This study aims at finding solutions of image quality problems in the area of biophotonics. The resulting image quality depends on hardware capabilities of the object illumination, image sensor, optical system and image post processing (image storage format). Although several of the quality problems of the imaging systems may be prevented in advance, some flaws may not be removed as easily. For example, uneven illumination cases, where skin is not flat (for example: nose, ear). Due to that, it is not possible to create uniform illumination field and the resulting optical image has noticeable differences across it. Sometimes, it is the skin texture that could cause problems for the automatic malformation classification and diagnosis. In this case, image quality enhancement can be helpful for removing different image flaws and raise the precision of malformation classification.
In this research methods for solving different image quality problems in multispectral images of skin malformations are proposed. Multispectral image acquisition and proposed methods are tested on noncontact skin cancer analyzing device prototype. Nevertheless, it could be applied on other multispectral image analysis algorithms. Pilot studies of filtering methods show good results when trying to deal with uneven lighting problems in images. Quality enhancement methods include high pass filtering, extraction of nonskin fragments (hair, markers, etc.), image stabilization and other methods. The image quality enhancement techniques were clinically tested on multispectral images of different skin malformations and the results of the study are presented in this paper.