Multicut Algorithms for Neurite Segmentation

Correlation clustering, or multicut partitioning is widely used for image segmentation and graph partitioning. Given an undirected edge weighted graph with positive and negative weights, correlation clustering partitions the graph such that the sum of cut edge weights is minimized. Since the optimal number of clusters is automatically chosen, multicut partitioning is well suited for clustering neural structures in EM connectomics datasets where the optimal number of clusters is unknown a-priori. Due to the NP-hardness of optimizing the multicut objective, exact solvers do not scale and approximative solvers often give unsatisfactory results. In chapter 2 we investigate scalable methods for correlation clustering. To this end we define fusion moves for the multicut objective function which iteratively fuses the current and a proposed partitioning and monotonously improves the partitioning. Fusion moves scale to larger datasets, give near optimal solutions and at the same time show state of the art anytime performance. In chapter 3 we generalize the fusion moves frameworks for the lifted multicut ob- jective, a generalization of the multicut objective which can penalize or reward all decompositions of a graph for which any given pair of nodes are in distinct compo- nents. The proposed framework scales well to large datasets and has a cutting edge anytime performance. In chapter 4 we propose a framework for automatic segmentation of neural structures in 3D EM connectomics data where a membrane probability is predicted for each pixel with a neural network and superpixels are computed based on this probability map. Finally the superpixels are merged to neurites using the techniques described in chapter 3. The proposed pipeline is validated with an extensive set of experiments and a detailed lesion study. This work substantially narrows the accuracy gap between humans and computers for neurite segmentation. In chapter 5 we summarize the software written for this thesis. The provided imple- mentations for algorithms and techniques described in chapters 2 to 4 and many other algorithms resulted in a software library for graph partitioning, image segmentation and discrete optimization

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 377)

This bibliography lists 223 reports, articles, and other documents recently introduced into the NASA Scientific and Technical Information System. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Towards fully automated analysis of sputum smear microscopy images

Sputum smear microscopy is used for diagnosis and treatment monitoring of pulmonary tuberculosis (TB). Automation of image analysis can make this technique less laborious and more consistent. This research employs artificial intelligence to improve automation of Mycobacterium tuberculosis (Mtb) cell detection, bacterial load quantification, and phenotyping from fluorescence microscopy images. I first introduce a non-learning, computer vision (CV) approach for bacteria detection, employing ridge-based approach using the Hessian matrix to detect ridges of Mtb bacteria, complemented by geometric analysis. The effectiveness of this approach is assessed through a custom metric using the Hu moment vector. Results demonstrate lower performance relative to literature metrics, motivating the need for deep learning (DL) to capture bacterial morphology. Subsequently, I develop an automated pipeline for detection, classification, and counting of bacteria using DL techniques. Firstly, Cycle-GANs transfer labels from labelled to unlabeled fields of view (FOVs). Pre-trained DL models are used for subsequent classification and regression tasks. An ablation study confirms pipeline efficacy, with a count error within 5%. For downstream analysis, microscopy slides are divided into tiles, each of which is sequentially cropped and magnified. A subsequent filtering stage eliminates non-salient FOVs by applying pre-trained DL models along with a novel method that employs dual convolutional neural network (CNN)-based encoders for feature extraction: one encoder is dedicated to learning bacterial appearance, and the other focuses on bacterial shape, which both precede into a bottleneck of a smaller CNN classifier network. The proposed model outperforms others in accuracy, yields no false positives, and excels across decision thresholds. Mtb cell lipid content and length may be related to antibiotic tolerance, underscoring the need to locate bacteria within paired FOV images stained with distinct cell identification and lipid detection, and to measure bacterial dimensions. I employ a proposed UNet-like model for precise bacterial localization. By combining CNNs and feature descriptors, my method automates reporting of both lipid content and cell length. Application of the approaches described here may assist clinical TB care and therapeutics research

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity