Joakim Lindblad

Algorithms for cytoplasm segmentation of fluorescence labelled cells

Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation

Rac1 is a GTP‐binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist‐induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells.

Surface area estimation of digitized 3D objects using weighted local configurations

We present a method for estimating surface area of three-dimensional objects in discrete binary images. A surface area weight is assigned to each 2x2x2 configuration of voxels. The total surface area of a digital object is given by a summation of the local area contributions. Optimal area weights are derived in order to provide an unbiased estimate with minimum variance for randomly oriented digitized planar surfaces. Due to co-appearance of certain voxel combinations, the optimal solution is not uniquely defined for planar surfaces. A Monte Carlo-based optimization of the estimator performance on the distribution of digitized balls of increasing radii is performed in order to uniquely determine the optimal surface area weights. The method is further evaluated on various objects in a range of sizes. A significant reduction of the error for small objects is observed. The algorithm is appealingly simple; the use of only a small local neighborhood enables efficient implementations in hardware and/or in parallel architectures.

High-Precision Boundary Length Estimation by Utilizing Gray-Level Information

We present a novel method that provides an accurate and precise estimate of the length of the boundary (perimeter) of an object by taking into account gray levels on the boundary of the digitization of the same object. Assuming a model where pixel intensity is proportional to the coverage of a pixel, we show that the presented method provides error-free measurements of the length of straight boundary segments in the case of nonquantized pixel values. For a more realistic situation, where pixel values are quantized, we derive optimal estimates that minimize the maximal estimation error. We show that the estimate converges toward a correct value as the number of gray levels tends toward infinity. The method is easy to implement; we provide the complete pseudocode. Since the method utilizes only a small neighborhood, it is very easy to parallelize. We evaluate the estimator on a set of concave and convex shapes with known perimeters, digitized at increasing resolution. In addition, we provide an example of applicability of the method on real images, by suggesting appropriate preprocessing steps and presenting results of a comparison of the suggested method with other local approaches.

Effects of aging and gender on the spatial organization of nuclei in single human skeletal muscle cells

The skeletal muscle fibre is a syncitium where each myonucleus regulates the gene products in a finite volume of the cytoplasm, i.e., the myonuclear domain (MND). We analysed aging‐ and gender‐related effects on myonuclei organization and the MND size in single muscle fibres from six young (21–31 years) and nine old men (72–96 years), and from six young (24–32 years) and nine old women (65–96 years), using a novel image analysis algorithm applied to confocal images. Muscle fibres were classified according to myosin heavy chain (MyHC) isoform expression. Our image analysis algorithm was effective in determining the spatial organization of myonuclei and the distribution of individual MNDs along the single fibre segments. Significant linear relations were observed between MND size and fibre size, irrespective age, gender and MyHC isoform expression. The spatial organization of individual myonuclei, calculated as the distribution of nearest neighbour distances in 3D, and MND size were affected in old age, but changes were dependent on MyHC isoform expression. In type I muscle fibres, average NN‐values were lower and showed an increased variability in old age, reflecting an aggregation of myonuclei in old age. Average MND size did not change in old age, but there was an increased MND size variability. In type IIa fibres, average NN‐values and MND sizes were lower in old age, reflecting the smaller size of these muscle fibres in old age. It is suggested that these changes have a significant impact on protein synthesis and degradation during the aging process.

Surface Area Estimation of Digitized Planes Using Weighted Local Configurations

We describe a method for estimating surface area of three-dimensional binary objects. The method assigns a surface area weight to each 2 × 2 × 2 configuration of voxels. The total surface area is given by a summation of the local area contributions for a digital object. We derive optimal area weights, in order to get an unbiased estimate with minimum variance for randomly oriented planar surfaces. This gives a coefficient of variation (CV) of 1.40% for planar regions. To verify the results and to address the feasibility for area estimation of curved surfaces, the method is tested on convex and non-convex synthetic test objects of increasing size. The algorithm is appealingly simple and uses only a small local neighbourhood. This allows efficient implementations in hardware and/or in parallel architectures.

A graph-based framework for sub-pixel image segmentation

Many image segmentation methods utilize graph structures for representing images, where the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object representation, i.e., allowing partial belongingness of elements to image objects, the unavoidable loss of information when representing continuous structures by finite sets is significantly reduced, enabling feature estimates with sub-pixel precision. This work presents a framework for object representation based on fuzzy segmented graphs. Interpreting the edges as one-dimensional paths between the vertices of a graph, we extend the notion of a graph cut to that of a located cut, i.e., a cut with sub-edge precision. We describe a method for computing a located cut from a fuzzy segmentation of graph vertices. Further, the notion of vertex coverage segmentation is proposed as a graph theoretic equivalent to pixel coverage segmentations and a method for computing such a segmentation from a located cut is given. Utilizing the proposed framework, we demonstrate improved precision of area measurements of synthetic two-dimensional objects. We emphasize that although the experiments presented here are performed on two-dimensional images, the proposed framework is defined for general graphs and thus applicable to images of any dimension.

Estimation of moments of digitized objects with fuzzy borders

Error bounds for estimation of moments from a fuzzy representation of a shape are derived, and compared with estimations from a crisp representation. It is shown that a fuzzy membership function based on the pixel area coverage provides higher accuracy of the estimates, compared to binary Gauss digitization at the same spatial image resolution. Theoretical results are confirmed by a statistical study of disks and squares, where the moments of the shape, up to order two, are estimated from its fuzzy discrete representation. The errors of the estimates decrease both with increased size of a shape (spatial resolution) and increased membership resolution (number of available grey-levels).

Sub-pixel Segmentation with the Image Foresting Transform

The Image Foresting Transform (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned user-defined labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. In its original form, the IFT produces crisp segmentations, i.e., each image element is assigned the label of exactly one seed-point. Here, we propose a modified version of the IFT that computes region boundaries with sub-pixel precision by allowing mixed labels at region boundaries. We demonstrate that the proposed sub-pixel IFT allows properties of the segmented object to be measured with higher precision.

Extracting 3D information on bone remodeling in the proximity of titanium implants in SRµCT image volumes

Bone-implant integration is measured in several ways. Traditionally and routinely, 2D histological sections of samples, containing bone and the biomaterial, are stained and analyzed using a light microscope. Such histological section provides detailed cellular information about the bone regeneration in the proximity of the implant. However, this information reflects the integration in only a very small fraction, a 10 μm thick slice, of the sample. In this study, we show that feature values quantified on 2D sections are highly dependent on the orientation and the placement of the section, suggesting that a 3D analysis of the whole sample is of importance for a more complete judgment of the bone structure in the proximity of the implant. We propose features describing the 3D data by extending the features traditionally used for 2D-analysis. We present a method for extracting these features from 3D image data and we measure them on five 3D SRμCT image volumes. We also simulate cuts through the image volume positioned at all possible section positions. These simulations show that the measurement variations due to the orientation of the section around the center line of the implant are about 30%.