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An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF

Rupinder Kaur and Er. Garima Malik
Global Institute of Management and Emerging Technology, Amritsar, 143501, (Pb.) India

Abstract—Image segmentation and evaluation are awfully not easy but significant tribulations in computer vision. In this article we have presented an improved FCM Watershed Algorithm for image segmentation. In this techniques, Decision based median filter used for noise removal, is best for salt and pepper noise reduction. Secondly, Fuzzy C-Means used for cluster selection and for final segmentation modified watershed segmentation used is integrated with FCM. The aim of this method is reduce the number of segments after proposed method and to overcome problem faced by this method which is over-segmentation and noise sensitivity. Computer simulations verify extensive improvement of this proposed method with existing methods in terms of PSNR, SSIM, CQM, MSE, RMSE and BER. These experimental results computed on MATLAB software with Image Processing toolbox.1

Index Terms—image segmentation, fuzzy C-means, watershed segmentation, decision based median filter, dynamic thresholding, morphology operation and masking

Cite: Rupinder Kaur and Er. Garima Malik, "An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF," Journal of Image and Graphics, Vol. 2, No. 2, pp. 106-112, December 2014. doi: 10.12720/joig.2.2.106-112