TY - GEN
T1 - Analyzing Retinal Optical Coherence Tomography Images Using Differential Spatial Pyramid Matching
AU - Chundi, Parvathi
AU - Subramaniam, Mahadevan
AU - Sabet, Keivan
AU - Margalit, Eyal
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - Spatial pyramid matching (SPM) has achieved impressive successes in analyzing and classifying images across several domains. SPM computes a similarity measure over images by using bag of words similarity score over different levels of coarseness of the images. In this paper we propose a novel, simple approach based on SPM, differential SPM (DSPM) that incorporates finer differences among images while determining image similarity. The approach propagates the differences seen at fine levels to dampen the similarity observed at the coarser levels, thereby highlighting differences among images at small, localized regions. The resulting similarity scores among images can better separate images that match at coarse levels, but have subtle differences. DSPM integrated with K-nearest neighbor classification approaches was used to identify and analyze retinal Optical Coherence Tomography (OCT) images containing normal retinal scans as well as those from subjects with AMD (age-related macular degeneration) and DME (diabetic macular edema). The proposed approach achieved higher classification accuracy with smaller training overheads in comparison to SPM in all cases in our experiments.
AB - Spatial pyramid matching (SPM) has achieved impressive successes in analyzing and classifying images across several domains. SPM computes a similarity measure over images by using bag of words similarity score over different levels of coarseness of the images. In this paper we propose a novel, simple approach based on SPM, differential SPM (DSPM) that incorporates finer differences among images while determining image similarity. The approach propagates the differences seen at fine levels to dampen the similarity observed at the coarser levels, thereby highlighting differences among images at small, localized regions. The resulting similarity scores among images can better separate images that match at coarse levels, but have subtle differences. DSPM integrated with K-nearest neighbor classification approaches was used to identify and analyze retinal Optical Coherence Tomography (OCT) images containing normal retinal scans as well as those from subjects with AMD (age-related macular degeneration) and DME (diabetic macular edema). The proposed approach achieved higher classification accuracy with smaller training overheads in comparison to SPM in all cases in our experiments.
KW - K-nearest neighbor classification
KW - Retinal optical coherence tomogrphy
KW - Spatial pyramid matching
UR - http://www.scopus.com/inward/record.url?scp=85011030475&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011030475&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2016.67
DO - 10.1109/BIBE.2016.67
M3 - Conference contribution
AN - SCOPUS:85011030475
T3 - Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016
SP - 316
EP - 323
BT - Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016
Y2 - 31 October 2016 through 2 November 2016
ER -