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TopIntroduction
The flow diagram consists of three major steps i.e. bilateral filtering, entropy multithresholding and artificial neural network (ANN) based edge detection (Sharifi, Fathy, & Mahmoudi, 2002). The acquired image is preprocessed by using bilateral filter to smoothen any spurious pixels present in acquired image. As mammographic images have low contrast and single thresholding Binarization is not inadequate for mammogram images. Therefore, three threshold levels are calculated by using entropy technique for binarization (Heindel, Wige, & Kaup, 2016). This multi threshold entropy binarization method helps to manifest maximum detail out of low contrast breast images. The true edges are filtered out by using Artificial Neural Network which is trained by using 3 × 3 Binary images. Finally, the output of ANN is edge map of lessen masses present in mammogram images. The complete details of these steps are described in following subsections. (Joshi, Yadav, & Allwadhi, 2016).
The detail flow diagram of proposed method is shown in Figure 1.
Figure 1. Flow diagram of proposed algorithm
TopBack Propagation Neural Network (Bpnn)
Back propagation neural network (BPNN) is a multi-layer network introduced. It is basically a supervised network use to train the network for edge detection by using the different Training Samples. Training means adjustment of Weights and Biases of Neural Network according to different input and output relation (Chickanosky & Mirchandani, 1998).
Suppose x is input training sample where x = (
), t is the output target given by t = (
). 
is the error at output unit 
, 
is the error at hidden unit 
, α is the learning rate,
is the bias of hidden layer neuron j, 
is the bias of hidden layer neuron k, 
and 
is the output of hidden layer and output neuron.
Output of hidden layer neuron is given by
(1)
is the activation function and output of 
hidden neuron which is given by
(2)
(3)And output of 
output node is given by
(4)
Error at
output node is given by
(5)