Assessment of the Wavelet Transform for Noise Reduction in Simulated PET Images

نویسندگان

  • Bahareh Shalchian Ph.D. Student, Medical Physics Dept., Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
  • Hamid Soltanian-Zadeh Professor, Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Dept., University of Tehran, Tehran, Iran
  • Hossein Rajabi Associate Professor, Medical Physics Dept., Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
چکیده مقاله:

Introduction: An efficient method of tomographic imaging in nuclear medicine is positron emission tomography (PET). Compared to SPECT, PET has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. However, high noise levels in the image limit its diagnostic utility. Noise removal in nuclear medicine is traditionally based on Fourier decomposition of images. This method is based on frequency components, irrespective of the spatial location of the noise or signal. The wavelet transform presents a solution by providing information on the frequency content while retaining spatial information. This alleviates the shortcoming of the Fourier transform and thus, wavelet transform has been extensively used for noise reduction, edge detection and compression. Materials and Methods: In this research, we used the SimSET software to simulate PET images of the NCAT phantom. The images were acquired using 250 million counts in a 128×128 matrix. For the reference image, we acquired an image with high counts (6 billion). Then, we reconstructed these images using our own software developed in MATLAB. After image reconstruction, a 250 million counts image (noisy image) and a reference image were normalized and then root-mean-square error (RMSE) was used to compare the images. Next, we wrote and applied de-noising programs. These programs were based on using 54 different wavelets and 4 methods. De-noised images were compared with the reference image using RMSE. Results: Our results indicate that the Stationary Wavelet Transform and Global Thresholding are more efficient at noise reduction compared to the other methods that we investigated. Discussion: The wavelet transform is a useful method for de-noising of simulated PET images. Noise reduction using this transform and loss of high-frequency information are simultaneous with each other. It seems that we should attend to the mutual agreement between noise reduction and the visual quality of the image

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

assessment of the wavelet transform for noise reduction in simulated pet images

introduction: an efficient method of tomographic imaging in nuclear medicine is positron emission tomography (pet). compared to spect, pet has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. however, high noise levels in the image limit its diagnostic utility. noise removal in nuclear medicine is traditionally based on fourier decomposition o...

متن کامل

Assessment of the wavelet transform in reduction of noise from simulated PET images.

UNLABELLED An efficient method for tomographic imaging in nuclear medicine is PET. Higher sensitivity, higher spatial resolution, and more accurate quantification are advantages of PET, in comparison to SPECT. However, a high noise level in the images limits the diagnostic utility of PET. Noise removal in nuclear medicine is traditionally based on the Fourier decomposition of the images. This m...

متن کامل

De-Noising SPECT Images from a Typical Collimator Using Wavelet Transform

Introduction: SPECT is a diagnostic imaging technique the main disadvantage of which is the existence of Poisson noise. So far, different methods have been used by scientists to improve SPECT images. The Wavelet Transform is a new method for de-noising which is widely used for noise reduction and quality enhancement of images. The purpose of this paper is evaluation of noise reduction in SPECT ...

متن کامل

Speckle Noise Reduction for the Enhancement of Retinal Layers in Optical Coherence Tomography Images

Introduction One of the most important pre-processing steps in optical coherence tomography (OCT) is reducing speckle noise, resulting from multiple scattering of tissues, which degrades the quality of OCT images. Materials and Methods The present study focused on speckle noise reduction and edge detection techniques. Statistical filters with different masks and noise variances were applied on ...

متن کامل

Fusion of PET and CT images using wavelet transform.

While information about anatomy is available in CT images, information about physiology and metabolism is available in PET images. To integrate both information, the two images are fused. Image fusion methods include simple methods like pixel averaging and sophisticated methods like wavelet transformation. An advantage of using wavelet transformation is that it preserves significant parts of ea...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}


عنوان ژورنال

دوره 6  شماره 2

صفحات  41- 50

تاریخ انتشار 2009-06-01

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023