Authors : Hau Lee Tong, Mohammad Faizal Ahmad Fauzi, Su Cheng Haw, Tzen Vun Timothy Yap and Hu Ng
Abstract: The main objective is to annotate and classify different types of hemorrhagic slices such as intra-axial, subdural and extradural slices. A two-segregated annotation is proposed to classify hemorrhagic slices due to their different shapes and locations in the brain. The first annotation is to identify the intra-axial hemorrhage slice whereas the second annotation is to classify the subdural and extradural slices. All the extracted features from both annotations will be used as inputs to the Support Vector Machine (SVM) classifier. Experiments conducted on a set of 519 CT slices under the proposed method show significant results. From the findings, the proposed method yields 79.3, 85 and 89.2% correct classification rate for intra-axial, subdural and extradural. On overall, the CCR obtained for subdural and extradural slices is higher than intra-axial slices. This is contributed by more specific local shape features are employed for subdural and extradural which results in better recognition. Global features are adopted to classify the intra-axial slices due to their arbitrary shapes. The proposed approach can be used to create an automated retrieval system so that radiologists and medical students can use it to retrieve the hemorrhage images for further study and analysis.
Hau Lee Tong, Mohammad Faizal Ahmad Fauzi, Su Cheng Haw, Tzen Vun Timothy Yap and Hu Ng, 2017. Automated Annotation System for Hemorrhage Slices. Journal of Engineering and Applied Sciences, 12: 593-601.