CT image labeling using Hopfield neural network

Author

Kovacevic, Domagoj ; Loncaric, Sven

Author_Institution

Fac. of Electr. Eng. & Comput., Zagreb Univ., Croatia

Volume

1

fYear

1998

Firstpage

44

Abstract

A method for the computed tomography (CT) image labeling is presented. CT images used in this work are obtained from patients having the spontaneous intra-cerebral haemorrhage (ICH). The images are segmented into three tissue classes (skull, brain, and ICH) and the background. The method consists of two steps. In the the first step, the image is divided into a number of regions using the K-means clustering algorithm. Regions used are dark, medium dark and bright region. In the second step, the regions are labeled using the modified Hopfield (1985) neural network. The stable state of the network represents a possible solution to the labeling problem. Simulated annealing is used as algorithm for network simulation

Keywords

Hopfield neural nets; brain; computerised tomography; diagnostic radiography; image segmentation; medical image processing; pattern clustering; simulated annealing; CT image labeling; K-means clustering algorithm; background; brain; image regions; image segmentation; labeling problem solution; modified Hopfield neural network; network simulation algorithm; patients; simulated annealing; skull; spontaneous intra-cerebral haemorrhage; stable state; tissue classes; Clustering algorithms; Computed tomography; Hemorrhaging; Hopfield neural networks; Image segmentation; Information processing; Labeling; Neurons; Simulated annealing; Skull;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference, 1998. MELECON 98., 9th Mediterranean

Conference_Location

Tel-Aviv

Print_ISBN

0-7803-3879-0

Type

conf

DOI

10.1109/MELCON.1998.692188

Filename

692188