Computer
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A genetic algorithm-based method for feature subset selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
A new method of feature fusion and its application in image recognition
Pattern Recognition
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human visual system inspired multi-modal medical image fusion framework
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper describes a two-stage feature fusion method for ultrasonic liver tissue characterization. The proposed method hierarchically incorporates a genetic-algorithm-based feature selection to automatically select more efficient feature subset to discriminate among ultrasonic images of liver tissue in three states: normal liver, cirrhosis, and hepatoma. Multiple feature spaces are adopted in this paper, including the spatial gray-level dependence matrices (SGLDMs), multiresolution fractal feature vector and multiresolution energy feature vector. Features extracted from different feature spaces may contain complementary information. The feature subsets of different feature spaces are fused and the genetic-algorithm-based feature selection is applied onto the fused feature space to facilitate the two-stage feature fusion. The classification accuracy of the fused feature subset is up to 96.62%. Experimental results demonstrate that the proposed method is capable to select discriminative features among multiple feature vectors to achieve the early detection of hepatoma and cirrhosis based on ultrasonic liver imaging.