Modified Watershed Segmentation with Denoising of Medical Images
|Usha Mittal1, Sanyam Anand2
M.Tech student, Dept. of CSE, Lovely Professional University, Phagwara, Punjab, India1
Assistant Professor, Dept. of CSE, Lovely Professional University, Phagwara, Punjab, India2
|Related article at Pubmed, Scholar Google|
De-noising and segmentation are fundamental steps in processing of images. They can be used as preprocessing and post-processing step. They are used to enhance the image quality. Various medical imaging that are used in these days are Magnetic Resonance Images (MRI), Ultrasound, X-Ray, CT Scan etc. Various types of noises affect the quality of images which may lead to unpredictable results. Various noises like speckle noise, Gaussian noise and Rician noise is present in ultrasound, MRI respectively. With the segmentation region required for analysis and diagnosis purpose is extracted. Various algorithm for segmentation like watershed, K-mean clustering, FCM, thresholding, region growing etc. exist. In this paper, we propose an improved watershed segmentation using denoising filter. First of all, image will be de-noised with morphological opening-closing technique then watershed transform using linear correlation and convolution operations is applied to improve efficiency, accuracy and complexity of the algorithm. In this paper, watershed segmentation and various techniques which are used to improve the performance of watershed segmentation are discussed and comparative analysis is done.