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A Contextual-based Hopfield Neural Network
for Medical Image Edge Detection
出處：2004 IEEE International Conference on Multimedia and Expo
作者：Chuan-Yu Chan
指導教授：張財榮
學生：陳建宏
學號：M97G0209
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Outline
 Introduction
 The Contextual Hopfield Neural Network
 The CHNN Algorithm
 Experimental Results
 Conclusions
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Introduction
 Detection of edge
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Introduction
 一階導數：Sobel 濾波器
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 二階導數：Laplacian 濾波器
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Introduction
 一般影像
 醫學影像
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(a) Laplacian
(b) Canny’s
(c) Laplacian
(d) Canny’s
Introduction
 A two-layer Hopfield based neural network
 Competitive Hopfield Edge Finding Neural Network
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The architecture of the CHEFNN
Hopfield Neural Network
 Network Architecture
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Hopfield Neural Network
 The total input to neuron (x,i) is computed as
 The activation function is defined by
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Vx ,i  
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Net
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Net x ,i  
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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
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The Contextual Hopfield Neural Network
 The energy function of CHNN must satisfy
 dx,i;y,j is defined as ：
 Φx,i (y,j) is defined as：
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 From the above constraint
 The Lyapunov energy function
 Comparing Eq.(8) and Eq.(3)
 Applying the above Equations to Eq.(l)
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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.
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Step2
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Step3
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Experimental Results
Original phantom image
(a) Laplacian (b) Marr-Hildreth’s (c) wavelet
(d) Canny’s (e) CHEFNN
(f) CHNN
Added Noise(K=30)
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Experimental Results
The CT image
(a) Laplacian (b) Marr-Hildreth’s (c) wavelet
(d) Canny’s (e) CHEFNN
(f) CHNN
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Experimental Results
Original phantom image
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Conclusions
 This paper proposes a CHNN for edge detection
 The CHNN saved a half of neurons than CHEFNN
 Noises will be effectively removed
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