A Hybrid Approach to Detection of Brain Hemorrhage Candidates from Clinical Head CT Scans

Author

Li, Yonghong ; Hu, Qingmao ; Wu, Jianhuang ; Chen, Zhijun

Author_Institution

Shenzhen Inst. of Adv. Integration Technol., Chinese Acad. of Sci., Hong Kong, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

361

Lastpage

365

Abstract

In this paper we present an approach for detecting brain hemorrhage regions from clinical head computed tomography (CT) scans. Firstly, non-brain tissues are removed by thresholding based on Fuzzy C-means (FCM) clustering. Then, thresholding based on maximum entropy is employed for the candidate hemorrhage region detection. Finally, non-hemorrhage regions and other normal artifacts are differentiated from hemorrhage regions by a knowledge-based classification system. The approach has been validated against 30 clinical brain CT images and compared with Otsu thresholding as well as hierarchical FCM thresholding.

Keywords

biological tissues; brain; computerised tomography; diseases; image classification; medical image processing; Otsu thresholding; candidate brain hemorrhage region detection; clinical head CT scan; computed tomography; fuzzy C-means clustering; hierarchical FCM thresholding; knowledge-based classification system; maximum entropy; nonbrain tissues; Blood; Cardiac disease; Computational modeling; Computed tomography; Entropy; Head; Hemorrhaging; Hopfield neural networks; Simulated annealing; Spatial resolution; CT scans; hemorrhage detection; maximum entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.717

Filename

5358565