Browse Prior Art Database

Biomedical Image Processing Disclosure Number: IPCOM000131576D
Original Publication Date: 1983-Jan-01
Included in the Prior Art Database: 2005-Nov-11
Document File: 12 page(s) / 46K

Publishing Venue

Software Patent Institute

Related People

Stanley R. Sternberg: AUTHOR [+3]


The burden on image analysts in medicalfields has led to the automated processing of pictorial data. Here, a device called the cytocomputer searches for genetic mutations.

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Biomedical Image Processing

Stanley R. Sternberg,

CytoSystems Corporation

The burden on image analysts in medicalfields has led to the automated processing of pictorial data. Here, a device called the cytocomputer searches for genetic mutations.

A computer revolution has occurred not only in technical fields but also in medicine, where vast amounts of information must be processed quickly and accurately. Nowhere is the need for image processing techniques more apparent than in clinical diagnosis or mass screening applications where data take the form of digital images. New high-resolution scanning techniques such as computed tomography, nuclear magnetic resonance, positron emission tomography, and digital radiography produce images containing immense amounts of relevant information for medical analysis. But as these scanning techniques become more vital to clinical diagnosis, the work for specialists who must visually examine the resultant images increases. In many cases, quantitative data in the form of measurements and counts are needed to supplement nonimage patient data, and the manual extraction of these data is a time- consuming and costly step in an otherwise automated process. Furthermore, subtle variants of shade and shape can be the earliest clues to a diagnosis, placing the additional burden of complete thoroughness on the examining specialist.

For the last five years, the University of Michrigan and the Environmental Research Institute of Michigan have conducted a unique series of studies that involve the processing of biomedical imagery on a highly parallel computer specifically designed for image processing. System designers have incorporated the requirements of extracting a verifiable answer from an image in a reasonable time into an integrated approach to hardware and software design. The system includes a parallel pipelined image processor, called a cytocomputer, and a high-level language specifically created for image processing, C-3PL, the cytocomputer parallel picture processing language.

These studies have involved a great many people from both the medical and engineering communities and have highlighted the interdisciplinary aspects of biomedical image processing. The methods have been tested in anatomy, developmental biology, nuclear medicine, cardiology, and transplant rejection. The general consensus is that quantification by automated image analysis not only increases diagnostic accuracy but also provides significant data not obtainable from qualitative analysis alone.

One study in part...