Ela Claridge

Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions

SummaryBackground  Spectrophotometric intracutaneous analysis (SIA) is a new technique for imaging pigmented skin lesions and for diagnosing melanoma. The SIAscope produces eight narrow‐band spectrally filtered images of the skin over an area of 24 × 24 mm with radiation ranging from 400 to 1000 nm.

Computer screening for early detection of melanoma--is there a future?

Computer image analysis in the study of pigmented lesions is critically examined and discussed in the light of the current published data. The potential for objective analysis by computers as a possible screening aid for the inexperienced clinician is discussed. The future for this technology is exciting if handled with care.

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 The paper specifies the parameter ranges corresponding to normal colon tissue, including some previously unpublished ones.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.

Monte Carlo modelling of the spectral reflectance of the human eye

The interpretation of in vivo spectral reflectance measurements of the ocular fundus requires an accurate model of radiation transport within the eye. As well as considering the scattering and absorption processes, it is also necessary to account for appropriate histological variation. This variation results in experimentally measured spectra which vary, both with position in the eye, and between individuals. In this paper the results of a Monte Carlo simulation are presented. Three histological variables are considered: the RPE melanin concentration, the choriodal haemoglobin concentration and the choroidal melanin concentration. By considering these three variables, it is possible to generate model spectra which agree well with in vivo experimental measurements of the nasal fundus. The model has implications for the problem of extracting histological parameters from spectral reflectance measurements. These implications are discussed and a novel approach to interpretation of images of the ocular fundus suggested.

Morphological algorithm design for binary images using genetic programming

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.

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

The 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.

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.

Noninvasive Skin Imaging

An earlier model of colour formation within normal human skin was extended to include architectural distortions associated with various pigmented skin lesions, including malignant melanoma. The extended five-layer model makes it possible to derive parameters characterising the thickness and pigment composition of the skin layers from calibrated colour and infrared images of skin lesions. The extracted parameters can be used to reconstruct a full 3-dimensional model of the skin architecture which conveys information grossly comparable to that available through microscopical examination of biopsied skin tissue.

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 the 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. This approach was successfully applied to the analysis of images of pigmented skin lesions. Histological parametric maps showing the concentration of dermal and epidermal melanin, blood and collagen thickness across the imaged skin have been used to aid early detection of melanoma. A clinical study on a set of 348 pigmented lesions showed 80.1% sensitivity and 82.7% specificity.

An Artificially Evolved Vision System for Segmenting Skin Lesion Images

We present a novel technique where a medical image segmentation system is evolved using genetic programming. The evolved system was trained on just 8 images outlined by a clinical expert and generalised well, achieving high performance rates on over 90 unseen test images (average sensitivity 97% , average specificity 81%). This method learns by example and produces fully automatic algorithms needing no human interaction or parameter tuning, which although complex, runs in approximately 4 seconds.