We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.

Abstract-Data security becomes more and more important in telemammography which uses a public high-speed wide area network connecting the examination site with the mammography expert center. Generally, security is characterized in terms of privacy, authenticity and integrity of digital data. Privacy is a network access issue and is not considered in this paper. We present a method, authenticity and integrity of digital mammography, here which can meet the requirements of authenticity and integrity for mammography image (IM) transmission.The authenticity and integrity for mammography ( NOMENCLATURE AIDMAuthenticity and integrity for mammography image. DICOM Digital imaging and communication in medicine. FFDM Full-field digital mammography.

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This report describes the authors' experience in the design and implementation of two large scale picture archiving and communication systems (PACS) during the past 10 years. The first system, which is in daily clinical operation was developed at University of California, Los Angeles from 1983 to 1992. The second system, which continues evolving, has been in development at University of California, San Francisco (UCSF) since 1992. The report highiights the differences between the two systems and points out the gradual change in the PACS design concept during the past 10 years from a closed architecture to an open hospitalintegrated system. Both systems focus on system reliability and data integrity, with 24-hour on-line service and no Ioss of images. The major difference between the two systems is that the UCSF PACS infrastructure design is a completely open architecture and the system implementation uses more advanced technologies in computer software, digital communication, system interface, and stable industry standards. Such a PACS can withstand future technology changes without rendering the system obsolete, an essential criterion in any PACS design.

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