2015 International Conference on Computational Intelligence and Communication Networks
Comparative Study of Noise Removal Algorithms For Denoising Medical Image Using LabVIEW Sambit Satpathy, Mohan Chandra Pradhan, Subrat Sharma Department of embedded system design, Sambalpur University Institute of Information Technology, burla, india E-mail:
[email protected],
[email protected],
[email protected] Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention. Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes database of normal anatomy and physiology to make it possible to identify abnormalities
Abstract—Biomedical images are generally tainted by Gaussian noise, Salt & Pepper noise. Gaussian noises are additive where as salt & pepper noises are itself as separately occurring white & black pixels. This paper presents the study of five types of filters like Gaussian filter, Gabor filter, Box filter, Median filter, Adaptive median filter, which are design using LabView. These filters are used for removing of two type of noises like Gaussian noise, Salt & Pepper noise. Then respective PSNR value has been found out which is used for the comparative analysis of the filter.
G(x,y)
Keywords- Gaussian noise, Salt & Pepper noise, Gaussian filter, Gabor filter, Box filter, Median filter, Adaptive noise filter, PSNR ratio.
I.
A.
Denoising technique
Z(x,y) Fig-1 model of denoising III.
BASIC FILTERS
A. GAUSSIAN FILTER It is a filter whose impulse response is a Gaussian function. Image denoising is an important image processing task, both as a process itself, and as a component in other processes. Very many ways to denoise an image or a set of data exists. The main properties of a good image denoising model are that it will remove noise while preserving edges. Traditionally, linear models have been used. One common approach is to use a Gaussian filter. For filtering other than Gaussian filter we have used Gabor filter, box filter, median filter, adaptive median filter. Gaussian filters have the properties of having no overshoot to a step function input while minimizing the rise and fall time. This behavior is closely connected to the fact that the Gaussian filter has the minimum possible group delay. It is considered the ideal time domain filter, just as theism is the ideal frequency domain filter. Mathematically, a Gaussian filter modifies the input signal by convolution with a Gaussian function; mathematically it is represented by
ARCHITECTURE
MEDICAL IMAGE
Image denoising is an important image processing task, both as a process itself, and as a component in other processes. Very many ways to denoise an image or a set of data exists. The main properties of a good image denoising model are that it will remove noise while preserving edges. Traditionally, linear models have been used. One common approach is to use a Gaussian filter.
B. IMAGE DENOISING For filtering other than Gaussian filter we have used Gabor filter, box filter, median filter, adaptive median filter. possible , such as "box" or "cross" patterns. 978-1-5090-0076-0/15 $31.00 © 2015 IEEE DOI 10.1109/CICN.2015.67
W(x,y)
Fig-1 model of denoising
INTRODUCTION
Noise may be arised in creation, transmission, capturing process of images. Especially we considered medical images because these many importance in day-today life.In practical life various noises are added in medical images, like Gaussian noise, salt & pepper noise etc. In general we face several problems in MRI scan, x-ray, ultra sound, brain hemorrhage scanning. Here with real images various noises are added. So that images are not clearly visible. To overcome this process, we have to design various filters, through which noises are removed and we can see clear images. There are various soft wares available for filter designing, matlab gives convenient filter design tools. But here we used labview for designing various types of filters.Labview provides comprehensive tools that can be used to build any measurement or control application in significantly less time, it also gives idea about development environment for Finnovation, detection & accelerated results. II.
Linear operation
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structure in the image. These filters smooth the data while keeping the small and sharp details. The median is just the middle value of all the values of the pixels in the neighborhood. Note that this is not the same as the mean instead; the median has half the values in the neighborhood larger and half smaller. The median is a stronger "central indicator" than the average. In particular, the median is hardly affected by a small number of discrepant values among the pixels in the neighborhood.
Where x= the distance from the origin in the horizontal axis Y= the distance from the origin in the vertical axis. σ = is the standard deviation of the Gaussian distribution.
IV.
B. GABOR FILTER
LABVIEW MODEL DESIGNING AND IMPLEMENTATION
A. GAUSSIAN FILTER
Gabor filter named after Dennis Gabor, is a filter used for edge detection. Frequency and orientation representations of Gabor filters are similar to those of the human visual system, and they have been found to be particularly appropriate for texture representation and discrimination. In the spatial domain, a 2D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. Simple cells in the visual cortex of mammalian brains can be modeled by Gabor functions. Thus, image analysis with Gabor filters is thought to be similar to Perception in the human visual system. C. BOX FILTER Box filter is a spatial domain linear filter in which each pixel in The resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass filter and also called box linear filter which can written in this way like 3*3 Matrix of 1/9 * determinant matrix Due to its property of using equal weights.
Fig-2 Gaussian function implementation
B. GABOR FILTER D. MEDIAN FILTER The median filter is a nonlinear filtering technique, often used to remove noise. Such noise reduction is a typical pre-processing step to improve the results of later processing for example, edge detection on an image. Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise . The main idea of the median filter is to run through the signal entry by entry, replacing each entry with each entry with the median of neighboring entries. The pattern of neighbors is called the "window", which slides, entry by entry, over the entire signal. For 1D signals, the most obvious window is just the first few preceding and following entries, whereas for 2D signals such as images, more complex window patterns are possible , such as "box" or "cross" patterns.
E. ADAPTIVE MEDIAN FILTER The median filter is a nonlinear filtering technique, often used to remove noise. Such noise reduction is a typical pre-processing step to improve the results of later processing for example, edge detection on an image. Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise . The main idea of the median filter is to run through the signal entry by entry, replacing each entry with We see various application of median filter, As i compare it with advanced standard median filtering, the Adaptive Median Filter performs vital processing to preserve detail and smooth non-impulsive noise. A basic benefit to this adaptive approach of median filtering is that repeated applications of this Adaptive Median Filter do not erode away edges or other small
Fig-3 Gabor function implementation
A. BOX FILTER(3*3)
301
C.
ADAPTIVE MEDIAN FILTER USING MATLAB SCRIPT
Fig-4 box filter (3*3) implementation
C. BOX FILTER (11*11) Fig-7 block diagram of adaptive median filtering using matlab script. V.
EXPERIMENT
In this work we have collected real time medical images which contain salt & pepper noise, Gaussian noise. Then we passing medical noisy images through Gaussian filter, Gabor filter, box filter, median filter, adaptive median filter. As a result we get denoised images. Below we taken two Gaussian noise medical images and denoised images by Gaussian filter, Gabor filter, box filter, median filter, adaptive median filter respectively.
A.
ORIGINAL NOISED IMAGES
Fig-5 box filter (11*11) implementation
B. MEDIAN FILTER USING IMAQ TOOL
Fig-8.1 image containing Gaussian noise
Fig-6 block diagram of median filter using imaq Tool
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Fig-9.3 Box filter image
Fig-8.2 image containing salt & pepper noise B. DENOISED IMAGES(REMOVING GAUSSIAN NOISE)
Fig-9.4 Median filter image
Fig-9.1 Gaussian filter image
Fig-9.5 Adaptive median filter image C. DENOISED IMAGES (REMOVING SALT & PEPPER NOISE)
Fig-9.2 Gabor filter image
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Fig-10.5 Adaptive median filter image Fig-10.1 Gaussian filter image VI.
COMPARISONS OF PSNR RATIO
Noise densit y
Noisy image s
0.05
14.264 9 12.602 5 10.550 8 9.7685 1 8.7635 7 8.1773 5
0.10 0.15 0.20 0.30 0.40
Fig-10.2 Gabor filter image
Denoise d image using Gaussia n filter 23.7703
Denoise d image using Gabor filter 23.7785
Denoise d image using Box filter 5.71139
Denoise d image using Median filter 21.2472
Denoise d image using AMF filter 18.3179
21.3328
21.3354
5.36576
18.5115
15.2925
19.7279
19.7281
5.11506
16.8979
14.5096
18.6728
18.6723
22.3983
15.7698
13.6797
17.1371
17.1356
15.6226
14.2295
12.6284
16.2774
16.2774
13.2595
13.2595
12.0707
Fig-11 PSNR Ratio for 2d b (Gaussian noise)
Fig-10.3 Box filter image
Noise density
Noisy images
17.7227
Denoised image using Gaussian filter 26.3213
Denoised image using Gabor filter 26.3414
Denoised image using Box filter 5.95379
Denoised image using Median filter 38.1054
Denoised image using AMF filter 42.6656
0.05 0.10
14.7031
24.6245
24.6352
5.73889
36.6994
40.4358
0.15
12.864
22.8758
22.8807
5.51272
33.6396
37.8844
0.20
11.637
21.4984
21.5004
5.32434
29.7598
36.8312
0.30
9.89726
19.1226
19.1225
25.3836
23.45
33.5118
0.40
8.64189
17.1717
17.1702
15.4086
18.5472
31.3005
Fig-12 PSNR Ratio for 2d b (Salt & pepper noise) VII.
RESULT & DISCUSSION
In this research work, we have used LabView for image processing, and found it suitable. Here, we implemented five filters i.e. Gaussian filter, Gabor filter, Box filter, median filter and adaptive median filter for removing various noises (Gaussian and salt & pepper noise) from medical images. PSNR value is used for the comparative analysis of these filters. Based on PSNR values with different noises and concentration of noise present in images we observed that, adaptive median filter gives better results as compared to others for impulsive noise (salt & pepper noise). It also found that in Gabor filter when center frequency tends to 0.02
Fig-10.4 Median filter image
304
Hz it gives the maximum results for Gaussian noise otherwise the PSNR value decreases. Again as compared to Gaussian filter in Gabor filter, we found that in maximum cases PSNR value is higher by taking σ = 2. The performance of box filter is good for particular noise level. But in maximum cases Gaussian performance is much better than the box filter (depending on level of noise).
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