Brain segmentation in head CT images

• Author(s): Torres, Ana Sofia ; Monteiro, Fernando C.
• Date: 2012
• Persistent ID: http://hdl.handle.net/10198/10584
• Origin: Biblioteca Digital do IPB
• Subject(s): Brain segmentation; Graph clustering; Head CT images; Watershed transform
• Document Type: Conference Object
• Language: English

Description

Brain segmentation in head computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the brain diseases. In this paper we present a hybrid framework to brain segmentation which joints region-based information based on watershed transform with clustering techniques. A pre-processing step is used to reduce the spatial resolution without losing important image information. An initial partitioning of the image into primitive regions is set by applying a rainfalling watershed algorithm on the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. We have applied our approach on several head CT images and the results reveal the robustness and accuracy of this method.