Image Segmentation Methodology Research Articles

Sire and growth-path effects on sheep meat production. 3. Fascicular structure of lamb loin muscle (m. longissimus lumborum) and the impact on eating quality

The effects of sire genetics and growth path on the fascicular structure (FS) of lamb loin muscle (m. longissimus lumborum; LL) and the consequences for eating quality were studied in progeny (n = 299) of 20 Poll Dorset sires and Merino ewes. Sires were selected on the basis of their Australian sheep breeding values (ASBV) for post-weaning growth (PWWT), loin eye muscle depth (PEMD) and subcutaneous fat (PFAT). Growth of lambs was varied by time of weaning (early or late) and by feeding level within weaning group, where lambs were either fully fed throughout or were feed restricted for 55 days then re-alimented to achieve liveweight of ~45 kg at slaughter. The FS variables were determined in differentially stained histological sections of LL by using automated image segmentation and circle geometry methodology and were defined as percentage connective tissue, perimysial seam thickness and fascicular width. Sire ASBV was not a significant covariate to measures of FS, despite ASBV-related effects on growth, carcass leanness and fatness. Independent of weaning time, faster growth was induced by re-alimentation of feed-restricted lambs, resulting in LL with less connective tissue as a percentage of cross-sectional area and larger fascicles than for fully fed peers. The direction of the perimysial seam thickness response varied with treatment and time of weaning. Rapid growth of re-alimented early weaned restricted lambs resulted in thinner seams than for fully fed peers, whereas the opposite response was observed in the late-weaned lamb contrast. This suggests that the weight and age of the lamb at the time of nutritional compromise and re-alimentation are important factors in determining the thickness of perimysial connective-tissue seams. Whilst the evidence confirms differential accretion of the myofibrillar and connective tissues and despite reports of positive effects on tenderness brought about by such changes, variation in structural measures was not associated with variation in either objective measures (compression) or sensory scores for tenderness of the loin muscle. The data from the present study support the tenet that rapid growth before slaughter, combined with appropriate sire genetics, will not compromise the physical characteristics and sensory qualities of the meat.

Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images

BackgroundThe objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1–2 mm by a level set image segmentation methodology.MethodsLevel set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function. A speed function suitable for segmentation of CL's in ovarian ultrasound images was developed. An initial contour was manually placed and contour evolution was allowed to proceed until the rate of change of the area was sufficiently small. The method was tested on ovarian ultrasonographic images (n = 8) obtained ex situ. A expert in ovarian ultrasound interpretation delineated CL boundaries manually to serve as a "ground truth". Accuracy of the level set segmentation algorithm was determined by comparing semi-automatically determined contours with ground truth contours using the mean absolute difference (MAD), root mean squared difference (RMSD), Hausdorff distance (HD), sensitivity, and specificity metrics.Results and discussionThe mean MAD was 0.87 mm (sigma = 0.36 mm), RMSD was 1.1 mm (sigma = 0.47 mm), and HD was 3.4 mm (sigma = 2.0 mm) indicating that, on average, boundaries were accurate within 1–2 mm, however, deviations in excess of 3 mm from the ground truth were observed indicating under- or over-expansion of the contour. Mean sensitivity and specificity were 0.814 (sigma = 0.171) and 0.990 (sigma = 0.00786), respectively, indicating that CLs were consistently undersegmented but rarely did the contour interior include pixels that were judged by the human expert not to be part of the CL. It was observed that in localities where gradient magnitudes within the CL were strong due to high contrast speckle, contour expansion stopped too early.ConclusionThe hypothesis that level set segmentation can be accurate to within 1–2 mm on average was supported, although there can be some greater deviation. The method was robust to boundary leakage as evidenced by the high specificity. It was concluded that the technique is promising and that a suitable data set of human ovarian images should be obtained to conduct further studies.

Local Smoothing Image Segmentation for Spotted Microarray Images

Gene microarray data are used in a wide variety of applications, including pharmaceutical and clinical research. By comparing gene expression in normal and abnormal cells, microarrays can be used to identify genes involved in particular diseases, and these genes then can be targeted by therapeutic drugs. Most gene expression data are produced from spotted microarray images. A spotted microarray image consists of thousands of spots, with individual DNA sequences first printed at each spot and then equal amounts of probes (e.g., cDNA samples) from treatment and control cells mixed and hybridized with the printed DNA sequences. To obtain gene expression data, the image first must be segmented to separate foregrounds from backgrounds for individual spots, after which averages of foreground pixels are used to compute the gene expression data. Thus image segmentation of microarray images is directly related to the reliability of gene expression data. Several image segmentation procedures have been suggested in the literature and included in software packages handling gene microarray data. This article proposes a new image segmentation methodology based on local smoothing. Theoretical arguments and numerical studies show that it has good statistical properties and should perform well in applications.

Hierarchical image segmentation based on similarity of NDVI time series

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover–landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.

Sources of variation in multi-centre brain MTR histogram studies: body-coil transmission eliminates inter-centre differences

1. Identify sources of variation affecting Magnetisation Transfer Ratio (MTR) histogram reproducibility between-centres. 2. Demonstrate complete elimination of inter-centre difference. Six principle sources of variation were summarised and analysed. These are: the imager coil used for radiofrequency (RF) transmission, imager stability, the shape and other parameters describing the Magnetisation Transfer (MT) pulse, the MT sequence used (including its parameters), the image segmentation methodology, and the histogram generation technique. Transmit field nonuniformity and B1 errors are often the largest factors. PLUMB (Peak Location Uniformity in MTR histograms of the Brain) plots are a convenient way of visualising differences. Five multi-centres studies were undertaken to investigate and minimise differences. Transmission using a body coil, with a close-fitting array of surface coils for reception, gave the best uniformity. Differences between two centres, having MR imagers from different manufacturers, were completely eliminated by using body coil excitation, making a small adjustment to the MT pulse flip angle, and carrying out segmentation at a single centre. Histograms and their peak location and height values were indistinguishable. Body coil excitation is preferred for multi-centre studies. Analysis (segmentation and histogram generation) should ideally be carried out at a single site.

Iris segmentation methodology for non-cooperative recognition

An overview of the iris image segmentation methodologies for biometric purposes is presented. The main focus is on the analysis of the ability of segmentation algorithms to process images with heterogeneous characteristics, simulating the dynamics of a non-cooperative environment. The accuracy of the four selected methodologies on the UBIRIS database is tested and, having concluded about their weak robustness when dealing with non-optimal images regarding focus, reflections, brightness or eyelid obstruction, the authors introduce a new and more robust iris image segmentation methodology. This new methodology could contribute to the aim of non-cooperative biometric iris recognition, where the ability to process this type of image is required.

Robust image segmentation using genetic algorithm with a fuzzy measure

In this paper we present new region-based image segmentation methodology on gray-level images using a genetic algorithm with a fuzzy measure. We first propose a fuzzy validity function which measures a degree of separation and compactness between and within finely segmented regions, and an edge strength along boundaries of all regions. We apply the genetic algorithm to search a good or usable region segmentation, which maximizes the quality of regions generated by split- and-merge processing. The iterative algorithm provides a useful method for image segmentation without the need for critical parameters or threshold values, iterative visual interaction or a priori knowledge of an image.

Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging

A comprehensive methodology for image segmentation is presented. Tools for differential and intensity contouring, and outline optimization are discussed, as well as the methods for automating such procedures. After segmentation, regional volumes and image intensity distributions can be determined. The methodology is applied to nuclear magnetic resonance images of the brain. Examples of the results of volumetric calculations for the cerebral cortex, white matter, cerebellum, ventricular system, and caudate nucleus are presented. An image intensity distribution is demonstrated for the cerebral cortex.