Classification of solid textures using 3d mask patterns

Mask patterns based on Higher Order Local Autocorrelation (HLAC) have been used for various 2D image recognition and classification systems. Recently, research related to extended 2D HLAC mask patterns has shown that the use of mask patterns improves classification rates for certain 2D textures. We have applied a similar extension approach to 3D HLAC mask patterns which can analyze 3D voxel data. Since there are a large number of combinations for 3D HLAC mask patterns compared with the number of 2D HLAC mask patterns, some mask patterns have to be eliminated by using proper weighting algorithms. We have used a technique based on backpropagation networks for weighting shape descriptors which are extracted by extended 3D HLAC mask patterns. The backpropagation networks were trained by learning voxel data sets, and the network eliminated unnecessary shape descriptors. A database of artificially generated 3D voxel data was used for testing pattern classification efficiencies of the extended 3D HLAC mask patterns. Our preliminary experiments showed fair recall-precision results for retrieving similar 3D voxel data when extended 3D HLAC mask patterns are used in conjunction with backpropagation networks.

PurposeThe purpose of this paper is to describe innovative machine vision methods that have been employed for the capture and analysis of 3D skin textures; and the resulting potential for assisting with identification of suspicious lesions in the detection of skin cancer.Design/methodology/approachA machine vision approach has been employed for analysis of 3D skin textures. This involves an innovative application of photometric stereo for the capture of the textures, and a range of methods for analysing and quantifying them, including statistical methods and neural networks.Findings3D skin texture has been identified as a useful indicator of skin cancer. It can be used to improve realism of virtual skin reconstructions in tele‐dermatology. 3D texture features can also be combined with 2D features to obtain a more robust classifier for improving diagnostic accuracy, thereby assisting with the long‐term goal of implementing computer‐aided diagnostics for skin cancer.Originality/valueThe device developed for capturing 3D skin textures is known as the “Skin Analyser”, and as far as the authors know it is unique in the world in being able to recover 3D textures from pigmented lesions in vivo. There currently exist numerous methods for analysing lesions, including manual inspection (using established heuristics commonly known as ABCD rules), dermoscopy and SIAoscopy. The ability to capture and analyse 3D lesion textures complements these existing techniques and forms a valuable additional indicator for assisting with the early detection of dangerous skin cancers such as melanoma.

This paper presents a new feature extraction technique for 3D texture classification based on Local Binary Patterns-derived operators and state-of-the-art denoising techniques, with good invariance properties to different transformations and robust to noise. Our approach consists in the computation of textural features obtained using the Block Matching and 3D Filtering Extended Local Binary Patterns for each of the three orthogonal planes and their concatenation. The proposed method achieves a promising classification performance on a publicly available dataset of 3D textured images. The obtained results prove that the presented feature extraction technique is above traditional 2D texture descriptors and typical CNN deep learning-based methods.

Analytical approaches, based on digitised 2D texture models, for an automatic solid (3D) texture synthesis have been recently introduced to Computer Graphics. However, these approaches cannot provide satisfactory solutions in the usual case of natural anisotropic textures (wood grain for example). Indeed, solid texture synthesis requires particular care, and sometimes external knowledge to "guess" the internal structure of solid textures because only 2D texture models are used for analysis. By making some basic assumptions about the internal structure of solid textures, we propose a very efficient method based on a hybrid analysis (spectral and histogram) for an automatic synthesis of solid textures. This new method allows us to obtain high precision solid textures (closely resembling initial models) in a large number of cases, including the difficult case of anisotropic textures.

Many image acquisition techniques used in biomedical imaging, material analysis, and structural geology are capable of acquiring 3-D solid images. Computational analysis of these images is complex but necessary since it is difficult for humans to visualize and quantify their detailed 3-D content. One of the most common methods to analyze 3-D data is to characterize the volumetric texture patterns. Texture analysis generally consists of encoding the local organization of image scales and directions, which can be extremely diverse in 3-D. Current state-of-the- art techniques face many challenges when working with 3-D solid texture, where most approaches are not able to consistently characterize both scale and directional information. 3-D Riesz- wavelets can deal with both properties. One key property of Riesz filterbanks is steerability, which can be used to locally align the filters and compare textures with arbitrary (local) orientations. This paper proposes and compares three novel local alignment criteria for higher-order 3-D Riesz-wavelet transforms. The estimations of local texture orientations are based on higher- order extensions of regularized structure tensors. An experimental evaluation of the proposed methods for the classification of synthetic 3-D solid textures with alterations (such as rotations and noise) demonstrated the importance of local directional information for robust and accurate solid texture recognition. These alignment methods improved the accuracy of the unaligned Riesz descriptors up to 0.63, from 0.32 to 0.95 over 1 in the rotated data, which is better than all other techniques that are published and tested on the same database.

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

The purpose of the study was to determine if CT texture features of untreated hepatic metastatic colorectal cancer (CRC) relate to pathologic features and clinical outcomes. Tumor texture analysis was performed on single hepatic metastatic lesions on pre-treatment contrast-enhanced CT scans in 77 pts (mean age 58, 34F/43M) using a novel tool. Measures of heterogeneity, including entropy, kurtosis, skewness, mean, mean positive pixels (MPP), and standard deviation (SD) of pixel distribution histogram were derived with filter values corresponding to fine (spatial scaling factor (ssf) 2), medium (ssf 3, 4), and coarse textures (ssf 5, 6). Texture parameters were correlated with tumor grade, baseline serum CEA, and KRAS mutation status. Overall survival was also correlated using Cox proportional hazards models. Single-slice 2D vs. whole-tumor volumetric 3D texture analysis was compared in a subcohort of 20 patients. Entropy, MPP, and SD at medium filtration levels were significantly associated with tumor grade (MPP ssf 3 P=0.002, SD ssf 3 P=0.004, entropy ssf 4 P=0.007). Skewness was negatively associated KRAS mutation (P=0.02). Entropy at coarse filtration levels was associated with survival (Hazard ratio (HR) for death 0.65, 95% CI 0.44-0.95, P=0.03). Texture results for 2D and 3D analysis were similar. CT texture features, particularly entropy, MPP, and SD, are significantly associated with tumor grade in untreated CRC liver metastases. Tumor entropy at coarse filters correlates with overall survival. Single-slice 2D texture analysis appears to be adequate.

With the widespread scope of image processing in fields such as ground classification, segmentation of satellite images, biomedical surface inspection and content-based image retrieval (CBIR), texture analysis is an important domain with a wide scope. The process of texture analysis comprises the following important steps: texture classification, segmentation and synthesis. Texture is a significant property of images which is difficult to define even though the human eye may recognize it with ease. Texture classification has remained an intangible pattern recognition task despite numerous studies. The prime issue in any texture classification application is how to select the features and which features to consider for representing texture. Another major issue remains the type of metric to be used for comparing the feature vectors. The traditional way of texture classification is to convert the texture image into a vector representing the features using a set of filters. This is followed by classification with a few smoothening steps involved in between. This paper outlines a picture of the basic features of an image as texture, colour and shape which are to be extracted to form the feature vector. The concept of applying Machine learning for the purpose of classification is explored and implemented. The use of Soft Computing Technique—Artificial Neural Networks is illustrated for the purpose of classification. The texture features are extracted using the GLCM method and used as input and fed to the neural network. The algorithm used in training the neural networks is the traditional backpropagation algorithm. The results show which configuration of the multi-layer feedforward architecture is best suited according to our experimental set-up.

It is presented a method of texture recognition based on the analysis of multilevel morphological spectra of biomedical images under consideration. The n-th level morphological spectrum of the image is defined as a matrix whose elements have been obtained due to an rearrangement of the results of four standard algebraic operations performed on the components of the former, (n-1)-th level spectrum. The higher is the spectrum-level, the larger are the morphological structures being taken into consideration in texture analysis; the lowest (0-th) level spectrum corresponds to the bitmap of the original image. It is shown that the original image can be reconstructed on the basis of its morphological spectrum of any given level. The set of spectra can be represented in the form of a tree. Morphological spectra corresponding to selected branches of this tree are characteristic for certain classes of textures and, as such, they can be taken into account for effective discrimination and recognition of the given classes of textures.

In this paper, we present an improved synthesis algorithm for both 2D and solid textures. Based on the wavelet-based multi-resolution pyramids and the improved optimization of texture synthesis, a coarse texture was synthesized as a skeleton. By adding detail information to the coarse texture, the higher resolution results can be generated. This skeletal growth-like procedure makes the result more controllable. In addition, to make full use of the spatial relationships among texture pixels, wavelet coefficients rather than traditional RGB channels are used to search the nearest neighbor pixels. Experimental study shows that the method plays well for both 2D and solid texture synthesis.

We describe the use of traditional stereological methods to synthesize 3D solid textures from 2D images of existing materials. We first illustrate our approach for aggregate materials of spherical particles, and then extend the technique to apply to particles of arbitrary shapes. We demonstrate the effectiveness of the approach with side-by-side comparisons of a real material and a synthetic model with its appearance parameters derived from its physical counterpart. Unlike ad hoc methods for texture synthesis, stereology provides a disciplined, systematic basis for predicting material structure with well-defined assumptions.

A number of algorithms have been developed in the past for texture analysis and classification using the wavelet transform. In this paper we explore the effect of different wavelet bases on the classification performance. We use the algorithm developed by T. Chang and C.-C. Jay Kuo (see IEEE Trans. Image Process., vol.2, no.4, p.429-41, Oct. 1993) for texture analysis and classification with a tree-structured wavelet transform. The performance of the wavelet bases is measured in terms of the sensitivity and the selectivity for the classification of natural textures. The classification results show that the choice of wavelet basis has a considerable effect on the classification performance.

Chapter 4 - Deep Learning in Texture Analysis and Its Application to Tissue Image Classification

The aim of this study was to investigate the performance of texture analysis in texture classification and tissue discrimination between MS lesions, normal appearing white matter (NAWM) and normal white matter (NWM) in order to support early diagnosis of MS. T2-weighted MR images of sixteen relapsing remitting MS (RRMS) patients and sixteen healthy subjects were selected. Based on the lesion size, sixteen regions of interests (ROIs) were chosen from MS patient MR images and healthy subject MR images for MS lesions, NAWM and NWM respectively. Texture features extracted from grey level co-occurrence matrix (GLCM) were selected based on greatest feature difference. For statistical analysis, raw data analysis (RDA), principal component analysis (PCA) and nonlinear discriminant analysis (NDA) were applied to the texture features. The k-nearest neighbor (k-NN) and artificial neural network (ANN) methods were used for texture classification. Fisher coefficient and classification accuracy were used to evaluate the performance of texture analysis. The results demonstrated that (1) classification was successful (>90.00%) between MS lesions and NAWM or NWM, less successful (88.89%) among the three tissue groups and worst (66.67%) between NAWM and NWM; (2) In statistical analysis, NDA outperforms RDA and PCA; (3) ANN classified more accurately than k-NN method between NAWM and NWM, and among the three texture types. This study demonstrated that MRI texture analysis can achieve high classification accuracy in tissue discrimination between MS lesions and NAWM or NWM, which is valuable in supporting early diagnosis of MS.

The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.