Visibility Of Low-contrast Objects Research Articles

Implementation of hybrid filter technique for noise removal from medical images

Image denoising is used to eliminate the noise while retaining as much as possible the important signal features. The function of image denoising is to calculate approximately the original image form the noisy data. Image denoising still remains the challenge for researchers because noise removal introduces artifacts and causes blurring of the images. Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). MR images are typically corrupted with noise, which hinder the medical diagnosis based on these images. The presence of noise not only causes as undesirable visual quality as well as lowers the visibility of low contrast objects. in this paper noise removal approach has proposed using hybridization of three filter with DWT method.Results calculated in terms of PSNR,MSE & TIME.

IJARCCE - Computer and Communication Engineering

3 Abstract: Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). In the proposed method noisy image is first decomposed into sub-band by wavelet transform and the nonlocal means filter is applied to each sub-band. This proposed method preserves the wavelet coefficients corresponding to the structures, while effectively suppressing noisy ones. Experimental results are also compared with the other different techniques like median, wiener, wavelet, wavelet based wiener, non-local mean. The quality of the output images is measured by the statistical quantity measures such as peak signal-to-noise ratio (PSNR), signal-to- noise ratio (SNR) and Mean square error (MSE) .The quantitative and the qualitative measures used as the quality metrics demonstrate the ability of the proposed method for noise suppression. Magnetic Resonance Images (MRI) are widely used for diagnosis and the treatment of brain tumors. It is the most powerful imaging technique developed to study the structural features and the functional characteristics of the internal body parts. It possesses good contrast resolution for different tissues and has advantages over computerized tomography (CT) for brain tissue studies. The diagnostic and visual quality of the MR images are affected by the noise added while acquisition. Noise in MRI (1) is mainly due to thermal noise that is induced by the movement of the charged particles in the radio frequency coils as well as the small anomalies in the preamplifiers. The presence of noise not only produces undesirable visual quality but also lowers the visibility of low contrast objects. Noise removal is essential in medical imaging applications in order to enhance and recover anatomical details that may be hidden in the data. In recent years, wavelet transform shows a clear advantage in the field of signal and image denoising domains, and has many research results. The important property of a good image-denoising model is that it should completely remove noise as far as possible as well as preserve edges. In this paper effectiveness of six denoising algorithms viz. median filter (2),wiener filter(3),wavelet filter(4),wavelet based wiener(5),NLM(6),wavelet based NLM(7) using MRI images in the presence of additive white Gaussian noise is compared. Wavelet filter (4) removes noise pretty well in smooth regions but perform poorly along the edges. Image denoising has been extensively studied and thus there is a large amount of literature on denoising. Among these numerous works, we will briefly mention only a few of recently developed methods that are related with our method, specifically the wavelet domain coefficient thresholding and modeling (8),(9),(10) and nonlocal means filter (6).The problem with the conventional wavelet domain filtering is the removal of small but important coefficients while thresholding or the generation of unwanted coefficients in the probabilistic modeling approach as stated above. In this paper, it is expected that the nonlocal means filtering of the coefficients can alleviate these problems while effectively removing noisy coefficients. Specifically we propose a wavelet domain image denoising method where the nonlocal means filtering is applied to each of the subbands. By the nonlocal means filtering, the small wavelet coefficients which are part of important image structures are well kept while suppressing the noisy coefficients, whereas the conventional wavelet denoising methods sometimes suppress small but important coefficients as well.

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

Abstract Computer-aided detection(CAD) system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and cost-sensitive support vector machine(C-SVM) classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO) nodules(part solid and nonsolid). This method consists of several steps. Firstly, segmentation of regions of interest(ROIs), including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentatio...

Shading correction for on‐board cone‐beam CT in radiation therapy using planning MDCT images

Applications of cone-beam CT (CBCT) to image-guided radiationtherapy (IGRT) are hampered by shading artifacts in the reconstructed images. These artifacts are mainly due to scatter contamination in the projections but also can result from uncorrected beam hardening effects as well as nonlinearities in responses of the amorphous silicon flat panel detectors. While currently, CBCT is mainly used to provide patient geometry information for treatment setup, more demanding applications requiring high-quality CBCT images are under investigation. To tackle these challenges, many CBCT correction algorithms have been proposed; yet, a standard approach still remains unclear. In this work, we propose a shading correction method for CBCT that addresses artifacts from low-frequency projection errors. The method is consistent with the current workflow of radiation therapy. With much smaller inherent scatter signals and more accurate detectors, diagnostic multidetector CT (MDCT) provides high quality CT images that are routinely used for radiation treatment planning. Using the MDCT image as "free" prior information, we first estimate the primary projections in the CBCT scan via forward projection of the spatially registered MDCT data. Since most of the CBCT shading artifacts stem from low-frequency errors in the projections such as scatter, these errors can be accurately estimated by low-pass filtering the difference between the estimated and raw CBCT projections. The error estimates are then subtracted from the raw CBCT projections. Our method is distinct from other published correction methods that use the MDCT image as a prior because it is projection-based and uses limited patient anatomical information from the MDCT image. The merit of CBCT-based treatment monitoring is therefore retained. The proposed method is evaluated using two phantom studies on tabletop systems. On the Catphan 600 phantom, our approach reduces the reconstruction error from 348 Hounsfield unit (HU) without correction to 4 HU around the object center after correction, and from 375 HU to 17 HU in the high-contrast regions. In the selected regions of interest (ROIs), the average image contrast is increased by a factor of 3.3. When noise suppression is implemented, the proposed correction substantially improves the contrast-to-noise ratio (CNR) and therefore the visibility of low-contrast objects, as seen in a more challenging pelvis phantom study. Besides a significant improvement in image uniformity, a low-contrast object of approximately 25 HU, which is otherwise buried in the shading artifacts, can be clearly identified after the proposed correction due to a CNR increase of 3.1. Compared to a kernel-based scatter correction method coupled with an analytical beam hardening correction, our approach also shows an overall improved performance with some residual artifacts. By providing effective shading correction, our approach has the potential to improve the accuracy of more advanced CBCT-based clinical applications for IGRT, such as tumor delineation and dose calculation.

TU‐D‐I‐611‐03: Cone‐Beam Image Reconstruction for Circular‐Arc Trajectories Via Shift‐Invariant Horizontal and Non‐Horizontal Filtering

Purpose: To propose and validate a novel shift‐invariant filtered backprojection (FBP) type cone‐beam reconstruction algorithm for circular‐arc scanning trajectories. This algorithm has been proposed to address two shortcomings in the FDK algorithm; namely, limited visibility of low contrast objects in the off‐center planes and the inability to provide clinically acceptable reconstruction when the scanning path is shorter than the short‐scan condition. Method and Materials: Based upon the application of an exact cone‐beam reconstruction algorithm to the case of a circular trajectory, which does not fulfill the Tuy data sufficiency condition, a new algorithm has been developed. The new algorithm differs from the heuristic FDK algorithm in that multiple sets of filtered data are utilized in reconstructing the value of a single image point. The new algorithm introduces non‐horizontal filtering which is not achievable by heuristic extension fan‐beam FBP reconstruction. In this manner more of the cone‐beam data is utilized in the filtering operation. The new algorithm also enables region‐of‐interest (ROI) reconstruction in a super‐short scan mode (less than the short‐scan angular range). Results: Computer simulations were performed using analytical data generated for a standard low contrast phantom. The new algorithm provided improved visualization of structures that lie in off‐center planes when compared with the FDK algorithm. In addition simulations were performed to validate the ROI reconstruction using a super‐short scan mode. Conclusion: The circular‐arc scanning trajectories are used frequently in cone‐beam CT on interventional C‐Arm systems, micro CT systems and cone‐beam CT guided radiation therapy. A new shift‐invariant FBP type cone‐beam reconstruction algorithm has been proposed and validated for this common source trajectory. In comparison with the standard FDK algorithm the new algorithm provides improved visualization of structures that lie in off‐center planes, and it enables a super‐short scan mode for ROI reconstruction.

Characteristics and control of contrast in CT.

Understanding how contrast is produced and controlled in computed tomography (CT) is essential to proper application of this modality. In the typical CT scanner, a narrow x-ray beam in the section thickness direction and an air gap in the section plane are used to reduce scatter and improve contrast. High- and low-contrast detectability of a CT scanner are important performance parameters contributing to optimal image quality. The limits of detectability of high-contrast objects (ie, spatial resolution) are affected by detector aperture size, pixel size of the image, algorithm used to reconstruct the image, and section thickness. Visibility of low-contrast objects is limited by image noise and the algorithm. Contrast in CT images can be controlled by the window level and window width settings used to display the image. These settings dictate how the actual measurements of tissue attenuation are translated into a gray-scale image. Wide window widths can be used to provide an accurate representation of bone, and narrow widths are more useful for visualizing soft tissues.