Plant species recognition based on bark patterns using novel Gabor filter banks

Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.

In the recent days automated plant species recognition systems are developed to help the ordinary people in identification of the different species. But the automatic analysis of plant species by the computer is difficult as compared to the human interpretation. The research has been carried out in this field for the better recognition of plant species. Still these approaches lack with exact classification of the plant species. The problem is due to the inappropriate classification algorithm. Especially when we consider the recognition of medicinal plant species, the accuracy will be the primary criteria. The proposed system in this research adopts the deep learning method to obtain the high accuracy in classification and recognition process using computer vision techniques. This system uses the Convolutional Neural Network (CNN) and the machine learning algorithms for deep learning of medicinal plant images. This research work has been carried out on the leaf dataset of flavia from sourceforge website. This data is fed as the training dataset for the CNN and machine learning based proposed system. An accuracy of 98% has been achieved in the recognition of the medicinal plant species. All the performance metrics like precision, recall, F1-score and support are calculated. Also the achieved training and validation accuracies are nearly equal.

A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its reduced version after feature selection. The second ensemble of classifiers was generated by combining five different type of primary classifiers, two NNs, and three k-nearest neighbor classifiers. The primary classifiers of the second ensemble used identical input vectors, which resulted from the combination of the five texture feature sets, either directly or after proper feature selection. The decision of each ensemble of classifiers was extracted by applying voting schemes. © 2006 International Federation for Information Processing.

In this paper, the performance of similarity retrieval from a database of earth core images by using different sets of spatial and transformed-based texture features is evaluated and compared. A benchmark consisting of 69 core images from rock samples is devised for the experiments. We show that the Gabor feature set is far superior to other feature sets in terms of precision-recall for the benchmark images. This is in contrast to an earlier report by the authors in which we have observed that the spatial-based feature set outperforms the other feature sets by a wide margin for a benchmark image set consisting of satellite images when the evaluation window has to be small (32 X 32) in order to extract homogenous regions. Consequently, we conclude that optimal texture feature set for texture feature-based similarity retrieval is highly application dependent, and has to be carefully evaluated for each individual application scenario.

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix.

Currently, the underlying medical conditions in China lag behind those in urban areas. There are some problems such as lack of resources of primary ophthalmologists and insufficient fundus image of diabetic retinopathy (DR) with markers. To solve the above questions, an automated detection model of diabetic retinopathy based on the statistical method and Naïve Bayesian (NB) classifier is proposed in this paper. Firstly, three sets of texture features are extracted, which are gray-level co-occurrence matrix texture features, different statistical texture features, and gray-level run-length matrix texture features. Secondly, the extracted texture features are used as input of the Naïve Bayesian classifier to classify the fundus images of diabetic retinopathy into three categories. The proposed automatic detection model for diabetic retinopathy is validated by a data set consisting of 568 images from China diabetic retinopathy screening project. The positive predictive accuracy of the system is 93.44%, the sensitivity and specificity are 91.94% and 88.24%, respectively.

In this research we concentrate on adaptive texture feature extractors which are automatically extracted from an image collection. We use independent component analysis (ICA) to extract independent component filters (ICF). ICF have previously been shown to include edge filters, having properties similar to the receptive fields of simple cells of the human visual cortex. In this thesis we evaluate the utility of ICF-based collection-specific features in the context of content-based image retrieval (CBIR), with the view to demonstrate the viability of using such automatic collection-specific features. We find that global features extracted using a small number of ICF outperform those extracted by a bank of Gabor filters, even when very large number of Gabors are used. We also present comparisons against a variety of state-of-the-art features and show that ICF-based features perform better than these, without the need for any hand-tuning. ICA extracts large number of filters. In order to find a useful smaller subset we evaluate a previously-published variance-based filter selection method. We identify the shortcomings of this method. Our proposed improvements to filter-based feature selection and extraction, response scaling and locally normalised convolution, was a result of trying to address some of these shortcomings. Even with these improvements the variance-based method has an intrinsic flaw of being susceptible to selecting redundant filters. We propose a new filter selection method based on normalised cross-correlation and clustering. We show that our method selects a more useful subset compared to the previously published variance-based method. We also propose a salient-point based local feature which uses ICF and show that our proposed feature performs better than SIFT features and also the global ICF features. We further illustrate the utility of ICFSIFT by developing a grid-based adaptive local feature and we demonstrate that ICFSIFT extracts different and useful information compared to the ICF-based global and grid-based local features. After applying the techniques proposed in this thesis to four standard texture collections, we show that, while hand-tuned features can work well for their target collection, the automatic adaptive features perform better for the general case.

The study investigates the possibility of applying texture analysis (TA) for testing Duchenne Muscular Dystrophy (DMD) therapies. The work is based on the Golden Retriever Muscular Dystrophy (GRMD) canine model, in which 3 phases of canine growth and/or dystrophy development are identified: the first phase (0–4 months of age), the second phase (from over 4 to 6 months), and the third phase (from over 6 months to death). Two differentiation problems are posed: (i) the first phase vs. the second phase and (ii) the second phase vs. the third phase. Textural features are derived from T2-weighted Magnetic Resonance Imaging (MRI) images. In total, 37 features provided by 8 different TA methods (statistical, filter-based, and model-based) have been tested. The work focuses on finding such textural features that evolve along with the dog’s growth. These features are indicated by means of statistical analyses and eliminated from further investigation, as they may disturb the correct assessment of response to treatment in dystrophy. The relative importance of each remaining feature is then assessed with the use of the Monte Carlo (MC) procedure. Furthermore, feature selection based on the MC procedure is employed to find the optimal subset of age-independent features. Finally, three classifiers are used for evaluating different sets of textural features: Adaptive Boosting (AB), back-propagation Neural Network (NN), and nonlinear Support Vector Machines (SVM). The best subsets of age-independent features ensure 80.0% and 78.5% of correctly identified phases of dystrophy progression, for the first (i) and second (ii) differentiation problem respectively.

A Quantitative Study of Texture Features across Different Window Sizes in Prostate T2-weighted MRI

Contrast-enhanced spectral mammography is one of the latest diagnostic tool for breast care; therefore, the literature is poor in radiomics image analysis useful to drive the development of automatic diagnostic support systems. In this work, we propose a preliminary exploratory analysis to evaluate the impact of different sets of textural features in the discrimination of benign and malignant breast lesions. The analysis is performed on 55 ROIs extracted from 51 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. We extracted feature sets by calculating statistical measures on original ROIs, gradiented images, Haar decompositions of the same original ROIs, and on gray-level co-occurrence matrices of the each sub-ROI obtained by Haar transform. First, we evaluated the overall impact of each feature set on the diagnosis through a principal component analysis by training a support vector machine classifier. Then, in order to identify a sub-set for each set of features with higher diagnostic power, we developed a feature importance analysis by means of wrapper and embedded methods. Finally, we trained an SVM classifier on each sub-set of previously selected features to compare their classification performances with respect to those of the overall set. We found a sub-set of significant features extracted from the original ROIs with a diagnostic accuracy greater than . The features extracted from each sub-ROI decomposed by two levels of Haar transform were predictive only when they were all used without any selection, reaching the best mean accuracy of about . Moreover, most of the significant features calculated by HAAR decompositions and their GLCMs were extracted from recombined CESM images. Our pilot study suggested that textural features could provide complementary information about the characterization of breast lesions. In particular, we found a sub-set of significant features extracted from the original ROIs, gradiented ROI images, and GLCMs calculated from each sub-ROI previously decomposed by the Haar transform.

This study represents an innovative automatic method for black and white films colorization using texture features and a multilayer perceptron artificial neural network. In the proposed method, efforts are made to remove human interference in the process of colorization and replace it with an artificial neural network (ANN) which is trained using the features of the reference frame. Later, this network is employed for automatic colorization of the remained black and white frames. The reference frames of the black and white film are manually colored. Using a Gabor filter bank, texture features of all the pixels of the reference frame are extracted and used as the input feature vector of the ANN, while the output will be the color vector of the corresponding pixel. Finally, the next frames’ feature vectors are fed respectively to the trained neural network, and color vectors of those frames are the output. Applying AVI videos and using various color spaces, a series of experiments are conducted to evaluate the proposed colorization process. This method needs considerable time to provide a reasonable output, given rapidly changing scenes. Fortunately however, due to the high correlation between consecutive frames in typical video footage, the overall performance is promising regarding both visual appearance and the calculated MSE error. Apart from the application, we also aim to show the importance of the low level features in a mainly high level process, and the mapping ability of a neural network.

We present a framework for content based retrieval (CBR) of remotely sensed imagery. The main focus of our research is the segmentation step in CBR. A bank of Gabor filters is used to extract regions of homogeneous texture. These filter responses are utilized in a multiscale clustering technique to yield the final segmentation. Novel area morphological filters are utilized for the purpose of scaling. The resultant segmentation yields regions that are homogeneous in terms of texture and are significant in terms of scale. These regions are used for the purpose of extracting shape and textural features (on a global and local basis) that provide important similarity cues in CBR of remotely sensed imagery. In comparison to solutions which use region merging, the segmentation from the texture/scale space does not require heuristic post-processing, nor knowledge of the number of significant regions.

PurposeTo evaluate the feasibility of using an objective computer aided system to assess bladder cancer stage in CT Urography (CTU).Materials and MethodsA data set consisting of 84 bladder cancer lesions from 76 CTU cases was used to develop the computerized system for bladder cancer staging based on machine learning approaches. The cases were grouped into two classes based on pathological stage ≥T2 or below T2, which is the decision threshold for neoadjuvant chemotherapy treatment clinically. There were 43 cancers below stage T2 and 41 cancers at stage T2 or above. All 84 lesions were automatically segmented using our previously developed auto-initialized cascaded level sets (AI-CALS) method. Morphological and texture features were extracted. The features were divided into subspaces of morphological features only, texture features only, and a combined set of both morphological and texture features. The data set was split into Set 1 and Set 2 for two-fold cross validation. Stepwise feature selection was used to select the most effective features. A linear discriminant analysis (LDA), a neural network (NN), a support vector machine (SVM), and a random forest (RAF) classifier were used to combine the features into a single score. The classification accuracy of the four classifiers was compared using the area under the receiver operating characteristic (ROC) curve (Az).ResultsBased on the texture features only, the LDA classifier achieved a test Az of 0.91 on Set 1 and a test Az of 0.88 on Set 2. The test Az of the NN classifier for Set 1 and Set 2 were 0.89 and 0.92, respectively. The SVM classifier achieved test Az of 0.91 on Set 1 and test Az of 0.89 on Set 2. The test Az of the RAF classifier for Set 1 and Set 2 was 0.89 and 0.97, respectively. The morphological features alone, the texture features alone, and the combined feature set achieved comparable classification performance.ConclusionThe predictive model developed in this study shows promise as a classification tool for stratifying bladder cancer into two staging categories: greater than or equal to stage T2 and below stage T2.

In this paper, a computer-aided diagnostic (CAD) system is developed using a multiparametric magnetic resonance imaging (MPMRI) (T2-MRI and DW-MRI) to differentiate between BC staging, especially T1 and T2. The segmentation of the bladder wall (BW) and the localization of the whole BC area (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ) and its extent inside the wall (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> ) is first performed. Secondly, a set of functional, texture, and morphological features are estimated. Due to the massive difference between the wall and bladder lumen cells, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> is split into nested equidistance contours (i.e., iso-contours), and features are estimated for each iso-contours. The functional features are based on the cumulative distribution function (CDF) statistical measures for the estimated apparent diffusion coefficient (ADC) from DWMRI. Texture features, namely radiomic features, are derived from T2W-MRI, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> both carcinoma intensity and gradient images for each iso-contours from A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> , T2-MRI. Besides, morphological features are also incorporated to describe the tumors' geometric from T2W-MRI, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> . Finally, the estimated iso-features are augmented and used to train and test neural networks classifier as well as a statistical machine learning (ML) classifier. The system has been tested using a leave-one-subject-out approach on 42 data sets. The overall accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristics (ROC) are 92.86%, 97.05%, 100%, and 0.9705, respectively. We introduce the diagnostic accuracy of individual MRI modality for our proposal to highlight the advantage of fusion multiparametric iso-features that is confirmed by the ROC analysis. Furthermore, the accuracy of two different techniques statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)) and end-to-end convolution neural networks (i.e., ResNet50) is compared against our pipeline.

Objective: To investigate the utility of the pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment.Methods: Sixty-three patients with 72 lesions who received immunotherapy were enrolled in this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform from pre-immunotherapy contrast-enhanced CT by using Mazda software. Three different methods, namely, Fisher coefficient, mutual information measure (MI), and minimization of classification error probability combined average correlation coefficients (POE + ACC), were performed to select 10 optimal texture feature sets, respectively. The patients were divided into non-progressive disease (non-PD) and progressive disease (PD) groups. t-test or Mann–Whitney U-test was performed to test the differences in each texture feature set between the above two groups. Each texture feature set was analyzed by principal component analysis (PCA), linear discriminant analysis (LDA), and non-linear discriminant analysis (NDA). The area under the curve (AUC) was used to quantify the predictive accuracy of the above three analysis models for each texture feature set, and the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were also calculated, respectively.Results: Among the three texture feature sets, the texture parameter differences of kurtosis (2.12 ± 3.92 vs. 0.78 ± 1.10, p = 0.047), “S(2,2)SumEntrp” (1.14 ± 0.31 vs. 1.24 ± 0.12, p = 0.036), and “S(1,0)SumEntrp” (1.18 ± 0.27 vs. 1.28 ± 0.11, p = 0.046) between the non-PD and PD group were statistically significant (all p < 0.05). The classification result of texture feature set selected by POE + ACC and analyzed by NDA was identified as the best model (AUC = 0.812, 95% CI: 0.706–0.919) with a sensitivity, specificity, accuracy, PPV, and NPV of 88.2, 76.3, 81.9, 76.9, and 87.9%, respectively.Conclusion: Pre-immunotherapy contrast-enhanced CT-based texture provides a new method for clinical evaluation of the NSCLC immunotherapy efficacy prediction.