M. Anderson. Task-oriented lossy compression of magnetic resonance images. M.Sc. thesis, School of Computing Science, Simon Fraser University, 1995. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Measuring Border Irregularity And Shape Of Cutaneous Melanocytic.. - Lee (2001) (Correct)
....scale space filtering. 2.2.4.1. Wavelet transform Another popular multi scale method in active research is the wavelet transform, which linearly decomposes a signal f(x) into multi scales based on mother wavelet basis functions y k : 2. 9) where c k are the coefficients for the transformation [2, 52, 69, 141, 142]. When sinusoidal functions are used as the basis functions, wavelet transform becomes the well known Fourier Transform, which do not retain spatial information. However, the balance of retaining information on both spatial and frequency domains can be achieved for other basis functions with local ....
M. Anderson. Task-oriented lossy compression of magnetic resonance images. M.Sc. thesis, School of Computing Science, Simon Fraser University, 1995.
Dynamic Region-based Wavelet Coding for Telemedicine Applications - Li (1997) (Correct)
....at the edge of the mask, because after the segmentation, artificial sharp edges are introduced at the edges in both f in (i; j) and f out (i; j) and these sharp edges are hard to be perfectly coded, so artifacts are formed after the two images are merged together. This effect was observed in [1]. This leads to the idea that the region partition will have to be done in the transform domain. As the wavelet transform coefficients still have spatial localization, the spatial mask over the original image could be adapted to a mask over the transformed image. As the wavelet transformation ....
Mark C. Anderson. Task-oriented lossy compression of magnetic resonance images. M.Sc. thesis, Simon Fraser University, August 1995.
Intracranial Boundary Detection and Radio Frequency Correction.. - Mackiewich (1995) (2 citations) (Correct)
....43] In MRI scans of the head, doctors are usually more interested in the brain as opposed to the region outside the brain. For this reason, Anderson has developed a lossy MRI compression scheme that selectively compresses the region outside the brain at a higher compression ratio than the brain [2]. Thus, he achieves high compression ratios while maintaining image quality in the brain area. Obviously, automatic intracranial boundary detection is a prerequisite for such a scheme. 1.2 Goals The primary goal of this thesis is to develop an automatic preprocessing step for isolating the brain ....
....way of a tissue segmentation algorithm such as Johnston s [22] 4. Validation of the detected intracranial boundaries by way of a registration algorithm such as Zuk s [56] CHAPTER 9. SUMMARY 142 5. Validation of the detected intracranial boundaries by way of Anderson s MRI compression algorithm [2]. These tests have been initiated, but remain incomplete. Initial results, which are not be presented here, are favorable. One algorithm aspect of the new intracranial boundary detection scheme also requires investigation. As shown in the previous chapter, the Segment Head process does not ....
Mark Anderson. Task-oriented lossy compression of magnetic resonance images. Master 's thesis, Simon Fraser University, Computer Science Department, Burnaby, B.C., July 1995.
Task-Oriented Lossy Compression of Magnetic Resonance Images - Anderson, Atkins, Vaisey (1996) (3 citations) Self-citation (Anderson) (Correct)
No context found.
Mark C. Anderson. Task-oriented lossy compression of magnetic resonance images. M.Sc. thesis, Simon Fraser University, August 1995.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC