title: Segmentation in dermatological hyperspectral images: dedicated methods.
abstract: BACKGROUND
  Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefore, the authors proposed three new dedicated methods of hyperspectral image segmentation with special reference to the time of analysis.
  
  
  METHODS
  The segmentation methods presented in this paper were tested and profiled to the images acquired from different hyperspectral cameras including SOC710 Hyperspectral Imaging System, Specim sCMOS-50-V10E. Correct functioning of the method was tested for over 10,000 2D images constituting the sequence of over 700 registrations of the areas of the left and right hand and the forearm.
  
  
  RESULTS
  As a result, three new methods of hyperspectral image segmentation have been proposed: fast analysis of emissivity curves (SKE), 3D segmentation (S3D) and hierarchical segmentation (SH). They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE-2.3 ms, S3D-1949 ms, SH-844 ms for the computer with Intel(®) Core i7 4960X CPU 3.6 GHz) and more accurate (SKE-accuracy 79 %, S3D-90 %, SH-92 %) in comparison with typical methods known from the literature.
  
  
  CONCLUSIONS
  Profiling and/or proposing new methods of hyperspectral image segmentation is an indispensable element of developing software. This ensures speed, repeatability and low sensitivity of the algorithm to changing parameters.
authors.affiliation: Department of Biomedical Computer Systems, University of Silesia, Bedzinska 39, 41-200, Sosnowiec, Poland. koprow@us.edu.pl.
authors.forename: Robert
authors.initials: R
authors.lastname: Koprowski
authors.affiliation: Department of Community Pharmacy, School of Pharmacy and Division of Laboratory Medicine in Sosnowiec, Medical University of Silesia in Katowice, Kasztanowa 3, 41-200, Sosnowiec, Poland.
authors.forename: Paweł
authors.initials: P
authors.lastname: Olczyk
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D012867:Skin / Q000166:cytology; D013997:Time Factors; D055815:Young Adult
keywords: Conditional dilatation; Conditional erosion; Fast segmentation method; Hyperspectral imaging; Image processing; Thresholding