Hippocampal segmentation by Random Forest classification

Author

Monno, Laura ; Bellotti, Roberto ; Calvini, Piero ; Monge, Roberta ; Frisoni, Giovanni B. ; Pievani, Michela

Author_Institution

Dipt. di Fis. M. Merlin, Univ. degli Studi di Bari (Italy), Bari, Italy

fYear

2011

fDate

30-31 May 2011

Firstpage

536

Lastpage

539

Abstract

This paper presents an innovative approach for the hippocampal segmentation in magnetic resonance images (MRI). The core of the method consists of a Random Forest classifier, it is able to recognize hippocampal and not-hippocampal voxels on the basis of a very large number of discriminating features. Among them, in particular, the so called Haar-like features play a central role. This method is fully automated and it uses, as base of knowledge, a set of hippocampal images segmented by expert neuroradiologists. Such a system can be of paramount importance to assist the neuroradiologist in clinical diagnosis of probable Alzheimer´s Disease.

Keywords

biomedical MRI; diseases; image classification; image segmentation; medical image processing; Alzheimers disease; Haar-like features; MRI; clinical diagnosis; hippocampal image segmentation; magnetic resonance imaging; neuroradiologists; random forest classification; Alzheimer´s disease; Hippocampus; Image segmentation; Indexes; Radio frequency; Vegetation;

fLanguage

English

Publisher

ieee

Conference_Titel

Medical Measurements and Applications Proceedings (MeMeA), 2011 IEEE International Workshop on

Conference_Location

Bari

Print_ISBN

978-1-4244-9336-4

Type

conf

DOI

10.1109/MeMeA.2011.5966763

Filename

5966763