TY  - JOUR
T1  - Shape-based Automated Classification of Subdural and Extradural Hematomas
AU - Tong, Hau-Lee AU - Fauzi, Mohammad Faizal Ahmad AU - Haw, Su-Cheng AU - Ng, Hu AU - Yap, Timothy Tzen-Vun AU - Ho, Chiung Ching 
JO  - Journal of Engineering and Applied Sciences
VL  - 11
IS  - 3
SP  - 395
EP  - 401
PY  - 2016
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2016.395.401
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2016.395.401
KW  - Hematoma classification
KW  -computed tomography
KW  -image segmentation
KW  -CT
KW  -LDA
AB  - This study reports the classification of subdural and extradural hematomas in brain CT images. The major difference between subdural and extradural hematomas lies in their shapes, therefore eight shape descriptors are proposed to describe the characteristics of the two types of hematoma. The images will first undergo the pre-processing step which consists of two-level contrast enhancement separated by parenchyma extraction processes. Next, k-means clustering is performed to garner all Regions of Interest (ROIs) into one cluster. Prior to classification, shape features are extracted from each ROI. Finally for classification, fuzzy k-Nearest Neighbor (fuzzy k-NN) and Linear Discriminant Analysis (LDA) are employed to classify the regions into subdural hematoma, extradural hematoma or normal regions. Experimental results suggest that fuzzy k-NN produces the optimum accuracy. It manages to achieve over 93% correct classification rate on a set of 109 subdural and 247 extradural hematoma regions, as well as 629 normal regions.
ER  -