Biomedical subjects

Sameer Antani

Publications and source records attributed to Sameer Antani.

4 recordsLinked to original sources

PubMed2006

Digital tools for collecting data from cervigrams for research and training in colposcopy.

Colposcopy is a critical part of gynecologic practice but has documented deficiencies, including lack of correlation between the colposcopic appearance and the severity of underlying neoplasia, limited reproducibility, and difficulty in the optimal placement of colposcopically directed biopsies. In a collaborative effort to improve colposcopy, we are analyzing digitized cervigram images from National Cancer Institute-funded studies. Specifically, the National Cancer Institute has collected close to 100,000 cervigrams, digitized to create a database of images of the uterine cervix for research, training, and education. In addition to the cervigram images, this database contains clinical, cytologic, and molecular information at multiple examinations of 15,000 women, with password and ID labeling strategies to protect patient privacy. The National Library of Medicine has designed two web-accessible software tools. The Boundary Marking Tool allows experts on colposcopy to perform an evaluation of the pictures and to mark boundary regions of normal and abnormal regions of the uterine cervix; these evaluations are collected and saved in the database. The Multimedia Database Tool enables retrieval of test and image biomedical data according to specific queries, for example, all women with cervical intraepithelial neoplasia 3 whose cytologic results are atypical squamous cells of undetermined significance. The resource soon will be available as an open resource, via a teaching tool coordinated by a database manager, which will permit a variety of applications for teaching and research. In this article, we describe the perceived need for the resource and its components.

Biomedical Research↗

PubMed2004

Content-based image retrieval for large biomedical image archives.

Content-Based Image Retrieval (CBIR) has been a topic of research interest for nearly a decade. Approaches to date use image features for describing content. A survey of the literature shows that progress has been limited to prototype systems that make gross assumptions and approximations. Additionally, research attention has been largely focused on stock image collections. Advances in medical imaging have led to growth in large image collections. At the Lister Hill National Center for Biomedical Communication, an R&D division of the National Library of Medicine, we are conducting research on CBIR for biomedical images. We maintain an archive of over 17,000 digitized x-rays of the cervical and lumbar spine from the second National Health and Nutrition Examination Survey (NHANES II). In addition, we are developing an archive of a large number of digitized 35 mm color slides of the uterine cervix. Our research focuses on developing techniques for hybrid text/image query-retrieval from the survey text and image data. In this paper we present the challenges in developing CBIR of biomedical images and results from our research efforts.

Archives↗

PubMedDate not supplied

Anterior osteophyte discrimination in lumbar vertebrae using size-invariant features.

Radiologists often examine X-rays of cervical, thoracic and lumbar vertebrae for determining the presence of osteoarthritis and osteoporosis. For individual vertebra assessment, the boundary increasingly digresses from the general rectangular shape as the vertebra becomes less normal in appearance. For an abnormal vertebra, bony growths ('osteophytes') may appear at the vertebral comers, resulting in a change in the vertebra's shape. Image processing techniques are presented for computing size-invariant, convex hull-based features to highlight anterior osteophytes. Feature evaluation of 714 lumbar spine vertebrae using a multi-layer perceptron yielded normal and abnormal average correct discrimination of 90.5 and 86.6%, respectively.

Algorithms↗

PubMedDate not supplied

Image analysis techniques for characterizing disc space narrowing in cervical vertebrae interfaces.

Image analysis techniques are introduced for evaluating disc space narrowing of cervical vertebrae interfaces from X-ray images. Four scale-invariant, distance transform-based features are presented for characterizing the spacing between adjacent vertebrae. K-means and self-organizing map clustering techniques are applied to estimate the degree of disc space narrowing using a four grade (0-3) scoring system, where 0 and 3 represent normal spacing and significant narrowing, respectively. For a data set of 294 vertebrae interfaces, experimental results yield average correct grade assignment of greater than 82.10% for each of the four grades using a one grade window around the correct grade.

Algorithms↗