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Biomedical subjects

Sameer Singh

Publications and source records attributed to Sameer Singh.

10 recordsLinked to original sources
PubMed2005

Machine learning in bioinformatics: a brief survey and recommendations for practitioners.

Machine learning is used in a large number of bioinformatics applications and studies. The application of machine learning techniques in other areas such as pattern recognition has resulted in accumulated experience as to correct and principled approaches for their use. The aim of this paper is to give an account of issues affecting the application of machine learning tools, focusing primarily on general aspects of feature and model parameter selection, rather than any single specific algorithm. These aspects are discussed in the context of published bioinformatics studies in leading journals over the last 5 years. We assess to what degree the experience gained by the pattern recognition research community pervades these bioinformatics studies. We finally discuss various critical issues relating to bioinformatic data sets and make a number of recommendations on the proper use of machine learning techniques for bioinformatics research based upon previously published research on machine learning.

Algorithms↗
PubMed2005

An evaluation of contrast enhancement techniques for mammographic breast masses.

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.

Algorithms↗
PubMed2005

Economic impact of telemedicine: a survey.

The economic evaluation of telemedicine has faced difficulties, both in terms of the effectiveness and cost-benefit analyses. The main challenges that lie ahead for economic assessment are: (a) technological changes; (b) sustainability of applications; (c) availability of outcomes and other patient data; (d) generalisability of evaluation results. These challenges have lead to an unsatisfactory modeling of cost analysis of teleradiology systems versus non-teleradiology (visiting radiology services) applications.This paper presents the analysis on the impact of telemedicine on health care. It particularly emphasizes a model for teleradiology cost systems. We study and compare cost analysis of teleradiology system versus non-teleradiology systems. Finally, a model is presented which is made viable for computing the number of patients needed to demonstrate the viability of the telemedicine systems.We conclude the following: (a) that large number of patients is needed to validate the economic impact of telemedicine services; (b) cultural change in USA will bring most prominent effect in improving health care thereby bringing health care costs down. This when combined with improving cost effective technology like telemedicine services will bring the overall health care costs down.

Cost-Benefit Analysis↗
PubMed2004

A knowledge-based framework for image enhancement in aviation security.

The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images.

Algorithms↗
PubMed2004

Consent in orthopaedic surgery.

AIM: To investigate whether the guidelines set out by the UK Department of Health on informed consent are being followed nationally in orthopaedic surgery. METHODS: A postal questionnaire of UK orthopaedic consultants was undertaken asking about consenting procedures for an elective and a trauma situation. RESULTS: In 53 of 110 cases, the most junior member of the team takes consent, and patients are not being warned about specific complications and risks associated with surgery. CONCLUSIONS: The guidelines issued by the Department of Health are not being adhered to, and the consenting doctor needs to be aware of the medical and legal responsibilities in taking informed consent.

Guideline Adherence↗
PubMed2002

Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review.

The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, this paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. This paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as "level sets without regularizers." This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as "level sets with regularizers." In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis.

Algorithms↗