Automatic ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier
Paper i proceeding, 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.




Ischemic stroke lesion

Random forests


Mahmood Qaiser

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

A. Basit

Pakistan Institute of Nuclear Science and Technology

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 9556 266-274

1st International Workshop on Brainlesion, Brainles 2015 Held in Conjunction with International Conference on Medical Image Computing for Computer-Assisted Intervention, MICCAI 2015
München, Germany,


Data- och informationsvetenskap



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