© 2016 Sara Ibrahim Ibrahim, Mohamed Abd Allah Makhlouf, Ghada.S. El-Tawel and M.E. Wahed. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license. American Journal of Bioinformatics Original Research Paper Swarm Optimization Techniques for Segmenting Gel Electrophoresis Images Sara Ibrahim Ibrahim, Mohamed Abd Allah Makhlouf, Ghada.S. El-Tawel and M.E. Wahed Department of Information System, Suez Canal University, Ismailia, Egypt Article history Received: 22-07-2015 Revised: 05-10-2015 Accepted: 18-06-2016 Corresponding Author: Sara Ibrahim Ibrahim Department of Information System, Suez Canal University, Ismailia, Egypt Email: gana2491990ex@gmail.com Abstract: Gel Electrophoresis (GE) are discussed as the main tool to dissociate DNA sequences. It helps in analyzing the genome such that each image resulting from it consists of lanes that include several bands. Image segmentation plays the foremost role in image processing. It helps in producing accurate results in medical diagnosis. Image segmentation works by dividing an image into regions that cover the full image. Image segmentation methods can be implemented, but still have certain defects that cannot produce accurate results. On the other hand, Swarm Optimization methods produce results with high efficiency in image segmentation. In this study, swarm optimization techniques for image segmentation are proposed. The proposed technique depends on applying different segmentation methods as Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) is an extensively used in computer science considered a simple and easy algorithm to implement. It also depends on swarm intelligence. PSO useful in image segmentation because the result is more exact and efficient. Furthermore, Darwinian PSO (DPSO) and Fractional Order Darwinian PSO (FODPSO) produced precise results. The efficiency of the proposed approach is compared with other by computing image quality measurement parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and others. The proposed technique, especially FODPSO produces more accurate results to segment GE image. Keywords: DNA, Electrophoresis Gel, Image Denoising, Image Preprocessing, Image Segmentation, Clustering, FCM, PSO, DPSO and FODPSO Introduction Deoxyribonucleic Acid (DNA) is the backbone of all living organisms (Zhu et al., 2011). DNA comprises of double strands of sugar, connected together by nucleotide bases. It also has four bases; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T). The varieties of DNA of different living beings in the whole world depend on the variety in the length of the helix and its order. It is very easy to determine a sequence of bases if you know the sequence of bases on one side of the double strand. Since the two strands are compliments like (A) is complemented with (T) and (C) is complemented with (G). Figure 1 displays two strands of DNA. The DNA sequence is the process of specifying the accurate rank of the four bases of DNA or RNA. It’s helpful for human recognition, which is used in genetic testing and fingerprint, is unique for every person. There are several techniques for DNA sequence like Maxam- Gilbert sequencing in (1977) by Allan Maxamand Walter Gilbert and Sanger Sequencing, the popular technique for DNA sequence, by Fredrick Sangerat (1977) (França et al., 2002). These techniques depend on a number of bases. If DNA sequences are above 1000 base pairs, then use the Shotgun sequencing and electrophoresis. Electrophoresis, discovered by Fred Sanger, is the main tool for DNA sequencing. It divides a molecule in to several pieces of different sizes by restriction enzymes (Lee et al., 2011). Gel electrophoresis is used to divide DNA pieces according to size and an electric field. If the DNA is negatively charged, it will move towards the electrode of opposite charge (Fig. 2).