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

Abstract

IntroductionAmong the currently known imaging methods, there exists hyperspectral imaging. This imaging fills the gap in visible light imaging with conventional, known devices that use classical CCDs. A major problem in the study of the skin is its segmentation and proper calibration of the results obtained. For this purpose, a dedicated automatic image analysis algorithm is proposed by the paper’s authors.Material and methodThe developed algorithm was tested on data acquired with the Specim camera. Images were related to different body areas of healthy patients. The resulting data were anonymized and stored in the output format, source dat (ENVI File) and raw. The frequency λ of the data obtained ranged from 397 to 1030 nm. Each image was recorded every 0.79 nm, which in total gave 800 2D images for each subject. A total of 36'000 2D images in dat format and the same number of images in the raw format were obtained for 45 full hyperspectral measurement sessions. As part of the paper, an image analysis algorithm using known analysis methods as well as new ones developed by the authors was proposed. Among others, filtration with a median filter, the Canny filter, conditional opening and closing operations and spectral analysis were used. The algorithm was implemented in Matlab and C and is used in practice.ResultsThe proposed method enables accurate segmentation for 36’000 measured 2D images at the level of 7.8%. Segmentation is carried out fully automatically based on the reference ray spectrum. In addition, brightness calibration of individual 2D images is performed for the subsequent wavelengths. For a few segmented areas, the analysis time using Intel Core i5 CPU RAM M460@2.5GHz 4GB does not exceed 10 s.ConclusionsThe obtained results confirm the usefulness of the applied method for image analysis and processing in dermatological practice. In particular, it is useful in the quantitative evaluation of skin lesions. Such analysis can be performed fully automatically without operator’s intervention.

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

“…The application presented in the paper is designed for pre‐processing of hyperspectral images. It enables to eliminate problems related to: reading large (>1 GB) files – the method for converting them to a sequence of individual 2D images has been proposed (the author' source code in Matlab has been provided in the Supplementary Materials); performing image pre‐processing and calculations in the selected ROI – filtration, calibration and normalization (the source code in Matlab has been provided in the Supplementary Materials); automatic calibration provided that the described requirements are met ; high costs associated with the purchase of proprietary software . …”

“…By providing a perpendicular position of the camera relative to the object and even illumination, calibration can be simplified to direct registration of the object in the area, where, for example, two patterns, white and black, will be placed in the extreme corners . These areas may be of any size but not less than half the size of the median filter mask (if used).…”

“…There are also other methods of hyperspectral image calibration , but their application in practice is much less cumbersome. One of them is the use of black and white patterns located for several columns on the image edge.…”

“…Sensitivity range of the camera and its bandwidth are individually selected, most often at the time of buying the camera . Depending on the individual choice of these parameters and the selected scan area, the image L GRAY ( m , n , i ) is registered, where m ‐row, n ‐column, i ‐number of a 2D image in a sequence . 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.…”

Hyperspectral imaging of retinal microvascular anatomy

“…Diagnostically, hyperspectral imaging has demonstrated utility for identifying gastric cancer [28] , and in dermatology, for evaluating skin lesions [29] . Furthermore, studies in swine show that hyperspectral imaging devices have the potential to become a real-time intraoperative tool for identifying anatomical structures in minimally non-invasive surgeries, such as laparoscopic cholecystectomy, reducing the need for radioactive contrast and aiding surgeons to avoid adverse and serious injury to non-target, vital tissues [30,31] .…”