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

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

Introduction 3 Detection of edge

Introduction 4 一階導數： Sobel 濾波器 二階導數： Laplacian 濾波器

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

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

Hopfield Neural Network 7 Network Architecture

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

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

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

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

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 Step2Step3Step4

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)

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

Experimental Results 15 Original phantom image

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