title: ICA for ovary tissue classification of perfusion magnetic resonance images.
abstract: In this study, a method to segment ovary Magnetic Resonance (MR) images and distinguish healthy tissue from cysts has been described. Through the application of independent component analysis (ICA) to a set of perfusion MR images it was possible to extract the output independent components and their corresponding signal-time curves. After examining and analyzing this result, a polynomial approach was computed to represent the main features of each curve, and automated particular selection of independent components was obtained by applying a Bayesian information criterion able to show the most relevant components. The results shown in this work permit to conclude that the independent components with a step-like signal-time curve allow to distinguish healthy tissue from cysts, thus, giving very promising results for the application of ICA to ovary tissue segmentation of perfusion MR images.
authors.affiliation: Biomedical Synergy, Valencia University of Technology, Campus Gandia, 46730 Gandia, Valencia, Spain. jjrieta@ieee.org
authors.forename: José J
authors.initials: JJ
authors.lastname: Rieta
authors.forename: David
authors.initials: D
authors.lastname: Moratal
authors.forename: Luis
authors.initials: L
authors.lastname: Martí-Bonmatí
authors.forename: Raquel
authors.initials: R
authors.lastname: Molina-Mínguez
authors.forename: Ana
authors.initials: A
authors.lastname: Vallés-Lluch
authors.forename: Roberto
authors.initials: R
authors.lastname: Sanz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010048:Ovarian Cysts / Q000473:pathology*; D010053:Ovary / Q000098:blood supply / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity