Introduction

The goal of any imaging methodology used in dermatology is to diagnose melanoma in early stages, because on it depends effectiveness of treatment. Investigations shows, that early diagnosis is more than 90% curable and late is less than 50% [1]. The diagnosis and successful treatment is often supplemented with permanent monitoring of suspicious skin lesions.

Doctor's diagnosis is reliable, but this procedure takes lots of time, efforts. These routines can be automated. It could save lots of doctor's time and could help to diagnose more accurate. Besides using computerised means there are good opportunity to store information with diagnostic information in order to use it for further investigations or creation of new methods of diagnosis.

Skin lesion imaging methods

We found that there are number of various imaging methods of skin lesions [2]. The simplest visualisation method is photography. This method gives only top layer skin image. In order to get deeper layer image there is oil immersion used. It reduces reflections of surface and brightens the image of epidermis ? the second skin layer.

The better results are reached when photos are made with polarized light source. Then there are diminished reflections of light form stratum corneum (top layer of skin). Then is easer to estimate the lesion structures like dots, globules, nets that are the major indicators to melanoma diagnosis. The different illumination method called epiliuminescence can be used in order to get the image from deeper skin layers. The light is directed straight in to these layers and reflected goes back through lesion giving more information about consistence of light absorbers in these layers. This method of illumination improves diagnosis accuracy up to 10 percent [2].

Other interesting solution of getting more information from skin is using multi spectral photography [2]. There is used narrow frequency band of light illumination. Those images give information about consistence and concentration of absorbers and reflectors in the skin. The idea is that different pigment of skin absorbs different light wave, determining the colour of our skin. When those photos are made with range of light waves, we can calculate the reflectance frequency characteristics of skin. And comparing to normal skin characteristic there can be made diagnostic decisions about skin pigment consistency.

Other imaging method using laser is called CLSM (confocal scanning laser microscopy). It uses red or near infrared low power laser beam to scan skin surface. This beam can be focused in to different deep to get the image of it. The deep is limited to 300'm, because of absorbance [2]. The distance between two layers (axial resolution) can be about 2 ? 5 'm. The main disadvantage of this method is complicated acquisition of image from reflected laser beam.

Ultrasound visualisation is usually used to measure depth of melanoma [1]. The other uses of ultrasound are limited by very little tissue differences between normal skin and lesion. If there is no melanoma practically there is no any differences. When doctor diagnoses melanoma, then he uses high frequency ultrasound (over 30 MHz) to measure penetration depth in order to make correct cut during surgery.

In optical coherence tomography is used short near infrared light pulses focused to papillary dermis [2]. Reflected light is combined with reference light that is reflected from mirror system in order to determine the depth of papillary dermis. Measurement of the interference pattern allows determination of the position within the tissue where the light was reflected. Using recent technologies with ultra sort light pulse, the maximum obtained resolution is 2 ? 4 'm. Visualisation depth is 1 ? 1.5 mm.

The wide variety of methods shows, that there is no best universal visualisation. Some of them are used for different needs, other are very expensive. The choice of method depends on what features of skin lesions is wanted to visualise, and on availability of resources. Algorithms of skin image processing

Digital dermatoscopic images itself does not provide formal and determined information. To get diagnostic information the digital image processing is used. Commonly used methods are based on geometrical feature extraction from image with lesion. The USA national health institute offers ABCD rule for classification of dermatological images in to benign, suspicious and melanoma [3]. ABCD are the letters of first feature words: A (asymmetry), B (border), C (bolour), D (dermatoscopic structures). According to these four values there are total dermatoscopic value calculated by formula: TDV = A?1,3 + B?0,1 + C?0,5 + D?0,5 (1)