Multi-resolution system for artifact removal and edge enhancement in computerized tomography images

Arivazhagan, S.; Deivalakshmi, S.; Kannan, K.; Gajbhiye, B. N.; Muralidhar, C.; Lukose, Sijo N.; Subramanian, Mani

doi:10.1016/j.patrec.2007.05.003

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…These residual ring artifacts may appear due to the no linear response of the detector elements with the incident X-ray flux, which can change between acquisitions. Among the number of methods that have been presented [22,[41][42][43][44][45][46][47][48][49][50], we propose a correction algorithm that works on the projection data before reconstruction, as it can be efficiently included in the correction/reconstruction pipe-line.…”

Section: Ring Artifact Correctionmentioning

confidence: 99%

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Abella

Vaquero

Sisniega

et al. 2012

Computer Methods and Programs in Biomedicine

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Cone-beam t r a c tMost small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry. IntroductionMany small animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory [1][2][3][4]. This configuration presents advantages over other alternatives used in clinical and preclinical applications: reduction of acquisition time, large axial field of view (FOV) without geometrical distortions, and optimization of radiated dose [5]. proposed by Feldkamp et al. (FDK) [6] are still widely used for solving the 3D reconstruction task because of their straightforward implementation and computational efficiency [4]. Almost every aspect of the reconstruction process has been studied: there is literature on algorithm variations for different trajectories [7,8], optimizations using graphic processing units (GPUs) [9][10][11][12][13][14][15], strategies to reduce cone beam artifacts [16,17], study of consistency conditions [18], optimization of the back-projection step [19], etc. However, in a real practical system, the implementation of a reconstruction algorithm core such as FDK is just an initial step of the process, and there 1

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Section: Ring Artifact Correctionmentioning

confidence: 99%

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Abella

Vaquero

Sisniega

et al. 2012

Computer Methods and Programs in Biomedicine

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“…Traditional medical image enhancement technologies [7–11] are generally divided into three categories, namely the spatial domain methods, frequency domain methods, and deep learning methods. The method about spatial domain mainly involves direct image processing including histogram transformation [12], histogram equalisation [13], local grey level [14], edge extraction [15], and smooth filtering [16]. Meanwhile, the method about frequency domain achieves enhancement via image transformation processing.…”

Section: Introductionmentioning

confidence: 99%

Efficient medical image enhancement based on CNN‐FBB model

et al. 2019

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Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross‐validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel‐level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X‐ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases.

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“…The discrete wavelet transform is a valuable signal analysis tool and can highlight specific subband features (Arivazhagan et al 2007) through the decomposition of multiple scalars in complex data. Discrete wavelet transform is commonly applied for edge detections and contrast enhancements in various applications (Huang et al 2002;Nakashizuka et al 2004;Hatami et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

Zuo

Geng

2016

Earthq Sci

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Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform (SWT) algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolution ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT subbands for analysis, and a new multiscale adaptive anisotropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.

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Multi-resolution system for artifact removal and edge enhancement in computerized tomography images

Cited by 8 publications

References 25 publications

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Efficient medical image enhancement based on CNN‐FBB model

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

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