Authors:
Ahmed Afifi
;
Toshiya Nakaguchi
and
Norimichi Tsumura
Affiliation:
Chiba University, Japan
Keyword(s):
Liver segmentation, Shape prior segmentation, Optimization for segmentation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
Abstract:
The image segmentation is the first and essential process in many medical applications. This process is traditionally performed by radiologists or medical specialists to manually trace the objects on each image. In almost all of these applications, the medical specialists have to access a large number of images which is a tedious and a time consuming process. On the other hand, the automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. In this work, we propose a fully automated medical image segmentation framework. In this framework, the segmentation process is constrained by two prior models; a shape prior model and a texture prior model. The shape prior model is constructed from a set of manually segmented images using the principle component analysis (PCA) while the wavelet packet decomposition is utilized to extract the texture features. The fisher linear discriminate algorithm is employed to build the texture prior model from the se
t of texture features and to perform a preliminary segmentation. Furthermore, the particle swarm optimization algorithm (PSO) is used to refine the preliminary segmentation according to the shape prior model. In this work, we tested the proposed technique for the segmentation of the liver from abdominal CT scans and the obtained results show the efficiency of the proposed technique to accurately delineate the desired objects.
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