TY - JOUR
T1 - Automated annotation system for hemorrhage slices
AU - Tong, Hau Lee
AU - Fauzi, Mohammad Faizal Ahmad
AU - Haw, Su Cheng
AU - Yap, Timothy Tzen Vun
AU - Ng, Hu
N1 - Publisher Copyright:
© Medwell Journals, 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Extradural
KW - Hemorrhage annotation
KW - Intra-axial
KW - Retrieve the hemorrhage
KW - Subdural
UR - http://www.scopus.com/inward/record.url?scp=85017462698&partnerID=8YFLogxK
U2 - 10.3923/jeasci.2017.593.601
DO - 10.3923/jeasci.2017.593.601
M3 - Article
AN - SCOPUS:85017462698
SN - 1816-949X
VL - 12
SP - 593
EP - 601
JO - Journal of Engineering and Applied Sciences
JF - Journal of Engineering and Applied Sciences
IS - 3
ER -