Gray Level Co-occurrence Matrix Technique Research Articles

Cancer Guard: Early Detection of Breast Cancer

Breast cancer stands as the most prevalent form of cancer among women, globally contributing to the highest number of cancer-related deaths. Timely detection of abnormalities significantly enhances the prospects of successful treatment and reduces mortality rates. Hence an automatic detection will be very useful for medical practitioner. This research introduces a novel framework for enhancing breast cancer detection and stage classification by integrating image processing techniques such as Gray Level Co-occurrence Matrix (GLCM) and Convolutional Neural Network (CNN) techniques. Initially, mammographic images undergo preprocessing to improve quality, followed by GLCM feature extraction for capturing textural information. With the help of GLCM technique, accuracy of the network can be increased by extracting various features. A CNN model is then employed for automatic feature learning and classification. This framework enhances the accuracy of distinguishing between malignant and benign tissues and extends to stage detection, enabling classification into various stages. Experimental results demonstrate the effectiveness of the proposed approach in achieving high precision and recall rates, suggesting potential for clinical integration to improve patient outcomes and streamline healthcare workflows.

Sub-Surface Soil Characterization Using Image Analysis: Material Recognition Using the Grey Level Co-Occurrence Matrix Applied to a Video-CPT-Cone

The geotechnical characterization of the subsurface is a key requirement for most soil investigations, incl. those for reclaiming landfills and waste dumps associated with mining operations. New sensor technology, combined with intelligent analysis algorithms, allow for a faster and less expensive acquisition of the necessary information without loss of data quality. The use of advanced technologies to support and back up common site investigation techniques, such as cone penetration testing (CPT), can enhance the underground characterization process. This study aims to investigate the possibilities of image analysis for material recognition to advance the geotechnical characterization process. The grey level co-occurrence matrix (GLCM) image processing technique is used in a wide range of study fields to estimate textures, patterns and structure anomalies. This method was adjusted and applied to process the video recorded during a CPT sounding, in order to distinguish soil types by its changing surface characteristics. From the results of the video processing, it is evident that the GLCM technique can identify transitions in soil types that were captured in the video recording. This enables the prospect of image analysis not just for soil investigations, but also for monitoring of the conveyor belt in the mining field, to allow for efficient preliminary decision making, material documentation and quality control by providing information in a cost effective and efficient manner.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Skin cancer recognition using SVM image processing technique

According to some estimates, skin cancer is the leading cause of death for people in the modern period. Although it can happen anywhere on the body, the majority of the time, this sort of cancer targets particular body regions that are more prone to being exposed to light. Skin cells proliferate in an erratic or patchy manner as a symptom. The majority of skin cancers can be treated if they are discovered early. Therefore, a patient’s life can be saved by promptly and precisely diagnosing skin cancer. Modern technology has made it feasible to detect skin cancer in its earliest stages. Skin cancer can be diagnosed systematically using the biopsy procedure (1). Skin cells are removed and supplied as a sample, following which they are evaluated in several laboratories. It’s an extremely unpleasant and time-consuming process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on support vector machine (SVM). It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called SVM are used in the identification process. In particular, epiluminescence microscopy is utilized using an image and several preprocessing techniques which are used in the reduction of sound artifacts and improve the quality of images. Segmentation is done by using certain thresholding techniques like Otsu. The gray level co-occurrence matrix technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The supervised learning model called SVM is used to distinguish data sets. It determines whether a picture is cancerous or not.

A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

A brain tumor develops when abnormal cells in brain tissue multiply uncontrollably. For radiologists, finding and categorizing tumors manually has become a demanding and time-consuming task. When radiologists or other clinical professionals need to extract an infected tumor area from an MR picture, they have to go through a lengthy and laborious process. To improve performance and simplify the segmentation process, we investigate the FCM-predicted picture segmentation techniques in this study. In addition, classifiers for automating the detection and reclassification of encephalon tumors receive input consisting of critical information obtained from each segmented tissue. We have assessed, verified, and demonstrated the experimental efficacy of the proposed method. The purpose of this research was to develop a novel MFO (Moth-Flame Optimization) based LLRBFNN model for the automatic detection and classification of benign and malignant brain tumors. In order to alleviate the burden of manually detecting encephalon cancers from MR images, the suggested LLRBFNN model parameters are improved via MFO training. The Modified FCM method removes outlying nodes from the LLRBFNN model, and the MFO algorithm keeps the current of node centres in the aforementioned model. The proposed MFO-LLRBFNN model was evaluated alongside the Decision Tree and the Random Forest. To prove the reliability of this model, an MFO-based LLWNN (Local Linear Wavelet Neural Network) model for autonomously detecting brain cancers was presented. We extracted features from MR images using the MFCM (modified fuzzy C-Means) segmentation algorithm and the GLCM (Gray Level Co-occurrence Matrix) technique.

Comparative Study of Single Biological Neuron with an Artificial Neuro

A number of artificial neural models have been presented in the literature in an effort to suggest a more accurate representation of a single biological neuron. There are numerous publications on synthetic neurons that attemptedtoreplicateasinglebiologicalneuron,however,suchmodelswereunabletogeneratethespikingpatterns of a real biological neuron. Therefore, there is still scope to design and research improved spiking neural models that more accurately reflect the functions of a biological neuron. This motivation drives extensive modification of an artificial neuron model to produce the spike patterns of a real biological neuron. The modified single artificial neuronmodel thathasbeen proposedexhibitsthefunctions ofabiological neuron.It’sstill crucialtomodel spiking bio-neuronbehavior.Modelingaspikingbio-neuronisstillanimportantexerciseinviewofpossibleapplicationsof the underlying features in the areas of neuromorphic engineering, cognitive radio, and spiking neural networks.

Feature Extraction of Tea Leaf Images using Dual-Tree Complex Wavelet Transform and Gray Level Co-occurrence Matrix

Feature extraction is a very important part of machine learning to analyze and find relationships between objects of different categories. This paper aims to analyze the feature vectors of the tea leaf image produced by using a combination of dual-tree complex wavelet transform and gray level co-occurrence matrix techniques. The tea leaf image consists of four different categories, each representing different phases of tea growth and were acquired using a visible camera from eight different orientations. Feature extraction using Principle Component Analysis (PCA) shows that the texture features can identify a different category of the leaf images without being significantly affected by the difference in scale and orientation of the images.

Hybrid Watermarking Scheme with Dual Encryption and Channel Coding in YCbCr Color Space

Objectives: This study aims to Improve and Secure the watermarking scheme with dual encryption (chaotic maps and Arnold transform) and channel coding in YCbCr color space with embedding and extraction procedure. Methods: In this scheme, the cover watermarked image is encoded and the singular value is decomposed by singular value decomposition (SVD). The four levels of Discrete Wavelet Transform (DWT) are applied after that, the singular value matrixes are embedded into the Y, Cb, Cr components of the host image.The embedding factor for each component is calculated with singular vectors of the hl sub-band of DWT with bit selection automatically by Gray level co-occurrence matrix (GLCM). In this paper, the GLCM technique is used to enhance the performance of a watermarked image affected by degradation with the DWT method. An inefficient approach is chosen randomly for image embedding which is bit selection. There is degradation in the quality of the watermark image when randomly selecting the bits. To dynamically choose the embedding bit, this research applies the Grey Level Co-occurrence Matrix method. Findings: Different performance parameters like Mean Squared Error (MSE), Peak Signal to Noise ratio (PSNR), Bit Error Rate (BER), Normalized correlation coefficient (NCC), and Mean Structural Similarity Index Measure (MSSIM) has been used to compare the effectiveness of the proposed scheme. The achieved outcomes show that when applying dual encryption and FFT (Fast Fourier Transform) with the GLCM, around 10 to 15 percent improvement in the results can be obtained. Novelty: We have proposed a hybrid watermarking scheme with Chaotic maps, Arnold transform and Fast Fourier transform in YCbCr color space. Keywords: Encryption; Discrete wavelet transform; Singular value decomposition; Chaotic map; Arnold transforms; Channel coding

Identification of Anthracnose Disease in Mango Plant Using Clustering and Gray level Co- occurrence Matrix

The aim of this paper is to help farmers to identify disease without observing each and every mango plant by eyes. Farmers can use the pesticides according to the leaf disease identified. For identification of this leaf disease K-means clustering and GLCM (Gray level co-occurrence matrix) technique are used. Affected areas of the leaf are identified more accurately by using K-means clustering. GLCM is used to get the texture features which help for identifying the mango leaf disease. The signs of the diseases on the leaf should be identified at the early for the growth of productivity in mango plant, for this leaf disease identification is important.

APSO based LLRBFNN Model and Enhanced Fuzzy C Means algorithm for Brain Tumor Detection and Classification from Magnetic Resonance Image

This paper presents a novel APSO (Accelerated Particle Swarm Optimization) Predicated LLRBFNN (Local Linear Radial Basis Function Neural Network) model for automatic encephalon tumor detection and classification. The enhanced fuzzy c means algorithm (EnFCM) has been proposed for image segmentation and the GLCM (Gray Level Co-occurrence Matrix) technique for feature extraction from MR (Magnetic Resonance) images. This research work aims to utilize the hybrid models and algorithms for relegation and segmentation of encephalon tumors from the MR images. The extracted features have been alimented as input to the proposed APSO predicated LLRBFNN model for relegation of benign and malignant tumors. In this research work the proposed LLRBFNN model weights are optimized by utilizing APSO training which will provide unique solution to mitigation the hectic task of radiologist from manual detection of encephalon tumors from MR Images. Additionally the centers of the LLRBFNN model are culled by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO predicated LLRBFNN model has been compared with PSO-LLRBFNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results are presented. The experimental results obtained from the proposed model shows better relegation results as compared to the subsisting models proposed anteriorly.  

A hybrid feature extraction and machine learning approaches for epileptic seizure detection

Epileptic seizure detection from the brain EEG signals is an essential step for speeding up the diagnosis that assists researchers and medical professionals. For this, various classification signal processing techniques have been developed in the traditional works. Still, they limit with the problems of increased complexity, reduced performance and insufficient classification rate. This motivates to design an automatic system for classifying the normal and abnormal EEG signals. Thus, an efficient machine learning approaches are implemented in this work, to overcome the existing techniques limitations. Here, an enhanced curvelet transform technique is established in order to overcome the disadvantage of Gabor and Wavelet transformations data loss and indiscriminate orientations. This method has the capacity to furnish the all the signals data required with no information loss of shearlet transformation and hence implemented to preprocess the given EEG signal, which smoothen the signal by eliminating the noise. Then, a modified graph theory, fractal dimension and novel pattern transformation techniques are employed to extract the features and patterns. The extraction of features is crucial for classification and compression of huge volume of EEG signal that possess low information. This theory improves the precision and speed of the computational technique. Most of the current research, Graph theory is reflected in the area of quantitative description of the time series data. It is predominantly employed for the reduction of noise and not affected during choosing the factors. From the patterns, the statistical features are extracted by using a standard gray level co-occurrence matrix technique that comprises entropy, homogeneity, energy, correlation and maximum probability. This method calculates the linear dependency of the adjacent samples thereby effective measurement of information loss in the transmitted signal is accomplished. Then, these extracted features are fed to the classifier named as novel random forest classification for detecting and classifying the signal as healthy, ictal and interictal. During simulation, various performance measures have been used for evaluating the results of existing and proposed classification techniques and results validate the efficacy of proposed technique.

MASCA–PSO based LLRBFNN model and improved fast and robust FCM algorithm for detection and classification of brain tumor from MR image

A novel modified adaptive sine cosine optimization algorithm (MASCA) integrated with particle swarm optimization (PSO) based local linear radial basis function neural network (LLRBFNN) model has been proposed for automatic brain tumor detection and classification. In the process of segmentation, the fuzzy C means algorithm based techniques drastically fails to remove noise from the magnetic resonance images. So, for reduction of noise and smoothening of brain tumor magnetic resonance image an improved fast and robust fuzzy c means algorithm segmentation algorithm has been proposed in this research work. The gray level co-occurrence matrix technique has been employed to extract features from brain tumor magnetic resonance images and the extracted features are fed as input to the proposed modified ASCA–PSO based LLRBFNN model for classification of benign and malignant tumors. In this research work the LLRBFNN model’s weights are optimized by using proposed MASCA–PSO algorithm which provides a unique solution to get rid of the hectic task of radiologist from manual detection. The classification accuracy results obtained from sine cosine optimization algorithm, PSO and adaptive sine cosine optimization algorithm integrated with particle swarm optimization based LLRBFNN models are compared with the proposed MASCA–PSO based LLRBFNN model. It is observed that the result obtained from the proposed model shows better classification accuracy results as compared to the other LLRBFNN based models.

Computer-aided screening system for cervical precancerous cells based on field emission scanning electron microscopy and energy dispersive x-ray images and spectra

The capability of field emission scanning electron microscopy and energy dispersive x-ray spectroscopy (FE-SEM/EDX) to scan material structures at the microlevel and characterize the material with its elemental properties has inspired this research, which has developed an FE-SEM/EDX-based cervical cancer screening system. The developed computer-aided screening system consisted of two parts, which were the automatic features of extraction and classification. For the automatic features extraction algorithm, the image and spectra of cervical cells features extraction algorithm for extracting the discriminant features of FE-SEM/EDX data was introduced. The system automatically extracted two types of features based on FE-SEM/EDX images and FE-SEM/EDX spectra. Textural features were extracted from the FE-SEM/EDX image using a gray level co-occurrence matrix technique, while the FE-SEM/EDX spectra features were calculated based on peak heights and corrected area under the peaks using an algorithm. A discriminant analysis technique was employed to predict the cervical precancerous stage into three classes: normal, low-grade intraepithelial squamous lesion (LSIL), and high-grade intraepithelial squamous lesion (HSIL). The capability of the developed screening system was tested using 700 FE-SEM/EDX spectra (300 normal, 200 LSIL, and 200 HSIL cases). The accuracy, sensitivity, and specificity performances were 98.2%, 99.0%, and 98.0%, respectively.

An Approach for Real Time Plastic Waste Segregation

Plastic has found an irreplaceable part in human life. Human produces plastic wastes continually which results in pollution. In this paper GLCM (Gray Level Co-occurrence Matrix) technique is used for plastic segregation. Plastic segregation is necessary for recycling; hence they have to be segregated from other wastes. Manufacturing plastics from recycled plastic products uses only two-third of the energy used to make plastics from virgin materials. By the variation in intensity levels of reflected light from the plastic wastes it is segregated. This type of waste management gains attention in recent days due to enormous increase in the wastes generated. Robotic arm controlled using vision system that uses GLCM technique is used for segregation. The GLCM property of contrast is calculated for the given sample and it is compared with the stored pre-defined value. On successful comparison with the given condition a signal is sent through the serial port to the 8051 microcontroller using USART (Universal Synchronous/Asynchronous Receiver/Transmitter) feature. On receipt of this signal, the microcontroller provides movement of the robotic arm by controlling the servo motors.

Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques

When changing the format of an image from simple RGB to HSV, YIQ and Dithered image, the characteristics of image also change. In this paper, the similar images in the above formats are retrieved using statistical and structural retrieving techniques i.e. GLCM (Gray Level Co-occurrence Matrix) and Wavelet Decomposition techniques. The best results are coming for dithered, HSV images by using GLCM technique for feature extraction and by using Wavelet decomposition; HSV images are giving the best results. While other formats also give the correct retrieval but from the accuracy point of view, HSV images are retrieved with better accuracy using both techniques.

Identification of Machining Chatter Marks on Surfaces of Automotive Valve Seats

In this paper, we propose a method for inspecting machining chatter marks on automotive valve seats using image processing technique. Chatter or self-excited tool-workpiece relative vibration is unacceptable in the machining processes of valve seats, because it causes poor surface finish and can create internal leaks in the engine. Robust identification of chatter marks on machined surfaces is one of the main challenges in the realization of an automatic in-line inspection system. We suggest several methods for calculating the chatter marks severity, using circle fitting and gray level co-occurrence matrix technique, which was for the first time implemented on circular images.

Advances in Telemedicine: A Multimedia-Based Texture Recognition Diagnostic System

The purpose of this paper is to propose a multimedia based texture recognition diagnostic system that has tremendous applications in telemedicine. The challenges of medical diagnostic errors get compounded in telemedicine. On the other hand, telemedicine offers opportunities with regard to exploiting vast databases of medical images and their retrieval. We design a multimedia system based on Grey Level Co-occurrence Matrix (GLCM) for automatically recognizing skin texture. Three dermatological conditions – acne, ichthyosis, and keratosis – are tested using this system. The results show that the GLCM technique is able to diagnose between the three disease conditions based on the skin texture images with an accuracy of 80%. Applications in the telemedicine context are discussed.

Texture-based classification of cloud and ice-cap surface features

It is well known that the interpretation of high resolution (<100 m) visible and near infrared (e.g. Landsat) imagery of large ice masses is hindered by the uniform reflectivity of snow, ice and cloud surfaces. Such interpretation is at present largely performed manually, but there is a good prospect that it could be automated by the incorporation of image texture. This paper describes preliminary work towards the identification of the most appropriate texture technique, or combination of techniques, and assesses the likely performance of such methods.Different textures are identified with different types of surface cover, and the use of these differences to classify images is investigated. Specifically, we compare a traditional texture measure, the Grey Level Co-occurrence Matrix (GLCM), with a modification of a relatively new technique, fractional Brownian motion (FBM). These two methods are applied to three Landsat MSS images of the Nordaustlandet ice cap, Svalbard. The classification accuracy, computation time and memory required, advantages and limitations of the two methods are compared. The GLCM technique appears to be able to distinguish three groups of image classes, namely dry snow, wet snow, and melt features, ablation areas or cloud cover. The FBM technique is computationally more efficient, and though it performs in general less well than the GLCM technique it gives better discrimination of cloud cover.

Texture-based classification of cloud and ice-cap surface features

It is well known that the interpretation of high resolution (<100 m) visible and near infrared (e.g. Landsat) imagery of large ice masses is hindered by the uniform reflectivity of snow, ice and cloud surfaces. Such interpretation is at present largely performed manually, but there is a good prospect that it could be automated by the incorporation of image texture. This paper describes preliminary work towards the identification of the most appropriate texture technique, or combination of techniques, and assesses the likely performance of such methods. Different textures are identified with different types of surface cover, and the use of these differences to classify images is investigated. Specifically, we compare a traditional texture measure, the Grey Level Co-occurrence Matrix (GLCM), with a modification of a relatively new technique, fractional Brownian motion (FBM). These two methods are applied to three Landsat MSS images of the Nordaustlandet ice cap, Svalbard. The classification accuracy, computation time and memory required, advantages and limitations of the two methods are compared. The GLCM technique appears to be able to distinguish three groups of image classes, namely dry snow, wet snow, and melt features, ablation areas or cloud cover. The FBM technique is computationally more efficient, and though it performs in general less well than the GLCM technique it gives better discrimination of cloud cover.