CT圖像金屬偽影消除技術的文獻。共3篇。
Reduction of Metal Artifacts in X-Ray computed Tomography
XrayCT_artifacts
Reduction of CT Artifacts Caused by Metallic Implants
由Willi A. Kalender, PhD
Robert Hebel, Dipl Phys
Johannes Ebersberger, Dr rer nat撰寫
function y_cum = cum2x (x,y, maxlag, nsamp, overlap, flag)
％CUM2X Cross-covariance
％ y_cum = cum2x (x,y,maxlag, samp_seg, overlap, flag)
％ x,y - data vectors/matrices with identical dimensions
％ if x,y are matrices, rather than vectors, columns are
％ assumed to correspond to independent realizations,
％ overlap is set to 0, and samp_seg to the row dimension.
％ maxlag - maximum lag to be computed [default = 0]
％ samp_seg - samples per segment [default = data_length]
％ overlap - percentage overlap of segments [default = 0]
％ overlap is clipped to the allowed range of [0,99].
Each arc of a binary-state network has good/bad states. The system reliability, the probability
that source s communicates with sink t, can be computed in terms of minimal paths (MPs). An
MP is an ordered sequence of arcs from s to t that has no cycle. Note that a minimal path is
different from the so-called minimum path. The latter is a path with minimum cost.
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.
The frequency domain plays an important role in image
processing to smooth, enhance, and detect edges of images. Although
image data typically does not include imaginary values, the fast Fourier
transform (FFT) has been used for obtaining spectra. In this paper,
the fast Hartley transform (FHT) is used to transform two-dimensional
image data. Because the Hartley transform is real valued, it does
not require complex operations. Both spectra and autocorrelations of
two-dimensional ultrasound images of normal and abnormal livers were
computed.
