Paper Info
Title
Classification of ultrasound medical images using distance based feature selection and fuzzy-SVM
Abstract
This paper presents a method of classifying ultrasound medical images towards dealing with two important aspects: (i) optimal feature subset selection for representing ultrasound medical images and (ii) improvement of classification accuracy by avoiding outliers. An objective function combining the concept of between-class distance and within-class divergence among the training dataset has been proposed as the evaluation criteria of feature selection. Searching for the optimal subset of features has been performed using Multi-Objective Genetic Algorithm (MOGA). Applying the proposed criteria, a subset of Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) based statistical texture descriptors have been identified that maximizes separability among the classes of the training dataset. To avoid the impact of noisy data during classification, Fuzzy Support Vector Machine (FSVM) has been adopted that reduces the effects of outliers by taking into account the level of significance of each training sample. The proposed approach of ultrasound medical image classification has been tested using a database of 679 ultrasound ovarian images and 89.60% average classification accuracy has been achieved.
Year
DOI
Venue
2011
10.1007/978-3-642-21257-4_22
IbPRIA
Keywords
Field
DocType
feature selection
Data mining,Noisy data,Pattern recognition,Feature selection,Matrix (mathematics),Computer science,Outlier,Artificial intelligence,Fuzzy svm,Contextual image classification,Genetic algorithm,Ultrasound
Conference
Volume
ISSN
Citations 
6669
0302-9743
2
PageRank 
References 
Authors
0.38
6
4
Name
Order
Citations
PageRank
Abu Sayeed Md. Sohail1264.01
Prabir Bhattacharya21010147.90
Sudhir P. Mudur320145.52
Srinivasan Krishnamurthy4103.98