Review on skin lesion imaging, analysis and automatic classification

The goal of any imaging methodology used in dermatology is to diagnose melanoma in early stages because it depends on the 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 are 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 accurately. Besides using computerized means there are excellent 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 skin visualization 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 a polarized light source. Then there are diminished reflections of light form stratum corneum (top layer of skin). Then is easier to estimate the lesion structures like dots, globules, nets that are the primary indicators to melanoma diagnosis.

The different illumination method called epiliuminescence can be used to get the image from deeper skin layers. The light is directed straight into these layers and reflected goes back through lesion giving more information about the consistence of light absorbers in these layers. This method of illumination improves the diagnostic accuracy of 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 skin 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 300µm, because of absorbance [2]. The distance between two layers (axial resolution) can be about 2 – 5 µm. The main disadvantage of this method is complicated acquisition of image from reflected laser beam.

Ultrasound visualization is usually used to measure the depth of melanoma [1]. The other uses of ultrasound are limited by minimal tissue differences between healthy skin and lesion. If there is no melanoma practically, there are no differences. When the doctor diagnoses melanoma, then he uses high-frequency ultrasound (over 30 MHz) to measure penetration depth to make a 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 µm. Visualization depth is 1 – 1.5 mm.

The wide variety of methods shows that there is no best universal visualization. Some of them are used for different needs; others are costly. The choice of method depends on what features of skin lesions is wanted to visualize, and on the 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 (color), 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)

This score TDV contributes to the differentiation between benign and malignant lesions: 1,00 – 4,75 – benign skin lesion, 4,75 – 5,45 – suspicious, more than 5,45 – melanoma.

According to “Romedix” organization, there is presented comparison table of software solutions, using ABCD rule.


N – Not known
Most of those software solutions have some major disadvantages. Firs of all, they are analyzing geometrical and structural or color changes extracted from plain surface picture, which does not contain enough useful information. Analysis is not connected with physical nature of skin image. There are no associations with histological parameters of skin.

Another disadvantage is that diagnostic results do not always support diagnosis decisions. If there is support of databases, then those pictures with diagnostic results are stored and should be used in decisions support algorithms. Nowadays there is a tendency to request past data and knowledge to gain better diagnosis results in the future.

Digital dermatoscopic images firstly have to be parametrized for automatic classification. The in-depth study of skin nature has to be done before to parametrize it. For extraction skin or lesion optical features, it is beneficial to use a multilayer skin model [5].

The most common is the four-layer skin model: Stratum Cornea, Epidermis, Papillary dermis and Reticular dermis.

Stratum Corneum is a top thin layer, which is a protective layer consisting of keratin-impregnated cells and it varies considerably in thickness. Apart from scattering the light, it is optically neutral.

The epidermis is largely composed of connective tissue. It also contains the melanin-producing cells, the melanocytes, and their product, melanin. In this layer there is strong absorption of blue and ultraviolet light. Melanocytes absorb most of this light. It behaves like blue and ultraviolet filter, which characteristics depend on concentration of melanocytes. Within the epidermal layer there is very little scattering, with the small amount that occurs being forward directed. The result is that all light not absorbed by melanin can be considered to pass into the dermis. By this description, epidermis can be characterized by wavelength-dependent coefficients: absorption coefficient of melanin mam(l) and melanin concentration cm [5].

Dermis consist of two sub layers: papillary dermis and reticular dermis. Dermis itself consists of collagen fibers and, in contrast to the epidermis; it contains sensors, receptors, blood vessels and nerve ends. In papillary dermis the collagen fibers are thinner and it behaves as highly back-scattering layer. Any incident light is back scattered towards surface. Scattering is grater in red spectrum and going greater to infrared. Because infrared is not absorbed by melanin and blood, this part of spectrum is best for assessing thickness of papillary dermis. Papillary dermis can be described by the absorption coefficients for hemoglobin mah(l), the hemoglobin concentration ch, the scatter coefficient for collagen mspd, and the thickness of collagen layer dpd.

Within the reticular dermis, the large size of collagen fiber bundles causes highly forward-directed scattering. Thus any light, which gets to this layer, is passed deeper into the skin and does not contribute to the spectrum remitted from the skin. Reticular dermis can be described by scattering coefficient msrd, and thickness of layer drd.

Model-based feature detection methods

The parameters, describing skin pigmentation, can be found by modelling light transport through layered skin model. To implement this model there can be used Kubelka-Munk theory (Egan and Hilgeman, 1979). It calculates the remitted and transmitted light separately for each layer. And using Beer-Lambert theory of light absorption, there can be calculated concentrations of pigments in each layer, when reflectance coefficients are known [5].

This physical interpretation would let mathematically to define the color changes because of melanin concentration changes in each layer.

The more significant part of melanoma diagnostic information is hidden in the dermis. In more than 90% of skin cancer cases, the dermal melanin causes melanoma [6].

The biggest problem is to define the melanin concentration in reticular dermis because almost all light is back-scattered from papillary dermis and the rest of light is forward scattered into deeper tissues almost without reflection. To solve this problem there should help Beer-Lambert law combined with Monte-Carlo simulation. Monte-Carlo simulation itself requires much of calculations, but combining with Beer-Lambert theory, this time can be significantly reduced. When there are known characteristics of upper layers by using Monte–Carlo simulation there can be calculated optical properties of reticular dermis with adequate probability.

The main equations used in skin modeling calculations are [6]:





d – layer thickness, k ≈ μa – absorption coefficient, s ≈ μs- scattering coefficient of each layer, Ri and Ti – represents reflected and transmitted light intensities in i-th layer of the layered system.

Modeling enables to choose colors of lightening to obtain adequate spectroreflectoscopy images of skin lesions, also theoretically confirms relations between histological features and features indicating colors in images of benign skin lesion, suspicious lesion or melanoma lesion. Skin photography imaging with adapted lightening and the model based digital image processing, parametrization and classification of skin lesion images provides objective images and estimates for early melanoma diagnosis and contributes into future of dermatological decision support system.


The survey shows the possible objective evaluation of skin lesion images based on established relations between histological features of lesion and color of its image. Mathematical modeling validates relationship. The future work is planed to construct multicolor illumination adapter for widely distributed and cheap digital cameras, which might become affordable imaging and diagnosis tool for wide range of dermatologists.


1. Valiukevičienė S. Melanomų diagnostika ir gydymas, Kaunas, KMU, 2002: 85p.

2. Ashfaq A. Marghoob, Lucinda D. Swindle, Claudia Z. M. Moricz, Fitzgerald A. Sanchez Negron, Bill Slue, Allan C. Halpern, Alfred W. Kopf, Instruments and new technologies for the in vivo diagnosis of melanoma New York, 2003.

3. D.D. Verma and A. Fahr, Confocal laser scanning microscopy study using lipophilic fluorescent probe DiI incorporated in liposomes for investigating the efficacy of a new device for substance deposition into deeper layers of the skin : Institut für Pharmazeutische Technologie und Biopharmazie, Philipps-Universität Marburg, 2001,

4. W. Kopf, Skin oncology teaching center 2002 //

5. Leading Dermatological Imaging Systems Comparison Table, 2002 05 12//

6. Ela Claridge, Symon Cotton, Per Hall, Marc Moncrieff, From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions, School of Computer Science, The University of Birmingham, Birmingham, 2002,

7. Cotton S., Claridge E, Hall P., Detection the presence of dermal melanin through computer image interpretation, Abstract, Skin Research and Technology 1999.

One Comment:

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    That was an inspiring post,

    I have been reading alot about melonomas and skin cancer and this has by far been the best resource,

    Keep up the good work,

    Thanks for writing, most people don’t bother.

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