Methods for Automatic Cyst Detection and Classification in Ultrasound Images of the Female Genitalia Using Image Processing
Main Article Content
Keywords
Ovarian Cysts, Papillary growth, Watershed segmentation, Pre-Processing, Automatic cyst detection
Abstract
Ovarian cysts are a condition that affects female reproductive organs. Experts are able to detect ovarian cysts, which is a disorder that affects a woman's uterus, by examining the cyst's size and characteristics on an ultrasound device. Because the manual interpretation of ultrasound examination data for ovarian cyst size generally produces erroneous results, a tool is necessary to assess the size of the cyst and identify the characteristics of the cyst based on the papillary growth in the cyst. The method proposed here involves taking an ultrasound picture as its input and then running a pre-processing phase to eliminate noise before going on to a segmentation stage using the watershed approach. The last step of the process involves the extraction of individual features from the image. The findings of the segmentation are then utilised for feature extraction, namely, , to identify cysts and papillary and their diameters using contour analysis using the bounding box approach.
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2. Chang, C. Y., Liu, H. Y., Tseng, C. H., & Shih, S. R. (2010). Computer-aided diagnosis for thyroid graves' disease in ultrasound images. Biomedical Engineering: Applications, Basis and Communications, 22(2), 91-99. https://doi.org/10.4015/S1016237210001815
3. Chen, S. J., Chang, C. Y., Chang, K. Y., Tzeng, J. E., Chen, Y. T., Lin, C. W., Hsu, W. C., & Wei, C. K. (2010). Classification of the thyroid nodules based on characteristic sonographic textural feature and correlated histopathology using hierarchical support vector machines. Ultrasound in medicine & biology, 36(12), 2018-2026. https://doi.org/10.1016/j.ultrasmedbio.2010.08.019
4. Chornokur, G., Amankwah, E. K., Schildkraut, J. M., and Phelan, C. M. (2013). Global ovarian cancer health disparities. Gynecologic Oncology, 129(1), 258-264. https://doi.org/10.1016/j.ygyno.2012.12.016
5. Costa, A. C., Oliveira, H. C. R., Catani, J. H., de Barros, N., Melo, C. F. E. & Vieira, M. A. C. (2018) "Data augmentation for detection of architectural distortion in digital mammography using deep learning approach,", arXiv:1807.03167. [Online] Available: http://arxiv.org/abs/1807.03167
6. El-Nabawy, A., El-Bendary, N., Belal, N. (2018). Epithelial Ovarian Cancer Stage Subtype Classification using Clinical and Gene Expression Integrative Approach. Procedia Computer Science, 131, 23-30. https://doi.org/10.1016/j.procs.2018.04.181
7. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, 1097-1105.
8. Li, W., Cao, P., Zhao, D., & Wang, J. (2016). Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Computational and Mathematical Methods in Medicine, 6215085.https://doi.org/10.1155/2016/6215085
9. Lu, M., Fan, Z., Xu, B., Chen, L., Zheng, X., Li, J., Znati, T., Mi, Q., & Jiang, J. (2020). Using machine learning to predict ovarian cancer. International journal of medical informatics, 141, 104195. https://doi.org/10.1016/j.ijmedinf.2020.104195
10. Ma, S., Sigal, L. & Sclaroff, S. 2016. Learning Activity Progression in LSTMs for Activity Detection and Early Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 1942-1950, https://doi.org/10.1109/CVPR.2016.214
11. Martínez-Más J, Bueno-Crespo A, Khazendar S, Remezal-Solano M, Martínez-Cendán JP, Jassim S, Du H, Al Assam H, Bourne T, Timmerman D, "Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images," PLOS ONE, Available: 2019. https://doi.org/10.1371/journal.pone.0219388
12. Menchón-Lara, R-M., Sancho-Gómez, J-L., Bueno-Crespo, A. (2016). Early-stage atherosclerosis detection using deep learning over carotid ultrasound images. Applied Soft Computing, 49, 616-628. https://doi.org/10.1016/j.asoc.2016.08.055
13. Rahman, M.A., Muniyandi, R.C., Islam, K.T., & Rahman, M.M. (2019). Ovarian Cancer Classification Accuracy Analysis Using 15-Neuron Artificial Neural Networks Model. 2019 IEEE Student Conference on Research and Development (SCOReD), 33-38. https://doi.org/10.1109/SCORED.2019.8896332
14. Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., & Summers, R. M. (2014). A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17(Pt 1), 520-527. https://doi.org/10.1007/978-3-319-10404-1_65
15. Shibusawa, M., Nakayama, R., Okanami, Y., Kashikura, Y., Imai, N., Nakamura, T., Kimura, H., Yamashita, M., Hanamura, N., & Ogawa, T. (2016). The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images. Journal of medical ultrasonics (2001), 43(3), 387-394. https://doi.org/10.1007/s10396-016-0718-9
16. Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). Breast cancer histopathological image classification using Convolutional Neural Networks. International Joint Conference on Neural Networks (IJCNN), Vancouver, 2560-2567. https://doi.org/10.1109/IJCNN.2016.7727519
17. Torre, L. A., Trabert, B., DeSantis, C. E., Miller, K. D., Samimi, G., Runowicz, C. D., Gaudet, M. M., Jemal, A. and Siegel, R. L. (2018.). Ovarian cancer statistics, 2018. CA: A Cancer Journal for Clinicians. 68(4), 284-296. https://doi.org/10.3322/caac.21456
18. World Health Organization (WHO), Cancer Fact Sheet, 2019. Available: https://www.who.int/news-room/fact-sheets/detail/cancer
19. Wu, M., Yan, C., Liu, H., & Liu, Q. (2018). Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Bioscience reports, 38(3), BSR20180289. https://doi.org/10.1042/BSR20180289
20. Zhang, L., Huang, J., & Liu, L., "Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system," Journal of Medical Systems, Vol. 43, no. 8, pp.243 - 251, 2019. https://doi.org/10.1007/s10916-019-1356-8
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Published
Apr 11, 2023
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How to Cite
Ahmad Ali AlZubi, Hannoud Al Moghrabi, Mallak Ahmad AlZubi, & Sufian Ahmad AlZubi. (2023). Methods for Automatic Cyst Detection and Classification in Ultrasound Images of the Female Genitalia Using Image Processing. Journal of Population Therapeutics and Clinical Pharmacology, 30(6), 297–305. https://doi.org/10.47750/jptcp.2023.30.06.035
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