Dynamic texture based smoke detection using Surfacelet transform and HMT model

To segment regions of interest (ROIs) from ultrasound images, one novel dynamic texture based algorithm is presented with surfacelet transform, hidden Markov tree (HMT) model and parallel computing. During surfacelet transform, the image sequence is decomposed by pyramid model, and the 3D signals with high frequency are decomposed by directional filter banks. During HMT modeling, distribution of coefficients is described with Gaussian mixture model (GMM), and relationship of scales is described with scale continuity model. From HMT parameters estimated through expectation maximization, the joint probability density is calculated and taken as feature value of image sequence. Then ROIs and non-ROIs in collected sample videos are used to train the support vector machine (SVM) classifier, which is employed to identify the divided 3D blocks from input video. To improve the computational efficiency, parallel computing is implemented with multi-processor CPU. Our algorithm has been compared with the existing texture based approaches, including gray level co-occurrence matrix (GLCM), local binary pattern (LBP), Wavelet, for ultrasound images, and the experimental results prove its advantages of processing noisy ultrasound images and segmenting higher accurate ROIs.

Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (e.g., using the expectation-maximization algorithm). We greatly simplify the HMT model by exploiting the inherent self-similarity of real-world images. The simplified model specifies the HMT parameters with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also introduce a Bayesian universal HMT (uHMT) that fixes these nine parameters. The uHMT requires no training of any kind, while extremely simple, we show using a series of image estimation/denoising experiments that these new models retain nearly all of the key image structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms other wavelet-based estimators in the current literature, both visually and in mean square error.

The wavelet-domain hidden Markov tree (HMT) model provides a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. However, it is usually assumed that the subbands at the same level are independent in the traditional HMT model. This paper proposes a modified HMT model, SHMT-S, in which a vector constructed from the coefficients at the same location of the subbands of the same level, is controlled by a hidden state. Meanwhile we also use the vector Laplace mixture distributions to fit the wavelet coefficients vector, which is peakier in the center and has heavier tails compared with Gaussian distribution. By using the HMT segmentation framework, we develop SHMT-S based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the proposed method.

The authors propose a multiscale Bayesian texture segmentation algorithm that is based on a complex wavelet domain hidden Markov tree (HMT) model and a hybrid label tree (HLT) model. The HMT model is used to characterise the statistics of the magnitudes of complex wavelet coefficients. The HLT model is used to fuse the interscale and intrascale context information. In the HLT, the interscale information is fused according to the label transition probability directly resolved by an EM algorithm. The intrascale context information is also fused so as to smooth out the variations in the homogeneous regions. In addition, the statistical model at pixel-level resolution is formulated by a Gaussian mixture model (GMM) in the complex wavelet domain at scale 1, which can improve the accuracy of the pixel-level model. The experimental results on several texture images are used to evaluate the algorithm.

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 $$97.9\%$$ .

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

Efficient prediction and classification for cirrhosis disease using LBP, GLCM and SVM from MRI images

Detecting anomalous events in densely pedestrian traffic video scenes remains challenging task, due to object's tracking difficulties and noise in the scene. In this paper, a Novel Hybrid Generative-Discriminative framework is proposed for detecting and localising the anomalous events of illegal vehicles present in the scene. This paper introduces a novelty in the application of Hybrid usage of latent Dirichlet allocation (LDA) and support vector machines (SVMs) over dynamic texture at sub-region level. The proposed HLDA-SVM model consists mainly of three steps: first local binary patterns from twelve orthogonal planes (LBP-TwP) technique is applied in each spatio-temporal video patch to extract dynamic texture; then LDA technique is applied to the extracted dynamic textures for finding the latent topic distribution and finally, training is done on the distribution of topic vector for each video sequence using multi way SVM classifier. The proposed HLDA-SVM model is validated on UCSD dataset data set and is compared with mixture of dynamic texture and motion context technique. Experimental results show that the HLDA-SVM approach performs well in par with current algorithms for anomaly detection.

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.

Dynamic textures are the sequences of images of moving scenes having some stationary properties in time; these include sea-waves, smoke, foliage, whirlwind etc. In recent years, dynamic texture description and recognition has attracted growing attention. This paper presents a more effective completed modeling of volume local binary pattern (VLBP) to recognize dynamic textures. Due to the dependency on local binary pattern (LBP), traditional VLBP also suffers with noise sensitivity and may give the same LBP code to different structural patterns; thus limiting the discriminating power of a texture descriptor. To represent temporal textures we have used VLBP. Local region of a volume is represented using its center frame and local sign magnitude difference with the circularly symmetric neighborhood. We have proposed a new contrast operator to complement the sign information of the temporal texture. To add additional discriminative information, volume center pixel information is also fused with the sign magnitude difference of texture. By combining these features into hybrid distributions we get higher classification accuracy for rotation invariant texture classification. Experimental results on UCLA and Dyntex databases show that the proposed approach provides better performance in comparison to the existing approaches.

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%.

Diabetic Retinopathy (DR) is a microangiopathic complication of diabetes mellitus that can cause visual impairment to permanent blindness. Early detection of DR is essential to prevent disease progression, but conventional methods require time, cost, and expertise that are not always available. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in DR classification based on texture features extracted from retinal fundus images. The dataset used consists of 3,000 retinal fundus images obtained from the Kaggle platform, divided into 2,400 training data and 600 test data. Image preprocessing includes conversion to grayscale, resizing to a resolution of 128×128 pixels, and normalization. Feature extraction is performed using a combination of Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) to produce a 14-dimensional feature vector. Performance evaluation uses accuracy, precision, recall, F1-score, ROC curve, and 5-fold cross-validation metrics. The results showed that Random Forest significantly outperformed SVM with an accuracy of 96% compared to 64%, an AUC value of 0.99 compared to 0.72, and an average cross-validation accuracy of 94.5% compared to 63.42%. Random Forest also showed balanced performance in both classes with precision, recall, and F1-score of 0.96, while SVM experienced classification imbalance especially in the disease class. This study proves that Random Forest is a more optimal algorithm for an automatic DR detection system based on fundus image texture features and can support increasing the accessibility of DR screening in areas with limited specialist medical personnel.

We present a novel approach for prediction of satellite image frame that uses a fuzzy rule based framework. The input-output membership functions for the premise and consequent parts of the rules are derived using a Gaussian Mixture Model (GMM). The weights of the fuzzy rules are represented as the prior probabilities of the respective Gaussian components. For obtaining the predictive fuzzy model, the GMM parameters are estimated via EM algorithm using a spatiotemporal representation of image sequence or video clips. Minimum Description Length (MDL) criterion is used to obtain a suitable predictive fuzzy model. The resulting model is successfully applied on a sequence of satellite images of tropical cyclone, Nargis, that made landfall in Myanmar on May 2, 2008. The quality of the predicted image is assessed using two criteria. The proposed approach is found to predict image frame successfully.

The purpose of our study was to estimate the ability of structural MRI-based texture analysis (TA) to quantify the textural changes in corpus callosum (CC) caused by Progressive Supranuclear Palsy (PSP). Three distinct TA approaches: gray level co-occurrence matrix (GLCM), local binary pattern (LBP), oriented Gaussian derivative filter-bank (O-GDFB) were exploited and compared to present a novel study that can classify PSP ( n=16) by quantifying the textural changes in CC versus that in healthy controls (HC, n=16 using T1-W MRI. Different statistical texture features were extracted using GLCM and LBP, from the five distinct CC sub-regions: CC1 (genu), CC2 (anterior-mid), CC3 (mid-body), CC4 (posterior-mid) and CC5 (splenium). Unlike statistical based TA, a modified GDFB based TA was also performed to explore the differences in filter responses in the same CC sub-regions for different orientations and scales. Finally, texture features that showed statistically highest discrimibility were input to the SVM classifier in classifying PSP and HC. With leave-one-out cross validation, SVM classification results maximum accuracy of 88%, 94% and 84% at CC3 using GLCM, LBP and O-GDFB based TA respectively. The diagnostic potential of CC TA using structural MRI can be applied on routine radiology scan without needing any additional acquisition for quantitative analysis. Significant texture alteration found at CC3 may be of great importance in the diagnosis and understanding of this pathology.