Biomedical subjects

B B Chaudhuri

Publications and source records attributed to B B Chaudhuri.

9 recordsLinked to original sources

PubMed2004

Generation of digital time database from paper ECG records and Fourier transform-based analysis for disease identification.

ECG signals recorded on paper are transferred to the digital time database with the help of an automated data extraction system developed here. A flatbed scanner is used to form an image database of each 12-lead ECG signal. Those images are then fed into a Pentium PC having a system to extract pixel-to-pixel co-ordinate information to form a raw database with the help of some image processing techniques. These raw data are then ported to the regeneration domain of the system to check the captured pattern with the original wave shape. The sampling period of each ECG signal is computed after detection of QRS complex. Finally, discrete Fourier transform of the generated database is performed to observe the frequency response properties of every ECG signal. Some interesting amplitude properties of monopolar chest lead V4 and V6 are noticed which are stated.

Databases as Topic↗

PubMed2004

Recognition of online handwritten mathematical expressions.

This paper aims at automatic understanding of online handwritten mathematical expressions (MEs) written on an electronic tablet. The proposed technique involves two major stages: symbol recognition and structural analysis. Combination of two different classifiers have been used to achieve high accuracy for the recognition of symbols. Several online and offline features are used in the structural analysis phase to identify the spatial relationships among symbols. A context-free grammar has been designed to convert the input expressions into their corresponding T(E)X strings which are subsequently converted into MathML format. Contextual information has been used to correct several structure interpretation errors. A new method for evaluating performance of the proposed system has been formulated. Experiments on a dataset of considerable size strongly support the feasibility of the proposed system.

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↗

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↗

PubMed2000

Segmentation and counting of FISH signals in confocal microscopy images.

In this paper we have presented a semi-automatic method for segmenting and counting the Fluorescence In Situ Hybridization (FISH) signals per cell nucleus in a 3D tissue image. The number of FISH signals indicate the gain (trisomy) or loss (monosomy) of certain base-sequences in deoxyribonucleic acid (DNA). The quantitative evaluation of the loss or gain in DNA is necessary in qualitative diagnostic molecular pathology. Multispectral volumetric images are obtained using the Confocal Microscope. Each consists of a red channel depicting the 3D morphology of the tissue and green channel containing the FISH signals. The red channel tissue image is segmented first to determine the membership of the FISH signal to a particular nuclei. Various segmentation methods starting from simple local thresholding to volume growing and active volumes are used for segmentation. A brief comparative study of the visual signal count by pathologist and our automatic count is also presented.

Cell Nucleus↗

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↗

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↗

PubMed1998

Automatic cell segmentation in cyto- and histometry using dominant contour feature points.

Automatic cell segmentation has various application potentials in cytometry and histometry. In this paper, an automatic cluster (touching) cell segmentation approach using the dominant contour feature points has been presented. Dominant feature points are the locations of indentation on the contour of the cluster. First, dominant feature points on the contour of the cluster are detected by distance profile. Next, using shape features of the cells, these feature points are selected for segmentation. We compared the results of the proposed method with manual segmentation and observed that the method has an overall accuracy about to 82%.

Cell Nucleus↗

PubMedDate not supplied

Frequency-plane analysis of normal and pathological ECG signals for disease identification.

In this paper a frequency plane analysis of both normal and diseased ECG signals is performed specifically for disease identification. Image processing techniques are used to develop an automated data acquisition package of 12 lead ECG signals from paper records. A regeneration domain is also developed to check the captured pattern with the original wave shape. A QRS complex detector with an accuracy level approximately 98.4% in up to 30% signal to noise level is developed. Discrete Fourier transform (DFT) is performed to obtain the frequency spectrum of every ECG signal. Some interesting amplitude and phase response properties of chest lead V2, V3, V4, V6 and limb lead I, II, III, AVL, AVF are seen. Both amplitude and phase properties are different for normal and diseased subjects and can serve an important role in disease identification. A statistical analysis of amplitude property is carried out to show that this property is significantly different for normal and diseased subjects.

Adult↗