TY - GEN
T1 - The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation
AU - Ross-Howe, Sara
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for developing automated skin lesion segmentation. This paper investigates various pre- and post-processing techniques for improving the performance of U-Nets as measured by the Jaccard Index. The dataset provided as part of the '2017 ISBI Challenges on Skin Lesion Analysis Towards Melanoma Detection' was used for this evaluation and the performance of the finalist competitors was the standard for comparison. The pre-processing techniques employed in the proposed system included contrast enhancement, artifact removal, and vignette correction. More advanced image transformations, such as local binary patterns and wavelet decomposition, were also employed to augment the raw grayscale images used as network input features. While the performance of the proposed system fell short of the winners of the challenge, it was determined that using wavelet decomposition as an early transformation step improved the overall performance of the system over pre- and post-processing steps alone.
AB - Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for developing automated skin lesion segmentation. This paper investigates various pre- and post-processing techniques for improving the performance of U-Nets as measured by the Jaccard Index. The dataset provided as part of the '2017 ISBI Challenges on Skin Lesion Analysis Towards Melanoma Detection' was used for this evaluation and the performance of the finalist competitors was the standard for comparison. The pre-processing techniques employed in the proposed system included contrast enhancement, artifact removal, and vignette correction. More advanced image transformations, such as local binary patterns and wavelet decomposition, were also employed to augment the raw grayscale images used as network input features. While the performance of the proposed system fell short of the winners of the challenge, it was determined that using wavelet decomposition as an early transformation step improved the overall performance of the system over pre- and post-processing steps alone.
KW - Deep Learning
KW - Local Binary Patterns
KW - Skin Lesion Segmentation
KW - U-Nets
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=85056561512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056561512&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489717
DO - 10.1109/IJCNN.2018.8489717
M3 - Conference contribution
AN - SCOPUS:85056561512
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
Y2 - 8 July 2018 through 13 July 2018
ER -