Cancer cell detection and morphology analysis based on local interest point detectors

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Esteves, T (Author) Other; Oliveira, MJ (Author) FCUP; Quelhas, P (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Pages: 624-631

6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013

Funchal, Madeira, 5 June 2013 through 7 June 2013

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-008-DG4

DOI: 10.1007/978-3-642-38628-2_74

Abstract (EN): The automatic analysis of cancer cells has gained increasing relevance given the amount of data that biology researchers have to analyze. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells. While the classic approach for automatic cell detection is to use image segmentation, in the case of in vivo brightfield images, such approach is not robust to image quality changes. To detect cells with robustness and increased performance we propose the use of local interest point detectors. We perform a comparison study between the use of the Laplacian of Gaussian filter, a Bank of Ring Filters and local convergence filters. Based on experimental results we found that the Laplacian of Gaussian filter outperformed all other in cell detection obtaining an accuracy of 78%. Additionally, through the analysis of shape fit, we found that the Laplacian of Gaussian filter obtained a better approximation to the shape of the cells having a Dice's coefficient of 81%. © 2013 Springer-Verlag.

Language: English

Type (Professor's evaluation): Scientific