Enhancement Filter Research Articles

SU‐F‐J‐71: Improving CT Quality for Radiation Therapy Planning and Delivery Guidance Using a Non‐Linear Contrast Enhancement Technique

Purpose:Advanced image post‐processing techniques which enhance soft‐tissue contrast in CT have not been widely employed for RT planning or delivery guidance. The purpose of this work is to assess the soft‐tissue contrast enhancement from non‐linear contrast enhancing filters and its impact in RT. The contrast enhancement reduces patient alignment uncertainties.Methods:Non‐linear contrast enhancing methods, such as Best Contrast (Siemens), amplify small differences in X‐ray attenuation between two adjacent structure without significantly increasing noise. Best Contrast (BC) separates a CT into two frequency bands. The low frequency band is modified by a non‐linear scaling function before recombination with the high frequency band. CT data collected using a CT‐on‐rails (Definition AS Open, Siemens) during daily CT‐guided RT for 6 prostate cancer patients and an image quality phantom (The Phantom Laboratory) were analyzed. Images acquired with a standard protocol (120 kVp, 0.6 pitch, 18 mGy CTDIvol) were processed before comparison to the unaltered images. Contrast and noise were measured in the the phantom. Inter‐observer variation was assessed by placing prostate contours on the 12 CT study sets, 6 enhanced and 6 unaltered, in a blinded study involving 8 observers.Results:The phantom data demonstrate that BC increased the contrast between the 1.0% supra‐slice element and the background substrate by 46.5 HU while noise increased by only 2.3 HU. Thus the contrast to noise ratio increased from 1.28 to 6.71. Furthermore, the variation in centroid position of the prostate contours was decreased from 1.3±0.4 mm to 0.8±0.3 mm. Thus the CTV‐to‐PTV margin was reduced by 1.1 mm. The uncertainty in delineation of the prostate/rectum edge decreased by 0.5 mm.Conclusion:As demonstrated in phantom and patient scans the BC filter accentuates soft‐tissue contrast. This enhancement leads to reduced inter‐observer variation, which should improve RT planning and delivery.Supported by Siemens

Gradient Enhanced Image Pyramid for Improved Nonlinear Image Registration

In this paper we investigate the use of image pyramid within a hierarchical registration framework for improved nonlinear image registration. Gaussian image pyramid is commonly used to reduce image complexity so that registration can be performed in a coarse to fine manner. In this paper, we apply two edge preserving filters, the bilateral filter and the guided filter, to generate image pyramids that can preserve strong gradient so as to improve registration accuracy. In addition, we propose a bilateral fractional differential based image enhancement filter and combine its output with a guided filter to generate another image pyramid that further enhances the gradient of strong image components. Registration is performed within a hierarchical framework where the model complexity of a Discrete Cosine Transformation (DCT) based nonlinear model is increased to couple with the image pyramid. Different image pyramids are compared by using three types of synthetic deformation fields. Experimental results show that registration using the gradient enhanced image pyramid achieves more accurate registration than registration using the Gaussian image pyramid.

Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study

Background/Objectives: Digital image processing is used various fields for analyzing different applications such as medical sciences, biological sciences. Various image types have been used to detect plant diseases. This work is analyzed and compared two types of images such as Grayscale, RGB images and the comparative result is given. Methods/Statistical Analysis: We examined and analyzed the Grayscale and RGB images using image techniques such as pre processing, segmentation, clustering for detecting leaves diseases. Results/Finding: In detecting the infected leaves, color becomes an important feature to identify the disease intensity. We have considered Grayscale and RGB images and used median filter for image enhancement and segmentation for extraction of the diseased portion which are used to identify the disease level. Conclusion: RGB image has given better clarity and noise free image which is suitable for infected leaf detection than Grayscale image.

An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering

Many practical images captured from X-ray systems not only are corrupted by noise, but also are of non-uniformity and low-luminance. Enhancing these images by noise removal, gray-level transformation and/or contrast enhancement, is thus necessary and crucial before further processing.In this paper, a new scheme of enhancement method for X-ray image is presented, which consists of a fuzzy noise removal method and a homomorphic filtering method. The fuzzy noise removal method is composed of fuzzy noise detection and noise filtering. Region detection and degree detection are presented for fuzzy noise detection, and a modified weight median filter is proposed for filtering noise. Then the high-pass enhancement filter is introduced for image sharpening in the proposed homomorphic filtering method. A variety of degraded images and a real X-ray images have been handled by the proposed method, as well as the several existing methods. The comparisons of those experimental results show that the performance of our method overwhelms those of the existing ones.

An ellipsoid convex enhancement filter for detection of asymptomatic intracranial aneurysm candidates in CAD frameworks.

Various kinds of enhancement filters have been developed in computer-aided diagnostic (CAD) frameworks for asymptomatic intracranial aneurysms in magnetic resonance angiography (MRA). However, many bending or branching portions on vessels are also enhanced by the conventional filters as false positives in 3.0 T MRA, which can visualize smaller vessels compared with 1.5 T MRA. To overcome this problem, this study focused on developing an ellipsoid convex enhancement (ECE) filter, which can selectively enhance aneurysms while reducing false positive contrasts on bending or branching portions on vessels, for detection of asymptomatic intracranial aneurysm candidates in CAD frameworks. The ECE filter was mathematically designed to enhance various convex regions in the intensity space such as convex aneurysms, in which the ratio of the shortest and longest diameters for aneurysms corresponds to the ratio of reciprocals of the square roots of the first and third eigenvalues of a Hessian matrix. The proposed ECE filter was evaluated by measuring an average contrast for false positive models and free-response receiver operating characteristic curves between two simple CAD frameworks using the ECE and conventional filters based on a leave-one-out-by-patient test. MRA images for thirty patients (male: 10, female: 20; age: 48-86 yr, mean: 69.2) with 31 unruptured aneurysms (longest diameter: 2.0-5.5 mm, mean: 3.7 mm) were selected for this study. The average contrast for false positive models was reduced by 51.4% using the ECE filter, compared with the conventional filter for the convex regions with ratios of the shortest and longest diameters less than 0.4. The number of false positives per case was decreased from 41.1 to 22.8 on average at a sensitivity of 87% by using the ECE filter. The ECE filter would be useful for boosting the performance of the CAD framework of asymptomatic intracranial aneurysms by providing higher contrast aneurysms and lower contrast false positives such as bending or branching portions on vessels.

Non-linear polynomial filters for edge enhancement of mammogram lesions

Background and objectivesComputer aided analysis of mammograms has been employed by radiologists as a vital tool to increase the precision in the diagnosis of breast cancer. The efficiency of such an analysis is dependent on the employed mammogram enhancement approach; as its major role is to yield a visually improved image for radiologists. MethodsNon-linear Polynomial Filtering (NPF) framework has been explored previously as a robust approach for contrast improvement of mammographic images. This paper presents the extension of NPF framework for sharpening and edge enhancement of mammogram lesions. Proposed NPF serves to provide enhancement of edges and sharpness of the lesion region (region-of-interest) in mammograms, in a manner to minimize the dependencies on pre-selected thresholds. In the present work, Logarithmic Image Processing (LIP) model has been employed for the purpose of improvement in visualization of mammograms based on Human Visual System (HVS) characteristics. ResultsThe proposed NPF filtering framework yields mammograms with significant improvement in contrast, edges as well as sharpness of the lesion region. The performance of the proposed approach has been validated using state-of-art objective evaluation measures (of mammogram enhancement) like Contrast Improvement Index (CII), Peak Signal-to-Noise Ratio (PSNR), Average Signal-to-Noise Ratio (ASNR) and Combined Enhancement Measure (CEM); as well as subjective evaluation by radiologists’ opinions. ConclusionsThe proposed NPF provides a robust solution to perform noise controlled contrast as well as edge enhancement using a single filtering model. This leads to a better visualization of the fine lesion details predictive of their severity. The applicability of single filtering methodology for carrying out denoising, contrast and edge enhancement improves the worth of the overall framework.

Cluster Based Vector Attribute Filtering

Abstract Morphological attribute filters operate on images based on properties or attributes of connected components. Until recently, attribute filtering was based on a single global threshold on a scalar property to remove or retain objects. A single threshold struggles in case no single property or attribute value has a suitable, usually multi-modal, distribution. Vector-attribute filtering allows better description of characteristic features for 2D images. In this paper, we apply vector-attribute filtering to 3D and incorporate unsupervised pattern recognition, where connected components are classified based on the similarity of feature vectors. Using a single attribute allows multi-thresholding for attribute filters where more than two classes of structures of interest can be selected. In vector-attribute filters automatic clustering avoids the need for either setting very many attribute thresholds, or finding suitable class prototypes in 3D and setting a dissimilarity threshold. Explorative visualization reduces to visualizing and selecting relevant clusters. We show that the performance of these new filters is better than those of regular attribute filters in enhancement of objects in medical images.

Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter.

Pulmonary fissures are important landmarks for recognition of lung anatomy. In CT images, automatic detection of fissures is complicated by factors like intensity variability, pathological deformation and imaging noise. To circumvent this problem, we propose a derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation. Considering a typical thin curvilinear shape of fissure profiles inside 2D cross-sections, the DoS filter is presented by first defining nonlinear derivatives along a triple stick kernel in varying directions. Then, to accommodate pathological abnormality and orientational deviation, a [Formula: see text] cascading and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination. During the post-processing stage, our main contribution is to isolate the fissure patches from adhering clutters by introducing a branch-point removal algorithm, and a multi-threshold merging framework is employed to compensate for local intensity inhomogeneity. The performance of our method was validated in experiments with two clinical CT data sets including 55 publicly available LOLA11 scans as well as separate left and right lung images from 23 GLUCOLD scans of COPD patients. Compared with manually delineating interlobar boundary references, our method obtained a high segmentation accuracy with median F1-scores of 0.833, 0.885, and 0.856 for the LOLA11, left and right lung images respectively, whereas the corresponding indices for a conventional Wiemker filtering method were 0.687, 0.853, and 0.841. The good performance of our proposed method was also verified by visual inspection and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.

Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images.

GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.

Multi-Microphone Target Signal Enhancement Using Generalized Sidelobe Canceller Controlled by Phase Error Filter

We propose an improved generalized sidelobe canceller (GSC) utilizing a phase-error filter (PEF) for multi-channel signal enhancement. The PEF is first incorporated for improving the fixed beamformer (BF) of the GSC. Furthermore, the weight update of the GSC is formulated using the PEF spectral gain. The experimental results show that the proposed method provides better perceptual evaluation and intelligibility scores under multiple noise conditions than conventional BFs, including the GSC and PEF.

Enhancement of Image Sharpness with Bilateral and Adaptive Filter

the image. In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the un sharp mask. This new approach to slope restoration also differs previous slope restoration algorithms. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure.. The proposed method is effective at removing signal noise while enhancing the experimental results in perceptual quality both quantitatively and qualitatively.

Modified Filter for Enhancement of Power Quality in Distribution Networks

The recent rise in renewable energy sources has brought with it a corresponding rise in the utilization of Voltage Source Converters (VSCs). These typically contain a Pulse Width Modulation (PWM) switched element that can introduce harmonics into the power grid, resulting in equipment damage and failure. In order to suppress excessive harmonics and improve performance, various lowpass filters are inserted between the VSC and grid. This paper presents a comparative and systematic analysis of the LCL filter and its derivatives based on both theoretical and simulation results. The paper details the impact of each parameter on the system behaviour, as well as a performance analysis between different filter structures such as the conventional LCL filter, LLCL filter, LCL-LC filter and others. Then a design process of the modified filter is introduced with particular focus on development of a formal model which allows the filter components to be determined while maintaining filter performance and reducing damping loss.

An Approach of Remote Sensing and GIS for the Delineation of Lineaments in the Suru Valley (Ladakh-Himalayas)

An attempt has been made to map a high resolution lineament map of Suru valley (Ladakh) using remote sensing and GIS technology. The study is helpful to establish a new approach and new data acquisition of the lineament mapping, and to compare this remote, inaccessible and lesser known part of the Suru Valley by using the remote sensing source data in association with the field study of the area. In this study, Landsat ETM, Landsat PAN, LISS III images and digital elevation model were used. Different edge enhancement techniques were applied on Landsat ETM and LISS III to better identify the lineaments using different filters while 3×3 edge enhancement filter, Laplacian filter and sobel filter are mostly used. The enhanced image was merged with the PAN image in order to differentiate between the artificial linear features such as roads, canals etc. and the lineaments. In all, 36 distinct linear features were identified and digitized their latitude, longitude and the direction was analyzed by rose diagram. By the analysis of these lineaments, it was found that all lineaments are in NW-SE direction The Drainage map of the study area, was digitized from the Landsat ETM image, and extracted from DEM’s by manual digitization and automatic extraction by different software’s which shows Dendritic drainage pattern indicating that the Valley is almost composed of uniform rock type. The mapping of these geological structures improves the existing knowledge about the distribution and direction of the Stress in the Suru Valley.

Intensity Based Automatic Boundary Identification of Pectoral Muscle in Mammograms

The pectoral muscle detection is an important assignment to improve the diagnostic performance of the breast cancer detection. In this paper, we have proposed an intensity based approach for pectoral muscle boundary detection in mammograms. The enhancement filter mask of 3×2, have been proposed and applied on the image to enhance the pectoral region of the mammograms. The pectoral boundary points from the candidates were detected based on threshold technique. Finally, all the boundary points detected were connected to obtain the boundary of pectoral muscle. The proposed technique has been tested on 320 digitized mammograms form mini-Mammographic Image Analysis Society (MIAS) database of 322 mammograms, with an acceptance rate of 96.56% from expert radiologists. The mean False Positive (FP) and False Negative (FN) rate demonstrate the effectiveness of the proposed method.

A comparative study of various edge enhancement filters in spatial domain

Edge detection plays an important role in many Image processing applications. In any Image Segmentation technique edges are primary features. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. So, edge detection is an important part in image analysis and it is also playing a key role of solving many complex problems. In this paper, our main aim is to compare the various edge detection algorithms for image segmentations.

A coronary artery segmentation method based on multiscale analysis and region growing

Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast.

Pareto optimality between width of central lobe and peak sidelobe intensity in the far-field pattern of lossless phase-only filters for enhancement of transverse resolution.

Resolution capability of an optical imaging system can be enhanced by reducing the width of the central lobe of the point spread function. Attempts to achieve the same by pupil plane filtering give rise to a concomitant increase in sidelobe intensity. The mutual exclusivity between these two objectives may be considered as a multiobjective optimization problem that does not have a unique solution; rather, a class of trade-off solutions called Pareto optimal solutions may be generated. Pareto fronts in the synthesis of lossless phase-only pupil plane filters to achieve superresolution with prespecified lower limits for the Strehl ratio are explored by using the particle swarm optimization technique.

Fractional poisson enhancement model for text detection and recognition in video frames

Performing Laplacian operation on video images is a common technique to improve image contrast to achieve good text detection and recognition accuracies. However, it is a fact that when Laplacian operation enhances contrast, at the same time it introduces too many noises. To alleviate this, the existing methods propose different enhancement methods and filters. In this paper, we propose a generalized enhancement model based on fractional calculus to increase the quality of images obtained by Laplacian operation. The proposed method considers edges and their neighbor information to derive a mathematical model for enhancing low contrast information in video as well as in scene images. Experimental results of text detection and recognition methods on different databases show that the proposed enhancement model improves their accuracies significantly. The enhancement model is compared with standard enhancement models to show that the proposed model outperforms the existing models in terms of quality measures. The usefulness of the proposed model is validated through text detection and recognition experiments.

Intensity and edge based adaptive unsharp masking filter for color image enhancement

Enhancement of image quality is a fundamental process for a wide range of vision-based applications. Images captured under unfavorable environments are often degraded in information content, sharpness and colorfulness. In the attempts to improve an image, the unsharp masking filter is an attractive candidate for its computational efficiency. However, the filter is vulnerable to the over-range problem where pixel magnitudes are driven beyond permissible ranges. This drawback is particularly noticeable if a non-adaptive procedure is used in the enhancement. Hence, an adaptive gain adjustment method is proposed here aiming at minimizing the number of over-range pixels while maximizing the image sharpness and information content. In this method, colorfulness is improved via color channel stretching and contrast is enhanced by edge augmentation. Specifically, a hyperbolic-tangent function, whose scale is dependent on the original image intensity and detected edges, is constructed to adjust the gain in sharpness enhancement. A collection of natural images captured under poor illumination conditions are used in the test against conventional and mask-based image enhancement approaches. Results have demonstrated that the proposed method outperforms the others with regard to colorfulness, information content, and sharpness.

Integrated Dominant Brightness Level Analysis and Guided Image Filter for Satellite Image Enhancement

In digital image processing, image enhancement contributes a vital role. It is a procedure used for the modification of digital images. It is one of the essential vision applications which have ability to improve the visibility of images. It is used to enhance the superiority of poor images and low quality images into the high-quality images so that images can be much clearer for human observation. The key purpose of this dissertation has been to explore and verify the limitations of the existing image enhancement procedures. Several techniques have been predictable so far to enhance the superiority of the digital images. To enhance the photograph quality, image enhancement improves and bound various facts available in the input image. Several procedures have been projected so far for improving the satellite image enhancement; which may decrease the intensity of the original satellite image. To overcome this problem we have introduced an integrated approach. To evaluate the performance of dominant brightness level based image enhancement technique, several parameters has been used like Bit Error Rate, Cross Correlation and Average Difference.