Original investigationIdentification, Segmentation, and Image Property Study of Acute Infarcts in Diffusion-Weighted Images by Using a Probabilistic Neural Network and Adaptive Gaussian Mixture Model
Section snippets
Materials and method
The data consisted of 13 DWI cases. The DWI scans had in-plane resolutions of 1 mm × 1 mm or 1.5 mm × 1.5 mm, and 5-mm slice thickness. The number of slices in DWI scans was from 27 to 33. The matrix size of DWI scans was 256 × 256.
Results
The results of the algorithm were evaluated against the GT marked by the expert. The segmented slices and GT slices of a given volume were overlaid in order to estimate the following quantities:
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TPS: slice containing infarct and also identified as infarct slice by the algorithm.
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False-positive slice (FPS): slice not having an infarct but identified as infarct slice by the algorithm.
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TNS: slice without infarct and also identified as non infarct slice by algorithm.
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FNS: slice containing an infarct
Discussion
Speed and accuracy are critical in clinical acute stroke management. In this study, we propose an automatic method for segmentation of acute infarcts, which may speed up the assessment of stroke region for stroke management applications. The method uses techniques such as Gaussian mixture-based modeling, PNN-based selection of slices and use of common features for classification of artifact and infarct, and allows the user to fine tune parameters according to the requirements (eg, selection of
Conclusion
In this study, we have proposed a technique that may assist radiologists and clinicians in speeding up the process of stroke management. The algorithm can be considered as a semiautomatic method as it requires training of PNN and setting some thresholds. The results of localization were high, with only two FPS and three FNS. The average dice index was about 0.6 and average sensitivity and specificity were 81% and 99%, respectively. In the current method, we had fewer FNA voxels, but a higher
Acknowledgments
The authors would like to thank the Biomedical Research Council, Agency for Science, Technology and Research, Singapore. We would also like to thank our colleague Mrs. Aminah Beevi for her assistance in preparing the manuscript.
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