Nonlinear Unsharp Masking Research Articles

FUZZY WEIGHTED MEDIAN FILTER WITH UNSHARP MASKING FOR ENHANCEMENT OF DBT IMAGES IN BREAST CANCER DETECTION

Breast cancer survival rates can be increased by providing early treatment to patients; thereby, microcalcification detection is critical because microcalcifications are an early sign of breast cancer. The visibility of microcalcifications can be improved by using Digital Breast Tomosynthesis (DBT) images, which have been shown to improve the overlapping issue in mammograms. However, since DBT screening techniques generate blurry artefacts and noise, this study proposes a DBT image enhancement procedure. As a result, this study indicated an enhancement method based on Non-Linear Unsharp Masking filters (NLUM). A filter, such as the Median Filter in conventional NLUM, is required to complete the non-linear element in the algorithm. Other researchers have previously proposed and demonstrated the Fuzzy Weighted Median Filter (FWMF) to improve medical images; thus, these filters can be adapted to the NLUM and replaced with the conventional filter. Following that, the enhancement process's performance will be evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). When compared to the Median Filter, the results show that the FWMF is the best filter to use in NLUM and successfully enhances DBT images with MSE and PSNR averages of 0.0171 and 67.0574, respectively.

Human visual system based unsharp masking for enhancement of mammographic images

Non-Linear Polynomial Filters (NPF) consists of a schema of linear and quadratic filter components operating as a fusion of low-and high pass filters. NPF has shown distinguished performance when applied for mammogram enhancement. The role has been multifaceted, as there is visual contrast improvement of Region-of-Interest (ROI), i.e. the tumor region as well as those of the surrounding diagnostic features. This paper presents the usage of NPF in design of Non-Linear Unsharp Masking (UM) framework for the enhancement of X-ray mammograms (digital mammographic images). The UM approach presented consists of operational modules namely: edge preserving and contrast enhancement algorithms which are realized using different variants of NPF. Application of Human Visual System (HVS) based adaptive thresholding during contrast enhancement provides for an effective minimization of background noises. The responses of the different modules are then combined using non-linear fusion operators based on an improved logarithmic model of perception and human vision. The obtained enhancement results demonstrate noteworthy improvement in contrast of lesion region together with better visualization of lesion margins and fine details. It has been subjectively as well as objectively shown that the enhancement of the contrast and edges do not introduces unwanted overshoots in the ROI.

Mammogram image enhancement by two-stage adaptive histogram equalization

This paper introduces a two-stage adaptive histogram equalization for enhancement of mammogram images. Instead of using a single histogram specification, we employ two different histogram specifications for adaptive histogram equalization (AHE). Here one stage AHE follows another AHE. This proposed approach is tested on all Mammographic Image Analysis Society (MIAS) database images and the improved performances are compared with standard AHE, unsharp masking (UM) and recently proposed nonlinear unsharp masking (NLUS). The objective performances are analyzed by enhancement measure (EME) and entropy.

Sharpening enhancement of ultrasound images using contourlet transform

The furry regions of ultrasound images are to be enhanced for good quality visual perception. This paper proposes a contourlet transform (CT) based sharpening technique (ST) for contrast enhancement in ultrasound (US) images. While sharpening, noise emphasize is the drawback of the classical ST methods. The proposed ST is operated on the multiscale, multidirectional CT decomposition of the underlying US image. The new ST not only sharpens the US image but also control the noise effect with tunable parameters. The results are compared with common unsharp masking and recently proposed nonlinear unsharp masking. The parameters like enhancement measure, structural similarity, and blind image quality measure evaluate the improved performances of the proposed technique.

Directionlet transform based sharpening and enhancement of mammographic X-ray images

Due to difficulty in detecting the low contrast and noisy nature of X-ray mammography images, they have to be enhanced to obtain a clear and good view. Though Sharpening Technique (ST) is used to enhance the contrast, it introduces noise in the enhancement process, and they do not include anisotropic features. This paper proposes a ST, which uses multiscale linear and anisotropic geometrical features obtained from directionlet transform (DT). The newly formulated method that combines multidirectional geometrical information has various tunable parameters and improved noise control by means of multiscale features. The DT that uses skewed and elongated directional basis functions not only captures the point singularities, but also links them into linear structure. The performance of the proposed DT ST is compared with non-linear unsharp masking (NLUSM). While the DT and LoG based sharpened images are given to the input of standard AHE, their performance is improved. Enhancement Measure and structural similarity measure are used to analyze the performance of the proposed method. Though the images are enhanced, the quality of the image is not degraded. As a specific application, the enhanced images are used to detect the microcalcification and spiculated masses in mammograms.

Contourlet transform-based sharpening enhancement of retinal images and vessel extraction application

Low-contrast retinal images have to be enhanced for good visual perception to aid in retinal vessel analysis. Classical sharpening enhancement techniques such as unsharp masking (USM) improve the contrast and bring out the information along with noise. This article uses a shift-invariant anisotropic Contourlet transform (CT) to decompose the retinal image into subbands. A new nonlinear method is applied over the subbands to modify the CT coefficients, followed by inverse CT. The proposed method is compared with a nonlinear USM (NLUSM) technique and wavelet transform-based method. The objective performance is measured in terms of enhancement measure. We observed that the proposed methodology provides better result. We demonstrate that this sharpening algorithm can be used as a preprocessing step to (i) adaptive histogram equalization and (ii) retinal vessel extraction. Pratt's figure of merit was used to analyze the vessel extracted from the retinal images with their ground truth that were obtained from STARE and DRIVE databases.

Nonlinear Unsharp Masking for Mammogram Enhancement

This paper introduces a new unsharp masking (UM) scheme, called nonlinear UM (NLUM), for mammogram enhancement. The NLUM offers users the flexibility 1) to embed different types of filters into the nonlinear filtering operator; 2) to choose different linear or nonlinear operations for the fusion processes that combines the enhanced filtered portion of the mammogram with the original mammogram; and 3) to allow the NLUM parameter selection to be performed manually or by using a quantitative enhancement measure to obtain the optimal enhancement parameters. We also introduce a new enhancement measure approach, called the second-derivative-like measure of enhancement, which is shown to have better performance than other measures in evaluating the visual quality of image enhancement. The comparison and evaluation of enhancement performance demonstrate that the NLUM can improve the disease diagnosis by enhancing the fine details in mammograms with no a priori knowledge of the image contents. The human-visual-system-based image decomposition is used for analysis and visualization of mammogram enhancement.

Contrast enhancement of images using Partitioned Iterated Function Systems

A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The transformation of the gray levels is determined by two parameters which adjust the brightness and the contrast of the transformed block. The PIFS is used in order to create a lowpass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The proposed algorithm uses a predefined constant value for the contrast parameter, whereas, the parameters of the affine spatial transform, as well as the parameter adjusting the brightness, are calculated using k-dimensional trees. The lowpass version of the original image is obtained applying the PIFS on the original image repeatedly while using a value for the contrast parameter that is lower than the predefined one. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against four other widely used contrast enhancement methods; namely, linear and nonlinear unsharp masking, Contrast Limited Adaptive Histogram Equalization and Local Range Modification.

A Real-Time Implementation of Nonlinear Unsharp Masking with FPLDs

Real-time performance of image enhancement algorithms is required in many applications. In this paper we present a real-time image contrast enhancement scheme using non-linear unsharp masking, implemented with FPLDs (Field Programmable Logic Devices). A quadratic filter has been chosen for the nonlinear unsharp masking. The implementation performs well on real images from medical and non-medical fields. Performance figures of the nonlinear filter on mammograms show substantial improvements over software-only and other filter implementations using linear schemes, thus achieving real-time image enhancement of large images. The implementation has been tested as an application-specific co-processor to a standard PC, but can also be used as a functional unit of an application-specific image enhancement processor.

Nonlinear unsharp masking methods for image contrast enhancement

In the unsharp masking approach for image enhancement, a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version. The method is simple, but it suffers from two serious drawbacks. First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas. Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images. In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges. On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification. A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters. This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters. Computer simulations have verified the superior results obtained using these filters. In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods.

Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs

The first six digital radiographs of the human chest generated by the Diode Array Digital Radiography scanner of the University of Pittsburg have been evaluated using a statistically designed experiment to study image-processing methods for displaying different anatomical landmarks. Linear and nonlinear unsharp masking and local contrast stretching were used. The experimental procedure, statistical analysis of the data obtained, and the results obtained are described and discussed. >

Digital image processing of dentomaxillofacial radiographs

The improvement in the image quality of dentomaxillofacial radiographs (panoramic radiographs) with the use of a digital image-processing system was examined. Linear and nonlinear unsharp masking techniques were employed for image processing. The quality of the processed images was found to be markedly improved over that in original radiographs. The detail visibility of linearly enhanced images, especially in low-density areas, was superior to that of nonlinearly enhanced images, although the presence of more artifacts and more noise was noted in linearly enhanced images. Radiographic diagnosis in the dentomaxillofacial region can be improved with this system.