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