Friday 26th of April 2024
 

Fuzzy Watershed Algorithm: An enhanced algorithm for 2D gel electrophoresis image segmentation


Shaheera Rashwan, Amany M. Sarhan, Muhamed Talaat Faheem and Bayumy A.B. Youssef

An important issue in the analysis of two-dimensional electrophoresis images is the detection and quantification of protein spots. The main challenges in the segmentation of 2DGE images are to separate overlapping protein spots correctly and to find the abundance of weak protein spots. To enable comparison of protein patterns between different samples, it is necessary to match the patterns so that homologous spots are identified. In this paper, we describe a new robust technique to segment and model the different spots present in the gels. The Watershed segmentation algorithm is modified to handle the problem of over segmentation by initially partitioning the image to mosaic regions using the composition of fuzzy relations. The experimental results showed the effectiveness of the proposed algorithm to overcome the over segmentation problem associated with the available algorithm. We also use a wavelet denoising function to enhance the quality of the segmented image. The parameters of the wavelet function are obtained using the Genetic Algorithm search technique. The results of using the denoising function before the proposed Fuzzy Watershed segmentation algorithm is very promising as they are better than those without denoising.

Keywords: Protein Spot Detection, Watershed Segmentation, over-segmentation, Fuzzy Relations

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ABOUT THE AUTHORS

Shaheera Rashwan
None

Amany M. Sarhan
None

Muhamed Talaat Faheem
None

Bayumy A.B. Youssef
Researcher at Informatics Research Institute, City for Science and Technology, Borg ElArab, Alexandria, Egypt


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