Lila Iznita Izhar

Analysis of retinal fundus images for grading of diabetic retinopathy severity.

Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. In this article, a computerised DR grading system, which digitally analyses retinal fundus image, is used to measure foveal avascular zone. A v-fold cross-validation method is applied to the FINDeRS database to evaluate the performance of the DR system. It is shown that the system achieved sensitivity of >84%, specificity of >97% and accuracy of >95% for all DR stages. At high values of sensitivity (>95%), specificity (>97%) and accuracy (>98%) obtained for No DR and severe NPDR/PDR stages, the computerised DR grading system is suitable for early detection of DR and for effective treatment of severe cases.

Extraction and reconstruction of retinal vasculature.

Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and reconstructing retinal vasculature. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods, especially with low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database show that it outperforms many other published methods and achieved an accuracy range (ability to detect both vessel and non-vessel pixels) of 0.91 – 0.95, a sensitivity range (ability to detect vessel pixels) of 0.91 – 0.95 and a specificity range (ability to detect non-vessel pixels) of 0.88 – 0.94.

Determination of foveal avascular zone in diabetic retinopathy digital fundus images

Monitoring FAZ area enlargement enables physicians to monitor progression of the DR. At present, it is difficult to discern the FAZ area and to measure its enlargement in an objective manner using digital fundus images. A semi-automated approach for determination of FAZ using color images has been developed. Here, a binary map of retinal blood vessels is computer generated from the digital fundus image to determine vessel ends and pathologies surrounding FAZ for area analysis. The proposed method is found to achieve accuracies from 66.67% to 98.69% compared to accuracies of 18.13-95.07% obtained by manual segmentation of FAZ regions from digital fundus images.

Analysis of foveal avascular zone in colour fundus images for grading of diabetic retinopathy severity

Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. At present, the classification of DR is based on the International Clinical Diabetic Retinopathy Disease Severity. In this paper, FAZ enlargement with DR progression is investigated to enable a new and an effective grading protocol DR severity in an observational clinical study. The performance of a computerised DR monitoring and grading system that digitally analyses colour fundus image to measure the enlargement of FAZ and grade DR is evaluated. The range of FAZ area is optimised to accurately determine DR severity stage and progression stages using a Gaussian Bayes classifier. The system achieves high accuracies of above 96%, sensitivities higher than 88% and specificities higher than 96%, in grading of DR severity. In particular, high sensitivity (100%), specificity (>98%) and accuracy (99%) values are obtained for No DR (normal) and Severe NPDR/PDR stages. The system performance indicates that the DR system is suitable for early detection of DR and for effective treatment of severe cases.

Determination of Retinal Pigments from Fundus Images using Independent Component Analysis

In the macular area, there are fovea and foveal avascular zone. Foveal avascular zone is the fovea devoid of capillaries. Enlargement of the foveal avascular zone is found to be related to the severity of diabetic retinopathy. Particular colours observed in the retinal image correspond to the optical properties of the pigments and the structure of the retinal layers. In this work, we use independent component analysis to determine retinal pigments from fundus images. The results show that independent component analysis can be used to determine retinal pigments, namely haemoglobin, melanin, and macular pigment. This technique allows us to determine retinal blood vessels and the macula based on the distribution of haemoglobin and macular pigment. Contrast enhancement factor of 3.5 for digital retinal images is achieved. This improvement in contrast reduces the need of applying contrasting agent on patients.

Toward a Fully Automated DR Grading System

Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. Research shows that the foveal avascular zone (FAZ) could be used to diagnose DR as it enlarges in DR cases due to the loss of capillaries in the perifoveal capillary network. A computerised DR monitoring and grading system based on analysis of FAZ enlargement in colour fundus image has been developed and has high values of sensitivity, specificity and accuracy. However the system is still semi automated. A user interruption is needed in the selection of retinal vessel endpoints to determine the FAZ. Therefore, the objective of this paper is to improve the FAZ determination so that the computerised DR grading system is fully automated. Results show the fully automated system consistently maintains high sensitivity (>73%), specificity (>77%) and accuracy (>77%) for all DR stages. This indicates that the fully automated DR monitoring and grading system has a potential to be used for early detection of DR and for effective treatment of severe cases.

Analysis of Foveal Avascular Zone for grading of Diabetic Retinopathy

At present, it is difficult to determine Foveal Avascular Zone (FAZ) enlargement based on colour fundus images. Fundus image analysis presents several challenges such as high image variability, improper illumination and artifacts. A new approach for grading Diabetic Retinopathy (DR) by analysing FAZ enlargement in colour fundus image has been developed. Investigations show that FAZ area ranges can be used to indicate progression of the disease. The mean accuracy and standard deviation of ranges obtained are 92.2% and 3.22, respectively. This new approach is reliable, accurate and fast compared to the current method based on DR pathologies.

Image enhancement: A composite image approach using contourlet transform

Most existing image enhancement algorithms work on a single image. Their performance is limited by the capacity of the sensor by which the image is originally taken. In some cases they completely fail to provide us the necessary enhancements. Here we propose a composite image enhancement approach for enhancing still images. We combine the relevant features of the input images and produce a composite image which is rich in information content. The input images are first decomposed into multiple resolutions by using the contourlet transform which provides a better representation than the conventional wavelet transforms. Transformed coefficients are combined with a predefined fusion rules. The resultant image is found by performing inverse contourlet transformation of the composite image. The results found are encouraging and the algorithm does not introduce any artificial artefacts and distortion.

Cerebral vessel enhancement using bilateral and Hessian-based filter

Vessel enhancement in magnetic resonance angiography (MRA) is an important preprocessing step for stroke surgical planning and further processing. Bilateral filter has been widely used to reduce noise due to its ability for smoothing an image and preserve the edges. It suffers a drawback of over smoothing that leads to discontinuity of blood vessels when applied to MRA image. Hessian-based filter is known to have the ability of enhancing the vessels and preserving the geometrical structure. This paper presents the vessel enhancement technique which combines bilateral and Hessian-based filters to exploit the advantages of them. The bilateral filtered images show that weighted bilateral filter can reduce more noise when comparing the peak signal-to-noise ratio (PSNR) value. Then, Hessian-based filter is performed on several types of preprocessed images to compare the performance of this technique on different types of images. Our method shows a promising result in suppressing the noise that is enhanced by Hessian-based filter.

Role of EEG delta and beta oscillations during problem solving tasks

Problem solving is one of the higher-order thinking skills that have been studied by many researchers using Electroencephalography (EEG) brain signals. This paper concentrates on specific neural oscillations that can be observed in EEG signals which are delta (1.0–4.0 Hz) and beta (12.0–25.0 Hz) during problem solving task. Our aim is to investigate the role of the delta and beta neural oscillations during problem solving task at cortical area as compared to resting state (i.e. eyes open) using EEG. Eight volunteered healthy right-handed male students were recruited in this study. EEG 128-channel Hydro-Cel Geodesic (EGI Inc.) system was used in this study for data collection, but only 19 channels were used for data analysis. EEG recordings were taken during problem solving task (i.e. Ravens Advanced Progressive Matric (RAPM)) and during resting state (eyes open). Results showed that delta was significantly higher in almost all brain region and beta was significantly higher at prefrontal region only. Since we aim to investigate the role of delta and beta oscillation during problem solving, further investigation needs to be done with greater number of subjects in order to have more significant result to support this study. Further investigation could also help in finding quantitative indicators for intelligent assessment using brain signals.