PubMed Health⌕ Search

Biomedical subjects

D L McEachron

Publications and source records attributed to D L McEachron.

20 records · Page 2Linked to original sources

Image processing for the rest of us: the potential utility of inexpensive computerized image analysis in clinical pathology and radiology.

Recent progress in computer technology in both hardware and software, combined with marked cost reductions, have placed quantitatively accurate video densitometry systems within the reach of the individual clinician, biomedical researcher, and community hospital. While much of the attention generated by advances in image processing has focussed on larger scale procedures, such as CAT, chemical shift, and positron emission tomography, important applications can be found for considerably more modest systems. In this article, we discuss three such applications of DUMAS, a personal computer-based imaging system developed by the Image Processing Center at Drexel University. A potential technique for quantifying numbers of estrogen receptors in tumorous breast tissue samples as a predictor of patient responsiveness to hormonal therapy is described first, along with possible sources of error. The second application, also related to clinical pathology and cancer, outlines methods for relating changes in nuclear and cell morphology to the diagnosis of Sezary Cell Syndrome. The utility of binary image filtering methods in the classification of cell types is discussed. The third application involves the development of a semi-automatic procedure for the determination of vessel diameter in arteriograms. A detailed description of the optimization and curve-fitting algorithms is provided along with preliminary test results comparing various approaches. The need for user demand to fuel research and development in small-scale imaging systems is also discussed.

Algorithms↗

The variation in user drawn outlines on digital images: effects on quantitative autoradiography.

Variation in user outlines drawn to delineate neural regions is the only source of error added to autoradiographic images after the images have been digitized. By analyzing data compiled from many outlines drawn by several different investigators, it was demonstrated that this variation constitutes a major source of error affecting quantitative autoradiography. Both the gray level variation and average within a region, as well as the specific pixels included in an outline, were affected. However, using the intersection of multiple outlining attempts surrounding any given region and a consistent set of outlining criteria, it was possible to delineate a region which remained consistent from user to user. A technique for automating the consistent outlining process is discussed.

Animals↗