A Regularized Constrained Least-Squares De-blurring Method for SPECT Images
Nuclear medicine images suffer from blur due to the scattering of emitted radiations. An image processing technique is proposed in this paper to reduce blur in nuclear images. This is achieved in two main stages. A maximum likelihood estimate of the disto
- PDF / 184,643 Bytes
- 8 Pages / 439.37 x 666.142 pts Page_size
- 62 Downloads / 134 Views
Abstract Nuclear medicine images suffer from blur due to the scattering of emitted radiations. An image processing technique is proposed in this paper to reduce blur in nuclear images. This is achieved in two main stages. A maximum likelihood estimate of the distortion operator or the point spread function is computed from the image itself. Then, regularized least-squares filtering is performed constrained to the noise power computed from the image. Pre-filtering is also done to avoid unwanted high frequency drops. The algorithm is tested on real cardiac single-photon emission computed tomography images. Quantitative and qualitative evaluations of the algorithm show the potential of proposed algorithm in reducing blur while maintaining high peak signal-to-noise ratio.
⋅
⋅
Keywords Least-squares filtering Maximum likelihood estimate Nuclear medicine imaging Single-photon emission computed tomography imaging
⋅
1 Introduction Heart disease is a major health problem in the developed world [1]. According to the statistics in 2008, about 17.3 million people died from heart diseases, representing 30 % of all global deaths [2]. This statistic marks urgency for the early diagnosis of heart diseases. Nuclear medicine imaging is becoming common nowadays in the diagnosis of heart diseases [3]. Nuclear medicine imaging is a non-invasive technique in which a radioactive tracer is injected into the human body. This tracer is carried to the heart through blood flow. Using a rotating camera, multiple images in different directions are obtained which show the uptake of tracer
N. Sasi (✉) Government Model Engineering College, Ernakuam, Kerala, India e-mail: [email protected] V.K. Jayasree College of Engineering, Cherthala, Kerala, India © Springer Science+Business Media Singapore 2017 S.C. Satapathy et al. (eds.), Proceedings of the First International Conference on Computational Intelligence and Informatics, Advances in Intelligent Systems and Computing 507, DOI 10.1007/978-981-10-2471-9_14
137
138
N. Sasi and V.K. Jayasree
by the heart. Different heart slices, that is, short axis, horizontal long-axis and vertical long-axis slices, are then reconstructed from these multiple images [4]. Single-photon emission computed tomography (SPECT) image is a popular nuclear medicine imaging tool in the diagnosis of cardiac diseases. But its diagnostic accuracy is reduced by patient motion and photon scattering. This introduces blur in such type of images. This work proposes a method to improve the visual quality of cardiac SPECT images by reducing blur. Methods to improve the visual quality of nuclear images fall into two categories: algorithms performed during the reconstruction process and algorithms performed on the reconstructed images. The first category methods are based on the system point spread function (PSF) modeling [5] and the second category makes use of image processing techniques. Our method falls under the second category. A two-stage image de-convolution technique is employed using maximum likelihood estimate and re
Data Loading...
