Preparation of 2D sequences of corneal images for 3D model building

A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patient's cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior–posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences

Automatic image processing applied to corneal endothelium cell count and shape characterization

Corneal endothelium cell count, as well as cell hexagonality percent characterization, are of great importance nowadays to detect anomalies and pathologies of human eye, such as glaucoma. Prevalent technologies used are mainly based in both microscopy and a later image analysis. However, automatic cell count made by microscopes’ built-in software is rather inconsistent, therefore many laboratories opt for using manual count as the most reliable alternative. This count is a tedious and time-consuming task, that can lead to human error, for this reason, several proposals to automate the process have been made. Present communication shows a procedure for the automatic pre-processing, segmentation and analysis of the images obtained by a confocal microscope, using watershed transform, and the graphics user interface (GUI) created with Matlab® to apply this procedure. In order to quantify the procedure’s quality, 30 corneal endothelium images with a number of cells between 90 and 170 were analysed, resulting in a mean error in cell count of 4.3%, which can be considered a reasonably good result. However, results achieved for hexagonality percent using this method, and with the available image quality, are not as good as expected, which invites to improving image quality, focusing in areas with better cell homogeneity or even considering the application of other algorithms, such as neural networks, for future works.This work was supported by the Thematic Network for Co-Operative Research in Health (RETICS-RD16/0008/0012), financed by the Carlos III Health Institute and the European Regional Development Fund (FEDER)

An efficient system for preprocessing confocal corneal images for subsequent analysis

A confocal microscope provides a sequence of images of the various corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient's cornea. Preprocessing the confocal corneal images to make them suitable for analysis is very challenging due the nature of these images and the amount of the noise present in them. This paper presents an efficient preprocessing approach for confocal corneal images consisting of three main steps including enhancement, binarisation and refinement. Improved visualisation, cell counts and measurements of cell properties have been achieved through this system and an interactive graphical user interface has been developed

Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images.

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.The data and image files accompanying this thesis are not available online

Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly, a fully automatic nerve segmentation system for corneal confocal microscope images is proposed. The performance of the proposed system is evaluated against manually traced images with an execution time of the prototype is 13 seconds. Secondly, an automatic corneal nerve registration system is proposed. The main aim of this system is to produce a new informative corneal image that contains structural and functional information. Thirdly, an automated real-time system, termed the Corneal Endothelium Analysis System (CEAS) is developed and applied for the segmentation of endothelial cells in images of human cornea obtained by In Vivo CCM. The performance of the proposed CEAS system was tested against manually traced images with an execution time of only 6 seconds per image. Finally, the results obtained from all the proposed approaches have been evaluated and validated by an expert advisory board from two institutes, they are the Division of Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and the Manchester Royal Eye Hospital, Centre for Endocrinology and Diabetes, UK

Fully automated computer system for diagnosis of corneal diseases. Development of image processing technologies for the diagnosis of Acanthamoeba and Fusarium diseases in confocal microscopy images

Confocal microscopy demonstrated its value in the diagnosis of Acanthamoeba and fungal keratitis which considered sight-threatening corneal diseases. However, it can be difficult to find and train confocal microscopy graders to accurately detect Acanthamoeba cysts and fungal filaments in the images. Use of an automated system could overcome this problem and help to start the correct treatment more quickly. Also, response to treatment can be difficult to assess in infectious keratitis using clinical examination alone, but there is evidence that the morphology of filaments and cysts may change over time with the use of correct treatment. An automated system to analyse confocal microscopy images for such changes would also assist clinicians in determining whether the ulcer is improving, or whether a change of treatment is needed. This research proposes a fully automated novel system with GUI to detect cysts and hyphae (filaments) and measure useful quantitative parameters for them through many stages; Image enhancement, image segmentation, quantitative analysis for detected cysts and hyphae, and registration and tracking of ordered sequence of images. The performance of the proposed segmentation procedure is evaluated by comparing between the manual and the automated traced images of the dataset that was provided by the Manchester Royal Eye Hospital. The positive predictive values rate of cysts for Acanthamoeba images was 76%. For detected hyphae in Fusarium images, many standard measurements were computed. The accuracy of their values was quantified by calculating the percent error rate for each measurement and which ranged from 23% to 49%

Modelado geométrico personalizado de la córnea humana y su aplicación a la detección de ectasias corneales

[SPA] La córnea es una estructura biológica viva cuya arquitectura presenta una morfología singular, ya sea en un estado natural o patológico. Esta singularidad ha sido caracterizada a lo largo de toda la historia en el campo de la oftalmología y la óptica a través de la generación de modelos genéricos o de modelos personalizados de la córnea humana. Hoy en día, el desarrollo de nuevas tecnologías permite caracterizar la morfología corneal a partir de los denominados equipos topográficos; estos equipos aportan una caracterización personalizada de índole cualitativa y cuantitativa al médico oftalmólogo. Sin embargo, los sistemas de diagnóstico de las patologías corneales están basados en unos índices de valoración de la irregularidad de las superficies corneales que son calculados a partir de algoritmos específicos internos para cada topógrafo corneal y de los cuales se desconoce su programación. Por este motivo en esta tesis doctoral se establece un nuevo procedimiento fundamentado en la geometría computacional para obtener un modelo sólido 3D personalizado in vivo de la córnea humana utilizando herramientas de Diseño Geométrico Asistido por Ordenador. Este modelo virtual reconstruye fidedignamente las superficies de la cara anterior y posterior de la córnea, a partir de unos datos aportados por los topógrafos corneales denominados datos en bruto (sin ningún trato mediante algoritmo) tanto para los ojos de pacientes sanos como para los ojos de pacientes diagnosticados con la patología ectásica más común, el queratocono. A partir del nuevo modelo sólido obtenido, se definen unos índices de caracterización de la morfología corneal basados en variables geométricas, los cuales pueden ser utilizados como unos nuevos índices de diagnóstico de la patología ectásica objeto de estudio debido a que presentan una elevada sensibilidad y especificidad para su diagnóstico. [ENG] The cornea is a living biological structure whose architecture has a unique morphology, either in a natural or diseased condition. This uniqueness has been characterized throughout all history in the field of ophthalmology and optics through the generation of generic or customized models of human cornea. Today, the development of new technologies leads to characterize the corneal morphology from the so‐called topographic devices; these devices provide a personalized qualitative and quantitative characterization of its nature for the ophthalmologist. However corneal pathological diagnosis systems are based on indicators of the irregularity of the corneal surfaces, which are calculated from specific internal algorithms for each corneal topographer and whose programming is unknown. For that reason, this doctoral thesis establishes a new procedure based on computational geometry to obtain a 3D solid model, personalized and in vivo of the human cornea by using Computer Aided Geometrical Design tools. This virtual model represents accurately both the anterior and posterior corneal surfaces from a set of raw data (without any algorithm treatment) provided by the corneal topographers for both healthy corneas and corneas with the most common ectasic disease, the keratoconus. The new solid model obtained is later analyzed to define a set of indices that enable the characterization of the corneal morphology and that are based on geometric variables. These indices can be used as new indicators for the diagnosis of the keratoconus disease due to their high sensibility and specificity.[ENG] The cornea is a living biological structure whose architecture has a unique morphology, either in a natural or diseased condition. This uniqueness has been characterized throughout all history in the field of ophthalmology and optics through the generation of generic or customized models of human cornea. Today, the development of new technologies leads to characterize the corneal morphology from the so‐called topographic devices; these devices provide a personalized qualitative and quantitative characterization of its nature for the ophthalmologist. However corneal pathological diagnosis systems are based on indicators of the irregularity of the corneal surfaces, which are calculated from specific internal algorithms for each corneal topographer and whose programming is unknown. For that reason, this doctoral thesis establishes a new procedure based on computational geometry to obtain a 3D solid model, personalized and in vivo of the human cornea by using Computer Aided Geometrical Design tools. This virtual model represents accurately both the anterior and posterior corneal surfaces from a set of raw data (without any algorithm treatment) provided by the corneal topographers for both healthy corneas and corneas with the most common ectasic disease, the keratoconus. The new solid model obtained is later analyzed to define a set of indices that enable the characterization of the corneal morphology and that are based on geometric variables. These indices can be used as new indicators for the diagnosis of the keratoconus disease due to their high sensibility and specificity.Esta tesis se ha realizado en parte gracias a la financiación del proyecto del Fondo Europeo de Desarrollo Regional (FEDER) y del Ministerio Español de Economía y Competitividad, Instituto Carlos III, Red Temática de Investigación Cooperativa en Salud (RETICS) «Prevención, detección precoz y tratamiento de la patología ocular prevalente, degenerativa y crónica». Subprograma «dioptrio ocular y patologías frecuentes» (RD12/0034/0007).Escuela Internacional de DoctoradoUniversidad Politécnica de CartagenaPrograma Oficial de Doctorado en Tecnologías Industriale