Automatic method for the dermatological diagnosis of selected hand skin features in hyperspectral imaging

Abstract

IntroductionHyperspectral imaging has been used in dermatology for many years. The enrichment of hyperspectral imaging with image analysis broadens considerably the possibility of reproducible, quantitative evaluation of, for example, melanin and haemoglobin at any location in the patient's skin. The dedicated image analysis method proposed by the authors enables to automatically perform this type of measurement.Material and methodAs part of the study, an algorithm for the analysis of hyperspectral images of healthy human skin acquired with the use of the Specim camera was proposed. Images were collected from the dorsal side of the hand. The frequency λ of the data obtained ranged from 397 to 1030 nm. A total of 4'000 2D images were obtained for 5 hyperspectral images. The method proposed in the paper uses dedicated image analysis based on human anthropometric data, mathematical morphology, median filtration, normalization and others. The algorithm was implemented in Matlab and C programs and is used in practice.ResultsThe algorithm of image analysis and processing proposed by the authors enables segmentation of any region of the hand (fingers, wrist) in a reproducible manner. In addition, the method allows to quantify the frequency content in different regions of interest which are determined automatically. Owing to this, it is possible to perform analyses for melanin in the frequency range λ E ∈(450,600) nm and for haemoglobin in the range λ H ∈(397,500) nm extending into the ultraviolet for the type of camera used. In these ranges, there are 189 images for melanin and 126 images for haemoglobin. For six areas of the left and right sides of the little finger (digitus minimus manus), the mean values of melanin and haemoglobin content were 17% and 15% respectively compared to the pattern.ConclusionsThe obtained results confirmed the usefulness of the proposed new method of image analysis and processing in dermatology of the hand as it enables reproducible, quantitative assessment of any fragment of this body part. Each image in a sequence was analysed in this way in no more than 100 ms using Intel Core i5 CPU M460 @2.5 GHz 4 GB RAM.

Spectral-divergence based pigment discrimination and mapping: A case study on The Scream (1893) by Edvard Munch

“…In the remote sensing field, the concept of spectral variability has been used to solve unmixing tasks (Drumetz, Chanussot, and Jutten 2016). It also remains to be explored how feature vectors computed in this space can be used in medical applications, such as dermatological diagnosis based on hyperspectral imaging (Koprowski et al 2014).…”

Calibration and segmentation of skin areas in hyperspectral imaging for the needs of dermatology

“…The thus designated absolute difference values are the basis for the segmentation of individual objects. Figure 9 b) shows sample reference waveforms obtained based on the spread spectrum curves known from the literature which are the pattern [ 11 , 36 ]. Figure 9 c) shows the graph for each i -th pixel.…”

“…The normalized images L O ( m , n , k ) also enable automatic segmentation in accordance with the reference curve of melanin and haemoglobin content for each wavelength. The reference content of melanin and haemoglobin can be acquired from external sources, for example from literature data [ 36 ], or on the basis of the selected ROI. In the latter case, the result will be as follows - image L D ( m , n ), i.e.…”

Hyperspectral imaging in medicine: image pre‐processing problems and solutions in Matlab

“…The image L GRAY ( m , n , i ) has an M × N × I resolution, where M ‐number of rows, N ‐number of columns, I ‐number of 2D images, equal to the number of different wavelengths λ during acquisition. In relation to the known types of software (FreeMat, Matlab, Scilab, Octave) that use raster graphics and nomenclature used in hyperspectral imaging, there occur synonyms that are used later in this paper, lines (rows – symbol m ), samples (columns – symbol n ) and bands (image number for a given wavelength – symbol i ) . The method for saving this type of image on a computer is different and is closely dependent on the type of camera used.…”

“…For most hyperspectral cameras, e.g. Specim, these are *.dat or *.raw files . Both of these file formats are closely associated with the specificity of the camera operation itself.…”