Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Automatic vessel and telangiectases analysis in dermoscopy skin lesion images

The blood vessels are part of the circulatory system and function to transport blood throughout the body. Vessels have their own features such as distinctive color compared to surrounding skin as well as distinctive curved and/or linear shape. Telangiectases are small dilated blood vessels near the surface of the skin or mucous membranes, measuring between 0.5 and 1 millimeter in diameter. In this research, image analysis techniques are investigated to detect vessels in dermoscopy skin lesion images. Machine vision and neural network methods are explored to discriminate skin lesions containing telangiectases from those containing normal vessels. A vessels Detection technique is implemented firstly to find the possible vessels in dermatology skin lesion images. In addition, a noise filtering technique is applied, which filters out the noise such as hair, bubble and so one, according to their own features. Based on the fact that some of the images are fuzzy, a contrast enhancement technique can be added to increase the contrast. After obtaining the final masked regions containing vessel-like structures, features are computed to facilitate the discrimination of skin lesion with normal vessels from lesions containing telangiectases. The features are mostly about the number, shape and size of telangiectases mask --Abstract, page iii

Automatic Segmentation of Subfigure Image Panels for Multimodal Biomedical Document Retrieval

Biomedical images are often referenced for clinical decision support (CDS), educational purposes, and research. The task of automatically finding the images in a scientific article that are most useful for the purpose of determining relevance to a clinical situation is traditionally done using text and is quite challenging. We propose to improve this by associating image features from the entire image and from relevant regions of interest with biomedical concepts described in the figure caption or discussion in the article. However, images used in scientific article figures are often composed of multiple panels where each sub-figure (panel) is referenced in the caption using alphanumeric labels, e.g. Figure 1(a), 2(c), etc. It is necessary to separate individual panels from a multi-panel figure as a first step toward automatic annotation of images. In this work we present methods that add make robust our previous efforts reported here. Specifically, we address the limitation in segmenting figures that do not exhibit explicit inter-panel boundaries, e.g. illustrations, graphs, and charts. We present a novel hybrid clustering algorithm based on particle swarm optimization (PSO) with fuzzy logic controller (FLC) to locate related figure components in such images. Results from our evaluation are very promising with 93.64% panel detection accuracy for regular (non-illustration) figure images and 92.1% accuracy for illustration images. A computational complexity analysis also shows that PSO is an optimal approach with relatively low computation time. The accuracy of separating these two type images is 98.11% and is achieved using decision tree

Graphical Image Classification Combining an Evolutionary Algorithm and Binary Particle Swarm Optimization

Biomedical journal articles contain a variety of image types that can be broadly classified into two categories: regular images, and graphical images. Graphical images can be further classified into four classes: diagrams, statistical figures, flow charts, and tables. Automatic figure type identification is an important step toward improved multimodal (text + image) information retrieval and clinical decision support applications. This paper describes a feature-based learning approach to automatically identify these four graphical figure types. We apply Evolutionary Algorithm (EA), Binary Particle Swarm Optimization (BPSO) and a hybrid of EA and BPSO (EABPSO) methods to select an optimal subset of extracted image features that are then classified using a Support Vector Machine (SVM) classifier. Evaluation performed on 1038 figure images extracted from ten BioMedCentral® journals with the features selected by EABPSO yielded classification accuracy as high as 87.5%

The influence of coal mining subsidence on the movement and deformation of loess slope in the loess gully area of Northern Shaanxi

Introduction: How to solve the contradiction between coal mining and soil and water conservation is a key scientific problem to realize ecological environment protection and high-quality development in the middle reaches of the Yellow River.Methods: Using FLAC3D numerical simulation experiment method, the influence of loess slope surface shape and coal seam overburden structure coupling on slope movement and deformation is studied.Results: Under any surface slope shape, the average slope subsidence coefficient (q slope average) increases with the increase of sand layer coefficient after coal mining subsidence. When the sand layer coefficient is less than 0.71, the q slope average increases rapidly, with an increase of more than 2.86%, and when the sand layer coefficient is greater than 0.71, the q slope average tends to be stable. Under any surface slope shape, the q slope average decreases with the increase of sand-mud ratio. When the overburden structure characteristics of any coal seam and the natural slope of the surface slope are less than or equal to 5°, the q slope average of the convex slope is the largest, and the q slope average of the four slope types is ranked as follows: convex slope > straight slope ≈ composite slope > concave slope; When the structural characteristics of overlying strata in any coal seam and the natural slope of surface slope are more than 5°, the q slope average of concave slope is the largest, and the q slope average of four slope types is in the order of concave slope > straight slope ≈ composite slope > convex slope. With the increase of the natural slope of the surface slope, the q slope average first decreases and then increases, and the inflection point is 15°. The influence law of loess slope surface morphology and coal seam overburden structure on the average horizontal movement of slope surface is similar to that of average subsidence of slope surface.Discussion: The results can provide scientific basis for surface movement and deformation and soil and water conservation in the mining subsidence area of northern Shaanxi in the middle reaches of the Yellow River Basin in China

Chip-scale demonstration of hybrid III-V/silicon photonic integration for an FBG interrogator

Silicon photonic integration is a means to produce an integrated on-chip fiber Bragg grating (FBG) interrogator. The possibility of integrating the light source, couplers, grating couplers, de-multiplexers, photodetectors (PDs), and other optical elements of the FBG interrogator into one chip may result in game-changing performance advances, considerable energy savings, and significant cost reductions. To the best of our knowledge, this paper is the first to present a hybrid silicon photonic chip based on III–V/silicon-on-insulator photonic integration for an FBG interrogator. The hybrid silicon photonic chip consists of a multiwavelength vertical-cavity surface-emitting laser array and input grating couplers, a multimode interference coupler, an arrayed waveguide grating, output grating couplers, and a PD array. The chip can serve as an FBG interrogator on a chip and offer unprecedented opportunities. With a footprint of 5mm x 3mm, the proposed hybrid silicon photonic chip achieves an interrogation wavelength resolution of approximately 1 pm and a wavelength accuracy of about ±10 pm. With the measured 1 pm wavelength resolution, the temperature measurement resolution of the proposed chip is approximately 0.1°C. The proposed hybrid silicon photonic chip possesses advantages in terms of cost, manufacturability, miniaturization, and performance. The chip supports applications that require extreme miniaturization down to the level of smart grains

Complex reorientation dynamics of sizable glass-formers with polar rotors revealed by dielectric spectroscopy

We present the results of dielectric measurements for three sizable glass-formers with identical nonpolar cores linked to various dipole-labeled rotors that shed new light on the picture of reorientation of anisotropic systems with significant moment of inertia revealed by broadband dielectric spectroscopy. The dynamics of sizable glass-formers formed by partially rigid molecular cores linked to small polar rotors in many respects differs from that of typical glass-formers. For instance, the extraordinarily large prefactors (τ0 > 10−12 s) in the Vogel− Fulcher−Tammann equation were found. The rich and highly diverse relaxation pattern was governed by the location of a dipole, its ability to rotate freely, and the degree of coupling to the motion of the entire sizable system

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS

Tumor Suppressor Spred2 Interaction with LC3 Promotes Autophagosome Maturation and Induces Autophagy-Dependent Cell Death

The tumor suppressor Spred2 (Sprouty-related EVH1 domain-2) induces cell death in a variety of cancers. However, the underlying mechanism remains to be elucidated. Here we show that Spred2 induces caspase-independent but autophagy-dependent cell death in human cervical carcinoma HeLa and lung cancer A549 cells. We demonstrate that ectopic Spred2 increased both the conversion of microtubule-associated protein 1 light chain 3 (LC3), GFP-LC3 puncta formation and p62/SQSTM1 degradation in A549 and HeLa cells. Conversely, knockdown of Spred2 in tumor cells inhibited upregulation of autophagosome maturation induced by the autophagy inducer Rapamycin, which could be reversed by the rescue Spred2. These data suggest that Spred2 promotes autophagy in tumor cells. Mechanistically, Spred2 co-localized and interacted with LC3 via the LC3-interacting region (LIR) motifs in its SPR domain. Mutations in the LIR motifs or deletion of the SPR domain impaired Spred2-mediated autophagosome maturation and tumor cell death, indicating that functional LIR is required for Spred2 to trigger tumor cell death. Additionally, Spred2 interacted and co-localized with p62/SQSTM1 through its SPR domain. Furthermore, the co-localization of Spred2, p62 and LAMP2 in HeLa cells indicates that p62 may be involved in Spred2-mediated autophagosome maturation. Inhibition of autophagy using the lysosomal inhibitor chloroquine, reduced Spred2-mediated HeLa cell death. Silencing the expression of autophagy-related genes ATG5, LC3 or p62 in HeLa and A549 cells gave similar results, suggesting that autophagy is required for Spred2-induced tumor cell death. Collectively, these data indicate that Spred2 induces tumor cell death in an autophagy-dependent manner

Tacky Elastomers to Enable Tear-Resistant and Autonomous Self-Healing Semiconductor Composites

Mechanical failure of π-conjugated polymer thin films is unavoidable under cyclic loading conditions, due to intrinsic defects and poor resistance to crack propagation. Here, the first tear-resistant and room-temperature self-healable semiconducting composite is presented, consisting of conjugated polymers and butyl rubber elastomers. This new composite displays both a record-low elastic modulus