Medical Image Denoising Using Reinforced Gradient Minimization

Year 2023, Volume: 28 Issue: 1, 163 - 176, 30.04.2023

Metin Ertas

https://doi.org/10.17482/uumfd.1139249

Abstract

Medical images are naturally exposed to different types and levels of noise. The main purpose of algorithms used in the reconstruction of medical images is to use the most efficient method to remove this noise and to increase the resolution. With these techniques, the noise is intended to be removed using filters, regularizers, and noise removal operators. After the availability of compressed sensing in medical imaging, the total variation (TV) minimization, which transforms the image into a sparser form, reduces the image noise and preserves small details and edges. In this study, a local gradient operator TD has been redesigned as a stronger noise remover by increasing the level of the neighborhood used in partial gradient directions. The proposed reinforced gradient minimization's ability to reduce noise under various levels of Gaussian, Poisson, and Gauss+Poisson noise was compared to that of the traditional TV technique. The results were compared using peak signal-to-noise-ratio (PSNR), structure similarity (SSIM), contrast-to-noise-ratio (CNR) metrics, and visual analysis. It was shown that the proposed reinforced gradient minimization method has better noise removal potential than that of the TV algorithm.

Keywords

total variation , image denoising , Gaussian , Poisson , reinforced gradient minimization

GÜÇLENDİRİLMİŞ GRADYAN MİNİMİZASYONU KULLANARAK MEDİKAL GÖRÜNTÜLERDE GÜRÜLTÜ ARINDIRMA

Abstract

Medikal görüntüler doğası gereği farklı gürültü tipleri ve seviyelerine maruz kalmaktadır. Medikal görüntülerin oluşturulmasında kullanılan rekonstrüksiyon algoritmalarının temel amacı, oluşan bu gürültünün giderilmesi ve çözünürlüğün arttırılması için en verimli yöntemlerin kullanılmasıdır. Bu yöntemler kullanılırken filtreleme, düzenleyiciler ve gürültü giderici operatörler kullanıp gürültünün arındırılması amaçlanmaktadır. Sıkıştırılmış algılamanın medikal görüntülemede aktif olarak kullanılmaya başlanmasından sonra, görüntüyü daha seyrek forma dönüştüren toplam değişinti (TD) en küçüklemesi ile görüntü üzerindeki gürültü azaltılarak ufak detayların ve kenarların daha net biçimde korunması sağlanmıştır. Lokal bir gradyan operatörü olan toplam değişinti algoritması bu çalışmada kısmi gradyan yönlerinde kullanılan komşuluk seviyesi arttırılarak daha güçlü bir gürültü giderici olarak yeniden tasarlanmıştır. Çalışma kapsamında, tasarlanan bu yeni güçlendirilmiş gradyan minimizasyonunun medikal görüntülerde mevcut farklı Gauss, Poisson ve Gauss+Poisson gürültü seviyeleri üzerinde gürültü arındırma başarısı klasik TD ile karşılaştırılmıştır. Sonuçlar pik sinyal-gürültü oranı, yapısal benzerlik ve kontrast-gürültü oranı metrikleri ve görsel analiz kullanılarak karşılaştırılmış ve önerilen yeni güçlendirilmiş gradyan minimizasyonu yönteminin mevcut klasik TD algoritmasından daha iyi gürültü arındırma potansiyeline sahip olduğu gösterilmiştir.

Keywords

toplam değişinti , gürültü arındırma , Gauss , Poisson , güçlendirilmiş gradyan minimizasyonu

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