A Neural Network-Based Data Managing and Manipulating System

WANWU GUO ANTHONY WATSON School of Computer and Information Science

Edith Cowan University 2 Bradford Street, Mount Lawley, Western Australia 6050, Australia

Abstract: - In data processing it is common to have many data files resulted from using various operations on a given original data file for different purposes. Since each operation has its own algorithm and is designed for carrying out a specific task, each resultant data file will need an independent presentation. If we need to present the features processed by multiple operations into one presentation so that the relationships among the processed features in different files can be revealed, a new system that is capable of not only managing all the resultant data files, but also carrying out some manipulations on these data files is required. In this paper, we propose a neural network-based system that can not only coordinate all the results from traditional processing, but also integrate different data files into one presentation. The general structure of this neural system is described firstly, and then three examples are used to test the usefulness of this system. The results show that this neural system is adaptive and meets the needs of managing and manipulating digital data for different purposes. Key-Words: - Neural Network, Data Managing and Manipulation, Data Integration, Supervised Learning 1 Introduction Given a digital data file, one may process it using various operators for different purposes. Therefore it is common to have many data files resulted from using these operations in the end of the processing. Since each operation has its own algorithm and is designed for achieving a specific task, each resultant data file corresponds to an independent presentation. For example, directional filters are used to enhance linear features in a specific direction and normally based on selective horizontal derivatives [1]. In theory, the linear features perpendicular to the filter movement are enhanced and those parallel to the filter movement are minimized. To illustrate linear features in several directions of an image, we need to process the original data in each of these directions, and then make several independent images using the resultant data files in these directions. If we need to present the features enhanced in more than one direction into one image so as the relationships between the enhanced features in these directions can be revealed, a new system that is capable of not only managing all the resultant data files, but also carrying out some manipulations on these data files is required. Our study shows that neural networks can be used to facilitate such a system. In this paper, we propose a neural network-based system that can not only coordinate all the results from traditional processing,

but also integrate different data files into one presentation. We firstly describe the general structure of this neural system, and then use three examples to test the usefulness of this system. The examples used include a synthetic image, an aerial photograph and chest radiograph. 2 Structure of the Neural System The design of this neural system is sketched in Figure 1. This system consists of five parts in general. The first part is an input/store unit that holds the original data file and the processed files resulted from applying various data operations on the original data. This unit is also designed to store the final outputs of this neural system, which can also be sent to a separate device for printing or displaying. The second part of this system is a data processing unit that is responsible for performing the ‘normal’ data processing with various implemented operations. These operations can be implemented as spatial domain filters, Fourier transformations, Randon transformations and other existing methods [1][2][3][4]. This processing unit takes the original data file as input and then produces many files according to different operations used. All the resultant files are sent back to the input/store unit. This unit can be detached if the original data and other

processed data are readily stored into the input/store unit by other means. Assuming data operations have been applied to an original file (f0) and the resultant files are f1, f2, …, fn, all the files now in the input/store unit are available for selection to the neural operations taken in the neuron unit that is the fourth part of the system. Deciding which files should be chosen and how they can be weighted individually for neural operations is determined by adjusting the parameters of the weighting unit that links the input/store unit to the neuron unit. This weighting unit is the third part in the system. The weighted files are further manipulated by individual neural operations in the neuron unit. All data from this neuron unit are then assembled at the output unit as the final result O(f,W,N) of this system that can be sent either back to the input/store unit for storage or to a device for printing or displaying. This process can be expressed as

( ) ∑ ∑= =

=

n

j

n

iijij WfNNWfO

0 0,, (1)

A set of supervised learning mechanisms can also be enforced into this sending-back procedure for iterative processing. This can be expressed in general as ( ) kkkk TOA εδ <−= , (2) or min)( =kF δ , (3) where Ak is the k th assessment rule; Tk is the k th target; δk is the difference between Ak and Tk after one iteration; εκ is the predetermined error threshold for iteration control; F is a δk dependent function used for optimising determination.

Figure 1. Schematic dia gram of the neural system

3 A Synthetic Example We first use a synthetic example to demonstrate the usefulness of this neural system for managing and manipulating directional filtering data. The synthetic model is consists of three squared-diamonds and a ‘multiplication’ sign in the middle of the diamonds

(Fig. 2a). All objects in Figure 2a are not clearly shown because the differences between adjacent objects in the middle area are not profound. As a result, the central ‘multiplication’ sign is hidden. After applying directional filtering northeasterly and northwesterly to the original data f0, the resulted data

(f1, f2) are stored in the input/store unit along with the original data as the input to the neural system. Selection of W0 = [1, 0, 0], W1 = [0, 0, 0], W2 = [0, 0, 0], and N0 = 1 produces the original image as shown in Figure 2a. A combination of W0 = [0, 0, 0], W1 = [0, 1, 0], W2 = [0, 0, 0], and N1 = 1 produces an image that shows the result of northeastern filtering (Fig. 2b), in which northwestern edges of all objects are outlined but northeastern edges of all the objects

are removed. On the other hand, selection of W0 = [0, 0, 0], W1 = [0, 0, 0], W2 = [0, 0, 1], and N2 = 1 returns the northwesterly filtered image as shown in Figure 2c, in which northeastern edges of all the objects are outlined but northwestern edges of all the objects are removed. All these presentations are the normal functions that a conventional system should have. This means our new system has the capability for traditional data management.

a b c

d e f

Figure 2. Synthetic model (a), and other processed images (b-f) using the neural system shown in Figure 1

A combination of W0 = [0, 0, 0], W1 = [0, 1, 0], W2 = [0, 0, 1], and N1 = N2 = 1 generates an image that combines both northeastern filtering and northwestern filtering onto the same image, in which all the objects including the hidden ‘multiplication’ sign are clearly outlined (Fig. 2d). It should be noticed that the boundaries of the same object have different grey scales in Figure 2d. This is because directional filtering always results in a pair of ‘positive’ and ‘negative’ changes along the parallel boundaries of a symmetric shape. The combination

W0 = [0, 0, 0], W1 = [0, 1, 0], W2 = [0, 0, 1], and N1 = N2 = abs makes all the boundaries of an object equally grey-scaled (Fig. 2e). The other way to produce an equally grey-scaled image is to use a different neural operation. For example, the combination W0 = [0, 0, 0], W1 = [0, 1, 0], W2 = [0, 0, 1], and N1 = N2 = normalization generates an image with all the boundaries of an object equally grey-scaled (Fig. 2f). Compared with Figure 2e with a neural operation abs that turns all negative values to the positive without change any

value itself, neural operation normalization changes not only the sign of a value, but also the value itself to a predefined uniform level. Thus, all the edges, no matter which object they belong to, should be highlighted uniformly. Figures 2d-f show that our new system also offers more power for advance data manipulation, in addition to the capability for conventional data management.

4 An Aerial Photograph In this example, we use an aerial photograph to show the manipulation of original data and edge detected

data resulted from a conventional Laplace operator. The original photograph is shown in Figure 3a. Building roofs, streets, and sport grounds are predominate features on the photograph, but they are blurry. Since this photograph contains more information in east-west and north-south directions, a conventional Laplace edge detector with 4 in the centre and -1 to its north, east, south, and west is applied to this photograph, and the resultant image (f1) is shown in Figure 3b. In terms of linear feature enhancement, this result is good. However, it is evident that this image lacks of both contrast depth and details inside a specific object.

a b

c d

Figure 3. Original aerial photograph (a), edge-detected image (b), and images (c and d) manipulated using the

neural system shown in Figure 1

Figure 3c shows the result of using a combination of W0 = [1,0,0], W1 = [0,1,0], W2 = [0,0,1], and N0 = N1 = N2 = 1. Compared with both original and Laplace images (Figs. 3a-b), this neural operation returns a combination of these two images. However, some linear features in this image are still unclear, eg, the conjugate marks in the tennis court, the paths in the garden in the southwestern building, and the NW trending strips on the roof of the building to east of the main street are still blurry. To outline these features, we use a system combination of W0 = [1,0,0], W1 = [0,2,0], W2 = [0,0,2], and N0 = N1 = N2 = 1 to further enhance the linear features in the image. The resultant image is shown in Figure 3d. In addition to keeping a good contrast depth with the image, the linear features, such as the marks in the tennis court, paths in the garden, and NW strips on the building roof, are clearly outlined. Furthermore, some nondirectional features, such as the spots on top of some buildings, are also enhanced. 5 Radiograph Processing The original chest radiograph is shown in Figure 4a. As we do not know for this case whether the textural information or structural information should be more concerned, for general practice of image processing techniques, our effort is made on achieving the best possible statistics using our proposed system. A statistics-based supervised learning mechanism is used to adjust the system parameters to approach a pre-defined statistical model. The model can be defined by users in many different ways depending on different requirements. Here we use a simple model that is designed to control the overall quality of the processed image so that its statistical mean is within 127 ± 127×10% (or 114 < mean < 140) and its standard deviation (σ) corresponding to this range of mean is within 39 < σ = 43 < 47. This model is based on the fact that the mean value of a normal distribution should be 127 for a grey-scale image that has a pixel range of 0-255. These two sets of rules are summarised in Table 1. Based on these two rules, the following optimising control function is used for iteration control. It should be noted that only those

iterations that satisfy both rules are further assessed using this formula.

min2

2

2

2

1

13 =

+

=

TTδδ

δ . (4)

Table 1. Control parameters for supervised learning

iteration in the radiograph processing δ i Ai Ti ε i δ1 A1 = mean T1 =127 ε1 = 13 δ2 A2 = S. D. T2 = 43 ε2 = 4

Figures 4b-d shows the results after using the proposed neural system to the original image. The original image data and two directionally filtered data files in the northeast and northwest are used as system input. As inter-neuron actions are not needed, each of the weighting vectors becomes a single value. Since the processing is iterative based on the two rules of statistical mean and standard deviation, showed in Figure 4 are also some selected results from ‘middle steps’ of this iteration. The statistical data of these images are shown in Table 2. The processing is initiated with a combination of W0 = 1.25, W1 = W2 = 0.5, N0 = N1 = N2 = 1. Although the structural features are enhanced by this operation, no significant improvement is visually achieved (Fig. 4b). Statistics show that the mean (131.51) of this image is within the tolerance of 114-140, but the standard deviation (36.37) is out of the acceptable range (Table 2), and thus this result is not further assessed by formula (4). Based on this initial statistics, the supervised learning mechanism of this system automatically adjusts the system parameters for further processing until reaching the ‘best-fit’ statistics. Figure 4c and 4d are produced during this iteration. In fact, the statistics of Figure 4c has fallen into the tolerance, but it is the optimistic result (δ3 = 13.26). Thus more iteration is called and the final result is produced with δ3 = 3.52, which is the closest to the middle of the tolerance range (Figure 4d and Table 2). This final image indeed shows significant enhancement in both the structural and textural features over the original image.

Table 2. Statistical data of original image and processed images in Figure 4

Image W0 W1 W2 Pixel Range Mean S.D. δ1 (<13) δ2 (<4) δ3 (min) Fig. 4a 1 0 0 0 – 255 105.32 29.06 21.68 13.94 - Fig. 4b 1.25 0.5 0.5 0 – 255 131.51 36.37 4.51 6.63 - Fig. 4c 1.33 1.33 1.33 0 – 255 139.65 39.03 12.65 3.97 13.26 Fig. 4d 1.25 3.75 3.75 0 – 255 130.48 42.45 3.48 0.55 3.52

a b

c d

Figure 4. Original image (a) and images (b-d) resulted from the supervised learning processing

6 Conclusion The synthetic and real examples presented above prove that the neural system is useful in not only managing digital data in a conventional way, but also manipulating the existing data files for different purposes. The adjustable weighting factors and selective neural operations in the neural system offer a great flexibility in data processing and integration. The last example also shows the system is adaptive for various supervised learning mechanisms to be implemented.

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Algorithms and Scientific Applications, Springer, 1997.

[2] S.R. Deans, Radon Transform and Some of Its Applications, John Wiley and Sons, 1983.

[3] G. Beylkin, Discrete Radon Transform, IEEE Trans. Acoustics, Speech and Signal Processing, Vol.35, 1987, pp.162-172.

[4] J.G. Proakis, and D.G. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications, Prentice-Hall International, 1996.