In six districts of West Bengal arsenic has been found in ground water above the maximum permissible limit recommended by the WHO of 0.05 mg l-1. This water is used by the villagers for drinking, cooking and other household purposes. These six districts have an area of 34,000 km2 and hold a population of 30 million. Over the last five years we have surveyed only a few small areas of these six affected districts and our survey revealed that, at present, at least 800,000 people from 312 villages in 37 blocks are drinking contaminated water and more than 175,000 people are showing arsenical skin lesions that are the late stages of manifestation of arsenic toxicity. Most of the three stages of arsenic-related clinical manifestations are observed amongst the affected people. The common symptoms are conjunctivitis, melanosis, depigmentation, keratosis and hyperkeratosis; cases of gangrene and malignant neoplasms are also observed. The source of arsenic is geological. We have analysed thousands of arsenic contaminated water samples. Most of the water samples contain a mixture of arsenite and arsenate and in none of them could we detect methylarsonic or dimethylarsenic acid. We have also analysed a large number of urine, hair and nail samples, several skin-scales and some liver tissues (biopsy samples) of the people drinking the arsenic contaminated water and showing arsenical skin lesions. Flow injection hydride generation atomic absorption spectrometry (FI-HGAAS) was used for the analysis of hair, nails, urine and skin-scale after decomposition by various techniques. The liver tissues were analysed by Zeeman corrected-ETAAS using a few milligrams of the biopsy samples.

In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.

A scheme for enhancing local contrast of raw images based on multiscale morphology is presented in this paper. The conventional theoretical concept of local contrast enhancement has been extended in the regime of mathematical morphology. The intensity values of the scale-speci"c features of the image extracted using multiscale tophat transformation are modi"ed for achieving local contrast enhancement. Locally enhanced features are combined to reconstruct the "nal image. The proposed algorithm has been executed on a set of raw images for testing its e$cacy and the result has been compared with that of other standard methods for getting idea about its relative performance.2000 Elsevier Science B.V. All rights reserved. Zusammenfassung Re2 sume2Nous preH sentons dans cet article un scheH ma de rehaussement du contraste local dans des images, reposant sur de la morphologie multi-eH chelle. Le concept theH orique conventionne du rehaussement de contraste local a eH teH eH tendu dans le reH gime de la morphologie matheH matique. Les valeurs d'intensiteH des caracteH ristiques speH ci"ques de l'eH chelle dans l'image, extraites en utilisant la transformation du chapeau haut-de-forme multi-eH chelle, sont modi"eH es pour atteindre un rehaussement du contraste local. Less caracteH ristiques rehausseH es localement sont combineH es pour reconstruire l'image "nale. L'algorithme proposeH a eH teH exeH cuteH sur un ensemble d'images a"n de tester sont e$caciteH et les reH sultats ont eH teH 0165-1684/00/$ -see front matter 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 -1 6 8 4 ( 9 9 ) 0 0 1 6 1 -9 compareH s avec ceux d'autres meH thodes standard a"n d'avoir une ideH e de ses performances relatives.2000 Elsevier Science B.V. All rights reserved.

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