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ABCD method for screening skin cancer

To improve diagnostic accuracy, the  ABCD rule of lesion screening is widely used which is based on asymmetry (A), border (B), color (C), and differential structure (D) measuring. •         A total dermatoscopic value (TDV) results from the calculation TDV = A·1,3 + B·0,1 + C·0,5 + D·0,5 •         This score 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 Asymmetry A – Asymmetry of Shape, Structure, and Color. The skin lesion is divided into four regions, and there is symmetry inspected across x or y-axis. If asymmetry is only by one axis, it gives 1 point and if on both axes there are 2 points calculated. So for shape, structure, and color, there can be 6 points maximum.

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MoleExpert micro software

The MoleExpert software is a product is based on experiences of many years with the automated analysis of pigmented skin lesions. Essential requirement with this software project was the usefulness of the software with the most different photograph systems. Qualitatively high-quality, evenly and well illuminated top illumination-microscopic pictures of the lesions is the most essential condition for the operability of this software. MoleExpert micro was developed for the support of diagnostic identification. The system spends no diagnosis for this reason, but supplies as results of measurement data to asymmetry, for the delimitation of the lesion, to the color and the size. These parameters of the ABCD rule are recognized for some years as important dermatoscopic parameters. According to a particular algorithm adapted on the image analysis, the four ABCD values are combined into a total core, which can take values between zero to unify. With lesions with high Score, it acts with higher probability around a Melanoma, than with lesions with low Score. Download demo version from here: MoleExpert micro

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Recognizing skin cancer symptoms using model based imaging

When a quality skin model is constructed – recognizing skin cancer symptoms can be more comfortable as many factors indicate the threat of skin cancer. Of course, this can’t give 100% results, as there are many shortcomings connected with skin lesion variety and interpretation errors. But some guides may help. Three main factors can indicate the risk of skin cancer. Recognizing skin cancer symptoms can be based on them. They are: Melanin presence in the papillary dermis; The thickness of papillary dermis; Blood behaviour around the lesion and inside it. Firs important factor is melanin present in the dermis. This is the main factor in recognizing skin cancer symptoms. If melanin spread in the papillary dermis or even dermis, this is a significant probability of being skin cancer symptoms, but not always. Several subfactors in this issue, like melanin spreading figure, depth, and melanin density within this shape. If there are more irregularities in the spreading area, there are more risks. Another factor in recognizing skin cancer symptoms is papillary layer thickness. In not going into deep too much there can be said, that thinner this layer, the more significant risk.

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SkinSeg – segmentation of melanoma

SkinSeg is a simple tool used for skin lesion segmentation. This program was developed by Intelligent Systems laboratory students: L. Xu, M. Jackowski, A. Goshtasby, C. Yu, D. Roseman, S. Bines, A. Dhawan, A. Huntley. Their method is working similarly as in my earlier experiment with the MATLAB pigmented lesion boundary tracing algorithm. The first image is converted to intensity image, and then the lesion edges are detected. ant test results: The more informative description you can find here The program can be downloaded from here: https://www.cs.wright.edu/people/faculty/agoshtas/skinseg.zip  This version of the Skin Cancer Segmentation program (skinseg) runs on the Windows 95/NT platforms. Make sure all files reside in the same directory after extraction. No setup program is required to install skinseg on your machine. To run, execute the program skinseg.exe.

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DullRazor – digital skin hair shaver

DullRasor uses image processing techniques to analyze and segments skin areas with dark hair. This program removes dark hairs form images, and makes skin lesion images clean to further processing.    Many skin images contain various numbers of hairs. Other skin segmentation programs may mislead because of hairs – especially dark ones. One solution can be shaving skin before taking pictures of it. But shaving of skin adds more time to processing, and this is uncomfortable and in some cases unaesthetic.  Hence, a software approach for dark, thick hair removal from skin images is needed.

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Skin Cancer causing factors

Substance Where this can be found How to avoid Arsenic Pesticides, wood preservatives, alloy additive non-ferrous metals. Use protective clothing when working with arsenic substances Creosote Wood preservative Use protective clothing when working with  creosote substances Ionizing radiation Ionizing radiation is specific industrial sterilization sources Limit exposure if possible. Wear a dosimeter while working with radiation. Sunlight Summer, and when on a sun holiday. Avoid strong sunlight, especially at midday. Wear protective clothing to protect your skin. Cover exposed skin with sunscreen of factor 15 or higher. Tar Coal tar Use protective clothing Glutaraldehyde Glutaraldehyde is used as a disinfectant. It is also can be found in X-ray films. Use protective clothing when dealing with glutaraldehyde. Work only in well-ventilated areas. Soot Black particles of carbon, produced by incomplete combustion of coal, oil, wood, or other fuels Use protective clothing Pitch It is made by the destructive distillation of wood or coal tar Use protective clothing Asphalt Sticky, black and highly viscous liquid or semi-solid that is present in most crude petroleum and in some natural deposits Use protective clothing Paraffin wax   A member of the alkenes series Use Gloves Smoking   Smoking cigarettes increase your risk of cell carcinoma…

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Healthy skin reflectance model

This pilot study is intended to investigate possibilities of skin nevus imaging using digital still image camera. The main objective is to develop method of dermatology images interpretation, which enables the looking on the skin lesions and nevus from the optical background of skin coloration. Kubelka-Munk calculation method for light transport and reflection from multilayered complex media is applied in modeling of light reflection spectra of skin. Calculation of model shows that red, green, blue and infrared colors lighting is satisfactory to access distribution of comparative estimates of the following skin parameters: volume fraction of melanin in epidermal layer, volume fraction of hemoglobin in dermal layer, presence of dermal melanin and thickness of papillary layer. Performance of image processing method on fourteen samples of images of common melanocytic nevi, dysphasic melanocytic nevi, Spitz nevus, thrombotic hemangioma and surrounding healthy skin were made. Skin spectral properties Understanding how light interacts with skin, can assist in designing physics based dermatological image processing. The key is understanding how light interacts with skin tissue. Skin consists of different layers with different spectral properties. Fig 1. Skin model and its physical view When incident light is applied to skin layer, the part of it absorbed…

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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.

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Skin lession boundary tracing algorithm

I found a Matlab to be a convenient tool which allows easily to trace boundaries of objects in a picture. So I adopted it to skin lesions. This can be used for automatic detection of skin irregularities and utilized to calculate lesion properties like the asymmetry of shape, or border irregularities, who can help in detecting melanoma. There are numerous investigations done, so I only put a few examples of how it looks. I will give you my source code so that you can try it on your own. Look at my results: 1) And it also finds the center of mass:

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