M. H. Ahmad Fadzil

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.

Area assessment of psoriasis lesions for PASI scoring

Psoriasis is a skin disorder which is caused by a genetic fault. 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 measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the determination of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameters, the lesion area. The method isolates healthy and healed skin areas from lesion areas by analysing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue–chroma space are determined from selected sample. The Euclidean distance of all pixels from each centroid is calculated. Pixels are assigned to either healthy skin or psorasis lesion classes based on the minimum Euclidean distance. The study involves patients from different ethnic origins having three different skin tones. Results obtained show that the proposed method is able to determine lesion areas with accuracy higher than 90% for 28 out of 30 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.

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.

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.

Modelling of reflectance spectra of skin phototypes III

In dermatology, study of human skin colour is related to skin phototype (SPT) in which the Fitzpatricks scale is the most used skin photo type classification. Assessment of skin response to UV for various reasons plays an important role in dermatology. This is however not easy to be performed because of two reasons. Firstly, skin areas may have different skin tone resulting in different reflectance spectra and secondly, different modalities may produce different reflectance spectra. We hypothesize that the underlying pattern of reflectance spectra must be similar regardless of the modalities use and the skin areas where it is obtained, for a particular person. An observational clinical study involving 21 participants with SPT III was performed to study the relationship between reflectance spectra of facultative skin colour and constitutive skin colour obtained using two different instruments namely spectrophometer and multispectral camera. The reflectance spectra is then modelled by different linear regressions over different intervals of wavelength (piecewise linear regressions). Results show that correlation between the modelled reflectance and reflectance obtained from different skin samples using different instruments is very high (R-squared >0.965). For this, it can be inferred that the reflectance model based on piecewise linear regressions is suitable to model SPT III.

Thickness Characterization of 3D Skin Surface Images Using Reference Line Construction Approach

Irregular elevation is commonly formed in skin surface during dermatological diseases. Thickness is one of the parameters to assess the severity. In this research, the thickness is defined as the elevation of lesion surface from its constructed reference line which is generated by smoothing the lesion surface using moving average filter. This method is applied in dermatological disease which caused by disorder cell growth. In the clinical trial, Dermatologist classifies the thickness severity into 4 classes. The classes are divided by its thickness appearance. Dermatologist assesses 40 3D images of skin lesion taken from 16 patients. The quantitative and objective measurement of the lesions performed in this research has characterized the thickness range of each class as well as met the doctors thickness classification.

Modeling psoriasis lesion colour for PASI erythema scoring

Skin colour reflects the pathological condition beneath the skin and is commonly used to indicate extent of a lesion. Psoriasis is a skin disease that is indicated by the appearance of red plaques. Although there is no cure for psoriasis, many treatment modalities exist that can help control the disease. To evaluate treatment efficacy, the Psoriasis Area and Severity Index (PASI) as a gold standard method is used to measure psoriasis severity. Commonly, the erythema (a PASI parameter) of a lesion is assessed visually but may lead to subjective and inconsistent results. Since psoriasis lesions can have a wide variety of colour, modeling the psoriasis colour is necessary. In this work, we proposed a colour model of psoriasis lesion that can be used in PASI erythema scoring. The colour of psoriasis lesion is modeled by hue (H), saturation (S), and value (V). Correlation coefficient between HSV parameters of lesion references with PASI erythema score is calculated. Among the three parameters, hue and saturation are the two most correlated parameters with PASI erythema scores. Larger numbers of lesion references are required in order to model the distribution of lesions with different scores in hue-saturation plane.

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.