Computer assisted detection of polycystic ovary morphology in ultrasound images

Polycystic ovary syndrome (PCOS) is an endocrine abnormality with multiple diagnostic criteria due to its heterogenic manifestations. One of the diagnostic criterion includes analysis of ultrasound images of ovaries for the detection of number, size, and distribution of follicles within the ovary. This involves manual tracing of follicles on the ultrasound images to determine the presence of a polycystic ovary (PCO). A novel method that automates PCO morphology detection is described. Our algorithm involves automatic segmentation of follicles from ultrasound images, quantifying the attributes of the segmented follicles using stereology, storing follicle attributes as feature vectors, and finally classification of the feature vector into two categories. The classification categories are PCO morphology present and PCO morphology absent. An automatic PCO diagnostic tool would save considerable time spent on manual tracing of follicles and measuring the length and width of every follicle. Our procedure was able to achieve classification accuracy of 92.86% using a linear discriminant classifier. Our classifier will improve the rapidity and accuracy of PCOS diagnosis, and reduce the chance of the severe health implications that can arise from delayed diagnosis

Construction of artificial skin tissue with placode-like structures in well-defined patterns using dielectrophoresis

During embryonic development of animal skin tissue, the skin cells form regular patterns of high cell density (placodes) where hair or feathers will be formed. These placodes are thought to be formed by the aggregation of dermal cells into condensates. The aggregation process is thought to be controlled by a reaction-diffusion mechanism of activator and inhibitor molecules, and involve mechanical forces between cells and cells with the matrix. In this project, placode formation in chicken embryonic skin cells was used as a model system for the study of the mechanism by which the placodes are formed. Artificial aggregates of chicken embryonic skin cells were created by suspending them in a 300 mM low conductivity sorbitol solution and attracting them by positive dielectrophoresis to high field regions within microelectrode arrays by applying a 10 - 20 Vpk-pk 1 MHz signal across the microelectrodes. It was demonstrated that using this method aggregates can be produced in a large variety of patterns and that the distance between the aggregates and aggregate size and shape within the pattern can be controlled effectively. Custom-built image analysis tools were developed in LabVIEW to analyze the patterns formed. The formation of aggregates by dielectrophoresis was followed by an immobilization phase of the resulting patterns inside a gel matrix, forming an artificial skin. Nutrients and oxygen were supplied externally. Long-term incubation of the artificial skin shows that embryonic skin cells in the aggregates were viable and showed behavior similar to that of developing embryonic skin, including further aggregation of the cells and the formation of cell condensates. The domain size was shown to have an influence on the condensation process, with cells in small aggregates forming only one condensate near the centre of the aggregate, and several condensates in larger aggregates. Whilst the distribution of cell condensates within the aggregates in round large aggregates is predominantly random, some line formation could be observed in linear aggregations, indicating some self-organization may be occurring

Biomedical Photoacoustic Imaging and Sensing Using Affordable Resources

The overarching goal of this book is to provide a current picture of the latest developments in the capabilities of biomedical photoacoustic imaging and sensing in an affordable setting, such as advances in the technology involving light sources, and delivery, acoustic detection, and image reconstruction and processing algorithms. This book includes 14 chapters from globally prominent researchers , covering a comprehensive spectrum of photoacoustic imaging topics from technology developments and novel imaging methods to preclinical and clinical studies, predominantly in a cost-effective setting. Affordability is undoubtedly an important factor to be considered in the following years to help translate photoacoustic imaging to clinics around the globe. This first-ever book focused on biomedical photoacoustic imaging and sensing using affordable resources is thus timely, especially considering the fact that this technique is facing an exciting transition from benchtop to bedside. Given its scope, the book will appeal to scientists and engineers in academia and industry, as well as medical experts interested in the clinical applications of photoacoustic imaging

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Automatic Detection of Critical Dermoscopy Features for Malignant Melanoma Diagnosis

Improved methods for computer-aided analysis of identifying features of skin lesions from digital images of the lesions are provided. Improved preprocessing of the image that 1) eliminates artifacts that occlude or distort skin lesion features and 2) identifies groups of pixels within the skin lesion that represent features and/or facilitate the quantification of features are provided including improved digital hair removal algorithms. Improved methods for analyzing lesion features are also provided

Proteomic and clinical insights into polycystic ovary syndrome in adolescents

Despite its high prevalence, our understanding of the pathophysiology of polycystic ovary syndrome (PCOS) is lacking. Consequently, the way we diagnose and manage this common condition is inadequate, which is especially true for adolescents. This thesis aims to expand the body of knowledge regarding PCOS in adolescents. It will explore the clinical phenotype of PCOS, in addition to using proteomic techniques to better understand the biological mechanisms which underpin this condition. However, to do this, we must first comprehend ‘normal’ menstrual patterns in these pubertal years. As such, this thesis begins by seeking to define menstrual and ovulatory ‘normality’ in the first year following menarche, by systematically reviewing relevant literature. Following this, data are presented from a longitudinal study evaluating the clinical presentation and phenotype of adolescents with a suspected diagnosis of PCOS. The latter part of the thesis focuses on the use of proteomic techniques to broaden our understanding of PCOS. Discovery proteomic analysis of urine samples is employed firstly to explore the biological pathways associated with PCOS, and secondly to identify specific proteins which are differentially expressed in adolescents with PCOS, which may form a pool of non-invasive candidate biomarkers. Inflammation was identified as the most significant biological process associated with PCOS in discovery analysis, and these findings were validated in subsequent targeted proteomic panels. Validation studies were undertaken in a larger cohort of adolescents with PCOS, and then comparison was also made to adults with PCOS. Finally, all results from this thesis are summarised, the findings discussed, and their implications considered, alongside future work

Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images

Morphological analysis is the starting point for the diagnostic approach of more than 80% of the hematological diseases. However, the morphological differentiation among different types of abnormal lymphoid cells in peripheral blood is a difficult task, which requires high experience and skill. Objective values do not exist to define cytological variables, which sometimes results in doubts on the correct cell classification in the daily hospital routine. Automated systems exist which are able to get an automatic preclassification of the normal blood cells, but fail in the automatic recognition of the abnormal lymphoid cells. The general objective of this thesis is to develop a complete methodology to automatically recognize images of normal and reactive lymphocytes, and several types of neoplastic lymphoid cells circulating in peripheral blood in some mature B-cell neoplasms using digital image processing methods. This objective follows two directions: (1) with engineering and mathematical background, transversal methodologies and software tools are developed; and (2) with a view towards the clinical laboratory diagnosis, a system prototype is built and validated, whose input is a set of pathological cell images from individual patients, and whose output is the automatic classification in one of the groups of the different pathologies included in the system. This thesis is the evolution of various works, starting with a discrimination between normal lymphocytes and two types of neoplastic lymphoid cells, and ending with the design of a system for the automatic recognition of normal lymphocytes and five types of neoplastic lymphoid cells. All this work involves the development of a robust segmentation methodology using color clustering, which is able to separate three regions of interest: cell, nucleus and peripheral zone around the cell. A complete lymphoid cell description is developed by extracting features related to size, shape, texture and color. To reduce the complexity of the process, a feature selection is performed using information theory. Then, several classifiers are implemented to automatically recognize different types of lymphoid cells. The best classification results are achieved using support vector machines with radial basis function kernel. The methodology developed, which combines medical, engineering and mathematical backgrounds, is the first step to design a practical hematological diagnosis support tool in the near future.Los análisis morfológicos son el punto de partida para la orientación diagnóstica en más del 80% de las enfermedades hematológicas. Sin embargo, la clasificación morfológica entre diferentes tipos de células linfoides anormales en la sangre es una tarea difícil que requiere gran experiencia y habilidad. No existen valores objetivos para definir variables citológicas, lo que en ocasiones genera dudas en la correcta clasificación de las células en la práctica diaria en un laboratorio clínico. Existen sistemas automáticos que realizan una preclasificación automática de las células sanguíneas, pero no son capaces de diferenciar automáticamente las células linfoides anormales. El objetivo general de esta tesis es el desarrollo de una metodología completa para el reconocimiento automático de imágenes de linfocitos normales y reactivos, y de varios tipos de células linfoides neoplásicas circulantes en sangre periférica en algunos tipos de neoplasias linfoides B maduras, usando métodos de procesamiento digital de imágenes. Este objetivo sigue dos direcciones: (1) con una orientación propia de la ingeniería y la matemática de soporte, se desarrollan las metodologías transversales y las herramientas de software para su implementación; y (2) con un enfoque orientado al diagnóstico desde el laboratorio clínico, se construye y se valida un prototipo de un sistema cuya entrada es un conjunto de imágenes de células patológicas de pacientes analizados de forma individual, obtenidas mediante microscopía y cámara digital, y cuya salida es la clasificación automática en uno de los grupos de las distintas patologías incluidas en el sistema. Esta tesis es el resultado de la evolución de varios trabajos, comenzando con una discriminación entre linfocitos normales y dos tipos de células linfoides neoplásicas, y terminando con el diseño de un sistema para el reconocimiento automático de linfocitos normales y reactivos, y cinco tipos de células linfoides neoplásicas. Todo este trabajo involucra el desarrollo de una metodología de segmentación robusta usando agrupamiento por color, la cual es capaz de separar tres regiones de interés: la célula, el núcleo y la zona externa alrededor de la célula. Se desarrolla una descripción completa de la célula linfoide mediante la extracción de descriptores relacionados con el tamaño, la forma, la textura y el color. Para reducir la complejidad del proceso, se realiza una selección de descriptores usando teoría de la información. Posteriormente, se implementan varios clasificadores para reconocer automáticamente diferentes tipos de células linfoides. Los mejores resultados de clasificación se logran utilizando máquinas de soporte vectorial con núcleo de base radial. La metodología desarrollada, que combina conocimientos médicos, matemáticos y de ingeniería, es el primer paso para el diseño de una herramienta práctica de soporte al diagnóstico hematológico en un futuro cercano

Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones

Tesis por compendio[ES] En el presente trabajo se realiza un análisis completo de las técnicas de iluminación y registro de imagen desarrollados hasta el momento y que permiten emplear la fluorescencia intrínseca de estructuras biológicas para aumentar la capacidad de identificación, detección y análisis de lesiones y anomalías que puedan presentarse. El trabajo se ha enfocado principalmente en a) el análisis, validación y desarrollo de técnicas de detección precoz de lesiones asociadas al Carcinoma Escamoso Epidermoide (oncología otorrinolaringológica), así como posibles lesiones precursoras y b) el análisis y desarrollo de una metodología que permita registrar imágenes de fluorescencia y cuantificar mediante la aplicación de técnicas de procesado de imagen la afección provocada por el Acné Vulgaris (dermatología). Se proponen nuevas formas de adquisición, registro y procesado de imágenes de fluorescencia que mejoran de forma objetiva la capacidad de detección y gestión de las anteriores patologías. El desarrollo de la Tesis ha dado lugar a varios resultados. Parte de los resultados se han estructurado en forma de artículos de investigación y trabajos publicados en revistas JCR. Así, la tesis se va a desarrollar por Compendio de Artículos, incluyéndose: a) Artículo de Investigación 1 publicado en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Artículo de Investigación 2 publicado en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Artículo de Investigación publicado en Congreso Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudio Observacional (modalidad de ensayo clínico para técnicas no invasivas) con DICTAMEN FAVORABLE para su realización con fecha 29 de Septiembre de 2022. El Estudio Observacional ha sido evaluado por los miembros del Comité Ético de Investigación con medicamentos del Departamento Arnau de Vilanova-Llíria. A causa de la pandemia causada por la COVID-19, la ejecución del trabajo se ha visto pospuesta y se iniciará en el último trimestre de 2022. Título: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De forma adicional a los trabajos publicados, se ha redactado en forma de review (susceptible de ser publicado) el estado del arte que ha permitido desarrollar el OBJETIVO ESPECÍFICO 3. Se adjunta como Artículo de Investigación susceptible de publicación en revista JCR. Título: Segmentation of acne vulgaris images algorithms. La ejecución del Estudio Observacional se plantea como la línea de investigación a seguir y que da continuidad a la investigación iniciada en la presente Tesis Doctoral. El documento de Tesis está estructurado en 7 capítulos y 11 Anexos. Para el desarrollo del presente trabajo se han planteado tres objetivos específicos. Cada artículo o trabajo publicado se corresponde con el desarrollo de cada uno de los tres objetivos específicos. Así, cada uno de los capítulos 3, 4 y 5 plantea el escenario, desarrollo y conclusiones obtenidas que han dado como resultado cada uno de los trabajos publicados de forma independiente.[CAT] En el present treball es realitza una anàlisi completa de les tècniques d'il·luminació i registre d'imatge desenvolupats fins al moment i que permeten emprar la fluorescència intrínseca d'estructures biològiques per a augmentar la capacitat d'identificació, detecció i anàlisi de lesions i anomalies que puguen presentar-se. El treball s'ha enfocat principalment en a) l'anàlisi, validació i desenvolupament de tècniques de detecció precoç de lesions associades al Carcinoma Escatós Epidermoide (oncologia otorrinolaringològica), així com possibles lesions precursores i b) l'anàlisi i desenvolupament d'una metodologia que permeta registrar imatges de fluorescència i quantificar mitjançant l'aplicació de tècniques de processament d'imatge l'afecció provocada per l'Acne Vulgaris (dermatologia). Es proposen noves formes d'adquisició, registre i processament d'imatges de fluorescència que milloren de manera objectiva la capacitat de detecció i gestió de les anteriors patologies. El desenvolupament de la Tesi ha donat lloc a diversos resultats. Part dels resultats s'han estructurat en forma d'articles d'investigació i treballs publicats en revistes JCR. Així, la tesi es desenvoluparà per Compendi d'Articles, incloent-se: a) Article d'Investigació 1 publicat en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Article d'Investigació 2 publicat en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Article d'Investigació publicat en Congrés Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudi Observacional (modalitat d'assaig clínic per a tècniques no invasives) amb DICTAMEN FAVORABLE per a la seua realització amb data 29 de Setembre de 2022. L'Estudi Observacional ha sigut avaluat pels membres del Comité Ètic d'Investigació amb medicaments del Departament Arnau de Vilanova-Llíria. A causa de la pandèmia causada per la COVID-19, l'execució del treball s'ha vist posposada i s'iniciarà en l'últim trimestre de 2022. Títol: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De manera addicional als treballs publicats, s'ha redactat en forma de review (susceptible de ser publicat) l'estat de l'art que ha permés desenvolupar l'OBJECTIU ESPECÍFIC 3. S'adjunta com a Article d'Investigació susceptible de publicació en revista JCR. Títol: Segmentation of acne vulgaris images algorithms. L'execució de l'Estudi Observacional es planteja com la línia d'investigació a seguir i que dona continuïtat a la investigació iniciada en la present Tesi Doctoral. El document de Tesi està estructurat en 7 capítols i 11 Annexos. Per al desenvolupament del present treball s'han plantejat tres objectius específics. Cada article o treball publicat es correspon amb el desenvolupament de cadascun dels tres objectius específics. Així, cadascun dels capítols 3, 4 i 5 planteja l'escenari, desenvolupament i conclusions obtingudes que han donat com a resultat cadascun dels treballs publicats de manera independent.[EN] In the present work, a complete analysis is made of the illumination and image recording techniques developed so far that allow the use of intrinsic fluorescence of biological structures to increase the capacity of identification, detection and analysis of lesions and anomalies that may occur. The work has focused mainly on a) the analysis, validation and development of techniques for the early detection of lesions associated with Squamous Epidermoid Carcinoma (otorhinolaryngological oncology), as well as possible precursor lesions, and b) the analysis and development of a methodology for recording fluorescence images and quantifying the condition caused by Acne Vulgaris (dermatology) through the application of image processing techniques. New ways of acquisition, registration and processing of fluorescence images are proposed to objectively improve the capacity of detection and management of the previous pathologies. The development of the Thesis has led to several results. Part of the results have been structured in the form of research articles and papers published in JCR journals. Thus, the thesis is going to be developed by Compendium of Articles, including: a) Research Article 1 published in JCR journal. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Research Article 2 published in JCR journal. Hough Transform Sensitivity Factor Calculation Model Applied to the Analysis of Acne Vulgaris Skin Lesions. c) Research Article published in International Congress. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Observational study (clinical trial modality for non-invasive techniques) with FAVORABLE OPINION for its realization on September 29, 2022. The Observational Study has been evaluated by the members of the Ethics Committee for Research with Medicines of the Arnau de Vilanova-Llíria Department. Due to the pandemic caused by COVID-19, the execution of the work has been postponed and will start in the last quarter of 2022. Title: ANALYSIS OF AUTOFLUORESCENCE IMAGES FOR POTENTIAL USE AS A NON-INVASIVE SYSTEM IN THE DETECTION OF POTENTIALLY MALIGNANT ORAL LESIONS. In addition to the published works, the state of the art that has allowed the development of SPECIFIC OBJECTIVE 3 has been written in the form of a review (susceptible of being published). It is attached as a Research Article susceptible of being published in a JCR journal. Title: Segmentation of acne vulgaris images algorithms. The execution of the Observational Study is proposed as the line of research to be followed and which gives continuity to the research initiated in the present Doctoral Thesis. The Thesis document is structured in 7 chapters and 11 Annexes. Three specific objectives have been set for the development of this work. Each article or published work corresponds to the development of each of the three specific objectives. Thus, each of the chapters 3, 4 and 5 presents the scenario, development and conclusions obtained that have resulted in each of the works published independently.Moncho Santonja, M. (2022). Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191027Compendi