A Modular Approach to Lung Nodule Detection from Computed Tomography Images Using Artificial Neural Networks and Content Based Image Representation

Lung cancer is one of the most lethal cancer types. Research in computer aided detection (CAD) and diagnosis for lung cancer aims at providing effective tools to assist physicians in cancer diagnosis and treatment to save lives. In this dissertation, we focus on developing a CAD framework for automated lung cancer nodule detection from 3D lung computed tomography (CT) images. Nodule detection is a challenging task that no machine intelligence can surpass human capability to date. In contrast, human recognition power is limited by vision capacity and may suffer from work overload and fatigue, whereas automated nodule detection systems can complement expert’s efforts to achieve better detection performance. The proposed CAD framework encompasses several desirable properties such as mimicking physicians by means of geometric multi-perspective analysis, computational efficiency, and the most importantly producing high performance in detection accuracy. As the central part of the framework, we develop a novel hierarchical modular decision engine implemented by Artificial Neural Networks. One advantage of this decision engine is that it supports the combination of spatial-level and feature-level information analysis in an efficient way. Our methodology overcomes some of the limitations of current lung nodule detection techniques by combining geometric multi-perspective analysis with global and local feature analysis. The proposed modular decision engine design is flexible to modifications in the decision modules; the engine structure can adopt the modifications without having to re-design the entire system. The engine can easily accommodate multi-learning scheme and parallel implementation so that each information type can be processed (in parallel) by the most adequate learning technique of its own. We have also developed a novel shape representation technique that is invariant under rigid-body transformation and we derived new features based on this shape representation for nodule detection. We implemented a prototype nodule detection system as a demonstration of the proposed framework. Experiments have been conducted to assess the performance of the proposed methodologies using real-world lung CT data. Several performance measures for detection accuracy are used in the assessment. The results show that the decision engine is able to classify patterns efficiently with very good classification performance

3-D Content-Based Retrieval and Classification with Applications to Museum Data

There is an increasing number of multimedia collections arising in areas once only the domain of text and 2-D images. Richer types of multimedia such as audio, video and 3-D objects are becoming more and more common place. However, current retrieval techniques in these areas are not as sophisticated as textual and 2-D image techniques and in many cases rely upon textual searching through associated keywords. This thesis is concerned with the retrieval of 3-D objects and with the application of these techniques to the problem of 3-D object annotation. The majority of the work in this thesis has been driven by the European project, SCULPTEUR. This thesis provides an in-depth analysis of a range of 3-D shape descriptors for their suitability for general purpose and specific retrieval tasks using a publicly available data set, the Princeton Shape Benchmark, and using real world museum objects evaluated using a variety of performance metrics. This thesis also investigates the use of 3-D shape descriptors as inputs to popular classification algorithms and a novel classifier agent for use with the SCULPTEUR system is designed and developed and its performance analysed. Several techniques are investigated to improve individual classifier performance. One set of techniques combines several classifiers whereas the other set of techniques aim to find the optimal training parameters for a classifier. The final chapter of this thesis explores a possible application of these techniques to the problem of 3-D object annotation

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum

Shape Retrieval Methods for Architectural 3D Models

This thesis introduces new methods for content-based retrieval of architecture-related 3D models. We thereby consider two different overall types of architectural 3D models. The first type consists of context objects that are used for detailed design and decoration of 3D building model drafts. This includes e.g. furnishing for interior design or barriers and fences for forming the exterior environment. The second type consists of actual building models. To enable efficient content-based retrieval for both model types that is tailored to the user requirements of the architectural domain, type-specific algorithms must be developed. On the one hand, context objects like furnishing that provide similar functions (e.g. seating furniture) often share a similar shape. Nevertheless they might be considered to belong to different object classes from an architectural point of view (e.g. armchair, elbow chair, swivel chair). The differentiation is due to small geometric details and is sometimes only obvious to an expert from the domain. Building models on the other hand are often distinguished according to the underlying floor- and room plans. Topological floor plan properties for example serve as a starting point for telling apart residential and commercial buildings. The first contribution of this thesis is a new meta descriptor for 3D retrieval that combines different types of local shape descriptors using a supervised learning approach. The approach enables the differentiation of object classes according to small geometric details and at the same time integrates expert knowledge from the field of architecture. We evaluate our approach using a database containing arbitrary 3D models as well as on one that only consists of models from the architectural domain. We then further extend our approach by adding a sophisticated shape descriptor localization strategy. Additionally, we exploit knowledge about the spatial relationship of object components to further enhance the retrieval performance. In the second part of the thesis we introduce attributed room connectivity graphs (RCGs) as a means to characterize a 3D building model according to the structure of its underlying floor plans. We first describe how RCGs are inferred from a given building model and discuss how substructures of this graph can be queried efficiently. We then introduce a new descriptor denoted as Bag-of-Attributed-Subgraphs that transforms attributed graphs into a vector-based representation using subgraph embeddings. We finally evaluate the retrieval performance of this new method on a database consisting of building models with different floor plan types. All methods presented in this thesis are aimed at an as automated as possible workflow for indexing and retrieval such that only minimum human interaction is required. Accordingly, only polygon soups are required as inputs which do not need to be manually repaired or structured. Human effort is only needed for offline groundtruth generation to enable supervised learning and for providing information about the orientation of building models and the unit of measurement used for modeling

3D Shape Similarity Through Structural Descriptors

Due to the recent improvements to 3D object acquisition, visualization and modeling techniques, the number of 3D models available is more and more growing, and there is an increasing demand for tools supporting the automatic search for 3D objects and their sub-parts in digital archives. Whilst there are already techniques for rapidly extracting knowledge from massive volumes of texts (like Google [htt]) it is harder to structure, filter, organize, retrieve and maintain archives of digital shapes like images, 3D objects, 3D animations and virtual or augmented reality. This situations suggests that in the future a primary challenge in computer graphics will be how to find models having a similar global and/or local appearance. Shape descriptors and the methodologies used to compare them, occupy an important role for achieving this task. For this reason a first contribution of this thesis is to provide a critical analysis of the most representative geometric and structural shape descriptors with respect to a set of properties that shape descriptors should have. This analysis is targeted at highlighting the differences between descriptors in order to better understand where a descriptor fails and another succeed. As a second contribution, the thesis investigates the problem of using a structural descriptor for shape comparison purposes. A large class of structural shape descriptors can be easily encoded as directed, a-cyclic and attributed graphs, thus the problem of comparing structural descriptors is approached as a graph matching problem. The techniques used for graph comparison have an exponential computational complexity and it is therefore necessary to define an algorithmic approximation of the optimal solution. The methods for structural descriptors comparison, commonly used in the computer graphics community, consist of heuristic graph matching algorithms for specific application tasks, while it is lacking a general approach suitable for incorporating different heuristics applicable in different application tasks. The second contribution presented in this thesis is aimed at defining a framework for expressing the optimal algorithm for the computation of the maximal common subgraph in a formalization which makes it straightforward usable for plugging heuristics in it, in order to achieving different approximations of the optimal solution according to the specific case. Implemented heuristics for robust graph matching with respect to graph structural noise are discussed and experimented on sub-part correspondence between similar 3D objects, and shape retrieval application with respect to different structural graph descriptors

Advances in Discrete Differential Geometry

Differential Geometr

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks