Brain Tumor Detection and Segmentation Using Hybrid Intelligent Algorithms: Design and Implementation
| dc.contributor.advisor | Alemu, Getachew(PhD) | |
| dc.contributor.author | Megersa, Yehualashet | |
| dc.date.accessioned | 2018-07-10T07:06:28Z | |
| dc.date.accessioned | 2023-11-04T15:14:37Z | |
| dc.date.available | 2018-07-10T07:06:28Z | |
| dc.date.available | 2023-11-04T15:14:37Z | |
| dc.date.issued | 2012-11 | |
| dc.description.abstract | In brain tumor diagnosis, clinicians integrate their medical knowledge and brain magnetic resonance imaging (MRI) scans to obtain the nature and pathological characteristics of brain tumors and to decide on treatment options. However, manually detecting and segmenting brain tumors in today’s brain MRI, where a large number of MRI scans taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. Therefore, there is a need for computer aided brain tumor detection and segmentation from brain MR images to overcome the tedium and observer variability involved in the manual segmentation. As result a number of methods have been proposed in recent years to fill this gap, but still there is no commonly accepted automated technique by clinicians to be used in clinical floor due to accuracy and robustness issues. In this thesis, an automatic brain tumor detection and segmentation framework that consists of techniques from skull stripping to detection and segmentation of brain tumors is proposed with fuzzy Hopfield neural network as its final tumor segmentation technique. Through preprocessing, image fusion and initial tumorous slice classification, the final hybrid intelligent fuzzy Hopfield neural network algorithm based tumor segmentation, and tumor region detection and extraction is achieved. The performance of the proposed framework is evaluated on various MR images including simulated and real, normal and tumorous. Quantitatively the method is validated against available ground truth (manual detection and segmentation) using commonly used validation metrics. The final segmentation mean and standard deviation result in Jaccard similarity index, Dice similarity score, sensitivity and specificity are 0.8569 0.0896, 0.9186 0.0638, 0.9480 0.0402 and 0.9917 0.0387 respectively. Quantitative and qualitative segmentation result indicates the potential of the proposed framework. Key words: Brain tumor, Fuzzy Hopfield Neural Network, Segmentation, detection, MRI | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/7496 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Brain Tumor | en_US |
| dc.subject | Fuzzy Hopfield Neural Network | en_US |
| dc.subject | Segmentation | en_US |
| dc.subject | Detection | en_US |
| dc.subject | MRI | en_US |
| dc.title | Brain Tumor Detection and Segmentation Using Hybrid Intelligent Algorithms: Design and Implementation | en_US |
| dc.type | Thesis | en_US |