Texture analysis of 18F-florbetapir PET brain images for the diagnosis of Alzheimer’s disease

Milara Hernando, Eva and Seiffert, Alexander Peter and Gómez Grande, Adolfo and Villarejo Galende, Alberto and Bueno Zamora, Héctor J. and Gómez Aguilera, Enrique J. and Sánchez González, Patricia (2019). Texture analysis of 18F-florbetapir PET brain images for the diagnosis of Alzheimer’s disease. In: "XXXVII Congreso Anual de la Sociedad Española de Ingeniería Biomédica, CASEIB 2019", 27/11/2019 - 29/11/2019, Santander, España. ISBN 978-84-09-16707-4. pp. 17-20.

Description

Title: Texture analysis of 18F-florbetapir PET brain images for the diagnosis of Alzheimer’s disease
Author/s:
  • Milara Hernando, Eva
  • Seiffert, Alexander Peter
  • Gómez Grande, Adolfo
  • Villarejo Galende, Alberto
  • Bueno Zamora, Héctor J.
  • Gómez Aguilera, Enrique J.
  • Sánchez González, Patricia
Item Type: Presentation at Congress or Conference (Article)
Event Title: XXXVII Congreso Anual de la Sociedad Española de Ingeniería Biomédica, CASEIB 2019
Event Dates: 27/11/2019 - 29/11/2019
Event Location: Santander, España
Title of Book: Actas del XXXVII Congreso Anual de la Sociedad Española de Ingeniería Biomédica, CASEIB 2019
Date: 2019
ISBN: 978-84-09-16707-4
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica y Bioingeniería
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Alzheimer’s disease is characterised by pathological plaques outside the neurons formed by amyloid-beta(Aβ)that start occurring in the preclinical phase of the disease. PET imaging based on Aβ-binding radiotracers is used in the diagnosis of AD. These include 11C-Pittsburgh compound B and fluorine-labelled tracers like florbetapir (FBP).The images are visually analysed and classified into amyloid negative(A-) and amyloid positive(A+). This classification is based on the uptake of the radiotracer in cortical brain regions and the difference to the adjacent white matter.Quantitative feature extraction of amy-loid PET images is proposed to help in the classification of difficult cases. First, the images are segmented into cortical brain regions. Then, Standard Uptake Value ratios(SUVR) and textural features based on the grey level co-occurrence matrix (GLCM) are extracted from the images. AnSVM model is computed to classify amyloid PET images based on the extracted features. SUVRs, textural features and a combination of both are evaluated. The results show thatfeature vectors composed of 9 textural features offer the highest prediction accuracy, sensitivity and specificity(0.97, 0.94 and 1.00, respectively). Therefore, textural features are shown to be potential image features to correctly classify PET-amyloid images into A-and A+.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainFPU16/06487UnspecifiedUnspecifiedBeca de Formación de Profesorado Universitario

More information

Item ID: 63211
DC Identifier: http://oa.upm.es/63211/
OAI Identifier: oai:oa.upm.es:63211
Official URL: http://caseib.es/2019/
Deposited by: Memoria Investigacion
Deposited on: 03 Nov 2020 15:52
Last Modified: 03 Nov 2020 15:52
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