On Mon, 7 Jan 2002, Herbert Voss wrote:
> Jan Warnking wrote:
>
> > This is not strictly a lyx question, rather LaTeX/BibTeX:
> > I use the apalike quotation style ([author et al., year]). My problem is
> > that bibtex seems to put protected spaces in there. It seems reasonable
> > not to split a single reference across lines, but as four references can
> > already (almost) fill a line, there are only three spaces left between
> > them to block justify the text. My understanding is that LaTeX thinks it
> > would look ugly to stretch those three spaces, and puts in a fifth
> > reference, exceeding the right margin substantially. Not very nice either.
> > Any way to let LaTeX adjust the with of a space, without allowing a
> > linebrak at that place (a semi-protected space)? Or a way of saying that
> > overflowing lines are really not appreciated?
>
>
> give an example file
I attached a very short example .lyx file (basically containing only a
citation reference and the BibTeX references), as well as the
corresponding .bib file.
Jan
@article{dale.1999:cortical,
author = { A. M. Dale and B. Fischl and M. I. Sereno },
title = { {C}ortical surface-based analysis. {I}. {S}egmentation and
surface
reconstruction. },
journal = {Neuroimage},
volume = 9,
number = 2,
pages = { 179-94 },
month = feb,
year = 1999,
keywords = { Brain Mapping/instrumentation Cerebral Cortex/*anatomy &
histology Human Image Processing,
Computer-Assisted/*instrumentation
Magnetic Resonance Imaging/*instrumentation Reference Values
Software },
affiliation = { Massachusetts General Hosp/Harvard Medical School, Building
149, Charlestown, Massachusetts, 02129, USA.
[EMAIL PROTECTED] },
abstract = { Several properties of the cerebral cortex, including its
columnar and laminar organization, as well as the topographic
organization of cortical areas, can only be properly
understood
in the context of the intrinsic two-dimensional structure
of the cortical surface. In order to study such cortical
properties in humans, it is necessary to obtain an accurate
and explicit representation of the cortical surface in
individual
subjects. Here we describe a set of automated procedures
for obtaining accurate reconstructions of the cortical
surface,
which have been applied to data from more than 100 subjects,
requiring little or no manual intervention. Automated routines
for unfolding and flattening the cortical surface are
described
in a companion paper. These procedures allow for the routine
use of cortical surface-based analysis and visualization
methods in functional brain imaging. },
file = { processingIRMs/Dale_NIMG_99_segmentation.pdf },
url = { http://www.idealibrary.com/links/doi/10.1006/nimg.1998.0395 },
annote = { on file: processingIRMs }
}
@article{germond.2000:cooperative,
author = { L. Germond and M. Dojat and C. Taylor and C. Garbay },
title = { {A} cooperative framework for segmentation of {MRI} brain
scans. },
journal = { Artif Intell Med },
volume = 20,
number = 1,
pages = { 77-93 },
month = aug,
year = 2000,
keywords = { Artificial Intelligence Automation Brain/*anatomy &
histology/*physiology
*Computer Simulation Human *Magnetic Resonance Imaging/methods
Phantoms, Imaging Support, Non-U.S. Gov't },
affiliation = { Laboratoire TIMC-IMAG, Institut Bonniot, Faculte de Medecine,
Domaine de la Merci, La Tronche, France. },
abstract = { Automatic segmentation of MRI brain scans is a complex task
for two main reasons: the large variability of the human
brain anatomy, which limits the use of general knowledge
and, inherent to MRI acquisition, the artifacts present
in the images that are difficult to process. To tackle these
difficulties, we propose to mix, in a cooperative framework,
several types of information and knowledge provided and
used by complementary individual systems: presently, a
multi-agent
system, a deformable model and an edge detector. The outcome
is a cooperative segmentation performed by a set of region
and edge agents constrained automatically and dynamically
by both, the specific gray levels in the considered image,
statistical models of the brain structures and general
knowledge
about MRI brain scans. Interactions between the individual
systems follow three modes of cooperation: integrative,
augmentative and confrontational cooperation, combined during
the three steps of the segmentation process namely, the
specialization of the seeded-region-growing agents, the
fusion of heterogeneous information and the retroaction
over slices. The described cooperative framework allows
the dynamic adaptation of the segmentation process to the
own characteristics of each MRI brain scan. Its evaluation
using realistic brain phantoms is reported. },
url = { http://www.elsevier.nl/locate/artmed?menu=cont&label=Table },
annote = { on file: processingIRMs }
}
@article{schnack.2001:automated,
author = { H. G. Schnack and Hulshoff Pol, H. E. and W. F. Baare and
W. G. Staal and M. A. Viergever and R. S. Kahn },
title = { {A}utomated separation of gray and white matter from {MR}
images
of the human brain. },
journal = {Neuroimage},
volume = 13,
number = 1,
pages = { 230-7 },
month = jan,
year = 2001,
keywords = { Adult Brain/*anatomy & histology/pathology Calibration Female
Human Image Processing, Computer-Assisted Magnetic Resonance
Imaging Male Middle Age Reproducibility of Results
Schizophrenia/pathology },
affiliation = { Department of Psychiatry, A01.126, University Medical Center
Utrecht, The Netherlands. },
abstract = { A simple automatic procedure for segmentation of gray and
white matter in high resolution 1.5T T1-weighted MR human
brain images was developed and validated. The algorithm
is based on histogram shape analysis of MR images that were
corrected for scanner nonuniformity. Calibration and
validation
was done on a set of 80 MR images of human brains. The
automatic
method's values for the gray and white matter volumes were
compared with the values from thresholds set twice by the
best three of six raters. The automatic procedure was shown
to perform as good as the best rater, where the average
result of the best three raters was taken as reference.
The method was also compared with two other histogram-based
threshold methods, which yielded comparable results. The
conclusion of the study thus is that automated threshold
based methods can separate gray and white matter from MR
brain images as reliably as human raters using a thresholding
procedure. Copyright 2001 Academic Press. },
file = { processingIRMs/Schnack_NIMG_01_automatedsegmentation.pdf },
url = { http://www.idealibrary.com/links/doi/10.1006/nimg.2000.0669 },
annote = { on file: processingIRMs }
}
@article{shattuck.2001:magnetic,
author = { D. W. Shattuck and S. R. Sandor-Leahy and K. A. Schaper
and D. A. Rottenberg and R. M. Leahy },
title = { {M}agnetic resonance image tissue classification using a
partial
volume model. },
journal = {Neuroimage},
volume = 13,
number = 5,
pages = { 856-76 },
month = may,
year = 2001,
affiliation = { Signal and Image Processing Institute, University of Southern
California, Los Angeles, California, 90089 },
abstract = { We describe a sequence of low-level operations to isolate
and classify brain tissue within T1-weighted magnetic
resonance
images (MRI). Our method first removes nonbrain tissue using
a combination of anisotropic diffusion filtering, edge
detection,
and mathematical morphology. We compensate for image
nonuniformities
due to magnetic field inhomogeneities by fitting a tricubic
B-spline gain field to local estimates of the image
nonuniformity
spaced throughout the MRI volume. The local estimates are
computed by fitting a partial volume tissue measurement
model to histograms of neighborhoods about each estimate
point. The measurement model uses mean tissue intensity
and noise variance values computed from the global image
and a multiplicative bias parameter that is estimated for
each region during the histogram fit. Voxels in the
intensity-normalized
image are then classified into six tissue types using a
maximum a posteriori classifier. This classifier combines
the partial volume tissue measurement model with a Gibbs
prior that models the spatial properties of the brain. We
validate each stage of our algorithm on real and phantom
data. Using data from the 20 normal MRI brain data sets
of the Internet Brain Segmentation Repository, our method
achieved average kappa indices of kappa = 0.746 +/- 0.114
for gray matter (GM) and kappa = 0.798 +/- 0.089 for white
matter (WM) compared to expert labeled data. Our method
achieved average kappa indices kappa = 0.893 +/- 0.041 for
GM and kappa = 0.928 +/- 0.039 for WM compared to the ground
truth labeling on 12 volumes from the Montreal Neurological
Institute's BrainWeb phantom. Copyright 2001 Academic Press.
},
file = { processingIRMs/Schattuck_NIMG_01_segmentation.pdf },
url = { http://www.idealibrary.com/links/doi/10.1006/nimg.2000.0730 },
annote = { on file: processingIRMs }
}
@article{teo.1997:creating,
author = { P. C. Teo and G. Sapiro and B. A. Wandell },
title = { {C}reating connected representations of cortical gray matter
for functional {MRI} visualization. },
journal = { IEEE Trans Med Imaging },
volume = 16,
number = 6,
pages = { 852-63 },
month = dec,
year = 1997,
keywords = { Cerebral Cortex/*anatomy & histology/physiology Computer
Simulation Human *Magnetic Resonance Imaging Support, Non-U.S.
Gov't Support, U.S. Gov't, Non-P.H.S. Support, U.S. Gov't,
P.H.S. },
affiliation = { Computer Science Department, Stanford University, CA 94305,
USA. },
abstract = { We describe a system that is being used to segment gray
matter from magnetic resonance imaging (MRI) and to create
connected cortical representations for functional MRI
visualization
(fMRI). The method exploits knowledge of the anatomy of
the cortex and incorporates structural constraints into
the segmentation. First, the white matter and cerebral spinal
fluid (CSF) regions in the MR volume are segmented using
a novel techniques of posterior anisotropic diffusion. Then,
the user selects the cortical white matter component of
interest, and its structure is verified by checking for
cavities and handles. After this, a connected representation
of the gray matter is created by a constrained growing-out
from the white matter boundary. Because the connectivity
is computed, the segmentation can be used as input to several
methods of visualizing the spatial pattern of cortical
activity
within gray matter. In our case, the connected representation
of gray matter is used to create a flattened representation
of the cortex. Then, fMRI measurements are overlaid on the
flattened representation, yielding a representation of the
volumetric data within a single image. The software is freely
available to the research community. }
}
@article{zeng.1999:segmentation,
author = { Zeng, X and Staib, L H and Schultz, R T and Duncan, J S },
title = { Segmentation and Measurement of the cortex from 3D MR images
using coupled-surfaces propagation },
journal = { IEEE Transactions on Medical Imaging },
volume = 18,
number = 10,
pages = { 927-937 },
year = 1999
}
#LyX 1.1 created this file. For more info see http://www.lyx.org/
\lyxformat 218
\textclass article
\begin_preamble
\renewcommand\@biblabel[1]{}
\end_preamble
\language english
\inputencoding default
\fontscheme pslatex
\graphics default
\float_placement !hp
\paperfontsize 12
\spacing double
\papersize a4paper
\paperpackage a4
\use_geometry 0
\use_amsmath 1
\paperorientation portrait
\secnumdepth 0
\tocdepth 2
\paragraph_separation indent
\defskip medskip
\quotes_language english
\quotes_times 2
\papercolumns 1
\papersides 1
\paperpagestyle default
\layout Standard
Several descriptions of brain segmentation algorithms have been published
\begin_inset LatexCommand
\cite{teo.1997:creating,dale.1999:cortical,zeng.1999:segmentation,germond.2000:cooperative,schnack.2001:automated,shattuck.2001:magnetic}
\end_inset
.
Most commonly, voxels are assigned to either of three tissue types: white
matter, gray matter or cerebrospinal fluid\SpecialChar \ldots{}
\layout Standard
\begin_inset LatexCommand \BibTeX[apalike]{bib_problem}
\end_inset
\the_end
