ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams

Magnetic Resonance Imaging plays a vital part in diagnosis of mutant cells in the endometrial layer of women reproductive system. This Endometrial carcinoma (EC) is one among the type of uterine cancer acts as a major challenge for the medical practitioners for early diagnosis and classification. This study explores advanced imaging techniques and artificial intelligence (AI) for improved EC identification. The Methodology incudes, an approach of extracting texture features using Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM) from the image data. These features are fused using a hybrid approach to capture complementary information. Machine learning classifiers including Random Forest (RF), Radial Basis Function (RBF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are trained and evaluated. Results demonstrate significant improvements in classification accuracy, with the hybrid feature extraction method achieving significant accuracy. Explainable AI (XAI) techniques are employed to interpret and visualize classifier decisions, providing details into the discriminative features contributing to EC classification.. The findings support the potential of integrating advanced imaging and machine learning, facilitated by XAI, for precise EC diagnosis and therapeutic planning.

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.

Texture is one of the most important features in the analysis of images. It has been increasingly used in computer vision applications. In this study, the ability of three statistical texture analysis measures to perform lamb grading were compared with respect to the classification accuracy. The texture measures examined were the grey level difference method (GLDM), the spatial grey level co-occurrence matrix (GLCM) and the grey level run length matrix (GLRM). In addition, some image geometric features were also measured. The dimensionality of the input feature space was reduced using principal component analysis (PCA). The classification was performed using individual reduced feature sets and their combinations. Both discriminant function analysis (DFA) and artificial neural network (ANN) analysis were used for classification of lamb chop images into different grades. The results indicated that GLCM is the best texture measure, of the three texture measures considered, for lamb grading. The geometric features also performed equally well. Both GLCM and geometric features performed better than GLRM and GLDM. The higher classification performance was achieved by combining feature sets. The ANN produced higher classifications than DFA

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1–79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVM produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.

PURPOSEMost factors identified in prior studies on meningioma enlargement are qualitative, with limited quantitative evaluation. Predicting future tumor growth from initial imaging of incidentally discovered meningiomas could inform treatment decisions. This study quantitatively evaluates imaging data at diagnosis to identify characteristics associated with tumor enlargement. METHODSWe compared MRI T2 features from the initial imaging of 10 patients in a clearly enlarged group with 10 in a non-enlarged group. Using 3D Slicer and Slicer Radiomics plug-ins, we created a program to set regions of interest (ROIs) in the tumor and calculate various features. Additional ROIs were placed in the outer tumor edge, brain, and white matter contralateral to the tumor. Inter-ROI ratios of features were calculated and compared using PyRadiomics software. We analyzed 107 features across seven classifications: one-dimensional statistics, shape features, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Gray Level Dependence Matrix, and Neighbouring Gray Tone Difference Matrix. A t-test was conducted to identify significant predictors. RESULTSTwenty-four features showed significant differences (p<0.05), including 10 one-dimensional statistics, 1 shape feature, 5 Gray Level Co-occurrence Matrix, 6 Gray Level Run Length Matrix, 1 Gray Level Size Zone Matrix, and 1 Gray Level Dependence Matrix. CONCLUSIONSWe identified potential quantitative predictors of incidentally discovered meningioma growth. Future analysis with a larger sample size will refine these predictors and support the development of a predictive formula and corresponding program.

Relationship of CT Radiomics and Dose Texture to Radiation-Induced Swallow Dysfunction in Head and Neck Cancer

Mammogram—breast X-ray—is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesions exist, only 15–30 % of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256 × 256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features, and the 19 (nineteen) features extracted from gray-level run-length matrix and gray-level co-occurrence matrix could distinguish malignant masses from benign masses with an accuracy of 96.7 %. Further analysis was carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are as follows: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE; ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 93.6 %. Further analysis showed that area under the receiver operating curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it was concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of decision tree using image content to classify between normal and cancerous masses. The proposed system was shown to have the large potential for cancer detection from digital mammograms.

Traditionally, the quality traits of meat products have been determined by means of physico-chemical methods. As an alternative, computer vision algorithms applied on MRI have been proposed, mainly, because of the non-destructive, non-ionizing and innocuous nature of MRI. Usually, the computer vision algorithms developed to analyze meat quality are based in classical textures. In this paper, a new texture algorithm (called RTA, Radial Texture Algorithm) based on the radial distribution of the images and second order statistics is proposed. The results obtained by RTA were compared to the obtained by means of three well known classical texture algorithms: GLCM (Gray Level Co-occurrence Matrix), GLRLM (Gray Level Run Length Matrix) and NGLDM (Neighbouring Gray Level Dependence Matrix) and correlated to the results obtained by means of physico-chemical methods. GLRLM and NGLDM achieved correlation coefficients between 0.50 and 0.75 whereas RTA and GLCM reached very good to excellent relationship (R > 0.75) for the quality parameters of loins. RTA achieved the best results (0.988 for moisture, 0.883 for lipid content and 0.992 for salt content). These high correlation coefficients confirm the new algorithm as a firm alternative to the classical computational approaches in order to compute the quality traits of meat products in a non-destructive and efficient way.

Medical image denoising using multi-resolution transforms

BackgroundIntravascular ultrasound (IVUS) is a commonly used diagnostic imaging method for coronary artery disease. Virtual histology (VH) characterizes the plaque components into fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), or dense calcium (DC). However, VH can obtain only a single-frame image in one cardiac cycle, and specific software is needed to obtain the radio frequency data. This study proposed a novel intensity-based multi-level classification model for plaque characterization.MethodsThe plaque-containing regions between the intima and the media-adventitia were segmented manually for all IVUS frames. A total of 54 features including first order statistics, grey level co-occurrence matrix, Law’s energy measures, extended grey level run length matrix, intensity, and local binary pattern were estimated from the plaque-containing regions. After feature extraction, optimal features were selected using principle component analysis (PCA), and these were utilized as the input for the classification models. Plaque components were classified into FT, FFT, NC, or DC using an intensity-based multi-level classification model consisting of three different nets. Net 1 differentiated low-intensity components into FT/FFT and NC/DC groups. Then, net 2 subsequently divided FT/FFT into FT or FFT, whereas the remainder and high-intensity components were classified into NC or DC via net 3. To improve classification accuracy, each net utilized three different input features obtained by PCA. Classification performance was evaluated in terms of sensitivity, specificity, accuracy, and receiver operating characteristic curve.ResultsQuantitative results indicated that the proposed method showed significantly high classification accuracy for all tissue types. The classifiers had classification accuracies of 85.1%, 71.9%, and 77.2%, respectively, and the areas under the curve were 0.845, 0.704, and 0.783. In particular, the proposed method achieved relatively high sensitivity (82.0%) and specificity (87.1%) for differentiating between the FT/FFT and NC/DC groups.ConclusionsThese results confirmed the clinical applicability of the proposed approach for IVUS-based tissue characterization.

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

Cancer is now the leading causes of death. During treatment, if an abnormal mass is detected by routine mammography after histological examination, a biopsy will be performed for further diagnosis. However, the time required to review and evaluate the biopsy slide is very long. An effective algorithm that can identify cancer tissues and reduce the rate of misdiagnosis is urgently needed. The current research methods mainly include: Capsule Network, and Deep Convolutional Neural Networks (CNNs) used in image classification, etc. This paper mainly uses Capsule Network, CNN, CNN trained on Histogram of Oriented Gradient (HOG) images, and Grey Level Co-occurrence Matrix (GLCM) to classify benign and malignant images of the tumor micro-biopsy images. The first step is to download the breast tumor micro-biopsy images from Breakhis. The second step is to preprocess the images, mainly image enhancement, augmentation, and HOG feature extraction. In the third step, the processed image data is used to train CNN and Capsule Network. The last step is to visualize the accuracy of model training and use confusion matrix to calculate the accuracy of image classification, recall rate, and so on. The results show that the accuracy of CNN with the help of transfer learning can reach 98.32%, the accuracy of CNN trained with HOG image is only 82.99%, the accuracy of CNN trained with GLCM is 92.79%, and the accuracy of capsule network can reach 92.39%.

To recognize patients at high risk of refractory disease, the identification of novel prognostic parameters improving stratification of newly diagnosed Hodgkin Lymphoma (HL) is still needed. This study investigates the potential value of metabolic and texture features, extracted from baseline 18F-FDG Positron Emission Tomography/Computed Tomography (PET) and Contrast-Enhanced Computed Tomography scan (CECT), together with clinical data, in predicting first-line therapy refractoriness (R) of classical HL (cHL) with mediastinal bulk involvement. We reviewed 69 cHL patients who underwent staging PET and CECT. Lesion segmentation and texture parameter extraction were performed using the freeware software LIFEx 6.3. The prognostic significance of clinical and imaging features was evaluated in relation to the development of refractory disease. Receiver operating characteristic curve, Cox proportional hazard regression and Kaplan-Meier analyses were performed to examine the potential independent predictors and to evaluate their prognostic value. Among clinical characteristics, only stage according to the German Hodgkin Group (GHSG) classification system significantly differed between R and not-R. Among CECT variables, only parameters derived from second order matrices (gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) demonstrated significant prognostic power. Among PET variables, SUVmean, several variables derived from first (histograms, shape), and second order analyses (GLCM, GLRLM, NGLDM) exhibited significant predictive power. Such variables obtained accuracies greater than 70% at receiver operating characteristic analysis and their PFS curves resulted statistically significant in predicting refractoriness. At multivariate analysis, only HISTO_EntropyPET extracted from PET (HISTO_EntropyPET ) and GHSG stage resulted as significant independent predictors. Their combination identified 4 patient groups with significantly different PFS curves, with worst prognosis in patients with higher HISTO_EntropyPET values, regardless of the stage. Imaging radiomics may provide a reference for prognostic evaluation of patients with mediastinal bulky cHL. The best prognostic value in the prediction of R versus not-R disease was reached by combining HISTO_EntropyPET with GHSG stage.

Computer-aided diagnosis (CAD) system for breast ultrasound can effectively assist doctors in classifying benign and malignant of breast tumors. CAD system increases the work efficiency and diagnostic accuracy, and it also reduces the rate of misdiagnosis. This paper proposes a method based on a histogram of oriented gradients (HOG), local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) feature extraction combined with machine learning classifier – support vector machine (SVM) to classify benign and malignant breast tumor ultrasound images. Besides, due to the lack of the latest and comprehensive ultrasonic image dataset, this work collected a latest ultrasound image dataset, which considers 350 benign lesions and 257 malignant tumors proven by biopsy. The ability of the classifier to predict the pathological classes is measured by the receiver operating characteristic (ROC) curve and obtain good classification result.

Endovascular repair is a minimal invasive alternative to open surgical therapy. From a long term perspective, complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear influencing the evolution of treatment. The objective of this work is to develop a Computer-Aided Diagnosis system (CAD) for an automated classification of EndoVascular Aneurysm Repair (EVAR) progression from Computed Tomography Angiography (CTA) images. The CAD system is based on the extraction of texture features from segmented thrombus aneurysm samples and a posterior classification. Image segmentation is produced by an interactive Active Learning procedure based on Random Forest (RF) classification.. An initial set of labeled points are required to start training an RF. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. Three texture-analysis methods such as the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), and the gray level difference method (GLDM), were applied to each ROI to obtain texture features. Classification of the ROI is carried out by an ensemble of SVM classifiers (ECs). The final decision is based on the application of a voting scheme across the outputs of the individual SVMs. The performance of the classifier was evaluated using 10-fold cross validation and the average of accuracy (93.84% ± 0.29), sensitivity (94.72 ± 0.23), and specificity (91.48 ± 0.35) results.KeywordsAneurysmEVARsegmentationtexture featuresensembles of classifiers