The application of support vector machine classification to detect cell nuclei for automated microscopy
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
The detection of cell nuclei for diagnostic purposes is an important aspect of many medical laboratory examinations. Precise location of cell nuclei can aid in correct diagnosis and aid in automated microscopy applications, such as cell counting and tissue architecture analysis. In this paper, we investigate the use of support vector machine classification based on Laplace edge features for this task. Compared with existing applications, we used only one type of cell nucleus images to train the classifier but this classifier can locate other two types of cell nuclei with different stains and scales successfully. The results illustrate that such a data driven approach has remarkable detection and generalization performance.
|Journal||Machine Vision and Applications|
|Publication status||Published - 1 Jan 2012|