TY - GEN
T1 - Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker
AU - Oscanoa, Julio
AU - Doimi, Franco
AU - Dyer, Richard
AU - Araujo, Jhahaira
AU - Pinto, Joseph
AU - Castaneda, Benjamin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.
AB - Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not. First, the areas that belong to the carcinoma are automatically identified. Then, the algorithm evaluates features such as size and shape to correctly segment the nuclei in these fields. Finally, the Fuzzy C-Means algorithm is used to classify the detected nuclei. The method proposed was evaluated with a set of 10 images which contained 4093 cell nuclei. The algorithm correctly identified 93.1% of the nuclei, and sensitivity and specificity of the classification were 95.7% and 93.2% respectively.
UR - https://www.scopus.com/pages/publications/85009067455
U2 - 10.1109/EMBC.2016.7591213
DO - 10.1109/EMBC.2016.7591213
M3 - Conference contribution
C2 - 28268808
AN - SCOPUS:85009067455
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2399
EP - 2402
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -
