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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Skin imaging methods for melanoma diagnosis

There are many skin image capture methodologies developed and used. Here is a short review of them: Dermatoscopic photography The deepest layer of skin can be reached – Papillary dermis Resolution – depends on the optical system View of skin – Horizontal The main disadvantage is reflections of light from skin surface – stratum cornea.   Dermatoscopic oil immersion photography The deepest layer of skin can be reached – Papillary dermis Resolution – depends on the optical system View of skin – Horizontal Reflections of light from skin surface are smaller because of oil used between camera optics and skin.

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Polarizer test

I used simple lamp directed to glossy table surface. One polarizer is in front of lamp and other is in front of lens of digital camera. Both polarizes are perpendicularly oriented to each other. How does this work? There is a theory about an angle on which the incident polarized electromagnetic waves turn reflects from surface with polarization plane turned in 90 degrees. When light going through polarizer towards the surface, the light is polarized in one direction and when it reflects from surface it is turned by 90 degrees and those waves are filtered by another perpendicular polarizer in front of lens.

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Removing 60Hz from ECG using digital band stop filter

Let’s make a filter, which filters off the 60Hz frequency from ECG signal. As we know American power supply is 60Hz. This is common noise in biomedical signals, while they are powered from industrial power supply. This type of noise can be defined easily and can be filtered as parameters of noise are known. Here is one example of how to implement FIR filter using mathematical tools, like Matlab. This can be done by using microcontroller, like ARM or even ARM, because the frequencies are up to 1 KHz. Initial conditions: f0=60Hz – pover supply frequency; fs=500Hz – sampling rate; frequencies who define complex zeros: we get w0=0.754; Positions of complex zeros: Zeros and poles in z plane System Function From it we can calculate filter coefficients: And filter coefficients:         Also we know that: And here we get filter characteristics: We have band stop filter at 60Hz and its jam at 60Hz is -300dB. Bellow is filter structure: Now using this filter we can filter ECG signal: As you can see this is simple FIR filter. In other words there is nothing more than average function which doesn’t need much of resources. The other benefit of FIR filter…

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Imaging system with four different wavelengths obtained from LEDs

In order to evaluate skin pigmentation in different skin layers, there is a special light adapter needed to take multispectral pictures of skin. As there are different optical properties of skin pigments, four different light sources have been chosen. blue λ= 470 nm – highly absorbed by epidermal melanin green λ= 576 nm – hemoglobin peak red  λ= 660nm – epidermal-dermal boundary IR λ= 865 nm – low absorption, sensitive to scattering to measure papillary dermis thickness. There was lighting source for “Nikon Coolpix E3100” digital camera developed to take multispectral images of skin. Making adapter The drawing of  lighting adapter The lighting adapter isn’t very hard to build. You need to make a circular PCB and solder LEDs with protective resistors. The PCB image:

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