TY - GEN
T1 - A first glance on the enhancement of digital cell activity videos from glioblastoma cells with nuclear staining
AU - Calderon, Saul
AU - Moya, Daniel
AU - Cruz, Juan Carlos
AU - Valverde, Juan Manuel
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - In this work we explore a set of image enhancement techniques for improving contrast and removing noise from digital images of cell activity. The cells studied were extracted from cancerous brain tissue and exposed to different chemo-therapeutic agents, as microbiologists aim to analyze the behavior of cells exposed to different chemo-therapies. To ease and improve the precision of such analysis, an automatic cell tracking framework is of great interest. Thus, in this work we focus on the first stage of such framework, which refers to image preprocessing, aiming to noise removal and contrast enhancement, in order to improve cell segmentation and tracking performance. We compared the segmentation precision using different image preprocessing techniques based on the Deceived Weighting Average Framework (DeWAFF), reaching improvements of around 15 percent of segmentation accuracy, over no preprocessed images.
AB - In this work we explore a set of image enhancement techniques for improving contrast and removing noise from digital images of cell activity. The cells studied were extracted from cancerous brain tissue and exposed to different chemo-therapeutic agents, as microbiologists aim to analyze the behavior of cells exposed to different chemo-therapies. To ease and improve the precision of such analysis, an automatic cell tracking framework is of great interest. Thus, in this work we focus on the first stage of such framework, which refers to image preprocessing, aiming to noise removal and contrast enhancement, in order to improve cell segmentation and tracking performance. We compared the segmentation precision using different image preprocessing techniques based on the Deceived Weighting Average Framework (DeWAFF), reaching improvements of around 15 percent of segmentation accuracy, over no preprocessed images.
KW - Biomedical Imaging
KW - Contrast enhancement
KW - Digital Image processing
KW - Fluorescence based microscopy
KW - Noise removal
KW - Non lineal image filters
UR - http://www.scopus.com/inward/record.url?scp=85021415326&partnerID=8YFLogxK
U2 - 10.1109/CONCAPAN.2016.7942344
DO - 10.1109/CONCAPAN.2016.7942344
M3 - Contribución a la conferencia
AN - SCOPUS:85021415326
T3 - 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016
BT - 2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE Central American and Panama Convention, CONCAPAN 2016
Y2 - 9 November 2016 through 11 November 2016
ER -