Computer vision algorithms in DNA ploidy image analysis

Eleni Alexandratou; Anastasia Sofou; Haris Papasaika; Petros Maragos; Dido Yova; Nikolaos Kavantzas

doi:10.1117/12.646304

21 February 2006 Computer vision algorithms in DNA ploidy image analysis

Eleni Alexandratou, Anastasia Sofou, Haris Papasaika, Petros Maragos, Dido Yova, Nikolaos Kavantzas

Proceedings Volume 6088, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IV; 60880O (2006) https://doi.org/10.1117/12.646304
Event: SPIE BiOS, 2006, San Jose, California, United States

Abstract

The high incidence and mortality rates of prostate cancer have stimulated research for prevention, early diagnosis and appropriate treatment. DNA ploidy status of tumour cells is an important parameter with diagnostic and prognostic significance. In the current study, DNA ploidy analysis was performed using image cytometry technique and digital image processing and analysis. Tissue samples from prostate patients were stained using the Feulgen method. Images were acquired using a digital imaging microscopy system consisting of an Olympus BX-50 microscope equipped with a color CCD camera. Segmentation of such images is not a trivial problem because of the uneven background, intensity variations within the nuclei and cell clustering. In this study specific algorithms were developed in Matlab based on the most prominent image segmentation approaches that emanate from the field of Mathematical Morphology, focusing on region-based watershed segmentation. First biomedical images were simplified under non-linear filtering (alternate sequential filters, levelings), and next image features such as gradient information and markers were extracted so as to lead the segmentation process. The extracted markers are used as seeds; watershed transformation was performed to the gradient of the filtered image. Image flooding was performed isotropically from the markers using hierarchical queues based on Beucher and Meyer methodology. The developed algorithms have successfully segmented the cell from its background and from cells clusters as well. To characterize the nuclei, we attempt to derive a set of effective color features. By analyzing more than 50 color features, we have found that a set of color features, hue, saturation-weighted hue, I₁=(R+G+B)/3, I₂=(R-B),I₃=(2G-R-B)/2, Karhunen-Loeve transformation and energy operator, are effective.

Citation Download Citation

Eleni Alexandratou, Anastasia Sofou, Haris Papasaika, Petros Maragos, Dido Yova, and Nikolaos Kavantzas "Computer vision algorithms in DNA ploidy image analysis", Proc. SPIE 6088, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IV, 60880O (21 February 2006); https://doi.org/10.1117/12.646304

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