Supervised method for cell counting from bright field focus stacks
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) |
| Publisher | IEEE |
| Pages | 391-394 |
| Number of pages | 4 |
| ISBN (Print) | 9781479923502 |
| DOIs | |
| Publication status | Published - 15 Jun 2016 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING - Duration: 1 Jan 1900 → … |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 1945-7928 |
Conference
| Conference | IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING |
|---|---|
| Period | 1/01/00 → … |
Abstract
We present a novel method for cell counting using bright field focus stacks. Our method is based on the use of supervised learning and out-of-focus appearance of cells. Logistic regression was used for classification with intensity values of 25 focal planes as features. Binary erosion with a large circular structuring element was applied as post-processing step. With this simple method we obtained mean F\-score of 0.87 for cell counting with 12 test images, including images of extremely dense populations. The most important features were obtained from out-of-focus images. Thus, we conclude that using several focal planes provides valuable intensity information for cell counting from bright field microscopy.
ASJC Scopus subject areas
Keywords
- Cells & molecules, Confocal, Fluorescence, Image segmentation, Microscopy - Light