Vaishali R, Nancy Kuchhal Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal Dr V Madhu Viswanatham (Guide) School of Computing Science and Engineering VIT University, Vellore, India - 632014 vaishali.r2014@vit.ac.in

1 ABSTRACT The reduction of unwanted noise contamination is required to improve the accuracy of analysis in any field that finds application with signals. In this paper we compare various Gaussian filters on Gray scale and medical images that are corrupted by various noise mixtures. we test them using noise ratio calculation formula (PSNR) and image experts for their quality analysis and propose a hybrid bilateral filter by the combination of switching bilateral filter and low pass Gaussian filters. We have achieved less amount of noise reduction but the visibility obtained while testing by the image experts and edge detection operators have proven it to be a good filter against the noise mixture contamination. Keywords— noise filtering , mixed noise, hybrid bilateral filter, Edge Detection and image enhancement Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

Input Image = Actual Image + Mixed Noise 2 INTRODUCTION Images are reliable resources for clinical diagnosis.The common noise forms that contaminate the image signals are Gaussian noise (otherwise known as distributed noise), salt & pepper noise (otherwise called impulse noise) , poisson noise and speckle noise (or multiplicative noise) [1-2]. We aim to denoise the mixed noise contamination from the corrupted image Signal Input Image = Actual Image + Mixed Noise Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

Poisson Noise Speckle Noise Gaussian Noise Salt & Pepper Noise 3 NOISE MODELS Poisson Noise Speckle Noise Gaussian Noise Salt & Pepper Noise All these noises are added to the input Image and the denoising algorithms are applied over them. Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

INPUT DATA FOR ANALYSIS 4 INPUT DATA FOR ANALYSIS Input Image (Above) after Mixed noise contamination (Below) C A B D Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

FILTER ANALYSIS OVER NOISY IMAGE 5 FILTER ANALYSIS OVER NOISY IMAGE Peak- Signal Noise Ratio, tabulated analysing the output filtered image with input noisy image Filters Img A Img B Img C Img D High pass 28.8840 29.3815 24.9914 27.2154 Low Pass 19.9231 20.9523 13.2207 20.6844 Bilateral 29.7652 30.4380 25.7716 28.3570 Fast Bilateral 35.1390 37.0380 30.6045 34.5429 Imp.Switchable 29.5159 29.9198 26.7755 27.6178 Hybrid 34.7052 35.8906 31.3860 34.7551 Original 28.6577 29.1315 24.9976 27.2153 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

FILTER ANALYSIS OVER NOISY IMAGE 6 FILTER ANALYSIS OVER NOISY IMAGE Decision Taken by the image experts over the filtered output image. In image expert quality prediction over the resultant images, we tabular the data of analysis as V- Visible, B- Blurred , N- Noisy,NV- noisy and Visible, NB- noisy Blurred and VB - Visible Blurred. A B C D Highpass N Lowpass Bilateral NV Fast shift VB Improved Switch NB Hybrid V Input Img Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

QUALITY ANALYSIS OVER NOISY IMAGE 7 QUALITY ANALYSIS OVER NOISY IMAGE (i) Noise Ratio detection PSNR is obtained by the calculating the result by implementing the formula [11]. P= 10* log10(m*n*peak2 /sum(sum(B- A) 2)) db (ii) Image expert judgement In image expert quality prediction over the resultant images, we tabular the data of analysis as V- Visible, B- Blurred , N- Noisy,NV- noisy and Visible, NB- noisy Blurred and VB - Visible Blurred. Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) GAUSSIAN HIGH PASS 8 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) GAUSSIAN LOW PASS 9 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) GAUSSIAN LOW PASS 10 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) BILATERAL FILTER 11 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) FAST SHIFT BILATERAL FILTER 12 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) IMP SWITCHABLE BILATERAL FILTER 13 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

IMAGE VISIBILITY ANALYSIS (noisy vs filtered) PROPOSED HYBRID BILATERAL FILTER 14 Enhancement of Medical and Gray scale images using Gaussian Filters and Edge detection operators Vaishali R, Nancy Kuchhal and Dr V Madhu Viswanatham (Guide)School of Computing Science and Engineering, VIT University vaishali.r2014@vit.ac.in

EDGE DETECTION OPERATORS 15 Edge Preservation test The main objective of filtering is to retain the edge features even after filtering. We apply five distinctive edge detection operators over our experimental result images and find which filtering produces best output tabulated in table (v). They are named as Sobel operator , Prewitt operator, Canny operator, Roberts operator and Log edge detection operations decision on visibility and Edge detection operators output, We arrive at a decision that the Hybrid Bilateral filter proposed, work efficiently on all the images to produce good quality of visibility by filtering the noise mixture contaminated images, compared to other filters, in spite of the noise levels.

EDGE DETECTION OPERATORS 16 1 c d a b e 2 A. Canny B. Roberts C. Prewitt D. Sobel E. Log 1. Prop. Hybrid Bilateral (best visibility) 2. Low pass gaussian (lowest PSNR)

CONCLUSION & RESULT 17 We arrive at a decision that the Hybrid Bilateral filter proposed, work efficiently on all the images to produce good quality of visibility by filtering the noise mixture contaminated images, compared to other filters, in spite of the noise levels.

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Vaishali R, Nancy Kuchhal THANK YOU! Vaishali R, Nancy Kuchhal Dr V Madhu Viswanatham (Guide) School of Computing Science and Engineering VIT University vaishali.r2014@vit.ac.in