Noise Reduction Filter Optimization For Prostate Cancer Localization
| Autor: | Imam Samil Yetik, Efe Arin |
|---|---|
| Přispěvatelé: | TOBB ETU, Faculty of Engineering, Department of Computer Engineering, TOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Yetik, İmam Şamil |
| Jazyk: | turečtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Noise reduction 0206 medical engineering Multispectral image 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences Prostate cancer Total variation symbols.namesake 0302 clinical medicine medicine Prostatic neoplasms medicine.diagnostic_test business.industry Wiener filter Process (computing) endorectal coil Cancer Magnetic resonance imaging Pattern recognition Filter (signal processing) medicine.disease Linear discriminant analysis 020601 biomedical engineering Noise symbols Artificial intelligence business prostate |
| Zdroj: | SIU |
| Popis: | 27th Signal Processing and Communications Applications Conference (2019: Sivas, Turkey) Multispectral Magnetic Resonance Imaging (MRI) images are commonly used in prostate cancer diagnosis. However noise in raw data makes it difficult to process images. Therefore MR images must be filtered as a pre-processing step prior to automated localization. In the literature, filters and their parameters are generally selected depending on experiences in the field. In this research, a method, not found in the literature, is proposed such that the system can choose optimal filter parameters to maximize cancer localization. In order to use on KEL, KEP, and IAUC 30, 60, 90 parameters, obtained from multispectral MR images, 3 different filters (wiener filter, total variance filter and wavelet thresholding) and a parameter for each filtering is chosen to maximize localization performance. Linear discriminant analysis is used for localization and observed that optimally selecting the filtering method and its parameter improves prostate cancer localization performance. © 2019 IEEE. |
| Databáze: | OpenAIRE |
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