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Biomedical subjects

J Sklansky

Publications and source records attributed to J Sklansky.

7 recordsLinked to original sources
PubMed2000

Computer-aided, case-based diagnosis of mammographic regions of interest containing microcalcifications.

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate the effectiveness of a mapped-database diagnostic system in reducing the incidence of benign biopsies and misdiagnosed cancers among mammographic regions of interest (ROIs). MATERIALS AND METHODS: A novel neural network was devised (a) to respond to a query ROI by recommending to biopsy or not to biopsy and (b) to map each ROI in the database as a dot on a computer screen. The network was designed so that clusters in the array of dots help the radiologist to find proved ROIs visually similar to the query ROI. This mapped-database diagnostic system was restricted to ROIs with visible microcalcifications. The neural network was trained with a stored database of 80 biopsy-proved ROIs. RESULTS: Four radiologists acting independently on 100 ROIs recommended biopsies for 18, 15, 28, and 18 benign ROIs and misdiagnosed cancers in 11, 12, 7, and eight ROIs, respectively. Interaction with the mapped-database system reduced the numbers of benign biopsies to 11, eight, 18, and 10 cases and of misdiagnosed cancers to eight, seven, four, and three cases, respectively. Statistical analysis indicated that three radiologists achieved significant improvements at P < or = .02 and the fourth achieved a substantial improvement at P < or = .07. CONCLUSION: By using a mapped database of proved mammographic ROIs containing microcalcifications, radiologists may statistically significantly reduce the numbers of benign biopsies and misdiagnosed cancers.

Biopsy↗
PubMed1999

Biplane X-ray angiograms, intravascular ultrasound, and 3D visualization of coronary vessels.

The technology for determination of the 3D vascular tree and quantitative characterization of the vessel lumen and vessel wall has become available. With this technology, cardiologists will no longer rely primarily on visual inspection of coronary angiograms but use sophisticated modeling techniques combining images from various modalities for the evaluation of coronary artery disease and the effects of treatment. Techniques have been developed which allow the calculation of the imaging geometry and the 3D position of the vessel centerlines of the vascular tree from biplane views without a calibration object, i.e., from the images themselves, removing the awkwardness of moving the patient to obtain 3D information. With the geometry and positional information, techniques for reconstructing the vessel lumen can now be applied that provide more accurate estimates of the area and shape of the vessel lumen. In conjunction with these developments, techniques have been developed for combining information from intravascular ultrasound images with the information obtained from angiography. The combination of these technologies will yield a more comprehensive characterization and understanding of coronary artery disease and should lead to improved and perhaps less invasive patient care.

Coronary Angiography↗
PubMed1986

Computing the skeleton of coronary arteries in cineangiograms.

In this paper we describe techniques to determine the skeleton of coronary arteries and to measure their medically significant geometric properties (such as boundaries and orientations) in cineangiograms. We use these geometric properties for segmentation, i.e., for separating the image of the arteries from the background. The purpose of this segmentation is to produce a structural description of the arterial tree for high-level processing. Our technique is completely automatic, efficient, and reliable. Our technique starts with the detection of skeleton points from a local ridge detector using density profiles of the arteries. Local orientations of skeleton points are obtained directly from the ridge detection or from local edge directions. Boundaries of the arteries are computed from the density profile along a straight line perpendicular to the skeleton. From this, a sequential and raster-scan tracking procedure is applied to obtain segments of the arteries from two scanning directions. The final segments are generated by merging all the segments.

Algorithms↗