Biomedical subjects

P S Umesh Adiga

Publications and source records attributed to P S Umesh Adiga.

5 recordsLinked to original sources

PubMed2003

Segmentation of volumetric tissue images using constrained active contour models.

In this article we describe an application of active contour model for the segmentation of 3D histo-pathological images. The 3D images of a thick tissue specimen are obtained as a stack of optical sections using confocal laser beam scanning microscope (CLSM). We have applied noise reduction and feature enhancement methods so that a smooth and slowly varying potential surface is obtained for proper convergence. To increase the capture range of the potential surface, we use a combination of distance potential and the diffused gradient potential as external forces. It has been shown that the region-based information obtained from low-level segmentation can be applied to reduce the adverse influence of the neighbouring nucleus having a strong boundary feature. We have also shown that, by increasing the axial resolution of the image stack, we can automatically propagate the optimum active contour of one image slice to its neighbouring image slices as an appropriate initial model. Results on images of prostate tissue section are presented.

Models, Anatomic↗

PubMed2002

An integrated system for feature evaluation of 3D images of a tissue specimen.

In this article we have proposed an integrated system for measurement of important features from 3D tissue images. We propose a segmentation technique, where we combine several methods to achieve a good degree of automation. Important histological and cytological three-dimensional features and strategies to measure them are described.

Algorithms↗

PubMed2001

Some efficient methods to correct confocal images for easy interpretation.

In this paper we have explained some efficient methods to correct artefacts in confocal laser beam scanning microscope (CLSM) images. The main aim is to enhance object features such that they become clearly visible for interactive evaluation and to reduce the overall noise so that the automatic segmentation and feature measurement can be done easily. A simple automatic-thresholding technique, and a straightforward method to restore the light intensity along the depth of the image stack are proposed. Another problem associated with the CLSM is the non-isotropic resolution. We have presented an interpolation technique based on XOR contouring and morphing to virtually insert the image slices in the image stack for improving the axial resolution. This interpolation technique has the merits of both contour- and intensity-based interpolations. Results of application of these methods on CLSM data are shown.

Carcinoma↗

PubMed1999

Deformable models for segmentation of CLSM tissue images and its application in FISH signal analysis.

In this paper we present an application of deformable models for the segmentation of volumetric tissue images. The three-dimensional images are obtained using confocal microscope. The segmented images have been used for the quantitative analysis of the Fluorescence In Situ Hybridization (FISH) signals. An ellipsoidal surface initialized around the cell of interest acts as a deformable model. The deformable model surface voxels are subjected to various internal and external forces derived from underlying image features as well as externally imposed constraints. The deformable model converges to the optimum cell shape when the vector sum of all the forces acting on the model is zero. The result of segmentation is used to confirm the cell membership of the FISH signals and to reject all the signals that lie outside the cell nuclei. Three-dimensional region isolation and labeling technique is used to label and count the FISH signals per cell nucleus. A simple study on the effect of different segmentation methods over a quantitative analysis of FISH signals is also presented.

Adenocarcinoma↗

PubMedDate not supplied

A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.

Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a computer screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.

Algorithms↗