Automatic ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier
Kapitel i bok, 2016

© Springer International Publishing Switzerland 2016. This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-spectral MRI images. The segmentation framework is validated on both training and testing data, obtained from MICCAI ISLES-2015 SISS challenge dataset. The performance of the framework is evaluated relative to the manual segmentation (ground truth). The experimental results demonstrate the robustness of the segmentation framework, and that it achieves reasonable segmentation accuracy for segmenting the sub-acute ischemic stroke lesion in multi-spectral MRI images.

Automatic

Segmentation

MRI

Ischemic stroke lesion

Random forests

Författare

Mahmood Qaiser

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Biomedicinsk elektromagnetik

A. Basit

Pakistan Institute of Nuclear Science and Technology

Lecture Notes in Computer Science

0302-9743 (ISSN)

266-274

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1007/978-3-319-30858-6_23

ISBN

9783319308579

Mer information

Skapat

2017-10-07