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Denoising Technique for Segmentation of Medical Images using Biorthogonal Wavelet Transform

Ritu Agrawal*, Manisha Sharma**

* Research Scholar, Chhattisgarh Swami Vivekananda Technical University, Bhilai, India.

** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.

Periodicity:July - September'2015
DOI : https://doi.org/10.26634/jip.2.3.3604

Abstract

The proposed paper is divided into two parts: firstly image denoising and secondly image segmentation. All medical images are corrupted by noise at the time of transmission and acquisition. The goal of denoising is to remove noise while retaining the visual quality of image. Biorthogonal wavelet transform via hard thresholding is used for denoising. The second part of the paper is designed for segmentation of Magnetic Resonance Angiography (MRA) cardio image after denoising. Image segmentation plays a significant role in medical field. The aim of image segmentation is to extract meaningful objects lying in the image. The analysis of MRA images using two segmentation methods namely region growing method and fuzzy c- means method are discussed in the presence of different noise level and comparison of two segmentation techniques based on accuracy has been performed. The experimental results shows that fuzzy cmeans method yields better segmentation result compared to the region growing method.

Keywords

Region Growing Method, Fuzzy c-means, Segmentation Accuracy, Biorthogonal Wavelet.

How to Cite this Article?

Agrawal, R., and Sharma, M. (2015). Denoising Technique for Segmentation of Medical Images using Biorthogonal Wavelet Transform. i-manager’s Journal on Image Processing, 2(3), 35-39. https://doi.org/10.26634/jip.2.3.3604

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