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.

Objective assessment of psoriasis erythema for PASI scoring.

Skin colour is vital information in dermatological diagnosis as it reflects the pathological condition beneath the skin. It is commonly used to indicate the extent of diseases such as psoriasis, which is indicated by the appearance of red plaques. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, the current gold standard method, PASI (Psoriasis Area and Severity Index), is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI and this condition is assessed visually, thus leading to subjective and inconsistent results. Current methods or instruments that assess erythema have limitations, such as being able to measure erythema well for low pigmented skin (fair skin) but not for highly pigmented skin (dark skin) or vice versa. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring for different (low to highly pigmented) skin types. The colour of psoriasis lesions are initially obtained by using a chromameter giving the values L*, a*, and b* of CIELAB colour space. The L* value is used to classify skin into three categories: low, medium and highly pigmented skin. The lightness difference (DeltaL*), hue difference (Deltah(ab)), chroma (DeltaC*(ab)) between lesions and the surrounding normal skin are calculated and analysed. It is found that the erythema score of a lesion can be distinguished by their Deltah(ab) value within a particular skin type group. References of lesion with different scores are obtained from the selected lesions by two dermatologists. Results based on 38 lesions from 22 patients with various level of skin pigmentation show that PASI erythema score for different skin types i.e. low (fair skin) to highly pigmented (dark skin) skin types can be determined objectively and consistent with dermatology scoring.

Objective assessment of psoriasis erythema for PASI scoring.

Skin colour is vital information in dermatological diagnosis. It reflects pathological condition beneath the skin and commonly being used to indicate the extent of a disease. Psoriasis is a skin disease which is indicated by the appearance of red plaques. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI. Commonly, the erythema is assessed visually, thus leading to subjective and inconsistent result. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring. The colour of psoriasis lesion is analyzed by DeltaL, Deltahue, and Deltachroma of CIELAB colour space. References of lesion with different scores are obtained from the selected lesions by two dermatologists. Results based on 38 lesions from 22 patients with various level of skin pigmentation show that PASI erythema score can be determined objectively and consistent with dermatology scoring.

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.