Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques

Abstract

This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.

Segmentation of microscopic images of bacteria in Bulgarian yoghurt by template matching

“…The method is used for counting nuclei in the cells recognized by voxels (three-dimensional equivalents of the pixels) which are used as templates. Kothari et al [12] used for the same purpose spherical templates. The advances in biotechnology led to the need to adapt the microbiological techniques in many fields of applied biology.…”

Segmentation-free quantification of spots on a homogeneous background

“…It illustrates the intended use of our method. In contrast to approaches that have focused on segmenting individual spots [2,3,4,5], our primary goal is to provide fast, but robust global summary measures that quickly allow the analyst to find trends and outliers in image sets from high-throughput microscopy. This should allow them to identify smaller subsets of images of particular interest, to which more sophisticated segmentation-based approaches or manual segmentation can subsequently be applied.…”

“…Analysis of such images requires counting and measuring the sizes of those spots. Even though methods based on wavelet multiscale product (WMP) operator [2], ellipse fitting [3], modified circular hough transform (CHT) [4], or spectral graph partitioning [5] have been introduced to detect and segment individual spots, practitioners still frequently resort to manual analysis because automated segmentation is not reliable enough in many real-world scenarios. In this work, we introduce a novel approach to this problem that derives per-pixel estimates of spot size and density that do not require reliable delineation of individual spots.…”

An Automatic Mobile Cell Counting System for the Analysis of Biological Image

“…Nowdays the wide spread of ubiquitous smart space environments can lead the cell counting process applicable to [4]. It is known that morphology itself is not an efficient solution when the shape or size of the cell is inconstant from one class to another and also within a class [5].…”