TY - GEN
T1 - Curvelet-based classification of brain MRI images
AU - Damseh, Rafat
AU - Ahmad, M. Omair
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Classification of brain MRI images is crucial in medical diagnosis. Automatic classification of these images helps in developing effective non-invasive procedures. In this paper, based on curvelet transform, a novel classification scheme of brain MRI images is proposed and a technique for extracting and selecting curvelet features is provided. To study the effectiveness of their use, the proposed features are employed into three different prediction algorithms, namely, K-nearest neighbours, support vector machine and decision tree. The method of K-fold stratified cross validation is used to assess the efficacy of the proposed classification solutions and the results are compared with those of various state-of-the-art classification schemes available in the literature. The experimental results demonstrate the superiority of the proposed decision tree classification scheme in terms of accuracy, generalization capability, and real-time reliability.
AB - Classification of brain MRI images is crucial in medical diagnosis. Automatic classification of these images helps in developing effective non-invasive procedures. In this paper, based on curvelet transform, a novel classification scheme of brain MRI images is proposed and a technique for extracting and selecting curvelet features is provided. To study the effectiveness of their use, the proposed features are employed into three different prediction algorithms, namely, K-nearest neighbours, support vector machine and decision tree. The method of K-fold stratified cross validation is used to assess the efficacy of the proposed classification solutions and the results are compared with those of various state-of-the-art classification schemes available in the literature. The experimental results demonstrate the superiority of the proposed decision tree classification scheme in terms of accuracy, generalization capability, and real-time reliability.
KW - Curvelet transform
KW - Feature extraction and classification
KW - MRI imaging
UR - http://www.scopus.com/inward/record.url?scp=85022189264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022189264&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59876-5_49
DO - 10.1007/978-3-319-59876-5_49
M3 - Conference contribution
AN - SCOPUS:85022189264
SN - 9783319598758
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 446
EP - 454
BT - Image Analysis and Recognition - 14th International Conference, ICIAR 2017, Proceedings
A2 - Cheriet, Farida
A2 - Karray, Fakhri
A2 - Campilho, Aurelio
PB - Springer Verlag
T2 - 14th International Conference on Image Analysis and Recognition, ICIAR 2017
Y2 - 5 July 2017 through 7 July 2017
ER -