Multiple Feature Extraction of Image using 2D CA

Several problems are encountered in modeling and processing of many life science applications such like tissue engineering, bioacoustics, and food processing detection. Recently, various image processing techniques have been developed in an attempt to solve these problems. Due to the rapid advance and high number of researches included in such field, there is always a room for improvement. In this work, advanced image processing approaches are presented and applied to different life science applications. The first approach utilizes image processing in the generation of micro-scaffolds for tissue engineering modeling. Furthermore, a spectrogram enhancement approach is developed to improve the pattern detection of weevils existence in grain storage, based on their detected and identified sounds. Finally, a novel edge detection method applied on intramuscular fat recognition is presented based on the energy and skewness as two smoothed versions of the meat image. Results, in comparison to experimental data, offer a maximum error of 7.81% in the specified detection process. 1. Material and Method 1.1 Generation of micro-porous approach The difficulty of modeling such complex micro-structures always lies in retrieving the geometry into the solver as edges. Edge detection algorithms are based upon highlighting discontinuities in images and were first developed for satellite images. The technique has become well-known and was further developed and widely used for other image processing applications. During the conventional detection procedures, usually all the image size is processed, which leads to redundant calculations of areas containing no edge information. In this study a redundant avoiding algorithm is developed which searches for the threshold value in the image and then propagates around it in a 2-D lattice in eight directions figure 1.1, until an edge is hit. Figure 1.1 The scanning procedure for gradient changes propagates in an eight directional lattice until an edge is hit 1.2 Spectrogram enhancement in bioacoustics This approach is based on detection of the sound pattern edges in the crest factor image, as shown in figure 1.2 and 1.3, which behaves as a smoothed version of the spectrogram image and avoids the use of edge detectors prior smoothing filters and their scaling constraints. Figure 1.2 Spectrogram with 40 dB limited dynamic range Figure 1.3 Crest factor distributions (left), the scaled crest factor image of the spectrogram (right). 1.3 Edge detection for intramuscular fat recognition A novel edge detection method applied on intramuscular fat is presented based on the energy and skewness as two smoothed versions of the meat image, as shown in the flow chart given in figure 1.4. Figure 1.4 flow chart edge detection 2. Results 2.1.Generation of micro-porous Approach The conventional approach for edge detection is based upon either thresholding the first derivative (gradient) of the image or locating zero crossings of its second derivative (Laplacian).The sample used, figure 2.1, for this simulation was of size 3 mm, 350 μm and 350 μm in z-direction, x-direction and y-direction, respectively. The geometry was uploaded on the solver by collective segmenting and edge detection techniques. The method has proven time saving and as well enables the possibility of parallel processing of the image, as each threshold value can be scanned and dealt with independently. After the predictions of the edges, the 2-d binary edge images are assembled forming a 3-D binary file of the scaffolds figure 2.1. Figure 2.1 From the micro-CT images 430 hundred 2-D binary edge images are assembled forming a 3-D binary file of the scaffolds 2.2.Spectrogram enhancement in bioacoustics The spectrogram enhancement approach is compared to other enhancement approaches, including multiband spectral subtraction and wavelet packet decomposition, for different structure bioacoustics calls including bats (ultrasound range) and birds (acoustic range), with additive white Gaussian noise added across a range of signal to noise ratios. Results measured subjectively and objectively, indicate that the proposed method is promising for efficiently spectrogram enhancing while preserving its temporal and spectral accuracy, as shown in figure 2.2. Figure 2.2 (a) One of Sitta Canadensis bird calls at SNR = 20 dB, and its enhanced spectrogram by (b) BP, (c) MBSS, (d) WF, (e) WPD, and (f) the proposed method approaches. 2.3.Edge detection for intramuscular fat recognition The generation of micro-scaffolds approach is applied on a sample meat image, which is scanned line by line and every noticeable variance in the color values or the grey levels is captured and the local thresholds values are estimated according to the highest drop gradient value. The local threshold values are averaged to estimate the global threshold for the whole image, as shown in figure 2.1. Figure 2.3 an image for a meat with fat (a), the x y scan of the image which does not show all the fat details (b) and the lattice scan of the image showing more details of the fat, as circled by red (c). For intramuscular fat, the developed edge detection approach is used to predict the fat content in marbled meat images, as shown in figure 2.3. Figure 2.4 (a) original marbled meat images, (b) edges images using the proposed method (c) detected fat regions, and (d) the percentage of fat content Conclusion As observed in the different approaches there is always a room for improvement that minimize the error observed in the specified detection process. The methods have proven time saving and as well enables the possibility of parallel processing of the image that leads to a more efficient performance highly desirable for industrial applications.

Cellular edge detection: Combining cellular automata and cellular learning automata

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

Cellular Automata (CA) is used in pseudo-random number generators (PRNG) to produce high-rate random numbers. The randomness of such numbers directly depends upon the CA rules and the number of neighbor cells. The two-dimensional (2D) CA has several limitations such as, finding the best CA rules, boundary cell problems, etc. To address the problems existing in the 2D CA, we propose a random number generator based on 3D cellular automata. The proposed generator is based on rule numbers 43, 85, 170, and 201, and incremental boundary conditions. The rules are randomly changed after each cycle depending on a clock bit mechanism. The output bits are then passed to the Diehard and ENT test suites to test its randomness [1]. The results reveal that the bit stream generated by the proposed scheme passes all the tests present in the test suites.

We propose a method to quantitatively evaluate image processing methods on how well they simulate human visual processing through psychological and statistical tests. First, we focus on edge detection, an image processing technique. To set all edge detection methods fairly, we collect the edge data detected by humans. Then, the signal processing parameters for each edge detection method are adjusted with the same optimizing method based on minimizing the error between the edges detected by the detector and by humans. Two psychological experiments are conducted, confirming whether the edge detection parameters are optimized and evaluating the performance of edge detection methods. The results indicate that the optimizing method functioned comparatively well. The effectiveness rank of edge detection methods is also obtained.

Brain tumor is the phenomenal growth of abnormal tissues in the human brain. A malignant tumor is known as a cancerous cell, and it is the major cause of death among people across the globe. Nowadays, the detection and classification of brain tumors from Magnetic Resonance Images (MRI) is a very crucial task. This chapter addresses various computing methods such as Edge Detection (ED), feature extraction, and classification techniques for the detection and classification of brain tumor regions from MRI datasets. Initially, MRI images are collected, and then various pre-processing steps such as filtering and edge detection are applied. Then, different edge detection methods such as Sobel ED, Robert’s ED, Prewitt ED, Canny ED, Laplacian ED, and Laplacian of Gaussian (LoG) with sigma 3 are applied for the MRI datasets. Subsequently, segmentation techniques are applied for detecting tumor regions from MRI, and essential features are extracted using Discrete Wavelet Transform (DWT) method. Otsu’s thresholding and K-means clustering segmentation methods are used for the investigation. Further, the support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbors (KNN), Back Propagation Neural Network (BPNN), and feedforward neural networks (FFNN) classifiers are employed for the MRI classification purpose. The experiments are 146conducted using the brain tumor dataset in the MATLAB 2019a software environment. The experimental results are analyzed in multiple dimensions, and it shows that the SVM with Otsu’s thresholding method exhibits better performance with 86.11% accuracy during the classification.

In this paper, we study 2-dimensional nite cellular automata dened by hexagonal local rule with periodic boundary over the eld Z3.We construct the rule matrix corresponding to the hexagonal cellular automata.For some given coecients and the number of columns of hexagonal information matrix, we prove that the hexagonal cellular automata are reversible.

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results.

Edge detection plays an important role in the field of image processing. Various edge detection techniques are obtained like Sobel, PSO Preweitt, Laplacian and Laplacian of Gaussian. These techniques consume some restrictions such as fixed edge thickness and some parameter like threshold is problematic to implement. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. High performance computing (HPC) is a technique used to assemble process and analyze more quantity of remote sensing (satellite) data which needs less processing time. HPC method is used in distributed & cluster Computing, parallel computing and following satellite data analyzing & processing approaches like geo-referencing, image edge detection, image classification, image mosaicking, Morphological/neural approach and image fusion for hyperspectral satellite data. Stimulated by means of the specific clustering of theory called gravitational theory, an original edge detection process. The conservative methods include the usage of linear time invariant filters. Recognition of edge is done by these filters as a These filters recognize an edge as a sudden conversion of grey scale pixel intensity. These techniques are well recognized and computationally effective. Canny, Sobel, Robert, Kirsch, Prewitt and LOG, on idea of spatial differential filters by using local grade. Within the less time these filters process the data and are inclined to noise. But in fuzzy technique, it does not include like this restriction, by altering the rules and output parameters, it is possible to change the thickness of the edge. In this paper, a new technique is created on fuzzy logic, which is suggested for detection of edge in digital images deprived of finding the threshold value. This method starts through segmenting the images into sections by means of fluctuating 3 $$\times $$ 3 binary matrixes. The pixels of edge are planned to a range of standards separated from each other. The results of this proposed technique is compared with the results obtained from PSO and neural network methods. This suggested technique provides a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. And sharpness in the curves.

In this paper, we propose a new method to obtain the transition rules of two-dimensional cellular automata (CA) that performs grayscale image processing. CA has the advantages of producing complex systems from the local interaction of simple elements, and has attracted increased research interest. The difficulty of designing CA's transition rules to perform a particular task has severely limited their applications. So, the evolutionary design of CA rules has been studied. In this method, an evolutionary algorithm was used to evolve CA. In recent years, this method has been applied to image processing. Rosin has studied the evolutionary design of two-dimensional CA to perform noise reduction, thinning and convex hulls. Batouche et al. and Slatnia et al. employed genetic algorithm to investigate the possibility of CA to perform edge detection. In the previous methods, 2-state CA was used for binary image processing. Unlike the previous methods, the present method uses 256-state CA rules to perform grayscale image processing. Gene Expression Programming (GEP) proposed by Ferreira is employed as a learning algorithm in which the chromosomes encode the transition rules as expression trees. Experimental results for the reduction of impulse noise, salt-and-pepper noise and gaussian noise show that the proposed method is equivalent to previous methods in performance and more than 100 times faster than the method proposed by Rosin. We show that the rule obtained by the proposed method employs symmetry-based strategy in the noise reduction process and this property can reduce complexity of CA.

Image classification is a crucial technique in digital image processing, used in various applications such as object recognition, surveillance systems, and medical image analysis. This research explores the use of gradient magnitude-based edge detection and Robert's Cross methods in improving the classification accuracy of grayscale images. Edge detection is used to identify object boundaries, while gradient magnitude amplifies intensity differences, thus clarifying existing patterns. Through experiments conducted on grayscale images, the results show that this method is able to detect edges with significant accuracy. The gradient values obtained from the combination of Rx and Ry matrices give a strong indication of the presence of edges, which plays an important role in image classification. With an accuracy of 75%, the method proved to be effective, although there are still challenges in dealing with images with high noise or low contrast. The conclusion of this study shows that the combination of edge detection and gradient magnitude is a promising approach for image classification, providing results that can be applied in various domains, including medical and surveillance. Further research is recommended to optimize this approach and extend its application to more complex datasets.

In this paper, a hybrid edge detection method based on fuzzy sets and cellular learning automata is proposed. At first, existing methods of edge detection and their problems are discussed and then a high performance method for edge detection, that can extract edges more precisely by using only fuzzy sets than by other edge detection methods, is suggested. After that the edges improve incredibly by using cellular learning automata. In the end, we compare it with popular edge detection methods such as Sobel and Canny. The proposed method does not need parameter settings as Canny edge detector does, and it can detect edges more smoothly in a shorter amount of time while other edge detectors cannot.

This study explores the application of digital image processing techniques to improve the diagnostic accuracy of coronary artery disease through enhanced analysis of angiographic images. A curated dataset of 40 grayscale, single-vessel angiograms was used to evaluate the effectiveness of various preprocessing and edge detection methods. Preprocessing steps included noise reduction, histogram equalization, and morphological operations aimed at improving image clarity and highlighting vascular structures. Three edge detection algorithms, Otsu, Canny, and Roberts, were applied, and their outputs were further combined using the Dempster-Shafer fusion theory. Performance was assessed using edge-based structural similarity (ESSIM) and the percentage error in vessel diameter measurements. The Canny algorithm demonstrated the highest individual accuracy, while the fusion of Canny and Roberts yielded superior results, achieving the lowest error and the highest similarity index.

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in a bounded range. The LIP model has been proved to be physically justified by that it is consistent with the multiplicative transmittance and reflectance image formation models, and with some important laws and characteristics of human brightness perception. This article addresses the edge detection problem using the LIP-model based differentiation. First, the LIP model is introduced, in particular, for the gray tones and gray tone functions, which represent intensity values and intensity images, respectively. Then, an extension of these LIP model notions, respectively called gray tone vectors and gray tone vector functions, is studied. Third, the LIP-model based differential operators are presented, focusing on their distinctive properties for image processing. Emphasis is also placed on highlighting the main characteristics of the LIP-model based differentiation. Next, the LIP-Sobel based edge detection technique is studied and applied to edge detection, showing its robustness in locally small changes in scene illumination conditions and its performance in the presence of noise. Its theoretical and practical advantages over several well-known edge detection techniques, such as the techniques of Sobel, Canny, Johnson and Wallis, are shown through a general discussion and illustrated by simulation results on different real images. Finally, a discussion on the role of the LIP-model based differentiation in the current context of edge detection is presented.