Biomedical subjects

P S Adiga

Publications and source records attributed to P S Adiga.

5 recordsLinked to original sources

PubMed2001

Integrated approach for segmentation of 3-D confocal images of a tissue specimen.

In this article we have proposed an integrated approach for segmentation of cells in volumetric image data obtained using the Confocal Microscope. The volumetric images are the stack of two-dimensional (2-D) images. Segmentation of cells in such an image stack is a difficult problem due to the complex structure of the objects and the spatial relationship of the object signatures in different image slices of the image stack. Here we have proposed a segmentation technique, which is a combination of several known and novel segmentation methods. Low-level techniques such as edge operators, middle-level techniques such as 3-D watershed, rule-based merging, and a high level technique, active surface model optimization, are integrated in one approach to get better segmentation with less human interaction. Some image enhancement and noise reduction techniques are also used to reduce the error in intermediate stages and speed up the segmentation process. Results are shown on 3-D images of prostate cancer tissue specimen.

Automation↗

PubMed2001

Automatic analysis of agarose gel images.

MOTIVATION: Automatic tools to speed up routine biological processes are very much sought after in bio-medical research. Much repetitive work in molecular biology, such as allele calling in genetic analysis, can be made semi-automatic or task specific automatic by using existing techniques from computer science and signal processing. Computerized analysis is reproducible and avoids various forms of human error. Semi-automatic techniques with an interactive check on the results speed up the analysis and reduce the error. RESULTS: We have successfully implemented an image processing software package to automatically analyze agarose gel images of polymorphic DNA markers. We have obtained up to 90% accuracy for the classification of alleles in good quality images and up to 70% accuracy in average quality images. These results are obtained within a few seconds. Even after subsequent interactive checking to increase the accuracy of allele classification to 100%, the overall speed with which the data can be processed is greatly increased, compared to manual allele classification. AVAILABILITY: The IDL source code of the software is available on request from jonathan.flint@well.ox.ac.uk

Animals↗

PubMed2000

Region based techniques for segmentation of volumetric histo-pathological images.

In this article we have presented the application of three region based segmentation techniques namely, seeded volume growing, constrained erosion-dilation techniques and 3-D watershed algorithm. The algorithms are suitably extended to apply on 3-D histo-pathological images. Suitable modifications and extension for each algorithm is done to obtain better segmentation. A quantitative as well as qualitative comparison of the three methods is presented. Modifications to these algorithms for obtaining better results are discussed. The modifications include, (1) design of adaptive similarity measures to control the seeded volume growing and (2) rule-based merging of the over-segmented cells in the case of the 3-D watershed algorithm. Some results and quantitative study is also presented.

Adenocarcinoma↗

PubMed1999

Efficient cell segmentation tool for confocal microscopy tissue images and quantitative evaluation of FISH signals.

In this paper we have presented a semi-automatic method for segmenting 3-D cell nuclei from tissue images obtained using Confocal Laser Scanning Microscope. This microscope can focus at different layers of the specimen and hence a stack of images giving a 3D representation can be obtained. The existing methods for segmenting the cells in 3-D confocal images are highly interactive and, hence, time consuming. We have developed an approach, where, given one segmented image-slice (optical section) of the set of confocal images, the remaining image-slices in the image stack can be automatically segmented in a layered approach. One of the image-slices in an image stack is considered as a representative image-slice. In this image-slice, overlapping boundary pixels are identified interactively while the remaining part of the cell boundary is marked using Laplacian of a Gaussian operator. This interactively traced portion of the boundary is considered as initial boundary for finding the overlapping boundary pixels in the neighboring image-slices. Simple basic search strategy is used for boundary search in the neighboring image-slices. The method minimizes the human interaction and is also found to be efficient and reasonably accurate. Some experimental results are presented to illustrate the usefulness of the technique. We have also given the application of our segmentation method to quantitative evaluation of fluorescence in situ hybridization (FISH) signals. A brief comparative study of visual FISH signal evaluation and the FISH signal counting by automatic image analysis is also given.

Automation↗

PubMed1997

Groping for quantitative digital 3-D image analysis: an approach to quantitative fluorescence in situ hybridization in thick tissue sections of prostate carcinoma.

In molecular pathology numerical chromosome aberrations have been found to be decisive for the prognosis of malignancy in tumours. The existence of such aberrations can be detected by interphase fluorescence in situ hybridization (FISH). The gain or loss of certain base sequences in the desoxyribonucleic acid (DNA) can be estimated by counting the number of FISH signals per cell nucleus. The quantitative evaluation of such events is a necessary condition for a prospective use in diagnostic pathology. To avoid occlusions of signals, the cell nucleus has to be analyzed in three dimensions. Confocal laser scanning microscopy is the means to obtain series of optical thin sections from fluorescence stained or marked material to fulfill the conditions mentioned above. A graphical user interface (GUI) to a software package for display, inspection, count and (semi-)automatic analysis of 3-D images for pathologists is outlined including the underlying methods of 3-D image interaction and segmentation developed. The preparative methods are briefly described. Main emphasis is given to the methodical questions of computer-aided analysis of large 3-D image data sets for pathologists. Several automated analysis steps can be performed for segmentation and succeeding quantification. However tumour material is in contrast to isolated or cultured cells even for visual inspection, a difficult material. For the present a fully automated digital image analysis of 3-D data is not in sight. A semi-automatic segmentation method is thus presented here.

Humans↗