Indian Journal of Science and Technology

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Statistically Inhomogeneity Correction and Image Segmentation using Active Contours


Affiliations
1 Department of Electronics and Communication Engineering, C S Patel Institute of Technology, Charotar University of Science and Technology, Changa − 388421, Gujarat, India
2 Department of Electronics and Communication Engineering, Babaria Institute of Technology, Varnama, Vadodara − 391240, Gujarat, India
 

Objective: To improve results on noisy image. Method: We have proposed Gaussian distribution density function based inhomogeneity correction method with active contour. We have implemented proposed method in MATLAB. Findings: It is shown through stimulated results that the noise and inhomogeneity both are reduced in segmented images. Experimental work is carried out for gray scale images only and it is proven that noise has been significantly reduced for different gray scale images. Applications: Proposed method can be applied for the segmentation and analysis of the various images.

Keywords

Active Contour, Gaussian Distribution, Gray Scale Image, Inhomogeneity, Segmentation.
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  • Statistically Inhomogeneity Correction and Image Segmentation using Active Contours

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Authors

Brijesh N. Shah
Department of Electronics and Communication Engineering, C S Patel Institute of Technology, Charotar University of Science and Technology, Changa − 388421, Gujarat, India
Jaymin K. Bhalani
Department of Electronics and Communication Engineering, Babaria Institute of Technology, Varnama, Vadodara − 391240, Gujarat, India

Abstract


Objective: To improve results on noisy image. Method: We have proposed Gaussian distribution density function based inhomogeneity correction method with active contour. We have implemented proposed method in MATLAB. Findings: It is shown through stimulated results that the noise and inhomogeneity both are reduced in segmented images. Experimental work is carried out for gray scale images only and it is proven that noise has been significantly reduced for different gray scale images. Applications: Proposed method can be applied for the segmentation and analysis of the various images.

Keywords


Active Contour, Gaussian Distribution, Gray Scale Image, Inhomogeneity, Segmentation.



DOI: https://doi.org/10.17485/ijst%2F2017%2Fv10i5%2F149993