Performance Analysis of Various Filters for De-Speckling of Thyroid Ultrasound Images
Poornima D.1, Asha Gowda Karegowda2
1Poornima D., Assistant Prof. Dept. of BCA, RRIT, Bangalore, India.
2Asha Gowda Karegowda, Associate Prof. Dept. of MCA, Siddaganga Institute of Technology, Tumkur, Karnataka, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 910-917 | Volume-9 Issue-2, December, 2019. | Retrieval Number: A2015109119/2020©BEIESP | DOI: 10.35940/ijeat.A2015.129219
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© The Authors. 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: Thyroid ultrasonography is the most common and extremely useful, safe, and cost effective way to image the thyroid gland and its pathology. However, an inherent characteristic of Ultrasound (US) imaging is the presence of multiplicative speckle noise. Speckle noise reduces the ability of an observer to distinguish fine details, make diagnosis more difficult. It limits the effective implementation of image analysis steps such as edge detection, segmentation and classification. The main objective of this study is to compare the performance of various spatial and frequency domain filters so as to identify efficient and optimum filter for de-speckling Thyroid US images. The performance of these filters is evaluated using the image quality assessment parameters Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE) and Root Mean Square Error (RMSE) for different speckle variance. Experimental work revealed that kuan filter resulted in higher PSNR, SNR, SSIM and least MSE, RMSE values compared to other filters.
Keywords: De-speckling, Filters, MSE, PSNR, RMSE, SNR, Speckle noise, SSIM, Thyroid Ultrasound.