%0 Conference Paper
%A Khan, S.A.
%A Yong, S.-P.
%D 2016
%F scholars:6770
%I Institute of Electrical and Electronics Engineers Inc.
%K Encoding (symbols); Imaging systems, Content information; Global Descriptors; Image modality; Local descriptors; Local feature; Matching score; Medical image categorization; performance evaluation, Medical imaging
%P 60-65
%R 10.1109/ISMSC.2015.7594028
%T Analysis of primitive features for medical image modality classification
%U https://khub.utp.edu.my/scholars/6770/
%X In this paper the performance of various descriptors is evaluated for medical image categorization. Many descriptors have been proposed in the literature for medical image categorization. It is unclear which descriptor encodes the content information efficiently. The descriptors that are calculated from these medical images should be descriptive, distinctive and robust to various transformations. The stability of these descriptors are evaluated under various transformations and are then analyzed for their discriminatory ability for the task of classification. In this study the criteria of transformations, repeatability, matching score and computations cost is used to evaluate the performance of these descriptors. The experimental results illustrates that among global descriptors local features patches histogram and among local descriptors SIFT encodes the content information quite efficiently. © 2015 IEEE.
%Z cited By 0; Conference of 2015 International Symposium on Mathematical Sciences and Computing Research, iSMSC 2015 ; Conference Date: 19 May 2015 Through 20 May 2015; Conference Code:124374