A New Approach for Edge Detection of Color Microscopic Image Using Modified Pulse Coupled Neural Networks Feiyan Cheng,Zhaobin Wang,Yide Ma†,Lizhen Yang
Qingxiang Gao
School of Information Science and Engineering Lanzhou University Lanzhou China
[email protected]
School of Life Science and Technology Lanzhou University Lanzhou China
[email protected]
Abstract—In this paper, a novel algorithm for microscopic color cell image edge detection based on Pulse Coupled Neural Networks (PCNN) is presented. Microscopic color image edge detection has proven to be a difficult task due to uneven brightness, cross-color existing between cell boundary and background caused by dyeing and noise. To solve these problems, this paper originally employs an improved PCNN model called multi-dimensional PCNN to implement edge detection of microscopic color cell image. PCNN is an advanced approach which aims at processing color images parellelly rather than separately dispose signal to every channel because it is characterized by synchronous neuronal burst and multi-dimensional convolution. To test the feasibility and effectiveness of multi-dimensional PCNN on edge detection of microscopic color cell images, experiments of several microscopic cell images are carried out. Empirical results show that multi-dimensional PCNN outperforms classical methods in terms of anti-noise and the accuracy of weak edge detection. Keywords-PCNN; multi-dimensional convolution; color edge detection; vector Gradient
I.
INTRODUCTION
With the development of image processing technology, the quantitative analysis of microscopic images is widely used to reduce the workload of doctors and substantially improve work efficiency. It has long been known that early edge detection for microscopic images is important because it is the key of the performance of the quantitative analysis for microscopic images. In early research, edge detectors are designed mainly for gray-level images, and as is known, there are several representatively classical algorithms for edge detection such as sobel, prewitt, loG, canny and so on [1,2,3,4]. The use of color as an additional dimension for edge detection was later proposed. Until recently there is still some lack of knowledge of microscopic color image edge detection. The problem, as can be seen, is that a microscopic image is very complex, as shown in Fig. 1:macromolecules such as protein and DNA are of deepest color and uneven distribution; Figure1..Microscopic imperfect dyeing results in image of plant embryo boundary diffusion and cross-color cells
existing between cytoplasm area and cell boundary; the nuclear material is of non-uniform distribution; background without dyeing, however maybe has uneven distribution of brightness; furthermore some impurities and stains make it difficult to research into microscopic images. It can be easily seen that early detection and segmentation research for microscopic images, the key of the performance of quantitative analysis, are important but difficult. Ekhorn et al. were inspired by the visual cortex of mammals and developed PCNN which is close to the function of the human low level visionary system[5] and it is very suitable for image processing especially for image segmentation and detection[6,7,8,9,10,11]. Early edge detection techniques using PCNN are mostly designed for binary image. Knowledge of PCNN edge detection methods must be enriched. A solution was later suggested by Yang, who determines the edge by saltation gray scale and synchronous neuronal burst Characteristics of PCNN [12]. Owing to the success of this technique, the edge of gray-scale image can be extracted effectively using PCNN. The problem calls for a more extensive applicability, so that the color image segmentation method based on PCNN was proposed by Bao[13]. They apply PCNN separately to the three-channel H, S and V of color images and each channel can be seen as a gray image, and then makes the outstanding performance of PCNN in the gray image now come to the color image detection. However, there are limits to what PCNN can do. It still can not be used directly on the color image edge detection. The proposed approach in this paper aims to directly deal with color images rather than separately dispose each channel, which is based on multi-dimensional PCNN with the addition of vector gradient. The results show that the algorithm performances are better than the classic methods in the detection of weak edges. The rest of this paper is organized as follows: Section II discuses vector gradient. In Section III multi-dimensional PCNN is explained. Section IV describes our algorithm in detail. Experimental results are given and discussed in section V. The paper is concluded in section VI. II.
VECTOR GRADIENT
The emphasis of this chapter is placed on vector gradient which plays a crucial role in many tasks of edge detection.
* This work was supported by the National Science Foundation of China under the Grant No.60572011 and No.60872109, Program for New Century Excellent Talents in University (NCET-06-0900), National Natural Science Foundation in Gansu Province under the Grant No. 0710RJZA015. † Corresponding author. Email:
[email protected]
978-1-4244-2902-8/09/$25.00 ©2009 IEEE
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Vector gradient is beneficial to eliminate the loss of the correlation that exists among channels of color images and to improve the accuracy of edge detection algorithm, while separate processing in each channel will neglect correlation information. In this paper, difference vector (DV) operator, which is based on the first derivatives and works on vector inputs [14], is imported to get the vector gradient. Each pixel in color images is denoted by a vector. The gradient in each direction could be obtained by using the corresponding convolution template (see Fig. 2) and eventually the maximum one is selected as the pixel gradient.
Figure 2. The convolution template used by DV operator[14]
III.
IV.
In the 1990s, Eckhorn et al. developed PCNN model. It is a single-layer, self-supervision and self-learning, no advance training and iteratively running neural network. Researchers have made many important achievements of PCNN both on the theoretical exploration and application research. PCNN, in recent years, has been playing an important role in image processing, noise removing, segmentation, edge extraction, feature extraction, etc. Because a color image provides more information than a gray image, color image processing is attracting more and more researcher’s attention. Until now, PCNN can only directly deal with gray images. This paper modifies PCNN, called multi-dimensional PCNN model by introducing the vector matrix and multi-dimensional convolution so that it can also be directly applied to colour images. It can be expressed with (1) ~ (5).
Fij [ n] = Sij
(1)
Lij [ n] = Yij [ n − 1] ⊗ W
(2) (3)
⎛ 1 Uij [ n ] > Eij [ n ] Yij [ n ] = ⎜ others ⎝0 (4)
Eij [n] = exp(−α E )Eij [n −1] + vE Yij [n −1] (5) Where S, F, L, U, Y, E and W are all vector matrix and all operations are based on vector matrix. S is the input stimulus (e.g. the vector gradient of pixel (i,j)), F the feeding input of the neuron, L the linking input, U the internal activity, Y the
THE EDGE DETECTION ALGORITHM
According to the discussion in the above section, a different approach is proposed here for color edge detection. The algorithm consists of DV operation and PCNN, and gives a good performance for edge detection. The flow chart of the algorithm is shown in Fig. 3. Color Image Input
MULTI-DIMENSIONAL PCNN MODEL
U ij [ n ] = Fij [ n − 1] (1 + β L ij [ n ])
output, E the dynamic threshold which gradually decreases at each iteration and αE its attenuation coefficient. The values in interconnection W are dependent on the distance between the center neuron and its neighbours. β is linking coefficient. ⊗ represents multi-dimensional convolution. PCNN is affected by both feeding input and linking input. U accumulates the signals until it exceeds a dynamic threshold, generating an output "1". Then, the dynamic threshold is modified exponentially according to (5).
DV operation
Multi-dimensional PCNN Processing
Edge Detection
Figure 3. The diagram of the logical flow of the algorithm
The process of the method proposed in this paper is described in detail as follows: (1)Calculate the vector gradient of the input color image using vector gradient operator DV, to reflect the change degree of color information in the neighborhood window and provide the basis for multi-dimensional PCNN to detect the color edge. (2)Initialize PCNN: E is the maximum of gradient map, which inhibits the pixels with small gradient values from igniting; Y is 0. (3)Input the gradient vector got from step (1) into the multi-dimensional PCNN model. During iterations, the threshold E decays from the maximum and the pixels, whose internal activity U is less than its threshold, will not be fired. Those pixels with large gradient values will first ignite, and the edge pixels where dramatic changes have occurred could be detected naturally. (4) Finally, the three-channel outputs of PCNN are combined into the final edge. Because the convolution in (2) is a multi-dimensional convolution and PCNN are vector operations, there is no need to run three channels respectively. Parallel computing brings decreased running time. Finally the ultimate edges are up to all channels. The algorithm can quickly obtain more accurate edges. V.
EXPERIMENTAL RESULTS AND DISCUSSION
Two 256×256 typical microscopic color images of embryo cells (see Fig. 4 (a), (c)), respectively, are used to demonstrate the performance of the proposed algorithm in color edge detection. Both images are found to contain a considerable amount of weak edges. For providing a more convincing performance comparison with other existing color edge detectors, this color edge
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detection algorithm proposed should be compared with the complex ones such as sobel, canny and loG operator. Among them, comparison of sobel operator is applied in two forms:
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(b)
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Figure4. A and c are the original test image.b and d are the corresponding subgraphs of a and c including the weak edges
(a)Original color image a
Figure 6. Edge detection results of the original subgraph image b. the graph order was as follows: the original image b, edge graph of b produced by sobel1, sobel2, canny, loG and PCNN.
1)transform color images to gray images and then apply sobel operator to the gray image, called sobel1; 2) Use sobel operator respectively to R, G, B channel of the color image for edge detection and in final step add three channels to get the edge, called sobel2.
(b) Edge produced by sobel1 (a)
(c) Edge produced by sobel2
(e) Edge produced by LoG
(f)
(d) Edge produced by canny
(f) Edge produced by PCNN
Figure5. Edge detection results of the original image a. The graph order was as follows: the original image a, edge graph of a produced by sobel1, sobel2, canny, loG and PCNN.
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Figure 7. Edge detection results of the original image c.The graph order was as follows: the original image c, edge graph of c produced by sobel1, sobel2, canny, loG and PCNN.
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The experiment results indicate this method is better than the traditional methods. The method proposed is superior to others in weak edges detection, continuity and anti-noise. VI.
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CONCLUSION AND FUTURE WORK
In order to improve the performance of weak edge detection and the capacity of anti-noise, a PCNN edge detection algorithm is proposed in this paper. The algorithm promoted the two-dimensional PCNN model to multidimensional PCNN which could process more color information of images per unit of time and therefore has an excellent quality on weak edge detection. It can be concluded as a breakthrough that PCNN can directly deal with color images. In addition, this method inherits the strong performance of anti-noise of traditional PCNN in image processing. In this paper, the parameters of PCNN are well fixed, not automatically adjusted by network. It may have served as an implement for future research. REFERENCES [1]
(d)
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Figure 8. Edge detection results of the original subgraph image d; The graph order was as follows: the original image d, edge graph produced by sobel1, sobel2, canny, loG and PCNN.
From Fig. 5~8, it can be seen that discontinuous, single pixel edges are detected but weak edges are lost whether sobel1 or sobel2 is adopted; the edge maps produced by the canny algorithm contains more continuous information but still loss some important information of weak edges; with loG method, the edge maps are more detailed but discontinuous in whole; however, PCNN method captures not only the continuous edges but also more information in detail particularly for weak edges. This is obvious in the comparison of the results of Fig. 4(b) and (d). For noised images, shown as Fig.9, the proposed method also achieves a satisfying result.
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Figure 9. Edge detection results of the original image corrupted with additive 16% salt & pepper noise; the graph order was as follows: the image under salt-pepper noise, edge graph produced by sobel1, loG and PCNN
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