Cell counting based on local intensity maxima grouping for in-situ microscopy

Author

Rojas, L.D. ; Martinez, G. ; Scheper, T.

Author_Institution

Image Process. & Comput. Vision Res. Lab. (IPCV-Lab.), Univ. de Costa Rica, San Jose, Costa Rica

fYear

2014

fDate

April 29 2014-May 2 2014

Firstpage

1344

Lastpage

1347

Abstract

In this contribution, a new algorithm to estimate the cell count from an intensity image of Baby Hamster Kidney (BHK) cells captured by an in-situ microscope is proposed. Given that the local intensity maxima inside a cell share similar location and intensity values, it is proposed to find all the intensity maxima inside each cell cluster present in the image, and then group those who share similar location and intensity values. The total number of cells present in an image is estimated as the sum of the number of groups found in each cluster. The experimental results show that the average cell count improved by 79%, and that the average image processing time improved by 42%.

Keywords

biomedical optical imaging; cellular biophysics; image segmentation; kidney; medical image processing; optical microscopy; baby hamster kidney cell counting; in-situ microscopy; intensity image processing; local intensity maxima grouping; Biomedical imaging; Clustering algorithms; Image edge detection; Image segmentation; Microscopy; Solids; Biomedical imaging; cell cluster segmentation; cell counting; cell image analysis; detection; grouping; in-situ microscopy; local intensity maxima;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on

Conference_Location

Beijing

Type

conf

DOI

10.1109/ISBI.2014.6868126

Filename

6868126