Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

Learning object behaviour models

The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences. The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine

Modelling the evolution of biological complexity with a two-dimensional lattice self-assembly process

Self-assembling systems are prevalent across numerous scales of nature, lying at the heart of diverse physical and biological phenomena. Individual protein subunits self-assembling into complexes is often a vital first step of biological processes. Errors during protein assembly, due to mutations or misfolds, can have devastating effects and are responsible for an assortment of protein diseases, known as proteopathies. With proteins exhibiting endless layers of complexity, building any all-encompassing model is unrealistic. Coarse-grained models, despite not faithfully capturing every detail of the original system, have massive potential to assist understanding complex phenomenon. A principal actor in self-assembly is the binding interactions between subunits, and so geometric constraints, polarity, kinetic forces, etc. can often be marginalised. This work explores how self-assembly and its outcomes are inextricably tied to the involved interactions through the use of a two-dimensional lattice polyomino model. %Armed with this tractable model, we can probe how dynamics acting on evolution are reflected in interaction properties. First, this thesis addresses how the interaction characteristics of self-assembly building blocks determine what structures they form. Specifically, if the same structures are consistently produced and remain finite in size. Assembly graphs store subunit interaction information and are used in classifying these two properties, the determinism and boundedness respectively. Arbitrary sets of building blocks are classified without the costly overhead of repeated stochastic assembling, improving both the analysis speed and accuracy. Furthermore, assembly graphs naturally integrate combinatorial and graph techniques, enabling a wider range of future polyomino studies. The second part narrows in on implications of nondeterministic assembly on interaction strength evolution. Generalising subunit binding sites with mutable binary strings introduces such interaction strengths into the polyomino model. Deterministic assemblies obey analytic expectations. Conversely, interactions in nondeterministic assemblies rapidly diverge from equilibrium to minimise assembly inconsistency. Optimal interaction strengths during assembly are also reflected in evolution. Transitions between certain polyominoes are strongly forbidden when interaction strengths are misaligned. The third aspect focuses on genetic duplication, an evolutionary event observed in organisms across all taxa. Through polyomino evolutions, a duplication-heteromerisation pathway emerges as an efficient process. This pathway exploits the advantages of both self-interactions and pairwise-interactions, and accelerates evolution by avoiding complexity bottlenecks. Several simulation predictions are successfully validated against a large data set of protein complexes. These results focus on coarse-grained models rather than quantified biological insight. Despite this, they reinforce existing observations of protein complexes, as well as posing several new mechanisms for the evolution of biological complexity

Optimal ship navigation and algorithms for stochactic obstacle scenes

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.This thesis is comprised of two different but related sections. In the first section, we consider the optimal ship navigation problem wherein the goal is to find the shortest path between two given coordinates in the presence of obstacles subject to safety distance and turn-radius constraints. These obstacles can be debris, rock formations, small islands, ice blocks, other ships, or even an entire coastline. We present a graph-theoretic solution on an appropriately-weighted directed graph representation of the navigation area obtained via 8-adjacency integer lattice discretization and utilization of the A∗ algorithm. We explicitly account for the following three conditions as part of the turn-radius constraints: (1) the ship’s left and right turn radii are different, (2) ship’s speed reduces while turning, and (3) the ship needs to navigate a certain minimum number of lattice edges along a straight line before making any turns. The last constraint ensures that the navigation area can be discretized at any desired resolution. We illustrate our methodology on an ice navigation example involving a 100,000 DWT merchant ship and present a proof- of-concept by simulating the ship’s path in a full-mission ship handling simulator at Istanbul Technical University. In the second section, we consider the stochastic obstacle scene problem wherein an agent needs to traverse a spatial arrangement of possible-obstacles, and the status of the obstacles may be disambiguated en route at a cost. The goal is to find an algorithm that decides what and where to disambiguate en route so that the expected length of the traversal is minimized. We present a polynomial-time method for a graph-theoretical version of the problem when the associated graph is restricted to parallel avenues with fixed policies within the avenues. We show how previously proposed algorithms for the continuous space version can be adapted to a discrete setting. We propose a gener- alized framework encompassing these algorithms that uses penalty functions to guide the navigation in realtime. Within this framework, we introduce a new algorithm that provides near-optimal results within very short execution times. Our algorithms are illustrated via computational experiments involving synthetic data as well as an actual naval minefield data set. Keywords: Graph theory, shortest path, ship navigation, probabilistic path planning, stochastic dynamic programming, Markov decision process, Canadian traveler’s problemContents Declaration of Authorship ii Abstract iv ¨ Oz v Acknowledgments vii List of Figures x List of Tables xi 1 Optimal Ship Navigation with Safety Distance and Realistic Turn Con- straints 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 The Optimal Ship Navigation Problem . . . . . . . . . . . . . . . . . . . . 4 1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Safety Distance Constraints . . . . . . . . . . . . . . . . . . . . . . 5 1.4.2 Lattice Discretization . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.3 Ship-Turn Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4.4 The A∗ Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.5 Smoothing the Optimal Path . . . . . . . . . . . . . . . . . . . . . 13 1.5 Ice Navigation Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.6 Simulator Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.7 Summary, Conclusions, and Future Research . . . . . . . . . . . . . . . . 18 2 Algorithms for Stochastic Obstacle Scenes 21 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 The Stochastic Obstacle Scene Problem: Continuous vs. Discrete Settings 23 2.2.1 Deciding Where to Disambiguate: Single Disk Case . . . . . . . . 23 2.2.2 Deciding Where to Disambiguate: Two Disks Case . . . . . . . . . 25 2.2.3 Discretization of the Continuous Setting: An Example . . . . . . . 27 2.3 Definition of the Stochastic Obstacle Scene Problem . . . . . . . . . . . . 27 2.3.1 Continuous SOSP . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.3.2 Discrete SOSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.3.3 Discretized SOSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4 A Polynomial Algorithm for Discrete SOSP on Parallel Graphs . . . . . . 29 2.5 Discrete Adaptation of the Simulated Risk Disambiguation Algorithm . . 30 2.5.1 Adaptation to Discrete SOSP . . . . . . . . . . . . . . . . . . . . . 30 2.5.2 Adaptation to Discretized SOSP . . . . . . . . . . . . . . . . . . . 32 2.6 Discrete Adaptation of the Reset Disambiguation Algorithm . . . . . . . . 33 2.7 Generalizing SRA and RDA: Penalty-Based Algorithms and DTA . . . . . 34 2.7.1 Illustration of the Algorithms . . . . . . . . . . . . . . . . . . . . . 36 2.8 Computational Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.8.1 Environment A (The COBRA Data) Experiments . . . . . . . . . 40 2.8.2 Environment B Experiments . . . . . . . . . . . . . . . . . . . . . 41 2.8.3 Environment C Experiments . . . . . . . . . . . . . . . . . . . . . 43 2.9 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 A Impact of Cost Change in Parallel Graphs 47 Bibliograph