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

200x173 284x211 300x225 512x512 1280x8000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Run Times of Canny Edge Detection

124816

n

p

Performance Analysis on OpenMP (contd.)

200x173 284x211 300x225 512x512 1280x8000

0.5

1

1.5

2

2.5

3

3.5

1 2 4 8 16n

p

200x173 284x211 300x225 512x512 1280x8000

0.2

0.4

0.6

0.8

1

1.2

1 2 4 8 16

n

p

Speedup of Canny Algorithm

Efficiency of Canny Algorithm

CS6260 Project Implementation

Canny Edge Algorithm Performance on CUDA

Canny Edge Detection Performance on CUDA With Different Block Size

128x128 256x256 512x512 1024x1024 2048x2048

1 10000 40000 140000 530000 2150000

2 70000 60000 80000 70000 90000

4 70000 50000 60000 60000 80000

8 60000 60000 60000 60000 70000

16 60000 60000 50000 60000 70000

32 50000 60000 60000 60000 80000

128x

128

256x

256

512x

512

1024

x102

4

2048

x204

80

20000

40000

60000

80000

100000

120000

1 2 4 8 16 32

RuntimesRuntimes serial vs. parallel

Canny Edge Detection Performance on CUDA With Different Block Size (Contd.)

128x128 256x256 512x512 1024x1024 2048x2048

1 1 1 1 1 1

20.142857

140.666666

67 1.75 7.5714285723.888888

9

40.142857

14 0.82.333333

33 8.83333333 26.875

80.166666

670.666666

672.333333

33 8.8333333330.714285

7

160.166666

670.666666

67 2.8 8.8333333330.714285

7

32 0.20.666666

672.333333

33 8.83333333 26.875

128x

128

256x

256

512x

512

1024

x102

4

2048

x204

805

101520253035404550

1 2 4 8 16 32

SpeedupSpeedup serial vs. parallel

Canny Edge Detection Performance on CUDA With Different Block Size (Contd.)

128x128 256x256 512x512 1024x1024 2048x2048

1 1 1 1 1 1

20.071428

570.333333

33 0.875 3.7857142911.9444444

40.035714

29 0.20.583333

33 2.20833333 6.71875

80.020833

330.083333

330.291666

67 1.104166673.8392857

1

160.010416

670.041666

67 0.175 0.552083331.9196428

6

32 0.006250.020833

330.072916

67 0.276041670.8398437

5

128x

128

256x

256

512x

512

1024

x102

4

2048

x204

80

2

4

6

8

10

12

14

1 2 4 8 16 32

EfficiencyEfficiency serial vs. parallel

Canny Edge Detection Performance on CUDA With Different #Threads

128x128 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

16 50000 220000 880000 3530000 14140000

32 20000 110000 450000 1780000 7120000

1 2 4 8 16 321000

10000

100000

1000000

10000000

100000000

1000000000

128x128 256x256 512x512 1024x10242048x2048

RuntimesRuntimes serial vs. parallel

Canny Edge Detection Performance on CUDA With Different #Threads (Contd.)

128x128 256x256 512x512 1024x1024 2048x2048

1 1 1 1 1 1

20.232558

140.228571

430.199714

69 0.189083131.9104318

5

40.476190

480.454545

45 0.4 0.375620133.8046363

5

80.909090

910.930232

560.795454

55 0.747531737.6025459

7

16 21.818181

821.590909

09 1.5014164315.205091

9

32 53.636363

643.1111111

1 2.9775280930.196629

2

1 2 4 8 16 321

6

11

16

21

26

31

36

128x128 256x256 512x5121024x1024 2048x2048

n

p

SpeedupSpeedup serial vs. parallel

Canny Edge Detection Performance on CUDA With Different #Threads (Contd.)

128x128 256x256 512x512 1024x1024 2048x2048

1 1 1 1 1 1

20.116279

070.114285

710.099857

35 0.094541560.9552159

2

40.119047

620.113636

36 0.1 0.093905030.95115909

80.113636

360.116279

070.099431

82 0.093441470.9503182

5

16 0.1250.113636

360.099431

82 0.093838530.9503182

5

32 0.156250.113636

360.097222

22 0.093047750.9436446

61 2 4 8 16 32

0

0.2

0.4

0.6

0.8

1

1.2

128x128 256x256 512x512

Linear (512x512) 1024x1024 2048x2048

p

EfficiencyEfficiency serial vs. parallel

Markov Chain Weather Model

Simple Markov Model of Weather

SUNNY(0.30)

RAINY(0.30)

CLOUDY(0.40)0.38

0.200.45

0.10

0.42 0.45

0.39

0.12

0.60

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

09335088.042.03.04

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!