Biomedical subjects

Muhammad-Amri Abdul-Karim

Publications and source records attributed to Muhammad-Amri Abdul-Karim.

3 recordsLinked to original sources

PubMed2005

Automated image analysis methods for 3-D quantification of the neurovascular unit from multichannel confocal microscope images.

BACKGROUND: There is a need for integrative and quantitative methods to investigate the structural and functional relations among elements of complex systems, such as the neurovascular unit (NVU), that involve multiple cell types, microvasculatures, and various genomic/proteomic/ionic functional entities. METHODS: Vascular casting and selective labeling enabled simultaneous three-dimensional imaging of the microvasculature, cell nuclei, and cytoplasmic stains. Multidimensional segmentation was achieved by (i) bleed-through removal and attenuation correction; (ii) independent segmentation and morphometry for each corrected channel; and (iii) spatially associative feature computation across channels. The combined measurements enabled cell classification based on nuclear morphometry, cytoplasmic signals, and distance from vascular elements. Specific spatial relations among the NVU elements could be quantified. RESULTS: A software system combining nuclear and vessel segmentation codes and associative features was constructed and validated. Biological variability contributed to misidentified nuclei (9.3%), undersegmentation of nuclei (3.7%), hypersegmentation of nuclei (14%), and missed nuclei (4.7%). Microvessel segmentation errors occurred rarely, mainly due to nonuniform lumen staining. CONCLUSIONS: Associative features across fluorescence channels, in combination with standard features, enable integrative structural and functional analysis of the NVU. By labeling additional structural and functional entities, this method can be scaled up to larger-scale systems biology studies that integrate spatial and molecular information.

Animals↗

PubMed2005

Automatic selection of parameters for vessel/neurite segmentation algorithms.

An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (p = 0.78).

Algorithms↗

PubMed2003

Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series.

Automated methods are described for tracing and analysis of changes in angiogenic vasculature imaged by a multiphoton laser-scanning confocal microscope. Utilizing chronic animal window models, time series of in vivo 3-D images were acquired on approximately the same target volume of the same specimen while undergoing angiogenic change (typically every 24 h for 7 days). Objective, precise, 3-D, rapid, and fully automated vessel morphometry was performed using an adaptive tracing algorithm that is based on a generalized irregular cylinder model of the vasculature. This algorithm was found to be not only adaptive enough for tracing angiogenic vasculature, but also very efficient in its use of computer memory, and fast, taking less than 1 min to trace a 768 x 512 x 32, 8-bit/pixel 3-D image stack on a Dell Pentium III 1-GHz computer. The automatically traced centerlines were manually validated on six image stacks and the average spatial error was measured to be 2 pixels, with an average concordance of 81% between manual and automated traces on a voxel basis. The tracing output includes geometrical statistics of traced vasculature and serves as the basis of statistical change analysis. The computer methods described here are designed to be scalable to much larger hypothesis testing studies involving quantitative measurements of tumor angiogenesis, gene expression relative to known vascular structures, and impact of drug delivery.

Algorithms↗