Modified method of image histogram hyperbolization

One of the methods to improve image quality, which consists in increasing the resolution of image details by contrast enhancement, is to hyperbolize the image histogram. Herewith this increase in local contrast is carried out indirectly. It is due to the nature of the change in the histogram of the transformed image. Usually the histogram of the input image is transformed so that it has a uniform distribution, which illustrates the same contribution of pixels gray level to the image structure. However, there is a method that is based on modeling the human visual system, which is characterized by the logarithmic dependence of the human reaction to light stimulation. It consists in the hyperbolic transformation of the histogram of the image. Then, due to its perception by the visual system, at its output, during the psychophysical perception of the image, an approximately uniform distribution of the histogram of the levels of gray pixels is formed. But the drawback is the lack of effectiveness of this approach for excessively light or dark images. The modified method of image histogram hyperbolization has been developed. It is based on the power transformation of the probability distribution function, which in the discrete version of the images is approximated by a normalized cumulative histogram. The power index is a control parameter of the transformation. to improve the darkened images we use the value of the control parameter less than one, and for light images more than one. The effectiveness of the proposed method is shown by examples.

Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging

Background To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference. Methods Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves. Results The mean, median, 5th, and 95th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2WKurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC5th & T2WKurtosis parameters was also similar to that of the ADC5th & ADCDiff−Variance. Conclusions Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.

Innovative approach to evaluate the wearing of nano TiO2 doped alumina ceramics in the light of image modelling

The present research is emphasized on the microscopic observation of post wear surface of nano TiO2 doped alumina ceramics to accesses wearing by promising image processing algorithms viz. entropy analysis, Sobel edge detection technique and entropy filtered image histogram analysis in relation to the extent of doping. The experimental results of specific wear rate showed an indicator with the extent of micro fracturing of grains, ploughing of materials and debris formation on the wear track after a long wear cycles in terms of entropy level, edge density index, entropy filtered image and the nature of histogram at different doping level. The lowest value of entropy level and edge density index is shown at the level of 1 wt.% TiO2 doped alumina ceramics due to the presence of low number of granularity and microfracture grains on the wear track causes the lowering of specific wear rate. The histogram of entropy filtered image for 1 wt.% doping is more uniformly distributed with the highest frequency and lowest skewness factor over a wide range of intensity values for 1 wt.% doping.

A secure image cryptosystem via multiple chaotic maps

This work focused on introducing a new two-dimensional (2D) chaotic system. It is combined of some existing maps, the logistic, iterative chaotic map with infinite collapse, and Henon maps; we called it 2D-LCHM. The assessment of the actual performance of 2D-LCHM presents its sensitivity to a tiny change in the initial condition. Besides, its dynamics behavior is very complicated. It also has hyperchaotic properties and good ergodicity. The proposed system is occupied with designing a new image encryption system. Changing the image pixels’ locations is the primary step in the encryption process. The original image is splitting into four blocks to create four different keys based on 2D-LCHM; this reduces the computation time and increases the complexity. To obtain the encryption image, we have to repeat the partitioning process several times for each block. The encryption image’s efficiency is shown through some performance analysis such as; image histogram, the number of pixels changes rate (NPCR), the unified average changing intensity (UACI), pixels correlation, and entropy. The proposed system is compared with some efficient encryption algorithms in terms of chaocity attributes and image performance; the analysis result showed consistent improvement.

A Fuzzy Cooperative Approach to Resolve the Image Segmentation Problem

The image segmentation problem is one of the most studied problems because it helps in several areas. In this paper, the authors propose new algorithms to resolve two problems, namely cluster detection and centers initialization. The authors opt to use statistical methods to automatically determine the number of clusters and the fuzzy sets theory to start the algorithm with a near optimal configuration. They use the image histogram information to determine the number of clusters and a cooperative approach involving three metaheuristics, genetic algorithm (GA), firefly algorithm (FA). and biogeography-based optimization algorithm (BBO), to detect the clusters centers in the initialization step. The experimental study shows that, first, the proposed solution determines a near optimal initial clusters centers set leading to good image segmentation compared to well-known methods; second, the number of clusters determined automatically by the proposed approach contributes to improve the image segmentation quality.

Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

IMAGE QUALITY IMPROVEMENT USING IMAGE PROCESSING METHOD IMAGE BRIGHTNESS CONTRAST AND IMAGE SHARPENING

The ability of computers that are increasingly reliable in various fields, especially in helping the image processing sector through improving image quality, is very much felt so that the empowerment of computers at any time needs to be improved. Image quality improvement can be made with various techniques, including Image Quality Improvement with Image Brightness and Image Sharpening methods. The process begins with capturing the image and then continues with increasing the intensity of brightness, image contrast and sharpening. Image processing results are indicated by changes in the resulting image and changes in the image histogram

Grayscale Drone Inspection Image Enhancement Framework for Advanced Bridge Defect Measurement

This paper presents a framework to better identify and measure defects in a bridge using drone-based inspection images integrated with grayscale image enhancement techniques. For this study, a DJI Matrice 210 drone was used for the inspection of a three-span timber bridge with concrete decking located in Keystone, South Dakota. During the inspection, the drone recorded a series of videos of the bridge using the MOVie (MOV, video file extension) video format. MOV-based image analysis was conducted to identify a variety of defect types (i.e., efflorescence, water leakage, spalling, and discoloration) on the bridge. For improvement of defect visibility, the grayscale image enhancement technique was applied to determine visually enhanced images for the individual defect. The technique used grayscale image histogram processing that can adjust images using realignment of contrast histograms, in which contrasts of each pixel of the grayscale images have their own number from 0 for black to 255 for white in the image. With the enhanced images, pixel-based measurement was conducted to quantify the defects, including efflorescence (3.75 m2), water leakage (4.21 m2), spalling (0.74 m2), and discoloration (2.12 m2). Based on these findings, the grayscale drone inspection image enhancement technique enabled the demonstration of defect visibility adjustment and improvement for more reliable identification and measurement of the defects in the bridge.