Quality enhancement of multispectral images for skin cancer optical diagnostics

Abstract

Melanoma is the least common but deadliest skin cancer, accounting for only about 1% of all cases, but is the cause of the vast majority of skin cancer death. In some parts of the world, especially among western countries, melanoma is becoming more common every year. The detection of melanoma in early stage can be helpful to cure it. Unfortunately, long ques and high prices for dermatology service can result in the skin cancer diagnosis at its later stage, thus increasing the risk of mortality for the patient. It is important to provide a non-invasive optical device for primary care physicians to help diagnose different skin malformation based on obtained optical images. Such device will be able to automatically classify different skin malformations, but the results of classification strongly rely on obtained image quality. 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.