TY - GEN
T1 - Segmentation of Breast Masses in Local Dense Background Using Adaptive Clip Limit-CLAHE
AU - Sajeev, Shelda
AU - Bajger, Mariusz
AU - Lee, Gobert
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Mass segmentation in mammograms is a challenging task if the mass is located in a local dense background. It can be due to the similarity of intensities between the overlapped normal dense breast tissue and mass. In this paper, a self-adjusted mammogram contrast enhancement solution called Adaptive Clip Limit CLAHE (ACL-CLAHE) is developed, aiming to improve mass segmentation in dense regions of mammograms. An optimization algorithm based on entropy is used to optimize the clip limit and window size of standard CLAHE. The proposed method is tested on 89 mammogram images with 41 masses localized in dense background and 48 masses in non-dense background. The results are compared with other standard enhancement techniques such as Adjustable Histogram Equalization, Unsharp Masking, Neutrosophy based enhancement, standard CLAHE and an Adaptive Clip Limit CLAHE based on standard deviation. The experimental results show that our method significantly improves the mass segmentation in local dense background without compromising the performance in local non-dense background.
AB - Mass segmentation in mammograms is a challenging task if the mass is located in a local dense background. It can be due to the similarity of intensities between the overlapped normal dense breast tissue and mass. In this paper, a self-adjusted mammogram contrast enhancement solution called Adaptive Clip Limit CLAHE (ACL-CLAHE) is developed, aiming to improve mass segmentation in dense regions of mammograms. An optimization algorithm based on entropy is used to optimize the clip limit and window size of standard CLAHE. The proposed method is tested on 89 mammogram images with 41 masses localized in dense background and 48 masses in non-dense background. The results are compared with other standard enhancement techniques such as Adjustable Histogram Equalization, Unsharp Masking, Neutrosophy based enhancement, standard CLAHE and an Adaptive Clip Limit CLAHE based on standard deviation. The experimental results show that our method significantly improves the mass segmentation in local dense background without compromising the performance in local non-dense background.
UR - http://www.scopus.com/inward/record.url?scp=84963680017&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2015.7371305
DO - 10.1109/DICTA.2015.7371305
M3 - Conference contribution
AN - SCOPUS:84963680017
T3 - 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
BT - 2015 International Conference on Digital Image Computing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Y2 - 23 November 2015 through 25 November 2015
ER -