1. 出處： 2004 IEEE International Conference on Multimedia and Expo 作者： Chuan-Yu Chan A Contextual-based Hopfield Neural Network for Medical Image Edge Detection 1 指導教授：張財榮 學生：陳建宏 學號： M97G0209

2. Outline Introduction The Contextual Hopfield Neural Network The CHNN Algorithm Experimental Results Conclusions 2

3. Introduction 3 Detection of edge

4. Introduction 4 一階導數： Sobel 濾波器 二階導數： Laplacian 濾波器 0 10 1-41 010 111 1-81 111 2 2-42 2 2 000 121 01 -202 01

5. Introduction 5 一般影像 醫學影像 (a) Laplacian(b) Canny’s (c) Laplacian(d) Canny’s

6. Introduction A two-layer Hopfield based neural network Competitive Hopfield Edge Finding Neural Network The architecture of the CHEFNN 6

7. Hopfield Neural Network 7 Network Architecture

8. Hopfield Neural Network The total input to neuron (x,i) is computed as The activation function is defined by 8

9. The Contextual Hopfield Neural Network CHNN is make up of MxN neurons The input is the original image The output is an edge-based feature map The architecture of the CHNN 9

10. The Contextual Hopfield Neural Network The energy function of CHNN must satisfy d x,i;y,j is defined as ： Φ x,i (y,j) is defined as ： 10

11. From the above constraint The Lyapunov energy function Comparing Eq.(8) and Eq.(3) Applying the above Equations to Eq.(l) 11

12. The CHNN algorithm Step 1 ： Assigning the initial neuron states as 1. Step 2 ： Use Eq.(11) to calculate the Net (x,i) Step 3 ： Apply Eq.(2) to obtain the new output Step 4 ： Repeat Step 2 and Step 3 Step 5 ： Edge detection results. 12 111111 111111 111111 111111 111111 111111 100000 010000 001000 000100 000010 000001 Step2Step3Step4

13. Experimental Results (a) Laplacian (b) Marr-Hildreth’s (c) wavelet (d) Canny’s (e) CHEFNN (f) CHNN 13 Original phantom image Added Noise(K=30)

14. Experimental Results 14 The CT image (a) Laplacian (b) Marr-Hildreth’s (c) wavelet (d) Canny’s (e) CHEFNN (f) CHNN

15. Experimental Results 15 Original phantom image

16. Conclusions This paper proposes a CHNN for edge detection The CHNN saved a half of neurons than CHEFNN Noises will be effectively removed 16