Pixel Intensity Distribution Research Articles

Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions

This narrative review explores texture analysis as a valuable technique in dentomaxillofacial diagnosis, providing an advanced method for quantification and characterization of different image modalities. The traditional imaging techniques rely primarily on visual assessment, which may overlook subtle variations in tissue structure. In contrast, texture analysis uses sophisticated algorithms to extract quantitative information from imaging data, thus offering deeper insights into the spatial distribution and relationships of pixel intensities. This process identifies unique “texture signatures”, serving as markers for accurately characterizing tissue changes or pathological processes. The synergy between texture analysis and radiomics allows radiologists to transcend traditional size-based or semantic descriptors, offering a comprehensive understanding of imaging data. This method enhances diagnostic accuracy, particularly for the assessment of oral and maxillofacial pathologies. The integration of texture analysis with radiomics expands the potential for precise tissue characterization by moving beyond the limitations of human eye evaluations. This article reviews the current trends and methodologies in texture analysis within the field of dentomaxillofacial imaging, highlights its practical applications, and discusses future directions for research and dental clinical practice.

AFIRE: Adaptive FusionNet for illumination-robust feature extraction in heterogeneous imaging environments

The fusion of infrared and visible images aims to synthesize a fused image that incorporates richer information by leveraging the distinct characteristics of each modality. However, the disparate quality of input images in terms of infrared and visible light significantly impacts fusion performance. To address this issue, we propose a novel deep adaptive fusion method called Adaptive FusionNet for Illumination-Robust Feature Extraction (AFIRE). This method involves the interactive processing of two input features and dynamically adjusts the fusion weights based on varying illumination conditions. Specifically, we introduce a novel interactive extraction structure during the feature extraction stage for both infrared and visible light, enabling the capture of more complementary information. Additionally, we design a Deep Adaptive Fusion module to assess the quality of input features and perform weighted fusion through a channel attention mechanism. Finally, a new loss function is formulated by incorporating the entropy and median of input images to guide the training of the fusion network. Extensive experiments demonstrate that AFIRE outperforms state-of-the-art methods in preserving pixel intensity distribution and texture details. Source code is available at: https://www.github.com/ISCLab-Bistu/AFIRE.

Desarrollo de un método de clasificación de pastas cerámicas basado en el análisis de imágenes de radiografía digital

X-rays have motivated the application of imaging techniques in Heritage Sciences, providing a tool that produces global information about the object. The radiography analysis generates information with physical meaning relating the material properties with the photon attenuation. In this work, the characterization of a digital radiography system is presented, and the basis of an analysis method for ceramics sherds is described, based on the study of histograms, which represent the distribution of pixel intensities that made the image. Thus, this study aims to provide a tool, based on a mathematical analysis, encompassing a Lorentzian fit to each image histogram and a cluster analysis, that leads to establish a contrast between ceramic pastes. By applying this method, we could distinguish six paste groups from a set of 275 ceramic objects (archaeological and contemporary) from the Maya region.

Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions

Symbolic data analysis is an emerging field in statistics with great potential to become a standard inferential technique. This research introduces a new approach to image feature extraction using the empirical cumulative distribution function (ECDF) and distribution function of distribution values (DFDV) as symbolic data. The main objective is to reduce the dimension of huge pixel data by organizing them into more coherent pixel-intensity distributions. We propose a partitioning method with different breakpoints to capture pixel intensity variations effectively. This results in an ECDF representing the proportion of pixel intensities and a DFDV representing the probability distribution at specific points. The novelty of this approach lies in using ECDF and DFDV as symbolic features, thus summarizing the data and providing a more informative representation of the pixel value distribution, facilitating image classification analysis based on intensity distribution. The experimental results underscore the potential of this method in distinguishing image characteristics among existing image classes. Image features extracted using this approach promise image classification analysis with more informative image representations. In addition, theoretical insights into the properties of DFDV distribution functions are gained.

A multi-scale information integration framework for infrared and visible image fusion

Infrared and visible image fusion aims at generating a fused image containing the intensity and detail information of source images, and the key issue is effectively measuring and integrating the complementary information of multi-modality images from the same scene. Existing methods mostly adopt a simple weight in the loss function to decide the information retention of each modality rather than adaptively measuring complementary information for different image pairs. In this study, we propose a multi-scale dual attention (MDA) framework for infrared and visible image fusion, which is designed to measure and integrate multi-scale complementary information in both structure and loss function at the image and patch level. In our method, the residual downsample block decomposes source images into three scales first. Then, dual attention fusion block integrates complementary information and generates a spatial and channel attention map at the same and adjacent scale for feature fusion. Finally, the output image is reconstructed by the residual reconstruction block. Loss function consists of image-level, feature-level and patch-level three parts, of which the calculation of the image-level and patch-level two parts are based on the weights generated by the complementary information measurement. Indeed, to constrain the pixel intensity distribution between the output and infrared image, a style loss is added. Our fusion results perform robust and informative across different scenarios. Qualitative and quantitative results on three datasets illustrate that our method is able to preserve both thermal radiation and detailed information from two modalities and achieve comparable results compared with the other state-of-the-art methods. Ablation experiments show the effectiveness of our information integration architecture and adaptively measure complementary information retention in the loss function. Our code is available at https://github.com/SSyangguang/MDA.

Modality specific infrared and visible image fusion based on multi-scale rich feature representation under low-light environment

Infrared (IR) and Visible (VIS) image fusion is a critical task in computer vision, which aims to produce high-quality fused images by combining two modal images with complementary information. However, current image fusion techniques neglect the severe degradation of texture and details in low-light environments and do not account for human visual perception. In this paper, a multi-scale image fusion algorithm adapted to the low-light environment is proposed. Firstly, the light distribution of the low-light image is estimated and adjusted based on the gray value probability distribution, resulting in an enhanced VIS image with rich details. Simultaneously, considering the imaging characteristics of the two modal images, a superpixel method is utilized to obtain a saliency map for the IR image, and a multi-scale feature extraction scheme is employed to establish a VIS detail map. Finally, we employ entropy to measure the amount of information in the saliency and detail maps to guide the fusion process. Experimental results from qualitative and quantitative evaluations demonstrate that the fused image conforms to human visual perception, maintains a uniform distribution of pixel intensity, and preserves rich texture features at the edge details.

Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions.

Many lumbar spine diseases are caused by defects or degeneration of lumbar intervertebral discs (IVD) and are usually diagnosed through inspection of the patient's lumbar spine MRI. Efficient and accurate assessments of the lumbar spine are essential but a challenge due to the size of the clinical radiologist workforce not keeping pace with the demand for radiology services. In this paper, we present a methodology to automatically annotate lumbar spine IVDs with their height and degenerative state which is quantified using the Pfirrmann grading system. The method starts with semantic segmentation of a mid-sagittal MRI image into six distinct non-overlapping regions, including the IVD and vertebrae regions. Each IVD region is then located and assigned with its label. Using geometry, a line segment bisecting the IVD is determined and its Euclidean distance is used as the IVD height. We then extract an image feature, called self-similar color correlogram, from the nucleus of the IVD region as a representation of the region's spatial pixel intensity distribution. We then use the IVD height data and machine learning classification process to predict the Pfirrmann grade of the IVD. We considered five different deep learning networks and six different machine learning algorithms in our experiment and found the ResNet-50 model and Ensemble of Decision Trees classifier to be the combination that gives the best results. When tested using a dataset containing 515 MRI studies, we achieved a mean accuracy of 88.1%.

Quantitative assessment of the visual quality of digital images based on the laws of human visual perception

The existing methods of quantitative assessment of the visual quality of digital images are studied. Among the main shortcomings of the studied methods, the following can be singled out. Most of them require a reference image, do not include all the components that affect visual quality and do not take into consideration the laws of human visual perception. It was decided to develop a method for quantitative assessment of the visual quality of images, which will work without a reference image and will take into account the regularities of human visual perception. There are characterized the main regularities of human visual perception, which are used in the development of the technique. A classification of the researched methods of quantitative assessment of image quality is proposed for structuring their analysis. It was decided to investigate methods of quantitative assessment of quality based on statistical analysis of image pixel intensities. There are described factors affecting the quality of images and methods of their control based on changes in the pixel intensity distribution histogram. A generalized expression of quantitative quality assessment based on moments is proposed. A methodology for quantitative assessment of the visual quality of images has been developed, which does not require a reference image and is based on the laws of human visual perception. This method was tested on an image processed by local contrast enhancement and low-pass filtering. The test results showed that the visual perception of image quality coincides with the quantitative assessment of its quality. It is possible to use the proposed method with some modifications to determine the quality of color images. Moreover, a potential avenue for advancing the proposed method involves adapting it for evaluating images afflicted by distortions induced by noise presence.

Texture characterization and classification of polarized images based on multi-angle orthogonal difference.

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

A dual-path residual attention fusion network for infrared and visible images

Infrared image contains typical targets characterized by pixel intensity distribution, while visible image preserves structural details represented by edges and gradient. Infrared and visible image fusion aims to merge their respective key information, and produce a more comprehensive image with superior target perception and better detail representation. Most existing methods cannot reach this goal well, and are easy to generate a limited fused result. Towards this end, a dual-path residual attention fusion network, namely DRAFusion, is proposed in this paper. We construct a multi-scale dense network architecture, which adequately aggregates horizontal and vertical intermediate features at all scales and layers. In the fusion layer, we develop a residual attention fusion module, cascading channel-wise and spatial-wise attention models, which is used to model the global contextual dependencies, and interact with feature maps to more focus on their respective key information. In addition, we design a novel feature adaptation loss function to control the proportion of their key information in the training phase, which can keep a better balance between fused results and source images. Extensive experiments demonstrate that our DRAFusion produces a remarkable performance on the three testing datasets, and is superior to other methods in terms of qualitative visual description and quantitative evaluation validation.

Initial Observation of Contrast Profiles for 2-Dimensional and 3-Dimensional Magnetic Resonance Imaging Sequences in Magnetic Resonance–Guided Radiation Therapy for Locally Advanced Pancreatic Cancer

In our experience treating locally advanced pancreatic cancer with magnetic resonance-guided radiation therapy (MRgRT), the true-fast imaging with steady-state free precession sequences used to generate both the real-time 2-dimensional (2D) magnetic resonance images (MRI; 2D cine) and the pretreatment high-resolution 3-dimensional (3D) MRI impart differing intensities for relevant structures between the 2 scans. Since these variations can confound target tracking selection, we propose that an understanding of the differing contrast profiles could improve selection of tracking structures. We retrospectively reviewed both 2D cine and 3D MRI images for 20 patients with pancreatic cancer treated with MRgRT. At simulation, an appropriate tracking target was identified and contoured on a single 3-mm sagittal slice of the 3D MRI. This sagittal slice was directly compared with the coregistered 7-mm 2D cine to identify structures with notable discrepancies in signal intensity. The 3D MRI was then explored in additional planes to confirm structure identities. For quantitative verification of the clinically observed differences, the pixel intensity distributions of 2D cine and 3D MRI digital imaging and communications in medicine data sets were statistically compared. In all patients reviewed, arteries (aorta, celiac, superior mesenteric artery, hepatic artery) appeared mildly hyperintense on both scans. However, veins (portal vein, superior mesenteric vein) appeared hyperintense on 2D cine but isointense on 3D MRI. Biliary structures appeared mildly hyperintense on 2D cine but starkly hyperintense on 3D MRI. The pixel intensity distributions extracted from 2D cine and 3D MRI images were confirmed to differ significantly (2 sample Kolmogorov-Smirnov test; test statistic, 0.40; P < .001). There are significant variations in image intensity between the immediate pretreatment 2D cine compared with the initial planning 3D MRI. Understanding variations of image intensity between the different MRI sequences used in MRgRT is valuable to radiation oncologists and may lead to improved target tracking and optimized treatment delivery.

An Improved Hybrid Network With a Transformer Module for Medical Image Fusion

Medical image fusion technology is an essential component of computer-aided diagnosis, which aims to extract useful cross-modality cues from raw signals to generate high-quality fused images. Many advanced methods focus on designing fusion rules, but there is still room for improvement in cross-modal information extraction. To this end, we propose a novel encoder-decoder architecture with three technical novelties. First, we divide the medical images into two attributes, namely pixel intensity distribution attributes and texture attributes, and thus design two self-reconstruction tasks to mine as many specific features as possible. Second, we propose a hybrid network combining a CNN and a transformer module to model both long-range and short-range dependencies. Moreover, we construct a self-adaptive weight fusion rule that automatically measures salient features. Extensive experiments on a public medical image dataset and other multimodal datasets show that the proposed method achieves satisfactory performance.

Sub-microscale precision repeatability position measurement using integrated polar microstructure and feature extraction method

This paper presents a novel vision-based way to achieve repeatability measurement with sub-microscale precision, two degrees of freedom, small footprint, easy installation, and low cost. This research theoretically analyzes the pixel intensity distribution mechanism of surface topography and develops the non-periodic pattern referred to be polar microstructure to achieve sub-microscale resolution. Considering polar microstructure features, this paper also develops an image matching-based measurement algorithm, leading to the integrated polar microstructure and feature extraction (PMFE) repeatability measurement method. To demonstrate the performance of PMFE, comparison experiments with the laser interferometer method were conducted. The experimental results show the measurement uncertainty of the PMFE method is 0.58 μm in the X direction and 0.33 μm in the Y direction respectively compared with the laser interferometer method. These results indicate that the PMFE system is reliable and show the potential application in the measurement of machine tool repeatability. is reliable and show the potential application in the measurement of machine tool repeatability.

Skin Lesion Classification Using Hybrid Convolutional Neural Network with Edge, Color, and Texture Information

Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 different features were extracted based on the edge, color, and texture features of the skin lesion image. For the edge features, a line-segment-type analysis algorithm was used, wherein the visual information of a dermoscopic image was precisely analyzed in terms of the units of pixels and was transformed into a structured pattern. Regarding the color features of skin lesions, the dermoscopic image was transformed into multiple color models, and the features were acquired by analyzing histograms showing information regarding the distribution of pixel intensities. Subsequently, texture features were extracted by applying the well-known Law’s texture energy measure algorithm. Feature data (50 × 256) generated via the feature extraction process above were used to classify skin lesions via a one-dimensional (1D) convolution layer-based classification model. Because the architecture of the designed model comprises parallel 1D convolution layers, fine-grained features of the dermoscopic image can be identified using different parameters. To evaluate the performance of the proposed method, datasets from the 2017 and 2018 International Skin Imaging Collaboration were used. A comparison of results yielded by well-known classification models and other models reported in the literature show the superiority of the proposed model. Additionally, the proposed method achieves an accuracy exceeding 88%.

Multi-mode fiber-based speckle contrast optical spectroscopy: analysis of speckle statistics.

Speckle contrast optical spectroscopy/tomography (SCOS/T) provides a real-time, non-invasive, and cost-efficient optical imaging approach to mapping of cerebral blood flow. By measuring many speckles (n>>10), SCOS/T has an increased signal-to-noise ratio relative to diffuse correlation spectroscopy, which measures one or a few speckles. However, the current free-space SCOS/T designs are not ideal for large field-of-view imaging in humans because the curved head contour cannot be readily imaged with a single flat sensor and hair obstructs optical access. Herein, we evaluate the feasibility of using cost-efficient multi-mode fiber (MMF) bundles for use in SCOS/T systems. One challenge with speckle contrast measurements is the potential for confounding noise sources (e.g., shot noise, readout noise) which contribute to the standard deviation measure and corrupt the speckle contrast measure that is central to the SCOS/T systems. However, for true speckle measurements, the histogram of pixel intensities from light interference follows a non-Gaussian distribution, specifically a gamma distribution with non-zero skew, whereas most noise sources have pixel intensity distributions that are Gaussian. By evaluating speckle data from static and dynamic targets imaged through an MMF, we use histograms and statistical analysis of pixel histograms to evaluate whether the statistical properties of the speckles are retained. We show that flow-based speckle can be distinguished from static speckle and from sources of system noise through measures of skew in the pixel intensity histograms. Finally, we illustrate in humans that MMF bundles relay blood flow information.

A Novel Classification Model of Date Fruit Dataset Using Deep Transfer Learning

Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%.

Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers

In this paper, we propose a textural information-based analysis of scalogram image obtained from continuous wavelet transform of heart rate variability (HRV) signal to study its dynamics during meditation. In addition to features from scalogram image, visibility graph-based complexity measures and multiscale permutation entropies (MPEs) from HRV signal are used to elucidate the modulation in autonomic activity of heart during meditative and non-meditative state. Significant changes in the probability distribution of pixel intensities of scalogram image and undiminished permutation entropy at higher scales are observed during the meditative state. From the extracted features, we have selected the top-ranked features based on ReliefF algorithm and minimum threshold weight of importance set at 0.04. Considering the small sample size (12 subjects) of meditation dataset, we have employed a novel data augmentation technique based on averaging of feature sets to overcome the issue of overfitting. To examine the efficacy of the proposed technique, both the non-augmented and augmented data are applied to four different types of classifiers, namely k-nearest neighbor, support vector machine (SVM), logistic regression and random forest classifiers. Experimental results demonstrate that performance of classifiers in distinguishing meditative and pre-meditative state are much superior with the augmented data as compared to that with regular non-augmented data. Out of these classifiers, radial basis function (RBF)-based SVM results in the best performance with an average accuracy of 96.67%, sensitivity of 95.83% and specificity of 97.5%.

Two U-net Architectures for Infant Brain Tissue Segmentation from Multi-Spectral MRI Data

The segmentation of brain tissues based on MRI data represents a widely investigated problem in medical image analysis. Infant brain tissues in the so-called isointense phase of development, around the age of 6 to 9 months, are difficult to segment due to the inherent myelination and maturation processes, which manifest in grey and white matter pixel intensity distributions severely overlapping in both T1-weighted and T2-weighted MRI data. This paper introduces two different U-net architectures, one with 2D convolution and this other with 3D convolution, and employs them in infant brain tissue segmentation based on multi-spectral MRI data. The proposed methods are trained and tested on the training data set of the iSeg-2017 challenge. All records were initially fed to histogram alignment that was independently performed on each data channel. Nine records were used for training and one record for testing in each evaluation round, but all ten records took their turn as testing data. Statistical accuracy indicators were employed to assess the segmentation accuracy of both network models. The U-net architecture preforming 3D convolution obtained the better segmentation, achieving 91.8% average rate of correct decisions, and 95.0%, 91.5% and 89.8% Dice similarity score for cerebrospinal fluid, grey matter, and white matter tissues, respectively.

Detailed Successive Layer Modeling and Design Factor Analysis for Single Micro-LED Pixel

The major factors associated with the manufactured structures and photon emission intensity distribution must be accurately identified for the complicated optical design parameters associated with micro-LED pixels. This study presents the methodologies and reveals the sequential calculating protocol in each layer for pixel-level optical simulations, based on which the boundary condition errors associated with common optical simulations can be corrected. Moreover, the optical effect on each epitaxial layer of the micro-LED design is revealed, and the rationality of the setting of the finite-radius receiver with a pseudo-extended substrate material is also explained with respect to the intrinsic and extrinsic pixel-intensity distribution properties. Finally, the design of experiments DOE L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sub> orthogonal array table is demonstrated by the Taguchi method to explore the principal factors for the parameter design of the micro-LED layers. The results indicate that the sapphire thickness, the P-GaN layer, the buffer layers, and an additional substrate plate are the four significant factors that primarily influence the luminous power and the corresponding uniformity on the exit pupil of micro-LEDs. Based on the luminous power uniformity, the signal-to-noise ratio in the sapphire thickness is 16.9, and the extended substrate plate is 9.8, which is ∼10 times > the interaction design parameter, such as the etching angle and reflective index of the extended substrate of the micro-LED pixel. These specific design factors can be the major optimization parameters for cost control and performance improvement.

Method of encrypting images based on two multidimensional chaotic systems using fuzzy logic

The subject of the study: it is proposed to develop a method of image encryption with pixel permutation implemented using fuzzy logic and Hainaut mapping, as well as diffusion, which is implemented using the Lorenz system. Study objectives: To propose an effective way to apply the rules of fuzzy logic in relation to the values generated by the Henon mapping to implement the permutation of pixels in the image, which will provide a random permutation and increase the efficiency of the encryption method. Also, to achieve better security in the process of image encryption, the use of the diffusion process implemented using the Lorenz system. In addition, to increase the sensitivity of the encryption method to change the initial value of the component colors of the pixels will also be used in the encryption process. Investigation methods and research results: developed and presented a method of image encryption with pixel permutation implemented using fuzzy logic and Henon mapping, as well as diffusion, implemented using the Lorenz system. The initial values for the Henon mapping and the Lorenz system will be determined from the entered keyword, and the control parameters are set by the operator, while the values of the component colors of the pixels will also participate in the encryption process. In addition, before the process of rearranging the pixels in the image, the rules of fuzzy logic are implemented by Henon mapping. Also, the values of the component pixels before and after the diffusion procedure will be reduced to a single interval. Thus, as a result of image encryption, the original image changes completely, loses its content and shape, and the color intensity distribution of pixels becomes uniform. The program implementation of the proposed encryption method was also carried out and the qualitative characteristics of the proposed image encryption method were evaluated, namely: analysis of histograms of original and encrypted images, correlation of adjacent image pixels, root mean square error (MSE), peak signal-to-noise ratio (PSNR), entropy before changing the color components of the pixels. Conclusions: the implementation of the method has shown that it has a large number of encryption keys, which makes brute force (the process of their selection) resource-intensive and complex, and the implementation of the encryption process in two stages and using two different chaotic systems significantly improves the security of the encrypted image. The resulting cryptosystem is also resistant to the following attacks: approximation of chaotic orbits, correlation, analytical and statistical attacks.