ADAMIS: a database for medical information systems

This paper describes the design and implementation of ADAMIS (‘A database for medical information systems’). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the ‘simplified medical query language’ (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

DSA image registration using non-uniform MRF model and pivotal control points

In order to reduce the motion artifacts in DSA, non-rigid image registration is commonly used before subtracting the mask from the contrast image. Since DSA registration requires a set of spatially non-uniform control points, a conventional MRF model is not very efficient. In this paper, we introduce the concept of pivotal and non-pivotal control points to address this, and propose a non-uniform MRF for DSA registration. We use quad-trees in a novel way to generate the non-uniform grid of control points. Our MRF formulation produces a smooth displacement field and therefore results in better artifact reduction than that of registering the control points independently. We achieve improved computational performance using pivotal control points without compromising on the artifact reduction. We have tested our approach using several clinical data sets, and have presented the results of quantitative analysis, clinical assessment and performance improvement on a GPU. (C) 2013 Elsevier Ltd. All rights reserved.

Is The Burden of Medical Education Affecting Ethics of Medical Treatment?

A substantial number of medical students in India have to bear an enormous financial burden for earning a bachelor's degree in medicine referred to as MBBS (bachelor of medicine and bachelor of surgery). This degree program lasts for four and one-half years followed by one year of internship. A postgraduate degree, such as MD, has to be pursued separately on completion of a MBBS. Every medical college in India is part of a hospital where the medical students get clinical exposure during the course of their study. All or at least a number of medical colleges in a given state are affiliated to a university that mainly plays a role of an overseeing authority. The medical colleges usually have no official interaction with other disciplines of education such as science and engineering, perhaps because of their independent location and absence of emphasis on medical research. However, many of the medical colleges are adept in imparting high-quality and sound training in medical practices including diagnostics and treatment. The medical colleges in India are generally of two types, i.e., government owned and private. Since only a limited number of seats are available across India in the former category of colleges, only a small fraction of aspiring candidates can find admission in these colleges after performing competitively in the relevant entrance tests. A major advantage of studying in these colleges is the nominal tuition fees that have to be paid. On the other hand, a large majority of would-be medical graduates have to seek admission in the privately run medical institutes in which the tuition and other related fees can be mind boggling when compared to their public counterparts. Except for candidates of exceptionally affluent background, the only alternative for fulfilling the dream of becoming a doctor is by financing one's study through hefty bank loans that may take years to pay back. It is often heard from patients that they are asked by doctors to undergo a plethora of diagnostic tests for apparently minor illnesses, which may financially benefit those prescribing the tests. The present paper attempts to throw light on the extent of disparity in cost of a medical education between state-funded and privately managed medical colleges in India; the average salary of a new medical graduate, which is often ridiculously low when compared to what is offered in entry-level engineering and business jobs; and the possible repercussions of this apparently unjust economic situation regarding the exploitation of patients.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.