Automated brain lesion classification method for diffusion-weighted magnetic resonance images
Diffusion-weighted magnetic resonance imaging plays an increasingly important role in the diagnosis of several brain diseases by providing detailed information regarding lesion based on the diffusion of water molecules in brain tissue. Conventionally, the differential diagnosis of brain lesions is p...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/54878/1/NorhashimahMohdSaadPFKE2015.pdf http://eprints.utm.my/id/eprint/54878/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:95563 |
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| Summary: | Diffusion-weighted magnetic resonance imaging plays an increasingly important role in the diagnosis of several brain diseases by providing detailed information regarding lesion based on the diffusion of water molecules in brain tissue. Conventionally, the differential diagnosis of brain lesions is performed visually by professional neuroradiologists during a highly subjective, time consuming process. Within this context, this study proposes a new technique for automatically detecting and classifying major brain lesions of four types: acute stroke, chronic stroke, tumor and necrosis. An analytical framework of the brain lesions consists of four stages which are pre-processing, segmentation, features extraction and classification. For segmentation process, adaptive thres holding, gray level co-occurrence matrix, region splitting and merging, semi-automatic region growing, automatic region growing and fuzzy C-means were proposed to segment the lesion region. The algorithm performance was then evaluated using Jaccard index, Dice index, and both false positive and false negative rates. Results demonstrated that automatic region growing offered the best performance for lesion segmentation while acute stroke gave the highest rate with 0.838 Dice index. Next, statistical features were extracted from the region of interest and fed into the rule based classifier designed to the best suit to the lesion’s features. The performance of the classifier was evaluated based on overall accuracy, sensitivity and specificity. The overall accuracy for the classification was 81.3%. In conclusion, the proposed automated brain lesion classification method has the potential to diagnose and classify major brain lesions. |
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