Soft Weighted Median Filter Method for Improved Image Segmentation with Noise

Siprianus Septian Manek, Handayani Tjandrasa

Abstract


Soft Weighted Median Filter Method (SWMF) is one of the new methods for noise filtering in image processing. This method is used for two types of noise in images, there is fixed valued noise (FVN) and random valued noise (RVN). Fixed valued noise is a noise type with an unchanged value, it changes the pixel value of the image to the maximum and minimum values (0 and 255), while random valued noise is a noise type with a changed value. An example of fixed valued noise is salt & pepper noise, while for random valued noise can be exemplified as gaussian, poisson, speckle, and localvar noise.

Based on previous research, SWMF method can be applied to all images with all kinds of noise (FVN and RVN) and able to reduce the noise well. This method has a higher PSNR value than other methods, especially for random valued noise types such as: gaussian, speckle, and localvar noise.

In this study, we propose to examine the performance of the SWMF method further by comparing this method with other methods such as Median Filter, Mean Filter, Gaussian Filter, and Wiener Filter in an image segmentation process. The image segmentation process in this research is based on area detection using Top-Hat transform and Otsu thresholding and line detection using Sobel edge detection. The performance measurement process uses the calculation of sensitivity value, specificity, and accuracy on the image segmentation with the groundtruh image.

The results show that Soft Weighted Median Filter method can improve the quality of image segmentation with the average accuracy of 95.70% by reducing fixed value noise and random valued noise in the images.


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References


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DOI: http://dx.doi.org/10.12962/j24068535.v16i2.a721

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