)
Original
Multi-Threaded (GPU-CUDA)
Performance Analysis on OpenMP
Run Times of Canny Edge Detection
0.7
0.6
0.5
1
p
0.4
2
4
0.3
8
0.2
16
0.1
0
200x173
284x211
300x225
n
512x512
1280x800
Performance Analysis on OpenMP
(contd.)
Speedup of Canny Algorithm
Efficiency of Canny Algorithm
3.5
1.2
3
1
2.5
0.8
2
p
p
0.6
1.5
0.4
1
0.2
0.5
0
0
200x173 284x211 300x225 512x512 1280x800
1
2
n
4
8
16
200x173 284x211 300x225 512x512 1280x800
1
2
n
4
8
16
Canny Edge Algorithm Performance
on CUDA
CS6260 Project Implementation
Canny Edge Detection Performance on
CUDA With Different Block Size
Runtimes
128x128
Runtimes serial vs. parallel
256x256
512x512
1024x1024
2048x2048
1
10000
40000
140000
530000
2150000
2
70000
60000
80000
70000
90000
4
70000
50000
60000
60000
80000
80000
8
60000
60000
60000
60000
70000
60000
120000
100000
40000
16
60000
60000
50000
60000
70000
20000
32
50000
60000
60000
60000
80000
0
1
2
4
8
16
32
Canny Edge Detection Performance on
CUDA With Different Block Size (Contd.)
Speedup
128x128
1
2
4
8
16
32
Speedup serial vs. parallel
256x256
512x512
1024x1024
2048x2048
1
1
1
1
1
0.142857 0.666666
23.888888
14
67
1.75 7.57142857
9
0.142857
2.333333
14
0.8
33 8.83333333
26.875
0.166666 0.666666 2.333333
30.714285
67
67
33 8.83333333
7
0.166666 0.666666
30.714285
67
67
2.8 8.83333333
7
0.666666 2.333333
0.2
67
33 8.83333333
26.875
50
45
40
35
30
25
20
15
10
5
0
1
2
4
8
16
32
Canny Edge Detection Performance on
CUDA With Different Block Size (Contd.)
Efficiency
128x128
256x256
Efficiency serial vs. parallel
512x512
1024x1024
2048x2048
14
1
2
4
8
16
32
1
1
1
1
1
0.071428 0.333333
11.944444
57
33
0.875 3.78571429
4
0.035714
0.583333
29
0.2
33 2.20833333
6.71875
0.020833 0.083333 0.291666
3.8392857
33
33
67 1.10416667
1
0.010416 0.041666
1.9196428
67
67
0.175 0.55208333
6
0.020833 0.072916
0.8398437
0.00625
33
67 0.27604167
5
12
10
8
6
4
2
0
1
2
4
8
16
32
Canny Edge Detection Performance on
CUDA With Different #Threads
Runtimes
128x128
Runtimes serial vs. parallel
256x256
512x512
1024x1024
2048x2048
1
100000
400000 1400000
5300000 215000000
2
430000 1750000 7010000
28030000 112540000
4
210000
880000 3500000
14110000 56510000
8
110000
430000 1760000
7090000 28280000
1000000000
100000000
10000000
1000000
16
50000
220000
880000
3530000 14140000
32
20000
110000
450000
1780000
7120000
100000
10000
1000
1
2
4
8
128x128
256x256
1024x1024
2048x2048
16
32
512x512
Canny Edge Detection Performance on
CUDA With Different #Threads (Contd.)
Speedup
128x128
Speedup serial vs. parallel
256x256
512x512
1024x1024
36
2048x2048
31
1
1
1
1
0.232558 0.228571 0.199714
2
14
43
69 0.18908313
0.476190 0.454545
4
48
45
0.4 0.37562013
0.909090 0.930232 0.795454
8
91
56
55 0.74753173
1.818181 1.590909
16
2
82
09 1.50141643
3.636363 3.111111
32
5
64
11 2.97752809
1
1.9104318
5
3.8046363
5
7.6025459
7
15.205091
9
30.196629
2
26
21
16
11
6
p
1
1
1
2
4
8
128x128
256x256
1024x1024
2048x2048
n
16
32
512x512
Canny Edge Detection Performance on
CUDA With Different #Threads (Contd.)
Efficiency
128x128
256x256
Efficiency serial vs. parallel
512x512
1024x1024
2048x2048
1.2
1
1
1
1
0.116279 0.114285 0.099857
2
07
71
35 0.09454156
0.119047 0.113636
4
62
36
0.1 0.09390503
0.113636 0.116279 0.099431
8
36
07
82 0.09344147
0.113636 0.099431
16
0.125
36
82 0.09383853
0.113636 0.097222
32 0.15625
36
22 0.09304775
1
0.9552159
2
0.9511590
9
0.9503182
5
0.9503182
5
0.9436446
6
1
0.8
0.6
0.4
0.2
0
p
1
-0.2
1
2
4
8
16
32
-0.4
128x128
256x256
512x512
1024x1024
2048x2048
Linear (512x512)
Markov Chain Weather Model
Simple Markov Model of Weather
0.42
0.45
SUNNY(0.30)
0.38
CLOUDY(0.40)
0.12
0.20
0.39
0.10
RAINY(0.30)
0.60
0.45
Prediction Based on State Transition
Probability
If we want to know probability of the sequence
SUNNY SUNNY SUNNY SUNNY SUNNY
 Take initial probablity of SUNNY day i.e. on a any
given day probability that it will be SUNNY is 0.30
 And for use to get another SUNNY day after a
SUNNY day is 0.42
 So, by using Markov Chain we can say prbability of
getting 5 consecutive SUNNY day is
0.3  0.42  0.09335088

4
Challenges Faced During OpenMP
Missing Edges
• Medical images consist of nerves and arteries which should
be treaded as an edge however their gradient magnitude
varies relative to region in image.
• Solution : Adaptive Thresholding
False Edges in Parallel Implementation
Summary


Canny and Markov Chain Model is a simple and
efficient way to perform edge detection however
canny performs poorly with images taken during
laparoscopy (good to get started)
Future work
 Contribute
improvements to MIS learning methodology.
REFERENCES






Image Convolution with CUDA – Victor Podlozhnyuk,
sdkfeedback@nvidia.com
Performance Evaluation of Feature Extraction Algorithm on GPGPU
– Namdev Sawant Dept. of Computer Science and Engg. Dinesh
Kulkarni Dept. of Information Technology, 2011 International
Conference on Communication Systems and Network Technologies
Canny Edge Detection on NVIDIA CUDA - Yuancheng “Mike” Luo
and Ramani Duraiswami, Perceptual Interfaces and Reality
Laboratory, Computer Science & UMIACS, University of Maryland,
College Park
Cuda-grayscale – Karlphil, karlphil...@gmail.com
Rich, E.A. 2007. Automata, computability and complexity: Theory and
applications, Upper Saddle River, NJ: Prentice Hall
Special thanks to Jason and Vasilije!