Universiti Teknologi Malaysia Institutional Repository

Which method is providing correct geometry of pancreas in filtering and segmenting ultrasound images?

Ramlee, M. H. and Ishak, M. F. and Supriyanto, E. and Derus, A. (2017) Which method is providing correct geometry of pancreas in filtering and segmenting ultrasound images? Nuclear Medicine and Biomedical Imaging, 2 (1). pp. 1-6. ISSN 2398-3361


Official URL: https://dx.doi.org/10.15761/NMBI.1000115


Pancreas is a small organ approximately six inches long, located at upper abdomen and adjacent to the small intestine. It is located at the back and deep into the human body. Therefore, it difficult to obtain a pancreas image clearly using ultrasound machine. In order to solve this problem, MATLAB software was used to filter and segment the ultrasound pancreas images using various methods. The images were selected based on high quality images produced from five subjects using AplioMX device from Toshiba ultrasound machine with a 3.5MHz convex transducer. In this study, there were various technique of filter used including Kaun filter, Wiener filter, Frost filter and Anisotropic Diffusion filter in order to reduce spackle noise of ultrasound pancreas images. Then, the filtered images were measured using Mean Square Error (MSE), Power Signal to Noise Ratio (PSNR), Average Difference (AD), Normalized Cross-Correlation (NCC), Maximum Difference (MD), Structural Content (SC), and Normalized Absolute Error (NAE) formula to evaluate the best quality images before undergo the segmentation process. As the result, Wiener filter was selected. In the segmentation process, the active countor method and level set method were evaluated. Then, the area of binary image and error percentage were calculated. As a conclusion, it shows that the filtering process using Wiener filter and segmentation method using level sets method has been successfully done to produce the best geometry of pancreas image.

Item Type:Article
Uncontrolled Keywords:Mean Square Error (MSE), Power Signal to Noise Ratio (PSNR)
Subjects:Q Science > QH Natural history > QH301 Biology
Divisions:Biosciences and Medical Engineering
ID Code:80706
Deposited By: Fazli Masari
Deposited On:27 Jun 2019 06:17
Last Modified:27 Jun 2019 06:17

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