Analysis and Detection of Ovarian Cyst Using Soft Computing Technique in MATLAB

Chandrashekhar R. Mankar, Sonali P. Pardhi, Chaitali Choudhari, Anurucha Asare, Prof. Sumit Chafale


Cyst and polycystic ovary syndrome is a disorder is a normal phenomenon that affect woman in the perlite age. The most important thing is that PCOS. PCOS syndrome is mainly found in women aging from 12 year to 60 year. In our project, we will be going to use more neighbour counter, water shade method, active counter models, Gaussian filtering and binary filtering method are going to be used in this paper to detect the size, shape and border of the ovarian cyst from echography images. In order to analyse the efficiency of segmentation and application developed in MATLAB software is proposed.

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Lemos, A.J.J.M., Peixoto, C.A., Teixeira, A.A.C., Luna, R.L.A., Rocha, S.W.S., Santos, H.M.P., Silva A.K.S., Nunes, A.K.S., Teixeira, V.W. (2014), “Effect of the combination of metformin hydrochloride and melatonin on oxidative stress before and during pregnancy, and biochemical and histopathological analysis of the livers of rats after treatment for polycystic ovary syndrome”, Toxicology and applied pharmacology, Volume 280, Issue 1, pp. 159−168.

Purnama, B., Wisesti, U.N., Nhita, F., Gayatri, A., Mutiah, T. (2015), “A classification of polycystic Ovary Syndrome based on follicle detection of ultrasound images”, 3rd International Conference on Information and Communication Technology (ICoICT), pp. 396−401.

Mehrotra, P., Chatterjee, J., Chakraborty, C., Ghoshdastidar, B., Ghoshdastidar, S. (2011), “Automated screening of polycystic ovary syndrome using machine learning techniques”, Annual IEEE India Conference (INDICON), pp. 1 −5.

Setiawati, E., Tjokorda (2015), “A.B.W. Particle swarm optimization on follicles segmentation to support PCOS detection”, 3rd International Conference on Information and Communication Technology, pp. 369−374.

Alotaibi, M., Alsinan, A. (2016), “A mobile Polycystic ovarian syndrome management and awareness system for Gulf countries: System architecture”, SAI Computing Conference (SAI), pp. 1164−1167.

Prasanna Kumar, H., Srinivasan, S. (2014) “Segmentation of polycystic ovary in ultrasound images”, 2nd International Conference on Current Trends in Engineering and Technology (ICCTET), pp. 237−240.

Deng, Y., Wang, Y., Shen, Y. (2011), “An automated diagnostic system of polycystic ovary syndrome based on object growing”, Artificial intelligence in medicine, Volume 51, Issue 3, pp. 199−209.

Mahmood, N.H, Ahmmad, S.N.Z., Hashim, H., Rani, S.N.N.A. (2012), “Ovary ultrasound image edge detection analysis: a tutorial using MATLAB”, Int J Eng Res Appl (IJERA) Volume 2, Issue 3, pp. 1635−1642.

Wojtusiak, J., Michalski, R.S., Simanivanh, T., Baranova, A.V. (2009), “Towards application of rule learning to the meta-analysis of clinical data: An example of the metabolic syndrome”, International journal of medical informatics, Volume 78, Issue 12, pp. e104−e111.

Alonso, F.C., Pérez, C.J., Arias-Nicolás, J.P., Martín (2013), “J. Computer-aided diagnosis system: A Bayesian hybrid classification method”, Computer methods and programs in biomedicine, Volume 112, Issue 1, pp. 104−113.

Usman, A.D., Isah, O.R., Tekanyi, A.M.S. (2015), “Application of Artificial Neural Network and Texture Features for Follicle Detection”, African Journal of Computing & ICT, Volume 8, Issue 4, pp. 111−118.

Padmapriya, B., Kesavamurthy, T. (2016), “Detection of Follicles in Poly Cystic Ovarian Syndrome in Ultrasound Images Using Morphological Operations”, Journal of Medical Imaging and Health Informatics, Volume 6, Issue 1, pp. 240−243.

Udupa, J.K., Odhner, D., Zhao, L., Tong, Y., Matsumoto, M.M.S., Ciesielski, K.C., Falcao, A.X., Vaideeswaran, P., Ciesielski, V., Saboury, B., Mohammadianrasanani, S., Sin, S., Arens, R., Torigian, D.A. (2014), “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images”, Medical image analysis, Volume 18, Issue 5, pp. 752−771.

Tegnoor, J.R. (2012), “Automated Ovarian Classification in Digital Ultrasound Images using SVM”, International Journal of Engineering Research & Technology (IJERT), Volume 1, Issue 6, pp. 1−17.

Cigale, B., Zazula, D. (2004), “Segmentation of ovarian ultrasound images using cellular neural networks”, International Journal of Pattern Recognition and Artificial Intelligence, Volume 18, Issue 4, pp. 563−581.

Prema T. Akkasaligar, Girijamma V. Malagavi (2014), “Finding of Cysts In Therapeutic Ultrasound Images of Ovary”, International Journal of Advances in Electronics and Computer Science, Volume 1, Issue 1, pp. 20−25.

Raj, A. (2013), “Detection of Cysts in Ultrasonic Images of Ovary”, International Journal of Science and Research (IJSR), Volume 2, Issue 8, pp. 185−189.

Gujral, S., Rathore, A., Chauhan, S. (2017), “Detecting and Predicting Diabetes Using Supervised Learning: An Approach towards Better Healthcare for Women”, International Journal of Advanced Research in Computer Science, Volume 8, Issue 5.

Hiremath, P.S., Tegnoor, J.R. (2010), “Automatic detection of follicles in ultrasound images of ovaries using edge based method. IJCA special issue on, Recent Trends in Image Processing and Pattern Recognition

Kumar, H.P., Srinivasan, S. (2014), “Despeckling of Polycystic Ovary Ultrasound Images by Improved Total Variation Method”, Inter. J. Engineering and Technology, Volume 6, Issue 4, pp. 1877−1884.

Hiremath, P.S., Tegnoor, J.R. (2013), “Automated ovarian classification in digital ultrasound images”, International Journal of Biomedical Engineering and Technology, Volume 11, Issue 1, pp. 46−65.

Meena, K., Manimekalai, M., Rethinavalli, S. (2015), “Correlation of Artificial Neural Network Classification and NFRS Attribute Filtering Algorithm for PCOS Data”, International Journal of Research in Engineering and Technology, Volume 4, Issue 3, pp. 519−524.

Hiremath, H.P., Tegnoor, J.R. (2014), “Fuzzy inference system for follicle detection in ultrasound images of ovaries”, Soft Computing Volume 18, Issue 7, pp. 1353–1362.

Raj, A. (2013), “Ovarian follicle detection for polycystic ovary syndrome using fuzzy c-means clustering”, International Journal of Computer Trends and Technology, Volume 4, Issue 7, pp. 2146−2149.


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