A Histogram Separation and Mapping Framework for Image Contrast Enhancement

Histogram equalization is a famous method for enhancing the contrast and image features. However, in few cases, it causes the overenhancement, and hence demolishes the natural display of the image. Therefore, in this article, a new fuzzy clustering based subhistogram scheme using discrete cosine transform (DCT) for contrast enhancement has been proposed. For preserving the distinctive appearance of the image, histogram division and separate histogram equalization is done on each subhistogram. The way of dividing histogram and calculating the numbers of parts for histogram division are the major problems which directly affects the quality of the output image. The proposed fuzzy-DCT scheme includes automatic calculation of a number of parts in which histogram is divided. Histogram division has done on the basis of density function and histogram separation is computed in such a way that each main peak can be divided in a different segment. The proposed scheme consists of four stages. The first stage includes the automatic calculation of number of clusters for image brightness levels. The second stage includes clustering of brightness levels by the fuzzy c -means clustering method and utilizing the given transfer function of histogram equalization. In the third stage, contrast enhancement is computed on each individual cluster separately. In the final stage, DCT is employed on the resulting image of the third step for better contrast and brightness preservation. The simulation results of the proposed scheme reveal not only clearer features along with a contrast enhancement, but also remarkably more natural look in the images.

Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction. We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds. Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (fh ) results in the minimization of quantum noise. Both subtraction weighting factor (wSub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration. We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, fh , and wSub as well as guiding the understanding of their effects on image contrast and noise.

A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality.

One of the most important preprocessing techniques is image Contrast Enhancement which is a technique that improve visual quality of an input image becoming more suitable for human analysis and perception. Numerous researches have been already developed for enhancement of Medical image for their important application, Traditional Gamma Correction is found to be one of the simplest technique for the contrast enhancement of medical image. This technique uses a set of varying parameter (y, c) which adjust effectively gray value intensity of the input image. Otherwise, Adaptive Gamma Correction technique have been appeared and have been proved its effectiveness by the use of adaptive (y, c) parameter which are determined adaptively from statistical information of input image. Traditional Gamma Correction (TGC) and Adaptive Gamma Correction (AGC) have been applied on three brain MRI modalities for patient with Multiple Sclerosis pathology. Qualitative and quantitative results are presented to illustrate the comparison of TGC and AGC to enhance the contrast of MRI images for better appearance of normal tissue and diseased tissue affected by MS pathology.

The aim of this study was to evaluate the degree of hepatic enhancement and image quality in patients with cirrhosis or chronic hepatitis who underwent multiphasic contrast-enhanced dynamic imaging on MDCT at least twice using standard (300 mg I/mL) and higher (370 mg I/mL) iodine concentrations in contrast medium during follow-up periods. This study included 20 patients with chronic liver diseases who underwent at least two multiphasic contrast-enhanced dynamic MDCT examinations using 100 mL of standard (300 mg I/mL = group A) and higher (370 mg I/mL = group B) iodine concentrations in contrast medium. After we obtained unenhanced CT scans, we performed multiphasic scanning at 30 sec (arterial phase), 60 sec (portal phase), and 180 sec (late phase) after the start of contrast medium injection. The CT values of hepatic parenchyma, abdominal aorta, and portal vein were measured. The mean enhancement value was defined as the difference in CT values between unenhanced and contrast-enhanced images. Visual image quality was also assessed on the basis of the degree of hepatic and vascular enhancement, rated on a 4-point scale. The mean hepatic parenchyma enhancement values in group B was significantly greater (p < 0.001) than those in group A during the portal phase (43.8 +/- 8.2 H vs 36.2 +/- 7.3 H) and the late phase (33.7 +/- 7.0 H vs 27.3 +/- 3.9 H), but the difference on the arterial phase images between the two groups (9.4 +/- 3.2 H vs 8.3 +/- 2.5 H) was not significant. The mean aorta-to-liver contrast during the arterial phase in group B was significantly higher (p < 0.001) than that in group A (236 +/- 40 H vs 193 +/- 32 H). For qualitative analysis, the mean visual scores for hepatic parenchyma and vasculature enhancement in group B were significantly higher than those in group A in arterial phase (p < 0.018), portal phase (p < 0.0001), and late phase (p < 0.0001). In the same patients with chronic liver diseases, a higher iodine concentration (370 mg I/mL) in the contrast medium improves contrast enhancement of liver parenchyma in the portal phase and late phase images, improves overall image quality, and helps improve diagnostic accuracy for liver diseases on multiphasic contrast-enhanced dynamic MDCT.

The aim of this study was to describe the changes in the MR appearance of painful lesions of the spine before and after treatment in 18 patients with multiple myeloma and to correlate these changes with the clinical response to treatment. Eighteen patients with multiple myeloma and tumor nodules in the spine had MR imaging of the same site in the spine before and after treatment. Unenhanced T1- and T2-weighted spin-echo images, dynamic ultrafast low-angle shot (turbo-FLASH) images before and after IV bolus injection of paramagnetic contrast material, and contrast-enhanced T1-weighted spin-echo images were obtained. Characteristic signal and contrast enhancement before and after treatment were compared and were correlated with clinical data and the results of immunochemical and pathologic tests. Changes in signal intensity and enhancement of the lesions occurred after treatment in 14 of 18 patients. The characteristics of the lesions after treatment had three patterns: (1) rim enhancement or no enhancement of the lesions, (2) early enhancing lesions associated with other nonenhancing lesions or lesions with rim enhancement, and (3) no change from the enhancement pattern seen before treatment. In 13 of 18 patients, these patterns correlated well with the response to treatment. A discrepancy was observed in five patients. The MR appearance of spinal myeloma is different before and after treatment. MR images, particularly contrast-enhanced images, may be helpful in monitoring the response to treatment of focal bone lesions of myeloma.

Contrast enhancement efficacy of iodinated contrast media: Effect of molecular structure on contrast enhancement

Image contrast enhancement is an essential operation for various image processing applications, especially for segmentation and feature extraction. However, different contrast enhancement methods contain various performance on the same images. To automatically choose the suitable algorithm for image contrast enhancement, in this paper, we put forward a different Retinex based method according to image feature fusion. First, five features, including gray value, contrast, definition, edge and entropy, are defined to characterize various image. Then, five fusion rules are designed to distinguish images with low contrast, foggy, monotonous color, rich in detail or color. Finally, the single scale Retinex (SSR), the multi-scale Retinex (MSR) and MSR in HSV color space (MSR_HSV) are introduced to improve image contrast. Experiments on various images drawn from different data sets prove the effectiveness of our proposed method.

The quality of the contrast enhancement, which is deemed to be vague in contrast between one region and another, is a problem that many doctors face once identifying their patients with CT scan images. Image correction was used in this study to help doctors gain good CT scan images. In addition to reducing errors in the administering of radiation doses during treatment, accurate images are used to locate and assess the extent of cancer in patients. In this study, a computer application program to improve image contrast was created using the linear regression equation method. In this investigation, the cancer area is still being manually marked by doctors. Additionally, the proportion of the cancer area in the image that the doctor marked from the corrected image is calculated by comparing the ratio of cancer pixels to body pixels. The severity of the cancer is estimated using the proportion of the affected area. The error percentage for the average improvement in application performance is 0.15689%.

Polychromatic phase-contrast radiography differs from traditional (absorption-only) radiography in that the method requires at least a partially coherent x-ray source and the resulting images contain information about the phase shifts of x-rays in addition to the traditional absorption information. In a typical embodiment, this effect results in a measurable enhancement in image contrast at the edges of objects. In this study, a phase-contrast imaging system was adapted to allow an object to be imaged at multiple projections, and these projections were used to generate phase-contrast computed tomography images. The images obtained with this technique show edge enhancements surrounding the objects within the image.

This paper comprehensively demonstrates the efficiency of balanced detection in a visible optical coherence tomography instrument employing a low-noise supercontinuum laser. By using an innovative technique for digitally aligning camera pixels, we achieved a noise floor reduction of up to 12.8 dB across the entire imaging depth range, particularly near the zero optical path difference between the interferometer arms. The instrument presented here operates at a central wavelength of 590 nm. It delivers high-resolution images with a sensitivity of up to 74 dB in a single spectrometer configuration and 92.8 dB in a balanced configuration. The enhancement in image contrast is exemplified through images of an optical phantom and in-vivo images of a human thumb and nail.

Origin of long afterglow in strontium aluminate phosphors: Atomic scale imaging of rare earth dopant clustering

Range-gated technology is well known for its good reliability, large field of view (FOV) and low cost in target detection through scattering or turbid medium. However, the tail-gating technology suffers from low signal-to-noise ratio in high turbidity levels due to superposition of photons multiply scattered from the medium and that reflected from the target. In this paper, polarization properties of multiply scattered photons emerging from the turbid medium are studied. Results demonstrate that diffusive photons are almost completely depolarized with no diattenuation and retardance. We combined the tail-gated technology with polarization detection method to effectively image in high level of turbidity. This approach showed about two times enhancement in image contrast as compared with the conventional range-gated technology.

Nowadays, High Intensity Focused Ultrasound (HIFU) is a common surgery option for the treatment of uterine fibroids in China, the immediate response of which is clinically evaluated using Contrast Enhanced (CE) imaging. However, the injection of gadolinium with its potential adverse effect is of concern in CE and therefore, it deserves efforts to find a better imaging method without the need for contrast agent injection for this task. To assess the role of diffusion-weighted imaging (DWI) in evaluating the immediate therapeutic response of HIFU treatment for uterine fibroids in comparison with CE. 68 patients with 74 uterine fibroids receiving HIFU treatment were enrolled, and immediate treatment response was assessed using post-surgical DWI images. Semi-quantitative ordinal ablation quality grading and quantitative nonperfusion volume (NPV) measurement based on DWI and CE imaging were determined by two experienced radiologists. Agreement of ablation quality grading between DWI and CE was assessed using the weighted kappa coefficient, while intraobserver, interobserver and interprotocol agreements of NPV measurements within and between DWI and CE were evaluated using the intraclass correlation (ICC) and Bland-Altman analysis. Grading of immediate HIFU treatment response showed a moderate agreement between DWI and CE (weighted kappa = 0.446, p < 0.001). NPV measured in 65 fibroids with DWI of Grade 3~5 showed very high ICCs for the intraobserver and interobserver agreement within DWI and CE (all ICC > 0.980, p < 0.001) and also for the interprotocol agreement between DWI and CE (ICC = 0.976, p < 0.001). DWI could provide satisfactory ablation quality grading, and reliable NPV quantification results to assess immediate therapeutic responses of HIFU treatment for uterine fibroids in most cases, which suggests that non-contrast enhanced DWI might be potentially used as a more costeffective and convenient method in a large proportion of patients for this task replacing CE imaging.

Color consistency optimization for multiple images is a challenging task in remote sensing and computer vision. To ensure that the visual quality of corrected images is satisfactory, not only should the color discrepancies between multiple images be invisible, but also the contrast of individual images should be visually appealing. Most color correction approaches focus on eliminating drastic color discrepancies, but ignore the problem of image contrast enhancement. Some color correction approaches even tend to degrade the image contrast to ensure that the image tones are consistent especially when the contrast of input images is low. To solve this problem, we present a contrast-aware color consistency correction approach in this paper. We attempt to eliminate the drastic color differences and enhance the contrast of input images simultaneously. We creatively integrate the problems of color consistency correction and image contrast enhancement into the same global energy optimization framework, and we also design a special cost function to minimize the color discrepancies and enhance the image contrast using the original color information. Thus, although the contrast of input images is low, our approach can still generate the corrected images with consistent tones and visually appealing contrast. At last, we select several challenging datasets to evaluate our approach. The experimental results visually and quantitatively demonstrate the effectiveness and superiority of the proposed contrast-aware color consistency correction approach. The results also demonstrate that our approach significantly outperforms the existing approaches, especially when the contrast of the input images is low.