TY - GEN
T1 - Automatic classification and retrieval of brain hemorrhages
AU - Tong, Hau Lee
AU - Fauzi, Mohammad Faizal Ahmad
AU - Haw, Su Cheng
AU - Ng, Hu
AU - Yap, Timothy Tzen Vun
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - In this work, Computed Tomography (CT) brain images are adopted for the annotation of different types of hemorrhages. The ultimate objective is to devise the semantics-based retrieval system for retrieving the images based on the different keywords. The adopted keywords are hemorrhagic slices, intraaxial, subdural and extradural slices. The proposed approach is consisted of three separated annotation processes are proposed which are annotation of hemorrhagic slices, annotation of intra-axial and annotation of subdural and extradural. The dataset with 519 CT images is obtained from two collaborating hospitals. For the classification, support vector machine (SVM) with radial basis function (RBF) kernel is considered. On overall, the classification results from each experiment achieved precision and recall of more than 79%. After the classification, the images will be annotated with the classified keywords together with the obtained decision values. During the retrieval, the relevant images will be retrieved and ranked correspondingly according to the decision values.
AB - In this work, Computed Tomography (CT) brain images are adopted for the annotation of different types of hemorrhages. The ultimate objective is to devise the semantics-based retrieval system for retrieving the images based on the different keywords. The adopted keywords are hemorrhagic slices, intraaxial, subdural and extradural slices. The proposed approach is consisted of three separated annotation processes are proposed which are annotation of hemorrhagic slices, annotation of intra-axial and annotation of subdural and extradural. The dataset with 519 CT images is obtained from two collaborating hospitals. For the classification, support vector machine (SVM) with radial basis function (RBF) kernel is considered. On overall, the classification results from each experiment achieved precision and recall of more than 79%. After the classification, the images will be annotated with the classified keywords together with the obtained decision values. During the retrieval, the relevant images will be retrieved and ranked correspondingly according to the decision values.
KW - Brain hemorrhages
KW - Image classification
KW - Image retrieval
UR - http://www.scopus.com/inward/record.url?scp=85053276099&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-2622-6_1
DO - 10.1007/978-981-13-2622-6_1
M3 - Conference contribution
AN - SCOPUS:85053276099
SN - 9789811326219
T3 - Lecture Notes in Electrical Engineering
SP - 1
EP - 11
BT - Computational Science and Technology
A2 - Alfred, Rayner
A2 - Ibrahim, Ag Asri Ag
A2 - Lim, Yuto
A2 - Anthony, Patricia
PB - Springer
T2 - 5th International Conference on Computational Science and Technology 2018
Y2 - 29 August 2018 through 30 August 2018
ER -