title: Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues.
abstract: Hyperspectral images can provide useful biochemical information about tissue samples. Often, Fourier transform infrared (FTIR) images have been used to distinguish different tissue elements and changes caused by pathological causes. The spectral variation between tissue types and pathological states is very small and multivariate analysis methods are required to describe adequately these subtle changes. In this work, a strategy combining multivariate curve resolution-alternating least squares (MCR-ALS), a resolution (unmixing) method, which recovers distribution maps and pure spectra of image constituents, and K-means clustering, a segmentation method, which identifies groups of similar pixels in an image, is used to provide efficient information on tissue samples. First, multiset MCR-ALS analysis is performed on the set of images related to a particular pathology status to provide basic spectral signatures and distribution maps of the biological contributions needed to describe the tissues. Later on, multiset segmentation analysis is applied to the obtained MCR scores (concentration profiles), used as compressed initial information for segmentation purposes. The multiset idea is transferred to perform image segmentation of different tissue samples. Doing so, a difference can be made between clusters associated with relevant biological parts common to all images, linked to general trends of the type of samples analyzed, and sample-specific clusters, that reflect the natural biological sample-to-sample variability. The last step consists of performing separate multiset MCR-ALS analyses on the pixels of each of the relevant segmentation clusters for the pathology studied to obtain a finer description of the related tissue parts. The potential of the strategy combining multiset resolution on complete images, multiset segmentation and multiset local resolution analysis will be shown on a study focused on FTIR images of tissue sections recorded on inflamed and non-inflamed palatine tonsils.
authors.affiliation: Chemometrics group, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain; Environmental Chemometrics Group, Department of Environmental Chemistry, Institute of Environmental Assessment and Water Diagnostic (IDAEA-CSIC), Barcelona, Spain. Electronic address: piqueras.sara@gmail.com.
authors.forename: S
authors.initials: S
authors.lastname: Piqueras
authors.affiliation: Leibniz Institute of Photonic Technology, Jena, Germany.
authors.forename: C
authors.initials: C
authors.lastname: Krafft
authors.affiliation: Leibniz Institute of Photonic Technology, Jena, Germany.
authors.forename: C
authors.initials: C
authors.lastname: Beleites
authors.affiliation: Leibniz Institute of Photonic Technology, Jena, Germany.
authors.forename: K
authors.initials: K
authors.lastname: Egodage
authors.affiliation: Leibniz Institute of Photonic Technology, Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, University Jena, Germany; Department of Otorhinolaryngology, Jena University Hospital, Germany.
authors.forename: F
authors.initials: F
authors.lastname: von Eggeling
authors.affiliation: Department of Otorhinolaryngology, Jena University Hospital, Germany.
authors.forename: O
authors.initials: O
authors.lastname: Guntinas-Lichius
authors.affiliation: Leibniz Institute of Photonic Technology, Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, University Jena, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Popp
authors.affiliation: Environmental Chemometrics Group, Department of Environmental Chemistry, Institute of Environmental Assessment and Water Diagnostic (IDAEA-CSIC), Barcelona, Spain.
authors.forename: R
authors.initials: R
authors.lastname: Tauler
authors.affiliation: Chemometrics group, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain.
authors.forename: A
authors.initials: A
authors.lastname: de Juan
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D014066:Palatine Tonsil / Q000737:chemistry*; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*; D014069:Tonsillitis / Q000175:diagnosis*
keywords: FTIR macroscopic imaging; Image multiset segmentation; Image resolution; Local MCR-ALS; Multiset analysis; Tonsil inflammation disease; Unmixing