Abstract
Image filtering consists of modifying the original image by logically "reimaging" it with a mathematical imaging device in which spatial response can be controlled by the user. Image filtering is performed by a mathematical operation called convolution, which is simply the successive replacement of each point in the original image by a new value produced by a weighted combination of the original point and its surrounding neighbour points. Filtering generally requires definition of a filtering kernel or small matrix; often a few filtering kernels are predefined in imaging computer systems. The filtering kernel is generally square with a matrix size of 3×3 pixels, 5×5 pixels or 7×7 pixels. We consider the use of two-dimensional, second-order derivatives for image enhancement. The approach basically consists of defining a discrete formulation of the second-order derivative and then constructing a filter mask based on that formulation. Ten spatial high-pass filters (masks) are developed, then implemented and tested in our laboratory by using programs that were written in Borland c++ and visual Fortran. The results of the application of the developped Laplacian and Laplacian high-pass digital filters (masks) on digital images (either edge detection, sharpening of high frequency regions (fine details) accentuation), comparing between the effect of different dimensions filters 3×3 and 5×5 and milder high pass effect are presented and demonstrated. As the size of the filter (mask) gets larger and/or the weight of the center pixel of the kernel gets higher, the sharpenning effect becomes more and more. Second-order derivatives have a strong response to fine detail, such as thin lines and isolated points.
Highlights
Image processing, with the intent of improving display information, was one of the first applications of the computer in nuclear medicine (Brown et al, 1971)
Image filtering consists of modifying the original image by logically “reimaging” it with a mathematical imaging device in which spatial response can be controlled by the user
Image filtering is performed by a mathematical operation called convolution, which is the successive replacement of each point in the original image by a new value produced by a weighted combination of the original point and its surrounding neighbor points
Summary
With the intent of improving display information, was one of the first applications of the computer in nuclear medicine (Brown et al, 1971). The practical result of this difference is that the logical imaging device used for image filtering can be specified to improve spatial resolution and make edge and count density transitions more obvious. The response can be specified so that filtering smooths the image and reduces the noise so that small differences in count densities can be more readily perceived by the viewer (Erickson, 1985). More information on general image processing may be obtained from different references (Gonzalez and Witz, 1977; Jain, 1988; Gonzalez and Woods, 2002) and more detailed descriptions of various nuclear medicine analysis techniques may be found in Gottschalk (1988; Erickson and Rollo, 1993)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: American Journal of Engineering and Applied Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
