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consonant modified by vowel sound) and CVM (M is a modifier that modifies the nature of the preceding In this paper, we describe a system for the automatic vowel, e.g. nasalization). Other Tare forms were recognition of isolated handwritten Devanagari omitted for the Purpose of this study. Given ICl = 35, characters obtained by linearizing consonant IVI = 11, IN\ = 10, /MI = 4, andreshcting ourselves to conjuncts. Owing to the large number of characters valid combinations, we are left with a total of 1487 and resulting demands on data acquisition, we use characters to recognize-Some examples of each of structural reconnition techniques to reduce some these categories of characters are shown in Figure 1.characters to others. The residual characters are then classiJied using the subspace method. Finally the results of structural recognition and feature-based matching are mapped to give final output. The proposed system is evaluated for the writer dependent scenario.

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We present a comparison of elastic matching schemes for writer dependent on-line handwriting recognition of isolated Tamil characters. Three different features are considered namely, preprocessed x-y co-ordinates, quantized slope values, and dominant point co-ordinates. Seven schemes based on these three features and dynamic time warping distance measure are compared with respect to recognition accuracy, recognition speed, and number of training templates. Along with these results, possible grouping strategies and error analysis is also presented in brief.

Oral, cervical and breast cancers, which are either preventable and/or amenable to early detection and treatment, are the leading causes of cancer-related morbidity and mortality in India. In this paper, we describe implementation science research priorities to catalyze the prevention and control of these cancers in India. Research priorities were organized using a framework based on the implementation science literature and the World Health Organization's definition of health systems. They addressed both community-level as well as health systems-level issues. Community-level or "pull" priorities included the need to identify effective strategies to raise public awareness and understanding of cancer prevention, monitor knowledge levels, and address fear and stigma. Health systems-level or "push" and "infrastructure" priorities included dissemination of evidencebased practices, testing of point-of-care technologies for screening and diagnosis, identification of appropriate service delivery and financing models, and assessment of strategies to enhance the health workforce. Given the extent of available evidence, it is critical that cancer prevention and treatment efforts in India are accelerated. Implementation science research can generate critical insights and evidence to inform this acceleration.

We present a novel region-based curve evolution algorithm which has three primary contributions: (i) nonparametric estimation of probability distributions using the recently developed NP windows method; (ii) an inequalityconstrained least squares method to model the image histogram with a mixture of nonparametric probability distributions; and (iii) accommodation of the partial volume effect, which is primarily due to low resolution images, and which often poses a significant challenge in medical image analysis (our primary application area). We first approximate the image intensity histogram as a mixture of non-parametric probability density functions (PDFs), justifying its use with respect to medical image analysis. The individual densities in the mixture are estimated using the recent NP windows PDF estimation method, which builds a continuous representation of discrete signals. A Bayesian framework is then formulated in which likelihood probabilities are given by the non-parametric PDFs and prior probabilities are calculated using an inequality constrained least squares method. The non-parametric PDFs are then learnt and the segmentation solution is spatially regularised using a level sets framework. The log ratio of the posterior probabilities is used to drive the level set evolution. As background to our approach, we recall related developments in level set methods. Results are presented for a set of synthetic and natural images as well

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In this paper we apply the random walk-based segmentation method to mesothelioma CT image datasets, aiming to estab lish an automatic segmentation routine that can provide volu metric assessments for monitoring progression of the disease and its treatments. We have validated the applicability of this method to our image data through a series of experimental tri als, and demonstrated the superior performance and benefits of random walk compared to other segmentation algorithms such as level sets.