Barry T. Thomas

Automated identification of diabetic retinal exudates in digital colour images

Aim: To identify retinal exudates automatically from colour retinal images. Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. Results: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification. Conclusions: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.

Comparison of colour spaces for optic disc localisation in retinal images

The location of the optic disc is of critical importance in retinal image analysis. In this work we improve on an approach introduced by Mendels, Heneghan and Thiran (1999) which localises an optic disc region through grey level morphology, followed by snake fitting. We propose and implement both the automatic initialisation of the snake and the application of morphology in colour space. We examine various methods of performing the morphology step (to remove the interference of blood vessels) and compare them against each other. We demonstrate that our proposed simple Lab colour morphology method is particularly suitable for the characteristics of our optic disc images. Results indicate 90.32% average accuracy in localising, the optic disc boundary.

Iterative refinement by relevance feedback in content-based digital image retrieval

Many image-database retr-ieual systems rely heavily on the success oj one-shot queries, using optimised jemkre set-s to obtain the best possible results. What is ojten missing jt-om this appToach is acceptance oj the fact that the user knows considerably mor-e about the query being made than cm. be conveyed in szch Relatively simple terms. Ij the query jails then the useT must try and improve the description using only the avai[able Jeatwe descriptors. This papeT describes how a query system can. exploit the user’s lrnourledge to a higher extent by employing relevance jeedback to itemtively rejine queries at ran-time. Subjects oj inierest aTe chosen by selection oj i-egions jrom pre-processed, segment ed imag~, giving access to object-specific, local information which zs not possible in a global pattern-matching approach. AfteT an initial retrieved attempt, jeedback is given in the jmm oj acceptance or rejection oj imagtx ofemd. This information b used as a collection oj positive and negative training ezamples joT a class-speci

Classification and Localisation of Diabetic-Related Eye Disease

c classijkation network by identifying clusterings in the data and the spread along jeatuTe axes. Each. networ-k consists OJ a set of Radial BaSiSFunction nodes with a non-linear perception output layer. Network training is carried out of-line mung the data gathered o%ing an on-line query session with the user. The aseT can i-eview and adjust the behaviotw of the network in the next session. Over time, collections oj these networks can be built into a bier-a~chical ck.ss database, resulting into highly usefil Tetm”eval tooi specifically trained jor the nature oj the user’s database.

Comparative Exudate Classification Using Support Vector Machines and Neural Networks

Retinal exudates are a characteristic feature of many retinal diseases such as Diabetic Retinopathy. We address the development of a method to quantitatively diagnose these random yellow patches in colour retinal images automatically. After a colour normalisation and contrast enhancement preprocessing step, the colour retinal image is segmented using Fuzzy C-Means clustering. We then classify the segmented regions into two disjoint classes, exudates and non-exudates, comparing the performance of various classifiers. We also locate the optic disk both to remove it as a candidate region and to measure its boundaries accurately since it is a significant landmark feature for ophthalmologists. Three different approaches are reported for optic disk localisation based on template matching, least squares arc estimation and snakes. The system could achieve an overall diagnostic accuracy of 90.1% for identification of the exudate pathologies and 90.7% for optic disk localisation.

Automatic segmentation and classification of outdoor images using neural networks.

After segmenting candidate exudates regions in colour retinal images we present and compare two methods for their classification. The Neural Network based approach performs marginally better than the Support Vector Machine based approach, but we show that the latter are more flexible given criteria such as control of sensitivity and specificity rates. We present classification results for different learning algorithms for the Neural Net and use both hard and soft margins for the Support Vector Machines. We also present ROC curves to examine the trade-off between the sensitivity and specificity of the classifiers.

Temporal video segmentation and classification of edit effects

The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perception is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.

Video cut detection using frequency domain correlation

Abstract The process of shot break detection is a fundamental component in automatic video indexing, editing and archiving. This paper introduces a novel approach to the detection and classification of shot transitions in video sequences including cuts, fades and dissolves. It uses the average inter-frame correlation coefficient and block-based motion estimation to track image blocks through the video sequence and to distinguish changes caused by shot transitions from those caused by camera and object motion. We present a number of experiments in which we achieve better results compared with two established techniques.

Road edge tracking for robot road following: a real-time implementation

A common video indexing technique is to segment a video sequence into shots and then select representative key-frames. The process of shot break detection is a fundamental component in automatic video indexing, editing, and archiving. This paper introduces a novel video cut detection technique which performs in the frequency domain. It is computationally tractable and robust with respect to sudden changes in mean intensity within a shot. The method uses the average interframe correlation coefficients to determine whether an abrupt shot change has occurred. We compare our method against three established techniques and present our results using different video sequences.

Practical generation of video textures using the auto-regressive process

Abstract The problem of visually navigating a robot along a road is approached by means of creating and updating a simple representation of the road from a sequence of images. The representation chosen is a 4-parameter model that describes the width, direction and simple curvature of the road in a vehicle centred (X, Y, Z) world coordinate system. The model is created by tracking along major edge features in an image and applying constraints to select road edge candidates. Updating consists of tracking a set of measured edge points from frame to frame (assuming that vehicle motion is known), and using a weighted least squares process to find the four parameters of the road model. A number of constraint and filtering processes representing knowledge of how a vehicle moves on a road have been applied. The algorithm has been developed to run at near real-time video rates.