title: Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry.
abstract: BACKGROUND
  Pressure ulcers have become subject of study in recent years due to the treatment high costs and decreased life quality from patients. These chronic wounds are related to the global life expectancy increment, being the geriatric and physical disable patients the principal affected by this condition. Injuries diagnosis and treatment usually takes weeks or even months by medical personel. Using non-invasive techniques, such as image processing techniques, it is possible to conduct an analysis from ulcers and aid in its diagnosis.
  
  
  METHODS
  This paper proposes a novel technique for image segmentation based on contrast changes by using synthetic frequencies obtained from the grayscale value available in each pixel of the image. These synthetic frequencies are calculated using the model of energy density over an electric field to describe a relation between a constant density and the image amplitude in a pixel. A toroidal geometry is used to decompose the image into different contrast levels by variating the synthetic frequencies. Then, the decomposed image is binarized applying Otsu's threshold allowing for obtaining the contours that describe the contrast variations. Morphological operations are used to obtain the desired segment of the image.
  
  
  RESULTS
  The proposed technique is evaluated by synthesizing a Data Base with 51 images of pressure ulcers, provided by the Centre IGURCO. With the segmentation of these pressure ulcer images it is possible to aid in its diagnosis and treatment. To provide evidences of technique performance, digital image correlation was used as a measure, where the segments obtained using the methodology are compared with the real segments. The proposed technique is compared with two benchmarked algorithms. The results over the technique present an average correlation of 0.89 with a variation of ±0.1 and a computational time of 9.04 seconds.
  
  
  CONCLUSIONS
  The methodology presents better segmentation results than the benchmarked algorithms using less computational time and without the need of an initial condition.
authors.affiliation: Mathematical Modeling Research Group, School of Sciences, Universidad EAFIT, Carrera 49 NO 7 Sur-50, Medellín, Colombia.
authors.forename: Ortiz P
authors.initials: OP
authors.lastname: David
authors.affiliation: Mathematical Modeling Research Group, School of Sciences, Universidad EAFIT, Carrera 49 NO 7 Sur-50, Medellín, Colombia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Sierra-Sosa
authors.affiliation: DeustoTech - Fundación Deusto, Avda/Universidades 24, 48007, Bilbao, Spain. mbgarciazapi@deusto.es.|Facultad Ingeniería, Universidad de Deusto, Avda/Universidades 24, 48007, Bilbao, Spain. mbgarciazapi@deusto.es.
authors.forename: Begoña García
authors.identifier: http://orcid.org/0000-0002-9356-1186
authors.initials: BG
authors.lastname: Zapirain
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D003668:Pressure Ulcer / Q000000981:diagnostic imaging*
keywords: Image segmentation; Medical images; Synthetic frequencies; Toroidal geometry