The application of support vector machine classification to detect cell nuclei for automated microscopy

Research output: Contribution to journal › Article › peer-review

Standard

The application of support vector machine classification to detect cell nuclei for automated microscopy. / Han, Ji Wan; Breckon, Toby P.; Randell, David A.; Landini, Gabriel.

In: Machine Vision and Applications, Vol. 23, No. 1, 01.01.2012, p. 15-24.

Research output: Contribution to journal › Article › peer-review

Harvard

Han, JW, Breckon, TP, Randell, DA & Landini, G 2012, 'The application of support vector machine classification to detect cell nuclei for automated microscopy', Machine Vision and Applications, vol. 23, no. 1, pp. 15-24. https://doi.org/10.1007/s00138-010-0275-y

APA

Han, J. W., Breckon, T. P., Randell, D. A., & Landini, G. (2012). The application of support vector machine classification to detect cell nuclei for automated microscopy. Machine Vision and Applications, 23(1), 15-24. https://doi.org/10.1007/s00138-010-0275-y

Vancouver

Han JW, Breckon TP, Randell DA, Landini G. The application of support vector machine classification to detect cell nuclei for automated microscopy. Machine Vision and Applications. 2012 Jan 1;23(1):15-24. https://doi.org/10.1007/s00138-010-0275-y

Author

Han, Ji Wan ; Breckon, Toby P. ; Randell, David A. ; Landini, Gabriel. / The application of support vector machine classification to detect cell nuclei for automated microscopy. In: Machine Vision and Applications. 2012 ; Vol. 23, No. 1. pp. 15-24.

Bibtex

@article{89529070360f42b8ae23579fe2df5f74,
title = "The application of support vector machine classification to detect cell nuclei for automated microscopy",
abstract = "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.",
author = "Han, {Ji Wan} and Breckon, {Toby P.} and Randell, {David A.} and Gabriel Landini",
year = "2012",
month = jan,
day = "1",
doi = "10.1007/s00138-010-0275-y",
language = "English",
volume = "23",
pages = "15--24",
journal = "Machine Vision and Applications",
issn = "0932-8092",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - The application of support vector machine classification to detect cell nuclei for automated microscopy

AU - Han, Ji Wan

AU - Breckon, Toby P.

AU - Randell, David A.

AU - Landini, Gabriel

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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.

AB - 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.

U2 - 10.1007/s00138-010-0275-y

DO - 10.1007/s00138-010-0275-y

M3 - Article

VL - 23

SP - 15

EP - 24

JO - Machine Vision and Applications

JF - Machine Vision and Applications

SN - 0932-8092

IS - 1

ER -