Brain Tissue Evaluation Based on Skeleton Shape and Similarity Analysis between Hemispheres
| Autor: | Simona Moldovanu, Amira S. Ashour, Nilanjan Dey, Luminita Moraru, Lenuta Pana |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Jaccard index
General Computer Science S-Jaccard (Skeleton Jaccard) SSIM (Structural Similarity Index) 02 engineering and technology Skeletonization lcsh:QA75.5-76.95 030218 nuclear medicine & medical imaging Theoretical Computer Science Silhouette 03 medical and health sciences 0302 clinical medicine Similarity (network science) Region of interest Morphological skeleton 0202 electrical engineering electronic engineering information engineering Cluster analysis Mathematics business.industry Applied Mathematics Pattern recognition Modeling and Simulation Metric (mathematics) inter-hemisphere brain similarity 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electronic computers. Computer science business skeletonization clustering |
| Zdroj: | Computation, Vol 8, Iss 31, p 31 (2020) Computation Volume 8 Issue 2 |
| ISSN: | 2079-3197 |
| Popis: | Background: The purpose of this article is to provide a new evaluation tool based on skeleton maps to assess the tumoral and non-tumoral regions of the 2D MRI in PD-weighted (proton density) and T2w (T2-weighted type) brain images. Methods: The proposed method investigated inter-hemisphere brain tissue similarity using a mask in the right hemisphere and its mirror reflection in the left one. At the hemisphere level and for each ROI (region of interest), a morphological skeleton algorithm was used to efficiently investigate the similarity between hemispheres. Two datasets with 88 T2w and PD images belonging to healthy patients and patients diagnosed with glioma were investigated: D1 contains the original raw images affected by Rician noise and D2 consists of the same images pre-processed for noise removal. Results: The investigation was based on structural similarity assessment by using the Structural Similarity Index (SSIM) and a modified Jaccard metrics. A novel S-Jaccard (Skeleton Jaccard) metric was proposed. Cluster accuracy was estimated based on the Silhouette method (SV). The Silhouette coefficient (SC) indicates the quality of the clustering process for the SSIM and S-Jaccard. To assess the overall classification accuracy an ROC curve implementation was carried out. Conclusions: Consistent results were obtained for healthy patients and for PD images of glioma. We demonstrated that the S-Jaccard metric based on skeletal similarity is an efficient tool for an inter-hemisphere brain similarity evaluation. The accuracy of the proposed skeletonization method was smaller for the original images affected by Rician noise (AUC = 0.883 (T2w) and 0.904 (PD)) but increased for denoised images (AUC = 0.951 (T2w) and 0.969 (PD)). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|