Abstract
The Polycystic Ovary Syndrome (PCOS) is an endocrine abnormality; it affects females during their reproductive cycle. It is hormone imbalance of female and it skips the menstrual cycle and makes it harder to get pregnant. The side effects of PCOS are causing blood pressure, heart disease, diabetes, obesity, etc. Thus, there is an imbalance in hormone that creates many cysts in ovary and it is called as polycystic ovary syndrome. It can be diagnosed by using ultrasound scan to identify the count, size, and severity of cyst. Preprocessing the medical image is the basic and initial step for medical image processing to remove the speckle noise, present in the ultrasound image and also helpful to create medical image applications. Compared with all other modalities of scan images, ultrasound scan is less cost-effective, but it contains more speckles due to image acquisition. Speckle is a granular noise that inherently exists in and it degrades the quality of the radar, ultrasound, and CT scan images. It also causes the difficulties for image interpretation. The aim of this research paper is to apply the modified Daubechies—discrete wavelet filters for ultrasonic scan image of PCOS for removing speckle noise for better diagnose cyst. This paper concludes the effectiveness of the proposed filter to identify cyst present in ultrasound PCOS. The following metrics—SNR, PSNR, and SSIM are used to measure the effectiveness of various categories of discrete wavelet transform.
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Vinodhini, R., Suganya, R. (2019). Identification of Cyst Present in Ultrasound PCOS Using Discrete Wavelet Transform. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_32
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DOI: https://doi.org/10.1007/978-3-030-00665-5_32
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