Adaptive neuro-fuzzy inference system for texture image classification

Collagen is the most prominent protein in the human body, making up 30% of the total protein content. Quantitative studies have shown structural differences between collagen fibers of the normal and diseased tissues, due to the remodeling of the extracellular matrix during the pathological process. The dominant orientation, which is an important characteristic of collagen fibers, has not been taken into consideration for quantitative collagen analysis. Based on the conventional gray level co-occurrence matrix (GLCM) method, the authors proposed the orientation-dependent GLCM (OD-GLCM) method by estimating the dominant orientation of collagen fibers. The authors validated the utility of the OD-GLCM method on second harmonic generation (SHG) microscopic images of tendons from rats with different ages. Compared with conventional GLCM method, the authors' method has not only improved the discrimination between different tissues but also provided additional texture information of the orderliness of collagen fibers and the fiber size. The OD-GLCM method was further applied to the differentiation of the preliminary SHG images of normal and cancerous human pancreatic tissues. The combination of SHG microscopy and the OD-GLCM method might be helpful for the evaluation of diseases marked with abnormal collagen morphology.

Information system security is a crucial aspect in the digital era, especially for protecting sensitive data. One method to enhance data security is steganography, a technique for concealing information within digital media. This study analyzes the effectiveness of the Discrete Cosine Transform (DCT) method in digital image steganography. The research follows several stages, including literature review, system design, DCT implementation using MATLAB/Python, and performance evaluation based on Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Normalized Correlation (NC). Experimental results indicate that the DCT method achieves a high PSNR value (averaging above 40 dB) and a low MSE value, ensuring minimal image distortion after data embedding. Compared to the Least Significant Bit (LSB) method, DCT provides better resistance to visual analysis and is more suitable for images with transform-based compressions, such as JPEG. This study concludes that the DCT method is an effective steganography technique for enhancing information system security, despite challenges related to computational complexity and data embedding capacity. Future research may explore hybrid methods that combine DCT with other techniques to improve efficiency and robustness against various steganographic analysis attacks.

Machine learning methods are used to detect elderly accidents while image processing is used to support machine learning performance so that detection performance can be better. This study used two methods used simultaneously, namely GLCM and ANN. The study consisted of preparation of human gesture datasets, preprocessing stage, application of GLCM, analysis of feature extraction results, classification using ANN and analysis of motion class detection results. Overall, the GLCM method with homogeneity parameters and ANN as a classifier obtained an accuracy of 24.32%. The GLCM method with contrast parameters and ANN as a classifier gets an accuracy of 99.84%. The GLCM method with mean parameters and ANN as the classifier gets an accuracy of 99.99%. The GLCM method with dissimilarity parameters and ANN as a classifier to classify hand movement images gets the best accuracy of 100%.

Breast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) could be used as an aid to the doctors and radiologist in diagnosing breast cancer from the mammogram images. The aim of this paper is to compare two feature extraction methods used in SVM, namely the Gray Level Co-Occurrence Matrix (GLCM) and first order with two kernels for each method, namely Gaussian and Polynomial. Classification using SVM method is carried out by testing several parameters such as the value of C, gamma, degree and varying the pixel spacing values ​​in GLCM, which usually in previous studies only used the default pixel spacing. The dataset consists of 500 mammogram images containing 250 benign and malignant images, respectively. This study is expected to find out the best method with the highest accuracy between these two texture feature extractions and and able to distinguish between benign and malignant classes correctly. The result achieved that Gray Level Co-Occurrence Matrix (GLCM) feature extraction method with both Gaussian and Polynomial kernel yields the best performance with an accuracy of 89%.

In this work, we extend our previous research on gray level co-occurrence matrix (GLCM) based watermark embedding in the discrete cosine transform (DCT) domain to the discrete wavelet transform (DWT) domain. The GLCM method incorporated human visual system information into the embedding process making the watermark more transparent. DWT techniques allow for more compression as fewer coefficients are required to reconstruct an image. In addition, DWT methods will not exhibit block artifacts commonly encountered when applying block based DCT methods. The watermark identification is further enhanced using neural networks. In this research, daubechies wavelets are utilized to evaluate the efficiency of the watermark identification while the method is subjected to multiple attacks such as filtering, compression, or rotation. The results are then compared with previously published methods by the authors such as LMS based correlation and adaptive DWT based watermark identification.

In this paper, two levels of classification have been carried out, the first being upper extremity recognition followed by bone abnormality detection. Support vector machines (SVMs), VGG16 and Xception V3 convolutional neural networks (CNNs) were implemented for this purpose, and their performances have been compared. The conventional method (SVM) uses the grey level co-occurrence matrix (GLCM) and local binary pattern (LBP) methods for feature extraction. In the first level, X-ray images from the MURA dataset were classified into seven categories. Next, the images were classified into two classes based on the presence of an abnormality. The VGG16-based model performed the best in the first level of classification with an average accuracy of 94.4% over three trials. In the second level, the SVM model performed the best with an accuracy of 66.33%.

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

هدف از انجام این پژوهش، تعیین بهینه رخساره‌های پتروفیزیکی از روی داده‌های نموداری چاه است. با استفاده از روش دسته‌بندی خودکار K نزدیک­ترین همسایه می‌توان رخساره‌های پتروفیزیکی را تعیین کرد؛ اما نه به­صورت بهینه. برای تعیین بهینه رخساره‌ها، روش K نزدیک­ترین همسایه با روش‌های تحلیل سریع مؤلفه­های مستقل و تبدیل کسینوسی گسسته تلفیق می‌شود که این امر موجب افزایش نرخ موفقیت روش K نزدیک­ترین همسایه و تعیین بهینه رخساره‌های پتروفیزیکی می‌شود که به ­دنبال آن می‌توان مدل‌سازی و توصیف مخازن هیدروکربنی را انجام داد. پژوهش در دو مسیر متفاوت انجام می‌شود. در مسیر اول، روش تحلیل سریع مؤلفه­های مستقل روی داده اعمال‌ و سپس توسط روش K نزدیک­ترین همسایه دسته‌بندی می‌شود و در مسیر دوم روش‌های تحلیل سریع مؤلفه­های مستقل و تبدیل کسینوسی گسسته روی داده‌ها اعمال و سپس توسط روش K نزدیک­ترین همسایه دسته‌بندی می‌شود. در پایان نرخ موفقیت دسته‌بندی توسط K نزدیک­ترین همسایه در هر دو مسیر به­ منظور تعیین بهینه رخساره‌های پتروفیزیکی مورد ارزیابی قرار می‌گیرد. ارزیابی انجام‌ شده نشان می‌دهد که اعمال روش مسیر دوم روی داده‌ها به‌طور معنی‌داری نرخ موفقیت دسته‌بندی توسط روش K نزدیک­ترین همسایه را افزایش می‌دهد که این امر موجب تعیین بهینه رخساره‌های پتروفیکی می‌شود که همان هدف از انجام این پژوهش است. داده‌های مورد استفاده، نمودارهای صوتی (Sonic Log)، پرتوی گاما (Gamma Ray)، چگالی (FDC یا RHOB)، تخلخل نوترون (CNL یا NPHI) و نگارهای القایی ژرف (ILD) از میدان نفتی مارون در جنوب کشور ایران هستند.

Batik is an Indonesian cultural heritage that is internationally recognized by UNESCO. However, knowledge about the types of batik, especially traditional Pekalongan batik, is increasingly forgotten due to globalization. This research aims to create a Pekalongan traditional batik image classification system through Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Artificial Neural Network (ANN) classification method. This system aims to make it easier for people to identify Pekalongan batik motifs without requiring special skills. The results showed that the GLCM and JST methods can be used to classify Pekalongan batik can predict correctly. The use of JST Backpropagation architecture with 3 hidden layers resulted in train data accuracy of 46.6% and test data accuracy of 55.5%. This system is expected to help preserve the cultural heritage of batik and increase public understanding of Pekalongan batik motifs.

A novel technique is proposed to reduce the computational complexity of discrete cosine transform (DCT)-based video encoders. The proposed method merges the DCT and quantization into a single procedure, which is referred to as the novel quantized DCT (NQDCT), such that the DCT output does not need to be explicitly quantized. Thus, a lot of computations related to quantization can be saved. The video encoder's performance using NQDCT is evaluated by comparing it with those using the traditional separate DCT and quantization method and the quantized DCT (QDCT) method. Simulation results demonstrate that the proposed NQDCT outperforms the other two methods in improving the real-time performance for video encoding.

Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole

COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.

This aim of this study was to assess the discriminatory value of fractal and grey level co-occurrence matrix (GLCM) analysis methods in standard microscopy analysis of two histologically similar brain white mass regions that have different nerve fiber orientation. A total of 160 digital micrographs of thionine-stained rat brain white mass were acquired using a Pro-MicroScan DEM-200 instrument. Eighty micrographs from the anterior corpus callosum and eighty from the anterior cingulum areas of the brain were analyzed. The micrographs were evaluated using the National Institutes of Health ImageJ software and its plugins. For each micrograph, seven parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, GLCM variance, fractal dimension, and lacunarity. Using the Receiver operating characteristic analysis, the highest discriminatory value was determined for inverse difference moment (IDM) (area under the receiver operating characteristic (ROC) curve equaled 0.925, and for the criterion IDM≤0.610 the sensitivity and specificity were 82.5 and 87.5%, respectively). Most of the other parameters also showed good sensitivity and specificity. The results indicate that GLCM and fractal analysis methods, when applied together in brain histology analysis, are highly capable of discriminating white mass structures that have different axonal orientation.

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.