TY - GEN
T1 - Sparsity-based liver metastases detection using learned dictionaries
AU - Ben-Cohen, Avi
AU - Klang, Eyal
AU - Amitai, Michal
AU - Greenspan, Hayit
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - In this work we explore sparsity-based approaches for the task of liver metastases detection in liver computed-tomography (CT) examinations. Sparse signal representation has proven to be a very powerful tool for robustly acquiring, representing, and compressing high-dimensional signals that can be accurately constructed from a compact, fixed set basis. We explore different sparsity based classification techniques and compare them to state of the art classification schemes. These methods were tested on CT examinations from 20 patients taken in different times, with overall 68 lesions. Best performance was achieved using the label consistent K-SVD (LC-KSVD) method, with detection rate of 91%, 0.9 false positive (FP) rate and classification accuracy (ACC) of 96%. The detection rates as well as the classification results are promising. Future work entails expanding the method to 3D analysis as well as testing it on a larger database.
AB - In this work we explore sparsity-based approaches for the task of liver metastases detection in liver computed-tomography (CT) examinations. Sparse signal representation has proven to be a very powerful tool for robustly acquiring, representing, and compressing high-dimensional signals that can be accurately constructed from a compact, fixed set basis. We explore different sparsity based classification techniques and compare them to state of the art classification schemes. These methods were tested on CT examinations from 20 patients taken in different times, with overall 68 lesions. Best performance was achieved using the label consistent K-SVD (LC-KSVD) method, with detection rate of 91%, 0.9 false positive (FP) rate and classification accuracy (ACC) of 96%. The detection rates as well as the classification results are promising. Future work entails expanding the method to 3D analysis as well as testing it on a larger database.
KW - CT
KW - Classification
KW - Liver-Metastases
KW - Sparsity
KW - super-pixels
UR - http://www.scopus.com/inward/record.url?scp=84978406291&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493480
DO - 10.1109/ISBI.2016.7493480
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AN - SCOPUS:84978406291
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1195
EP - 1198
BT - 2016 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Y2 - 13 April 2016 through 16 April 2016
ER -