Hanung Adi Nugroho

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.

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.

Independent component analysis for assessing therapeutic response in vitiligo skin disorder

This paper describes an image analysis technique that objectively measures skin repigmentation for the assessment of therapeutic response in vitiligo treatments. Skin pigment disorders due to the abnormality of melanin production, such as vitiligo, cause irregular pale patches of skin. The therapeutic response to treatment is repigmentation of the skin. However the repigmentation process is very slow and is only observable after a few months of treatment. Currently, there is no objective method to assess the therapeutic response of skin pigment disorder treatment, particularly for vitiligo treatment. In this work, we apply principal component analysis followed by independent component analysis to represent digital skin images in terms of melanin and haemoglobin composition respectively. Vitiligo skin areas are identified as skin areas that lack melanin (non-melanin areas). Results obtained using the technique have been verified by dermatologists. Based on 20 patients, the proposed technique effectively monitored the progression of repigmentation over a shorter time period of six weeks and can thus be used to evaluate treatment efficacy objectively and more effectively.

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.

Gaussian Bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy

Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression having a correlation factor up to 0.883 at significant levels better than 0.01. However, it is also found that the FAZ areas can belong to different DR severity but with different levels of certainty having a Gaussian distribution. In this research work, the suitability of the Gaussian Bayes classifier in determining DR severity level is investigated. A v-fold cross-validation (VFCF) process is applied to the FINDeRS database to evaluate the performance of the classifier. It is shown that the classifier 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 Gaussian Bayes classifier is suitable as part of a computerised DR grading and monitoring system 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.

Enhancement of colour fundus image and FFA image using RETICA

Diabetic Retinopathy (DR) is a vision loss impairment due to complications arising from the diabetic condition and affects the retina and resulting pathologies can be monitored by analysing the colour fundus image. However, in retinal fundus images, the contrast between the retinal vasculature and the background is very low and varies within the image making visualisation and analysis of small retinal vasculatures difficult. Therefore, enhancement of the fundus image is important to provide the best visualization of the retinal blood vessels. Fluorescein angiogram overcomes this imaging problem but it is invasive and leads to other physiological problems. In this research work, a non-invasive digital image enhancement technique called RETICA has been developed that overcomes the problem of varied and low contrast in fundus images. RETICA first normalises the varied contrast using a Retinex based method that separates the illumination from the reflectance part of the image followed by ICA that forms the original retinal pigment makeup namely the macular, haemoglobin and melanin retinal pigment. The haemoglobin image exhibits the highest contrast for retinal vessels. Results based on a dataset of 13 fundus images show that RETICA successfully normalises the low and varied contrast and enhances the retinal vessels. It achieved a better average contrast improvement factor of up to 5.56 compared to the invasive FFA with 5.34. This improvement in contrast reduces the need for fluorescein angiogram in DR assessment.

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.

Model-based retinal vasculature enhancement in digital fundus image using independent component analysis

Early detection of several diseases related to the retina can be analyzed from fundus images. However, in fundus images the contrast between retinal vasculature and the background is very low. Therefore, analyzing these tiny retinal blood vessels is difficult. Fluorescein angiogram overcomes this imaging problem; however, it is an invasive procedure that leads to other physiological problems. In this work, we develop a fundus image model based on probability distribution function of melanin, haemoglobin and macular pigment to represent melanin, retinal vasculature and macular region, respectively. Enhancement of the low contrast of retinal vasculature in the retinal fundus image is performed by separating the retinal pigments makeup, namely macular pigment, haemoglobin and melanin, using independent component analysis. Independent component image due to haemoglobin obtained exhibits higher contrast retinal blood vessels. Results show that this approach outperforms other non-invasive enhancement methods and can be beneficial for retinal vasculature segmentation. Contrast enhancement factor of 2.62 for a digital retinal fundus image model is achieved. This improvement in contrast reduces the need of applying contrasting agent on patients.