Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis

Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy

Novel color and local image descriptors for content-based image search

Content-based image classification, search and retrieval is a rapidly-expanding research area. With the advent of inexpensive digital cameras, cheap data storage, fast computing speeds and ever-increasing data transfer rates, millions of images are stored and shared over the Internet every day. This necessitates the development of systems that can classify these images into various categories without human intervention and on being presented a query image, can identify its contents in order to retrieve similar images. Towards that end, this dissertation focuses on investigating novel image descriptors based on texture, shape, color, and local information for advancing content-based image search. Specifically, first, a new color multi-mask Local Binary Patterns (mLBP) descriptor is presented to improve upon the traditional Local Binary Patterns (LBP) texture descriptor for better image classification performance. Second, the mLBP descriptors from different color spaces are fused to form the Color LBP Fusion (CLF) and Color Grayscale LBP Fusion (CGLF) descriptors that further improve image classification performance. Third, a new HaarHOG descriptor, which integrates the Haar wavelet transform and the Histograms of Oriented Gradients (HOG), is presented for extracting both shape and local information for image classification. Next, a novel three Dimensional Local Binary Patterns (3D-LBP) descriptor is proposed for color images by encoding both color and texture information for image search. Furthermore, the novel 3DLH and 3DLH-fusion descriptors are proposed, which combine the HaarHOG and the 3D-LBP descriptors by means of Principal Component Analysis (PCA) and are able to improve upon the individual HaarHOG and 3D-LBP descriptors for image search. Subsequently, the innovative H-descriptor, and the H-fusion descriptor are presented that improve upon the 3DLH descriptor. Finally, the innovative Bag of Words-LBP (BoWL) descriptor is introduced that combines the idea of LBP with a bag-of-words representation to further improve image classification performance. To assess the feasibility of the proposed new image descriptors, two classification frameworks are used. In one, the PCA and the Enhanced Fisher Model (EFM) are applied for feature extraction and the nearest neighbor classification rule for classification. In the other, a Support Vector Machine (SVM) is used for classification. The classification performance is tested on several widely used and publicly available image datasets. The experimental results show that the proposed new image descriptors achieve an image classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (ScSPM), the Kernel Codebook (KC) and the LBP

空間的なテクスチャ解析によるコンプレックスネットワークに基づくテクスチャ解析の改善

This thesis proposes a new texture analysis model which enhanced from traditional complex network-based model for texture characterization via spatial texture analysis. The conceptual framework of the proposed model is to synergize between pattern recognition and graph theory research areas. The results of experiment show that the proposed model can capture robust textural information under various uncontrolled environments using standard texture databases. Texture analysis has played an important role in the last few decades. There are a growing number of techniques described in the literature, one of new area research is a complex network for texture characterization, which has developed in recent years. Inspired by the human brain system, the relation among structure texture elements on an image can be derived using the complex network model. Compared to the task of texture classification, development of the original complex network model is required in order to improve classification performance in environment variations. To fulfill this requirement, the enhancing complex network by spatial texture analysis (i.e., spatial distribution and spatial relation) has been achieved in this thesis. The proposed approach addresses the above requirement by investigating and modifying the original complex network model by extracting more discriminative information. A new graph connectivity measurement has been devised, including local spatial pattern mapping, which is denoted as a LSPM, to encode and describe local spatial arrangement of pixels. To the best of the author\u27s knowledge, as investigated in this thesis, the encoding spatial information which has been adapted within the original complex network model presented here were first proposed and reported by the author. The essence of this proposed graph connectivity measurement describes the spatial structure of local image texture cause it can effectively capture and detect micro-structures (e.g., edges, lines, spots) information which is critical being used to distinguish various pattern structures and invariant uncontrolled environments. Moreover, the graph-based representation has been investigated for improving the performance of texture classification. Spatial vector property has been comprised of deterministic graph modeling which decomposing the two component of the magnitude and the direction. Then, the proposed hybrid-based complex network comprises the enhancing graph-based representation, and the new graph connectivity measurement has been devised as an enhancing complex network-based model for texture characterization in this thesis. The experiments are evaluated by using four standard texture databases include Brodatz, UIUC, KTH-TIPS, and UMD. The experimental results are presented in terms of classification rate in this thesis to demonstrate that: firstly, the proposed graph connectivity measurement (LSPM) approach achieved on-average 86.25%, 77.25%, 89.38% and 94.06% respectively based on four databases. Secondly, the proposed graph-based spatial property approach achieved on-average 90.92%, 87.92%, 96.56% and 92.65%, respectively; finally, the hybrid-based complex network model achieved on-average 88.92%, 85.46%, 95.14% and 95.52% respectively. Accordingly, this thesis has advanced the original complex network-based model for texture characterization.九州工業大学博士学位論文 学位記番号：生工博甲第329号 学位授与年月日：平成30年9月21日1 Introduction|2 Literature Review|3 Complex Network Model and Spatial Information|4 Graph-based Representation in Texture Analysis|5 Hybrid-based Complex Network Model|6 Conclusions九州工業大学平成30年