TY - GEN
T1 - Decision support system for liver cancer diagnosis using focus features in NSCT domain
AU - Balagourouchetty, Lakshmipriya
AU - Pragatheeswaran, Jayanthi K.
AU - Pottakkat, Biju
AU - Govindarajalou, Rammkumar
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/2
Y1 - 2019/2
N2 - Diagnosis of liver cancer by medical experts using imaging modalities is found to be sub-optimal as different lesions exhibit similar visual appearance in the spatial domain. Thus computer aided diagnostic tools play a significant role in providing a decision support system for radiologists to minimize the risk of false diagnosis. This paper proposes a different feature set using focus operators for classifying different classes of liver cancer. As computation of focus measure involves the local neighborhood of pixel, focus operator is believed to indirectly measure the intricate texture details of the image. This knowledge of focus operator is exploited in NSCT domain to capture the directional components as feature variables replacing the classic texture features. The results in terms of classification accuracy and kappa coefficient proclaim that the focus operators can be employed as feature variables for classification scenario as it outperforms the state-of-the art texture features.
AB - Diagnosis of liver cancer by medical experts using imaging modalities is found to be sub-optimal as different lesions exhibit similar visual appearance in the spatial domain. Thus computer aided diagnostic tools play a significant role in providing a decision support system for radiologists to minimize the risk of false diagnosis. This paper proposes a different feature set using focus operators for classifying different classes of liver cancer. As computation of focus measure involves the local neighborhood of pixel, focus operator is believed to indirectly measure the intricate texture details of the image. This knowledge of focus operator is exploited in NSCT domain to capture the directional components as feature variables replacing the classic texture features. The results in terms of classification accuracy and kappa coefficient proclaim that the focus operators can be employed as feature variables for classification scenario as it outperforms the state-of-the art texture features.
KW - Classification
KW - Feature extraction
KW - Feature selection
KW - Focus measure
KW - Outliers
UR - http://www.scopus.com/inward/record.url?scp=85067915352&partnerID=8YFLogxK
U2 - 10.1109/NCC.2019.8732219
DO - 10.1109/NCC.2019.8732219
M3 - Conference contribution
AN - SCOPUS:85067915352
T3 - 25th National Conference on Communications, NCC 2019
BT - 25th National Conference on Communications, NCC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th National Conference on Communications, NCC 2019
Y2 - 20 February 2019 through 23 February 2019
ER -