Hardware Implementation of Genetic Algorithm for Ovarian Cancer Image Segmentation
Devesh D. Nawgaje1, Rajendra D. Kanphade2

1Devesh D. Nawgaje, Department of Electronics and Telecommunication, Shri Sant Gajanan Maharaj College of Engineering Shegaon, Buldhana, Maharastra, India 443404. India.
2Dr. Rajendra D. Kanphade, Principal, NMIET, Pune Maharastra 410507. India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 304-306 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1202112612/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Imaging plays an important role in the diagnosis and treatment of ovarian cancer. An accurate segmentation is critical, especially when the ovarian tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. Traditionally, segmentation is performed manually in clinical environment that is operator dependent and very tedious and time consuming labor intensive work. In this paper genetic algorithm for selecting the optimal threshold in image segmentation is proposed. In the computational process, the GA adjusts crossover probability and mutation probability automatically according to the variance between the target and background. Moreover, the complete algorithm is implemented using Digital Signal Processor TMS320C6713 which decreases the run time greatly. 
Keywords: Genetic algorithm, Ovarian Cancer, Digital Signal Processor, Segmentation.