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Automatic ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier

Mahmood Qaiser (Institutionen för signaler och system, Biomedicinsk elektromagnetik) ; A. Basit
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (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.

Nyckelord: Automatic , Ischemic stroke lesion , MRI , Random forests , Segmentation

Denna post skapades 2016-05-10. Senast ändrad 2016-05-10.
CPL Pubid: 236139


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Institutioner (Chalmers)

Institutionen för signaler och system, Biomedicinsk elektromagnetik (2006-2017)


Data- och informationsvetenskap

Chalmers infrastruktur