Investigating the potential of Zernike polynomials to characterise spatial distribution of macular pigment.

It has been postulated that particular patterns of macular pigment (MP) distribution may be associated with the risk for eye diseases such as age-related macular degeneration (AMD). This work investigates the potential of Zernike polynomials (ZP) to characterise the level and distribution of MP, and their suitability as a representation for analysis of the effects of age and AMD on MP patterns. As the case study, MP distribution maps computed using an experimental method based on fundus reflectance (MRIA) were obtained for ninety volunteers representing three groups: under-fifty without AMD, fifty and over without AMD, and fifty and over with AMD. ZP with 105 coefficients were fitted to the maps using least-squares optimisation and found to represent MP maps accurately (RMSE<10-1). One-way MANOVA analysis carried out on ZP representations showed that the three subject groups have significantly different means (Wilk's Lambda 0.125, p<0.0001). Linear discriminant analysis with leave-one-out scheme resulted in accuracy, sensitivity and specificity of classification according to, respectively, disease status regardless of age (81% all); disease status in the age-matched groups (87%, 88%, 86%); age irrespective of disease status (81%, 83%, 73%); and age for subjects without AMD (83%, 88%, 80%). Mean MP distributions computed from ZP coefficients for the three groups showed more elevated and more peaked MP for the healthy under-fifty group; more irregular and more elevated peripheral levels in over-fifty AMD group than in over-fifty non-AMD group; and moderate radial asymmetry in non-AMD over-50 group. The results suggest that ZP coefficients are capable of accurately representing MP in a way that captures certain spatial patterns of its distribution. Using the ZP representation MP maps could be classified according to both age and disease status with accuracy significantly greater than chance, with peak elevation, pattern irregularity and radial asymmetry identified as important features.

Detection and characterisation of bone destruction in murine rheumatoid arthritis using statistical shape models.

Rheumatoid arthritis (RA) is an autoimmune disease in which chronic inflammation of the synovial joints can lead to destruction of cartilage and bone. Pre-clinical studies attempt to uncover the underlying causes by emulating the disease in genetically different mouse strains and characterising the nature and severity of bone shape changes as indicators of pathology. This paper presents a fully automated method for obtaining quantitative measurements of bone destruction from volumetric micro-CT images of a mouse hind paw. A statistical model of normal bone morphology derived from a training set of healthy examples serves as a template against which a given pathological sample is compared. Abnormalities in bone shapes are identified as deviations from the model statistics, characterised in terms of type (erosion / formation) and quantified in terms of severity (percentage affected bone area). The colour-coded magnitudes of the deviations superimposed on a three-dimensional rendering of the paw show at a glance the severity of malformations for the individual bones and joints. With quantitative data it is possible to derive population statistics characterising differences in bone malformations for different mouse strains and in different anatomical regions. The method was applied to data acquired from three different mouse strains. The derived quantitative indicators of bone destruction have shown agreement both with the subjective visual scores and with the previous biological findings. This suggests that pathological bone shape changes can be usefully and objectively identified as deviations from the model statistics.

Performance comparison of publicly available retinal blood vessel segmentation methods.

Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with ground truth segmentation of the vessels. Each method is tested under consistent conditions with two types of preprocessing, and the parameters of the methods are optimized for each database. Additionally, possibility to predict the parameters of the methods by the linear regression model is tested for each database. Resolution of the input images and amount of the vessel pixels in the ground truth are used as predictors. The results show the positive influence of preprocessing on the performance of the unsupervised methods. The methods show similar performance for segmentation accuracy, with the best performance achieved by the method by Azzopardi et al. (Acc 94.0) on ARIADB, the method by Soares et al. (Acc 94.6, 94.7) on CHASEDB1 and DRIVE, and the method by Nguyen et al. (Acc 95.8, 95.5) on HRF and STARE. The method by Soares et al. performed better with regard to the area under the ROC curve. Qualitative differences between the methods are discussed. Finally, it was possible to predict the parameter settings that give performance close to the optimized performance of each method.

Model based inversion for deriving maps of histological parameters characteristic of cancer from ex-vivo multispectral images of the colon.

A model-based inversion method was used to obtain quantitative estimates of histological parameters from multispectral images of the colon and to examine their potential for discriminating between normal and pathological tissues. Pixel-wise estimates of the mucosal blood volume fraction, density of the scattering particles and thickness were derived using a two-stage method. In the first (forward) stage reflectance spectra corresponding to given instances of the parameter values were computed using Monte Carlo simulation of photon propagation through a multi-layered tissue. In the second (inversion) stage the parameter values were obtained via optimization using an iterated conditional modes algorithm based on Discrete Markov Random Fields. The method was validated on computer generated data contaminated with noise giving a mean normalized root mean square deviation (NRMSD) of 2.04. Validation on ex vivo images demonstrated that parametric maps show gross correspondence with histological features of mucosa characteristic of cancerous, precancerous and noncancerous colon lesions. The key signs of abnormality were shown to be the increase in the blood volume fraction and decrease in the density of scattering particles.

An analytical Micro CT methodology for quantifying inorganic dentine debris following internal tooth preparation.

OBJECTIVES: MicroCT allows the complex canal network of teeth to be mapped but does not readily distinguish between structural tissue (dentine) and the debris generated during cleaning. The aim was to introduce a validated approach for identifying debris following routine instrumentation and disinfection. METHODS: The mesial canals of 12 mandibular molars were instrumented, and irrigated with EDTA and NaOCl. MicroCT images before and after instrumentation and images were assessed qualitatively and quantitatively. RESULTS: Debris in the canal space was identified through morphological image analysis and superimposition of the images before and after instrumentation. This revealed that the removal of debris is prohibited by protrusions and micro-canals within the tooth creating areas which are inaccessible to the irrigant. Although the results arising from the analytical methodology did provide measurements of debris produced, biological differences in the canals resulted in variances. Both irrigants reduced debris compared to the control which decreased with EDTA and further with NaOCl. However, anatomical variation did not allow definitive conclusions on which irrigant was best to use although both reduced debris build up. CONCLUSIONS: This work presents a new approach for distinguishing between debris and structural inorganic tissue in root canals of teeth. The application may prove useful in other calcified tissue shape determination. CLINICAL SIGNIFICANCE: Remaining debris may contain bacteria and obstruct the flow of irrigating solutions into lateral canal anatomy. This new approach for detecting the amount of remaining debris in canal systems following instrumentation provides a clearer methodology of the identification of such debris.

Sucrose cryo-protection facilitates imaging of whole eye sections by MALDI mass spectrometry.

Sucrose is used as a cryo-preservation agent on large mammalian eyes post formalin fixation and is shown to reduce freezing artefacts allowing the collection of 12-µm thick sections from these large aqueous samples. The suitability of this technique for use in MALDI imaging experiments is demonstrated by the acquisition of the first images of lipid distributions within whole sagittal porcine eye sections.

Auto-align - multi-modality fluorescence microscopy image co-registration.

Multi-modality microscopes incorporate multiple microscopy techniques into one module, imaging through a common objective lens. Simultaneous or consecutive image acquisition of a single specimen, using multiple techniques, increases the amount of measurable information available. In order to benefit from each modality, it is necessary to accurately co-register data sets. Intrinsic differences in the image formation process employed by each modality result in images which possess different characteristics. In addition, as a result of using different measurement devices, images often differ in size and can suffer relative geometrical deformations including rotation, scale and translation, making registration a complex problem. Current methods generally rely on manual input and are therefore subject to human error. Here, we present an automated image registration tool for fluorescence microscopy. We show that it successfully registers images obtained via total internal reflection fluorescence (TIRF), or epi-fluorescence, and confocal microscopy. Furthermore, we provide several other applications including channel merging following image acquisition through an emission beam splitter, and lateral stage drift correction. We also discuss areas of membrane trafficking which could benefit from application of Auto-Align. Auto-Align is an essential item in the advanced microscopist's toolbox which can create a synergy of single or multi-modality image data.

Modelling and validation of spectral reflectance for the colon.

The spectral reflectance of the colon is known to be affected by malignant and pre-malignant changes in the tissue. As part of long-term research on the derivation of diagnostically important parameters characterizing colon histology, we have investigated the effects of the normal histological variability on the remitted spectra. This paper presents a detailed optical model of the normal colon comprising mucosa, submucosa and the smooth muscle layer. Each layer is characterized by five variable histological parameters: the volume fraction of blood, the haemoglobin saturation, the size of the scattering particles, including collagen, the volume fraction of the scattering particles and the layer thickness, and three optical parameters: the anisotropy factor, the refractive index of the medium and the refractive index of the scattering particles. The paper specifies the parameter ranges corresponding to normal colon tissue, including some previously unpublished ones. Diffuse reflectance spectra were modelled using the Monte Carlo method. Validation of the model-generated spectra against measured spectra demonstrated that good correspondence was achieved between the two. The analysis of the effect of the individual histological parameters on the behaviour of the spectra has shown that the spectral variability originates mainly from changes in the mucosa. However, the submucosa and the muscle layer must be included in the model as they have a significant constant effect on the spectral reflectance above 600 nm. The nature of variations in the spectra also suggests that it may be possible to carry out model inversion and to recover parameters characterizing the colon from multi-spectral images. A preliminary study, in which the mucosal blood and collagen parameters were modified to reflect histopathological changes associated with colon cancer, has shown that the spectra predicted by our model resemble measured spectral reflectance of adenocarcinomas. This suggests that an extended model, which incorporates parameters corresponding to an abnormal colon, may be effective for differentiation between normal and cancerous tissues.

Predictive power of irregular border shapes for malignant melanomas.

BACKGROUND/PURPOSE: The Irregularity Index is a measure of border irregularity from pigmented skin lesion images. The measure attempts to quantify the degree of irregularity of the structural indentations and protrusions along a lesion border. A carefully designed study has shown that the parameters derived from the Irregularity Index were highly correlated with expert dermatologists' notion of border shape. This paper investigates the predictive power of these parameters on a set of data with known histological diagnosis. METHODS: A set of 188 pigmented skin lesions (30 malignant melanomas and 158 benign lesions) was selected for the study. Their images were segmented and their border shapes were analysed by the Irregularity Index, producing four border irregularity parameters. The predictive power of these four parameters was estimated by a series of statistical tests. RESULTS: The mean values of the four border irregularity parameters were significantly different between the melanoma group and the benign lesion group. When using the four parameters to predict its disease status, the leave-one-out classification rate was 82.4%, and the area under the receiver operating characteristic curve was 0.77. A malignant melanoma was 8.9 times more likely to have an irregular border than a benign lesion. CONCLUSION: This study confirmed that border irregularity is an important clinical feature for the diagnosis of malignant melanoma. It also indicates that the computer-derived measures based on the Irregularity Index capture to certain extent the kind of irregularity which is exhibited by melanomas.

Spectral filter optimization for the recovery of parameters which describe human skin.

This paper presents a method for finding spectral filters that minimize the error associated with histological parameters characterizing normal skin tissue. These parameters can be recovered from digital images of the skin using a physics-based model of skin coloration. The relationship between the image data and histological parameter values is defined as a mapping function from the image space to the parameter space. The accuracy of this function is determined by the choice of optical filters. An optimization criterion for finding the optimal filters is defined by combing methodology from differential geometry with statistical error analysis. It is shown that the magnitude of errors associated with the optimal filters is typically half of that for typical RGB filters on a three-parameter model of human skin coloration. Finally, other medical image applications are identified to which this generic methodology could be applied.

From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions.

Through an understanding of the image formation process, diagnostically important facts about the internal structure and composition of pigmented skin lesions can be derived from their colour images. A physics-based model of tissue colouration provides a cross-reference between image colours and the underlying histological parameters. It is constructed by computing the spectral composition of light remitted from the skin given parameters specifying its structure and optical properties. The model is representative of all the normal human skin colours, irrespective of racial origin, age or gender. Abnormal skin colours do not conform to this model and thus can be detected. Once the model is constructed, for each pixel in a colour image its histological parameters are computed from the model. Represented as images, these 'parametric maps' show the concentration of dermal and epidermal melanin, blood and collagen thickness across the imaged skin as well as locations where abnormal colouration exists. In a clinical study the parametric maps were used by a clinician to detect the presence of malignant melanoma in a set of 348 pigmented lesions imaged using a commercial device, the SIAscope. Logistic regression identified the presence of melanin in the dermis, the abnormal distribution of blood within the lesion and the lesion size as the most diagnostically informative features. Classification based on these features showed 80.1% sensitivity and 82.7% specificity in melanoma detection.

An inverse method for the recovery of tissue parameters from colour images.

The interpretation of colour images is presented as an inverse problem in which a mapping is sought between image colour vectors and the physiological parameters characterizing a tissue. To ensure the necessary one-to-one correspondence between the image colours and the parameters, the mapping must be unique. This can be established through testing the sign of the determinant of the Jacobian matrix, a multi-dimensional equivalent of a discrete derivative, over the space of all parameter values. Furthermore, an optimisation procedure is employed to find the set of filters for image capture which generate image vectors minimizing the mapping error. This methodology applied to interpretation of skin images shows that the standard RGB system of filters provides for a unique mapping between image values and parameters characterizing the normal skin. It is further shown that an optimal set of filters reduces the error of quantification by a factor of 2, on average.