Iterative H-minima-based marker-controlled watershed for cell nucleus segmentation

Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry

A supervised learning model for live cell segmentation [Canli hücre bölütlemesi i̧in gözeticili öǧrenme modeli]

Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE