Biomedical subjects

B S Manjunath

Publications and source records attributed to B S Manjunath.

3 recordsLinked to original sources

PubMed2006

Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images.

PURPOSE: To develop an automated tool that provides reliable, consistent, and accurate results for counting cell nuclei in tissue sections. METHODS: We propose a novel method based on an image processing algorithm to analyze large sets of digital micrographs. The nucleus detector design is based on a Laplacian of Gaussian filter. We use the leave-one-out cross validation method for estimating the generalization error, which is then used to choose the model and parameters of the proposed nucleus detector with both fluorescent and dye stained images. We also evaluate the performance of a nucleus detector by comparing the results with manual counts. RESULTS: When our nucleus detector is applied to previously unanalyzed images of feline retina, it correctly counts nuclei within the outer nuclear layer (ONL) with an average error of 3.67% ranging from 0 to 6.07%, and nuclei within the inner nuclear layer (INL) with an average error of 8.55% ranging from 0 to 13.76%. Our approach accurately identifies the location of cell bodies. Even though we have a relatively large error in the INL due to the large intra-observer variation, both manual counting and nucleus detector result in the same conclusion. This is the first time that cell death in the INL in response to retinal detachment is analyzed quantitatively. We also test the proposed tool with various images and show that it is applicable to a wide range of image types with nuclei varying in size and staining intensity. CONCLUSIONS: The proposed method is simple and reliable. It also has widespread applicability to a variety of sample preparation and imaging methods. Our approach will be immediately useful in quantifying cell number in large sets of digital micrographs and from high-throughput imaging. The tool is available as a plug-in for Image J.

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PubMed2006

Graph partitioning active contours (GPAC) for image segmentation.

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results.

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PubMed2004

Robust image-adaptive data hiding using erasure and error correction.

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as compression and limited amounts of image tampering and image resizing. The three main findings are as follows. 1) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use image-adaptive criteria in addition to statistical criteria based on information theory. 2) The use of local criteria to choose where to hide data can potentially cause desynchronization of the encoder and decoder. This synchronization problem is solved by the use of powerful, but simple-to-implement, erasures and errors correcting codes, which also provide robustness against a variety of attacks. 3) For simplicity, scalar quantization-based hiding is employed, even though information-theoretic guidelines prescribe vector quantization-based methods. However, an information-theoretic analysis for an idealized model is provided to show that scalar quantization-based hiding incurs approximately only a 2-dB penalty in terms of resilience to attack.

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