304 research outputs found
Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review
Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd
Advanced image processing techniques for detection and quantification of drusen
Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa,
Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots.
In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually.
A literature review on automated drusen detection showed that the works already
published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques.
This methodology is based on a detection and modelling algorithm to automatically
quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the
image, which is used to compute the affected areas.
To validate the methodology, two software applications, one for semi-automated
(MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first
was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have
taken place. These compared the automated method to the specialists and evaluated its
repeatability. The studies were analysed regarding several indicators, which were based on the
total affected area and on a pixel-to-pixel analysis. Due to the high variability among the
graders involved in the first study, a new evaluation method, the Weighed Matching Analysis,
was developed to improve the pixel-to-pixel analysis by using the statistical significance of
the observations to differentiate positive and negative pixels.
From the results of these studies it was concluded that the methodology proposed is
capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image
segmentation,image modelling and images comparison, which are also applicable to other image processing fields
Biometrics
Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book
Automatic extraction of retinal features to assist diagnosis of glaucoma disease
Glaucoma is a group of eye diseases that have common traits such as high eye
pressure, damage to the Optic Nerve Head (ONH) and gradual vision loss. It affects
the peripheral vision and eventually leads to blindness if left untreated. The current
common methods of diagnosis of glaucoma are performed manually by the clinicians.
Clinicians perform manual image operations such as change of contrast, zooming in
zooming out etc to observe glaucoma related clinical indications. This type of diagnostic
process is time consuming and subjective. With the advancement of image and
vision computing, by automating steps in the diagnostic process, more patients can be
screened and early treatment can be provided to prevent any or further loss of vision.
The aim of this work is to develop a system called Glaucoma Detection Framework
(GDF), which can automatically determine changes in retinal structures and imagebased
pattern associated with glaucoma so as to assist the eye clinicians for glaucoma
diagnosis in a timely and effective manner. In this work, several major contributions
have been made towards the development of the automatic GDF consisting of the
stages of preprocessing, optic disc and cup segmentation and regional image feature
methods for classification between glaucoma and normal images.
Firstly, in the preprocessing step, a retinal area detector based on superpixel classification model has been developed in order to automatically determine true retinal
area from a Scanning Laser Ophthalmoscope (SLO) image. The retinal area detector
can automatically extract artefacts out from the SLO image while preserving the computational
effciency and avoiding over-segmentation of the artefacts. Localization of
the ONH is one of the important steps towards the glaucoma analysis. A new weighted
feature map approach has been proposed, which can enhance the region of ONH for
accurate localization. For determining vasculature shift, which is one of glaucoma indications,
we proposed the ONH cropped image based vasculature classification model
to segment out the vasculature from the ONH cropped image. The ONH cropped image based vasculature classification model is developed in order to avoid misidentification
of optic disc boundary and Peripapillary Atrophy (PPA) around the ONH of
being a part of the vasculature area.
Secondly, for automatic determination of optic disc and optic cup boundaries, a
Point Edge Model (PEM), a Weighted Point Edge Model (WPEM) and a Region
Classification Model (RCM) have been proposed. The RCM initially determines the
optic disc region using the set of feature maps most suitable for the region classification
whereas the PEM updates the contour using the force field of the feature maps with
strong edge profile. The combination of PEM and RCM entitled Point Edge and
Region Classification Model (PERCM) has significantly increased the accuracy of optic
disc segmentation with respect to clinical annotations around optic disc. On the other
hand, the WPEM determines the force field using the weighted feature maps calculated
by the RCM for optic cup in order to enhance the optic cup region compared to rim
area in the ONH. The combination of WPEM and RCM entitled Weighted Point Edge
and Region Classification Model (WPERCM) can significantly enhance the accuracy
of optic cup segmentation.
Thirdly, this work proposes a Regional Image Features Model (RIFM) which can
automatically perform classification between normal and glaucoma images on the basis
of regional information. Different from the existing methods focusing on global
features information only, our approach after optic disc localization and segmentation
can automatically divide an image into five regions (i.e. optic disc or Optic Nerve
Head (ONH) area, inferior (I), superior(S), nasal(N) and temporal(T)). These regions
are usually used for diagnosis of glaucoma by clinicians through visual observation
only. It then extracts image-based information such as textural, spatial and frequency
based information so as to distinguish between normal and glaucoma images. The
method provides a new way to identify glaucoma symptoms without determining any
geometrical measurement associated with clinical indications glaucoma.
Finally, we have accommodated clinical indications of glaucoma including the CDR,
vasculature shift and neuroretinal rim loss with the RIFM classification and performed
automatic classification between normal and glaucoma images. Since based on the clinical
literature, no geometrical measurement is the guaranteed sign of glaucoma, the
accommodation of the RIFM classification results with clinical indications of glaucoma can lead to more accurate classification between normal and glaucoma images. The
proposed methods in this work have been tested against retinal image databases of
208 fundus images and 102 Scanning Laser Ophthalmoscope (SLO) images. These
databases have been annotated by the clinicians around different anatomical structures
associated with glaucoma as well as annotated with healthy or glaucomatous
images. In fundus images, ONH cropped images have resolution varying from 300 to
900 whereas in SLO images, the resolution is 341 x 341. The accuracy of classification
between normal and glaucoma images on fundus images and the SLO images is 94.93%
and 98.03% respectively
Análisis automático de retinografías en Retinopatía Diabética: evaluación de la calidad y localización de las estructuras anatómicas del ojo mediante técnicas de procesado digital de imágenes
El objetivo de este Trabajo de Fin de Máster (TFM) ha sido el diseño y desarrollo de métodos automáticos para la evaluación de la calidad en retinografías y la localización de la papila y la fóvea. Para ello se ha creado una base de datos de 381 retinografías de pacientes diabéticos. Las imágenes tenían distintos niveles de calidad y pertenecían a pacientes tanto con lesiones asociadas a la RD como sin ellas. Un oftalmólogo especialista estableció la calidad de cada imagen y marcó la localización del centro de la papila y la fóvea.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació
Recommended from our members
Crystal Cartography: Mapping Nanostructure with Scanning Electron Diffraction
Nanostructure describes the network of defective and distorted atomic structure existing
on the nanoscale within materials. This nanostructure bridges the gap between idealised crys-
talline structure and real materials, playing a deterministic role in tailoring physico-chemical
properties, as well as providing a basis for mechanistic understanding of complex processes
such as mechanical deformation and phase transformation. Characterising nanostructure, to
develop understanding of materials, requires experimental techniques capable of probing the
structure with spatial resolution on the order of nanometres and across regions of interest
up to micrometres. Recent developments in electron microscopy, enabling the acquisition
of numerous diffraction patterns in a spatially resolved manner, combined with modern
computational power, provides a route to meet this need as developed in this work.
Scanning electron diffraction (SED) involves the acquisition of a two-dimensional elec-
tron diffraction pattern at each probe position in a two-dimensional scan of a specimen. An
information rich 4-dimensional (4D-SED) dataset is obtained that can be analysed extensively
post-facto using a wide-range of computational methods. The acquisition of such 4D-SED
data from the specimen at numerous orientations may also enable the reconstruction of
nanostructure in three-dimensions via tomographic methods. In this work, methods for the
acquisition and analysis of 4D-SED data are developed and applied to reveal nanostructure in
two and three-dimensions. These methods are applied to various prototypical characterisation
challenges in materials science, particularly: strain mapping in two and three dimensions,
revealing inter-phase crystallographic relationships, mapping grains in two-dimensional
materials, and probing nanostructure in polyethylene
- …