Standard Grayscale Images Research Articles

Detail-preserving switching algorithm for the removal of random-valued impulse noise

This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities.

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Blind watermarking targets the challenging recovery of the watermark when the host is not available during the detection stage. This paper proposes Discrete Shearlet Transform (DST) as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a nonadditive scheme based on the statistical decision theory. It first computes the Probability Density Function (PDF) of the DST coefficients modeled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman–Pearson criterion to minimize the missed detection subject to a fixed false alarm probability. Our method is evaluated in terms of imperceptibility, robustness, and payload against different attacks (Gaussian noise, blurring, cropping, compression, and rotation) using 30 standard grayscale images covering different characteristics (smooth, more complex with a lot of edges, and high detail textured regions). The proposed method shows greater windowing flexibility with more sensitive to directional and anisotropic features when compared against discrete wavelet and contourlets.

Two-stage image denoising considering interscale and intrascale dependencies

A solution to the problem of reducing the noise of grayscale images is presented. To consider the intrascale and interscale dependencies, this study makes use of a model. It is shown that the dependency between a wavelet coefficient and its predecessors can be modeled by the first-order Markov chain, which means that the parent conveys all of the information necessary for efficient estimation. Using this fact, the proposed method employs the Kalman filter in the wavelet domain for image denoising. The proposed method has two stages. The first stage employs a simple denoising algorithm to provide the noise-free image, by which the parameters of the model such as state transition matrix, variance of the process noise, the observation model, and the covariance of the observation noise are estimated. In the second stage, the Kalman filter is applied to the wavelet coefficients of the noisy image to estimate the noise-free coefficients. In fact, the Kalman filter is used to estimate the coefficients of high-frequency subbands from the coefficients of coarser scales and noisy observations of neighboring coefficients. In this way, both the interscale and intrascale dependencies are taken into account. Results are presented and discussed on a set of standard 8-bit grayscale images. The experimental results demonstrate that the proposed method achieves performances competitive with the state-of-the-art denoising methods in terms of both peak-signal-to-noise ratio and subjective visual quality.

Image denoising in dual contourlet domain using hidden Markov tree models

Used in a wide variety of transform based statistical image processing techniques, the hidden Markov tree (HMT) model with Gaussian mixtures is typically employed to capture the intra-scale and inter-scale dependencies between the magnitudes of the transform coefficients. But, the conventional model does not consider the signs of the transform coefficients. In this paper, a new HMT model which exploits mixtures of one-sided exponential densities is used to consider the signs of transform coefficients. The present study has two main contributions: 1) for the first time, HMT with mixtures of one-sided exponential densities is used to denoise images, and 2) a new efficient model formed by two one-sided exponential densities and one Gaussian density is proposed. In addition, the proposed method uses the dual contourlet transform (DCT) which is formed by the combination of the directional filter bank (DFB) and the dual tree complex wavelet transform (DTCWT). This transform is (nearly) shift-invariant and is computationally less expensive than the NSCT (nonsubsampled contourlet transform). Thus, it is fast and efficient when applied to image processing tasks. Experimental results on several standard grayscale images show that the proposed method is superior to some state-of-the-art denoising techniques in terms of both subjective and objective criteria.

An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm

Abstract Segmentation is one of the important steps for image analysis. Multilevel thresholding image segmentation was more popular in image segmentation. Otsu and Kapur based methods are most popular for multilevel threshold image segmentation. Many authors implemented evolutionary algorithms for the optimal multilevel threshold selection based on the above methods. In this paper, an efficient approach for multilevel image segmentation has been proposed and implemented based on novel evolutionary algorithm Improved Differential Search (IDS). The feasibility of proposed approach/algorithm has been tested on standard gray scale images. To check the effectiveness of the proposed approach/algorithm, all experimental results are analyzed quantitatively and qualitatively.

Sparse regularization method for the detection and removal of random-valued impulse noise

In this paper, we propose a novel two-stage algorithm for the detection and removal of random-valued impulse noise using sparse representations. The main aim of the paper is to demonstrate the strength of image inpainting technique for the reconstruction of images corrupted by random-valued impulse noise at high noise densities. First, impulse locations are detected by applying the combination of sparse denoising and thresholding, based on sparse and overcomplete representations of the corrupted image. This stage optimally selects threshold values so that the sum of the number of false alarms and missed detections obtained at a particular noise level is the minimum. In the second stage, impulses, detected in the first stage, are considered as the missing pixels or holes and subsequently these holes are filled-up using an image inpainting method. Extensive simulation results on standard gray scale images show that the proposed method successfully removes random-valued impulse noise with better preservation of edges and other details compared to the existing techniques at high noise ratios.

High Resolution MRI Reveals Detailed Layer Structures in Early Human Fetal Stages: In Vitro Study with Histologic Correlation.

An understanding of normal fetal brain development is essential in detecting the early onset of brain disorders. It is challenging to obtain high-quality images that show detailed local anatomy in the early fetal stages because the fetal brain is very small with rapidly-changing complex structures related to brain development, including neurogenesis, neuronal migration, and axonal elongation. Previous magnetic resonance imaging (MRI) studies detected three layers throughout the fetal cerebral wall that showed differences in MR contrasts at 10 gestational weeks (GW), which is one of the earliest ages studied using MRI. Contrary to the MRI studies, histological studies found more layers at this fetal age. The purpose of this work is to study the development of brain structures from an early fetal period to an early second trimester stage using ex vivo MRI and compare it to histology. Special attention was paid to laminar structures in the cerebral wall. T2-weighted imaging was performed on fetal brain specimens ranging from 10 GW to 18 GW on a 4.7 tesla MR scanner. We obtained standard grayscale as well as color-coded images using weighted red-green-blue scales, and compared them with the histological images. Our study confirmed laminar structure in the cerebral wall in all the fetal specimens studied. We found that MRI detected four layers within the cerebral wall as early as 10 GW during the early fetal period (10–13 GW). Early second trimester (15–18 GW) was characterized by the emergence of subplate structures and five layers within the cerebral wall. The color-coded images were more useful than the standard grayscale images in detecting the laminar structures. Scans with appropriate parameters from a high tesla MR scanner showed detailed laminar structures even through a very small and thin cerebral wall at 10 GW ex vivo. A combination of high-resolution structural imaging and color-coding processing with histological analysis may be a potential tool for studying detailed structures of typical developing fetal brains, as well as fetal brains with developmental disorders as references for clinical MRI.

Design of a cryptosystem for DCT compressed image using Arnold transform and fractional Fourier transform

The authors have proposed an image cryptosystem on the compressed version of the secret image to reduce the computational overhead and also to utilise the limited bandwidth of the communication channel maintaining the security. The compression has been done using transformation tool like discrete cosine transform (DCT). In the proposed scheme, the insignificant DCT coefficients of the secret image are discarded for compression of the secret image and the significant DCT coefficients are encrypted employing Arnold transformation (ACM) followed by fractional Fourier transform (FrFt). The parameters used in Arnold transformation and fractional Fourier transformation are kept confidential and considered as secret keys. The proposed scheme has been tested on a set of standard gray scale images and satisfactory results have been found. Besides, the security analysis has been carried out to demonstrate the effectiveness of the proposed scheme against some possible cryptographic attacks.

Identification of endolymphatic hydrops in Ménière's disease utilizing delayed postcontrast 3D FLAIR and fused 3D FLAIR and CISS color maps.

The preferential delayed enhancement of the perilymphatic space enables detection of the non-enhancing endolymphatic hydrops present in patients with Ménière's disease. The aim of this study was to evaluate the diagnostic utility of delayed postcontrast 3D FLAIR images and a color map of fused postcontrast FLAIR and constructive interference steady state (CISS) images in the identification of endolymphatic hydrops in patients with clinically diagnosed Ménière's disease. Case control, blinded study. Tertiary referral center. Ten patients with Ménière's disease and five volunteer controls. Diagnostic. Two neuroradiologists blinded to the clinical history independently evaluated for the presence of endolymphatic hydrops on the images of both inner ears for test and control subjects. Both the standard gray-scale FLAIR images and the fused color map images were independently reviewed. The gray-scale 3D FLAIR images demonstrated 68.2% sensitivity and 97.4% specificity, and the fused color map images demonstrated 85.0% sensitivity and 88.9% specificity in the identification of endolymphatic hydrops in Ménière's disease. There was significant correlation between the gray-scale 3D FLAIR images and fused color map images with the categorization of involvement (p = 0.002). Inter-evaluator reliability was excellent (kappa = 0.83 for gray-scale images, kappa = 0.81 for fused color map). Delayed 3D FLAIR and fused 3D FLAIR-CISS color map images of the inner ears after intravenous contrast administration are potentially useful diagnostic tools in the evaluation of patients with suspected Ménière's disease.

Improved Listless Embedded Block Partitioning Algorithms for Image Compression

In this paper, two simple yet efficient embedded block-based image compression algorithms are presented. These algorithms not only improve the rate distortion performances of set partitioning in hierarchical trees (SPIHT) and set partitioning in embedded block coder (SPECK) at lower bit rates but also reduces the dynamic memory requirement by 91.1% in comparison to SPIHT. The former objective is achieved by better exploiting the coefficient decaying spectrum of the wavelet transformd images and the later objective is realised by improved listless implementation of the algorithms. The proposed algorithms explicitly perform breadth first search like SPECK. Extensive simulation conducted on various standard grayscale and color images indicate significant peak-signal-to-noise-ratio (PSNR) improvement over most of the state-of-the-art wavelet-based embedded coders including JPEG2000 at lower rates. The reduction of encoding and decoding time as well as improvement in coding efficiency at lower bit rates facilitate these coder as better candidates for multimedia applications.

Salt and Pepper Noise Detection and Removal in Gray Scale Images: An Experimental Analysis

Impulse noise removal is a mechanism for detection and removal of impulse noise from images. Median filters are preferred for removing impulse noise because of their simplicity and less computational complexity. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. Extensive simulations have been carried out on a set of standard gray scale images and the state of the art median filter variants are compared in terms of the well known image quality assessment metrics namely mean square error, peak signal to noise ratio and multiscale structural similarity index.

A Novel Design and FPGAImplementation of TheBiorthogonal 5/3 Filter Bank-eng

In this paper, the DWT introduced through the design of the biorthogonal 5/3 filter bank using the lattice structure. The lattice coefficients of the proposed structure are very suitable for implementation using shift- only operations (with a single shift and add operation) instead of multipliers to perform multiplications. This results in a recognizable hardware saving when implementing such lattice structurehardwarly. The designed filter bank implemented using Matlab programming for verification. Matlab programsare also used to find the PSNR values that are taken for a group of standard gray scale images as objective criteria for efficiency, resulting in high PSNR values of around 55dB. The efficiency of the results also measured subjectively, for some standard gray scale images by comparing the original input images with the resulting onesfromcombining both analysis and synthesis sides of the proposed DWTstructure.Theefficient FPGA implementation of such design is consideredto show its simplicity. Keywords: Bio 5/3 Filter Bank, DWT,Lattice Structures,FPGA Implementation.

A Region-based Image Segmentation Method Under P Systems

Due to the reason of iterative calculation, there are some drawbacks for existing image segmentation methods based on region growing, such as large computation cost and low real-timing. Membrane computing is a novel class of computing models inspired by the structure and function of living cells, also known as P systems, which have the advantage of distributed and parallel computing. In order to overcome the drawbacks of existing image segmentation methods based on region growing, a regionbased image segmentation method based on P systems is proposed in this paper. A tissue-like P system designed specially applies evolution rules and communication rules to achieve an adaptive region growing for image segmentation. The proposed image segmentation method based on tissue-like P systems is evaluated in several standard gray-scale images and compared with the traditional image segmentation method based on region growing.

An Algorithm for Grayscale Images Compression based on the forward Adaptive Quantizer Designed for Signals with Discrete Amplitudes

In this paper an algorithm for grayscale image compression is presented, based on implementation of forward adaptive quantizers designed for signals with discrete amplitudes. Experiments are done, applying this algorithm on standard grayscale images and obtained results show that significant reduction of the bit-rate can be achieved (about 40%), maintaining very high quality of the reconstructed image, i.e. near lossless compression is performed. Ill. 2, bibl. 6, tabl. 2 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.118.2.1166

Evaluation of longitudinal and radial left ventricular function by two-dimensional speckle-tracking echocardiography in a large cohort of normal probands

Speckle tracking echocardiography (STE) is a novel tool to analyze left ventricular (LV) deformation in standard grey-scale images. The aim of our study was to characterize global and regional LV function by STE in a large cohort of healthy adult probands across a wide range of age. 144 healthy individuals (71 men, 73 women; mean age 42 ± 14 years, range: 17-80 years) who had undergone a standard echocardiographic examination were retrospectively investigated by STE. Longitudinal strain (LS) was measured in three apical views, and radial strain (RS) in a short-axis view at papillary muscle level. Averaged global LS was -20.6 ± 2.6%, and averaged RS 48.2 ± 13.8%. On a regional basis, LS showed an increase from base to apex (basal vs. mid vs. apical segments: -18.5 ± 2.7% vs. -19.9 ± 2.6% vs. -22.9 ± 4.5%; P < 0.001). RS increased with age (P < 0.05), while LS decreased in basal, and slightly increased in the apical segments. LS and RS decreased with body surface area and LV size (P < 0.05). In the longitudinal dimension about 38% of the segments showed some degree of systolic pre-stretching, with a higher likelihood in basal segments (P = 0.02 vs. mid and apical segments). In the radial dimension pre-stretching was even more frequent (60%). STE offers a simple and rapid method to measure regional myocardial deformation. Our results, based on a large cohort of normal probands covering a wide range of ages, may serve as reference for comparison with different disease entities.

Accuracy of Stent Measurements Using ECG-Gated Greyscale Intravascular Ultrasound Images: A Validation Study

Greyscale intravascular ultrasound (IVUS) is an accurate tool for measuring stent dimensions and residual disease at the stent edge. Electrocardiographically (ECG)-gated IVUS is used in evolving second-generation IVUS systems, but this modality provides fewer greyscale cross-sectional images, and its accuracy to measure stent dimensions has not been assessed. This study was designed to validate the use of ECG-gated greyscale IVUS in measuring minimum stent area (MSA), stent length and reference dimensions compared to standard greyscale IVUS data. IVUS imaging was performed after drug-eluting stent implantation in 53 target lesions in 48 patients with acute coronary syndrome. The IVUS catheter was mechanically withdrawn at 0.5 mm/s, standard greyscale images were collected at 10 frames/s, and ECG-gated greyscale images were constructed from R-wave gated images. The MSA measured 6.20 ± 1.75 mm 2 vs. 5.98 ± 1.55 mm 2 on standard greyscale and ECG-gated greyscale IVUS, respectively (R 2 = 0.91, p = 0.005). The MSA position (R 2 = 0.66, p = 0.179) and stent length (R 2 = 0.99, p = 0.435) measurements were similar between modalities. Proximal reference vessel area was larger by ECG-gated IVUS, but proximal reference lumen and distal reference measurements were similar. Bland-Altman plots demonstrated good agreement between modalities. In conclusion, ECG-gated greyscale IVUS provides accurate and reliable measurements of stent length, area and reference segment plaque burden after stent implantation and is not inferior to standard greyscale IVUS. (E-mail: gsm18439@aol.com or gmintz@crf.org).

Reversibility improved lossless data hiding

Recently, lossless data hiding has attracted increasing interests. As a reversible watermark scheme, the host media and hidden data should be recovered without distortion. A latest lossless data hiding technique based on image blocking and block classification has achieved good performance for image authentication. However, this method cannot always fully restore all the blocks of host images and watermarks. For this purpose, we propose an improved algorithm, which is characterized by two aspects. First, a block skipping scheme (BSS) is developed for the host blocks selection to embed watermark; secondly, the embedding level is modified by a novel parameter model to guarantee that the host blocks can be recovered without distortion as well as the embedded data. Extensive experiments conducted on standard grayscale images, medical images, and color images have demonstrated the effectiveness of the improved lossless data hiding scheme.

COLOR INTENSITY PROJECTION OF DIGITALLY SUBTRACTED ANGIOGRAPHY FOR THE VISUALIZATION OF BRAIN ARTERIOVENOUS MALFORMATIONS

Reliable and rapid delineation of arteriovenous malformations enables the application of effective treatments such as stereotactic radiosurgery. We describe a new method to improve the speed and reliability of visualizing the flow of contrast images with digital subtraction angiography. In line with current practices, digital subtraction angiography was used to produce a sequence of grayscale images. The new method combines the standard grayscale images produced by digital subtraction angiography into a single composite color image that encodes the contrast arrival time at each point of the brain's circulatory system. The algorithm is simple, fast, and easy to implement. The technique allows the flow of contrast from a series of angiography images to be summarized in a single color image. This visualization method promises to improve the speed of manual delineation of arteriovenous malformations. Further studies are required to evaluate the clinical value of the use of color intensity projection images, supplemented by grayscale images as necessary, in comparison with contouring on grayscale images only.

Emergence of a region-based approach to image transmission

In the absence of a priori knowledge about regions of interest, does dysfunctional behavior in the subband pyramid lead to the emergence of a region-based approach to image transmission as complexity grows? A model of image transmission that links simplification of information and time to produce a transmission plan to the bit-rate cost of the transmission, and where the decoded output is affected negatively by those factors that reduce the quality of the transmission plan and delay its preparation, answers the question in two steps. First, we characterize the variables (degrees of simplification and subordination) through which we expect to control, at any truncation time, the optimum transmission system. We observe that transform-domain coefficients exhibit self-seeking behavior if errors that result from the transmission plan increase as the distance of the coefficient from the top of the hierarchy grows. Then we use the degree of detail that arrives at the central control in any system of progressive transmission to derive conditions under which the likelihood of emergence of a region-based approach to progressive transmission grows with the increase of complexity of the picture. We find that the presence of self-seeking coefficients leads to a higher likelihood of emergence of a region-based approach as complexity rises. On a data set composed of 100 standard gray-scale test images, 26% of the test images exhibit self-seeking behavior, from very low bit rates, using the state of the art codec based on set partitioning in hierarchical trees.

Mean myocardial velocity mapping in quantifying regional myocardial contractile reserve in patients with impaired left ventricular systolic function: Doppler myocardial imaging study

The aim of this study was to use Doppler myocardial imaging-derived mean myocardial velocity (MMV) at baseline and during low-dose dobutamine stress echocardiography (DSE) to quantify regional contractile reserve of the left ventricle (LV). Sixteen patients (mean age 59 ± 7 years) with coronary artery disease and regional left ventricular wall motion abnormalities were studied. During each increment of Dobutamine infusion, 6 2-dimensional transthoracic apical images were acquired in standard gray-scale and Doppler myocardial imaging modes at 30° steps over 180°. For the analysis, the LV was divided into 18 segments. For each segment, both wall motion score and MMV obtained in systole and both early and late diastole were measured at baseline and at each stage of DSE. In viable segments by wall motion score, MMV increased during DSE in systole and in early and late diastole. In contrast, in nonviable segments, MMV did not change during DSE. Mean myocardial velocity mapping is a promising new approach to quantify regional myocardial contractile reserve of the LV.