Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2751
Title: Noise reduction filter optimization for prostate cancer localization
Other Titles: Prostat Kanseri Lokalizasyonu Için Gürültü Temizleme Süzgeçleri Optimizasyonu
Authors: Arın, Efe
Yetik, İmam Şamil
Keywords: Prostatic neoplasms 
 prostate 
 endorectal coil
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Arın, E., and şamil Yetik, İ. (2019, April). Noise Reduction Filter Optimization For Prostate Cancer Localization. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
Abstract: 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.
URI: https://ieeexplore.ieee.org/document/8806426
https://hdl.handle.net/20.500.11851/2751
ISBN: 978-172811904-5
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

16
checked on Feb 6, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.