Paper Info
Title
Selection Of Optimal Texture Descriptors For Retrieving Ultrasound Medical Images
Abstract
Although feature selection has been proven to be very effective in machine learning and pattern classification applications, it has not been widely practiced in the area of image annotation and retrieval. This paper presents a method of selecting a near optimal to optimal subset of statistical texture descriptors in efficient representation and retrieval of ultrasound medical images. 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 optimality. Searching for the selection of optimal subset of image descriptors has been performed using Multi-Objective Genetic Algorithm (MOGA). The proposed feature selection based approach of image annotation and retrieval has been tested using a database of 679 ultrasound ovarian images and satisfactory retrieval performance has been achieved. Besides, performance of ultrasound medical image retrieval with and without applying feature selection based image annotation technique has also been compared.
Year
DOI
Venue
2011
10.1109/ISBI.2011.5872343
2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO
Keywords
Field
DocType
texture descriptors, feature selection, medical image retrieval, multi-objective optimization
Computer vision,Automatic image annotation,Feature selection,Pattern recognition,Image texture,Computer science,Medical imaging,Image retrieval,Feature extraction,Artificial intelligence,Contextual image classification,Genetic algorithm
Conference
ISSN
Citations 
PageRank 
1945-7928
2
0.39
References 
Authors
8
4
Name
Order
Citations
PageRank
Abu Sayeed Md. Sohail1264.01
Prabir Bhattacharya21010147.90
Sudhir P. Mudur320145.52
Srinivasan Krishnamurthy4103.98