STYLE: Replace \bf with \textbf

In modern LaTeX, you should not use the old two-letter command \bf.
Instead, you should use the recommended \textbf{...} command or the
\bfseries declaration for producing bold text.

Author: hjmjohnson

SoftwareGuide/Latex/01-PrintedPreamble-Book1.tex

@@ -1,6 +1,6 @@
 \begin{minipage}[t][1.0cm][b]{\textwidth}
 \begin{center}
 Printed and produced in the United States of America.\\
-\bf{ISBN 978-1-930934-35-1}
+\textbf{ISBN 978-1-930934-35-1}
 \end{center}
 \end{minipage}

SoftwareGuide/Latex/01-PrintedPreamble-Book2.tex

@@ -1,6 +1,6 @@
 \begin{minipage}[t][1.0cm][b]{\textwidth}
 \begin{center}
 Printed and produced in the United States of America.\\
-\bf{ISBN 978-1-930934-36-8}
+\textbf{ISBN 978-1-930934-36-8}
 \end{center}
 \end{minipage}

SoftwareGuide/Latex/DesignAndFunctionality/DemonsRegistration.tex

@@ -18,21 +18,21 @@
 of the displacement is derived from the instantaneous optical flow equation:
 
 \begin{equation}
-\bf{D}(\bf{X}) \cdot \nabla f(\bf{X}) = - \left(m(\bf{X}) - f(\bf{X}) \right)
+\textbf{D}(\textbf{X}) \cdot \nabla f(\textbf{X}) = - \left(m(\textbf{X}) - f(\textbf{X}) \right)
 \label{eqn:OpticalFlow}
 \end{equation}
 
-In the above equation, $f(\bf{X})$ is the fixed image, $m(\bf{X})$
-is the moving image to be registered, and $\bf{D}(\bf{X})$ is the displacement
+In the above equation, $f(\textbf{X})$ is the fixed image, $m(\textbf{X})$
+is the moving image to be registered, and $\textbf{D}(\textbf{X})$ is the displacement
 or optical flow between the images. It is well known in optical flow
 literature that Equation \ref{eqn:OpticalFlow} is insufficient to specify
-$\bf{D}(\bf{X})$ locally and is usually determined using some form of
+$\textbf{D}(\textbf{X})$ locally and is usually determined using some form of
 regularization. For registration, the projection of the vector on the
 direction of the intensity gradient is used:
 
 \begin{equation}
-\bf{D}(\bf{X}) = - \frac
-{\left(  m(\bf{X}) - f(\bf{X}) \right) \nabla f(\bf{X})}
+\textbf{D}(\textbf{X}) = - \frac
+{\left(  m(\textbf{X}) - f(\textbf{X}) \right) \nabla f(\textbf{X})}
 {\left\|  \nabla f \right\|^2 }
 \end{equation}
 
@@ -41,9 +41,9 @@
 re-normalizes the equation such that:
 
 \begin{equation}
-\bf{D}(\bf{X}) = - \frac
-{\left(  m(\bf{X}) - f(\bf{X}) \right) \nabla f(\bf{X})}
-{\left\|  \nabla f \right\|^2 + \left(  m(\bf{X}) - f(\bf{X}) \right)^2 / K }
+\textbf{D}(\textbf{X}) = - \frac
+{\left(  m(\textbf{X}) - f(\textbf{X}) \right) \nabla f(\textbf{X})}
+{\left\|  \nabla f \right\|^2 + \left(  m(\textbf{X}) - f(\textbf{X}) \right)^2 / K }
 \label{eqn:DemonsDisplacement}
 \end{equation}
 
@@ -52,26 +52,26 @@
 value of the pixel spacings. The inclusion of $K$ ensures the force computation is
 invariant to the pixel scaling of the images.
 
-Starting with an initial deformation field $\bf{D}^{0}(\bf{X})$, the demons
+Starting with an initial deformation field $\textbf{D}^{0}(\textbf{X})$, the demons
 algorithm iteratively updates the field using Equation
 \ref{eqn:DemonsDisplacement} such that the field at the $N$-th iteration is
 given by:
 
 \begin{equation}
-\bf{D}^{N}(\bf{X}) = \bf{D}^{N-1}(\bf{X}) - \frac
-{\left(  m(\bf{X}+ \bf{D}^{N-1}(\bf{X}))
-- f(\bf{X}) \right) \nabla f(\bf{X})}
+\textbf{D}^{N}(\textbf{X}) = \textbf{D}^{N-1}(\textbf{X}) - \frac
+{\left(  m(\textbf{X}+ \textbf{D}^{N-1}(\textbf{X}))
+- f(\textbf{X}) \right) \nabla f(\textbf{X})}
 {\left\|  \nabla f \right\|^2 + \left(
-m(\bf{X}+ \bf{D}^{N-1}(\bf{X}) )
- - f(\bf{X}) \right)^2 }
+m(\textbf{X}+ \textbf{D}^{N-1}(\textbf{X}) )
+ - f(\textbf{X}) \right)^2 }
 \label{eqn:DemonsUpdateEquation}
 \end{equation}
 
 Reconstruction of the deformation field is an ill-posed problem where
 matching the fixed and moving images has many solutions. For example, since
 each image pixel is free to move independently, it is possible that all
-pixels of one particular value in $m(\bf{X})$ could map to a single image
-pixel in $f(\bf{X})$ of the same value. The resulting deformation field may
+pixels of one particular value in $m(\textbf{X})$ could map to a single image
+pixel in $f(\textbf{X})$ of the same value. The resulting deformation field may
 be unrealistic for real-world applications. An option to solve for the field
 uniquely is to enforce an elastic-like behavior, smoothing the deformation
 field with a Gaussian filter between iterations.
@@ -89,9 +89,9 @@ \subsection{Asymmetrical Demons Deformable Registration}
 used in this case is
 
 \begin{equation}
-\bf{D}(\bf{X}) = - \frac
-{2 \left(  m(\bf{X}) - f(\bf{X}) \right) \left(  \nabla f(\bf{X}) +  \nabla g(\bf{X}) \right) }
-{\left\|  \nabla f + \nabla g \right\|^2 + \left(  m(\bf{X}) - f(\bf{X}) \right)^2 / K }
+\textbf{D}(\textbf{X}) = - \frac
+{2 \left(  m(\textbf{X}) - f(\textbf{X}) \right) \left(  \nabla f(\textbf{X}) +  \nabla g(\textbf{X}) \right) }
+{\left\|  \nabla f + \nabla g \right\|^2 + \left(  m(\textbf{X}) - f(\textbf{X}) \right)^2 / K }
 \label{eqn:DemonsDisplacement2}
 \end{equation}
 

SoftwareGuide/Latex/DesignAndFunctionality/ImageMetrics.tex

@@ -88,8 +88,8 @@
 In the following sections, we describe the ITKv4 metric types in detail.
 You can check ITK descriptions in doxygen for details about ITKv3 metric classes.
 
-For ease of notation, we will refer to the fixed image $f(\bf{X})$
-and transformed moving image $(m \circ T(\bf{X}))$ as images $A$ and $B$.
+For ease of notation, we will refer to the fixed image $f(\textbf{X})$
+and transformed moving image $(m \circ T(\textbf{X}))$ as images $A$ and $B$.
 
 \subsection{Mean Squares Metric}
 \label{sec:MeanSquaresMetricv4}

SoftwareGuide/Latex/DesignAndFunctionality/LevelSetsSegmentation.tex

@@ -20,11 +20,11 @@
 The paradigm of the level set is that it is a numerical method for tracking
 the evolution of contours and surfaces. Instead of manipulating the contour
 directly, the contour is embedded as the zero level set of a higher
-dimensional function called the level-set function, $\psi(\bf{X},t)$. The
+dimensional function called the level-set function, $\psi(\textbf{X},t)$. The
 level-set function is then evolved under the control of a differential
 equation.  At any time, the evolving contour can be obtained by extracting
-the zero level-set $\Gamma(\bf{X},t) =
-\{\psi(\bf{X},t) = 0\}$ from the output.  The main advantages of using level
+the zero level-set $\Gamma(\textbf{X},t) =
+\{\psi(\textbf{X},t) = 0\}$ from the output.  The main advantages of using level
 sets is that arbitrarily complex shapes can be modeled and topological
 changes such as merging and splitting are handled implicitly.
 
@@ -73,7 +73,7 @@
 those inputs will be cast to an image of appropriate type when the filter is
 executed.
 
-Most filters require two images as input, an initial model $\psi(\bf{X},
+Most filters require two images as input, an initial model $\psi(\textbf{X},
 t=0)$, and a \emph{feature image}, which is either the image you wish to
 segment or some preprocessed version.  You must specify the isovalue that
 represents the surface $\Gamma$ in your initial model. The single image

SoftwareGuide/Latex/DesignAndFunctionality/Registration.tex

@@ -23,8 +23,8 @@ \section{Registration Framework}
 Let's begin with a simplified typical registration framework where its components
 and their interconnections are shown in Figure \ref{fig:RegistrationComponents}.
 The basic input data to the registration process are two images: one is defined
-as the \emph{fixed} image $f(\bf{X})$ and the other as the \emph{moving} image
-$m(\bf{X})$, where $\bf{X}$ represents a position in N-dimensional space.
+as the \emph{fixed} image $f(\textbf{X})$ and the other as the \emph{moving} image
+$m(\textbf{X})$, where $\textbf{X}$ represents a position in N-dimensional space.
 Registration is treated as an optimization problem with the goal of finding the
 spatial mapping that will bring the moving image into alignment with the fixed image.
 
@@ -37,7 +37,7 @@ \section{Registration Framework}
 \label{fig:RegistrationComponents}
 \end{figure}
 
-The \emph{transform} component $T(\bf{X})$ represents the spatial mapping of
+The \emph{transform} component $T(\textbf{X})$ represents the spatial mapping of
 points from the fixed image space to points in the moving image space. The
 \emph{interpolator} is used to evaluate moving image intensities at non-grid
 positions. The \emph{metric} component $S(f,m \circ T)$ provides a measure of

SoftwareGuide/Latex/DesignAndFunctionality/Transforms.tex

@@ -251,8 +251,8 @@ \subsection{Transform General Properties}
 indicates for a point in the input space how much its mapping on the output
 space will change as a response to a small variation in one of the transform
 parameters. Note that the values of the Jacobian matrix depend on the point in
-the input space. So actually the Jacobian can be noted as $J(\bf{X})$, where
-${\bf{X}}=\{x_i\}$. The use of transform Jacobians enables the efficient
+the input space. So actually the Jacobian can be noted as $J(\textbf{X})$, where
+${\textbf{X}}=\{x_i\}$. The use of transform Jacobians enables the efficient
 computation of metric derivatives.  When Jacobians are not available, metrics
 derivatives have to be computed using finite differences at a price of $2M$
 evaluations of the metric value, where $M$ is the number of transform
@@ -371,14 +371,14 @@ \subsection{Scale Transform}
 
 \begin{equation}
 \begin{array}{lccccccc}
-\mbox{Point }          & \bf{P'} &  =  & T(\bf{P})  & : & \bf{P'}_i &  = & \bf{P}_i \cdot S_i \\
-\mbox{Vector}          & \bf{V'} &  =  & T(\bf{V})  & : & \bf{V'}_i &  = & \bf{V}_i \cdot S_i \\
-\mbox{CovariantVector} & \bf{C'} &  =  & T(\bf{C})  & : & \bf{C'}_i &  = & \bf{C}_i /     S_i \\
+\mbox{Point }          & \textbf{P'} &  =  & T(\textbf{P})  & : & \textbf{P'}_i &  = & \textbf{P}_i \cdot S_i \\
+\mbox{Vector}          & \textbf{V'} &  =  & T(\textbf{V})  & : & \textbf{V'}_i &  = & \textbf{V}_i \cdot S_i \\
+\mbox{CovariantVector} & \textbf{C'} &  =  & T(\textbf{C})  & : & \textbf{C'}_i &  = & \textbf{C}_i /     S_i \\
 \end{array}
 \end{equation}
 
-where $\bf{P}_i$, $\bf{V}_i$ and $\bf{C}_i$ are the point, vector and covariant
-vector $i$-th components while $\bf{S}_i$ is the scaling factor along dimension
+where $\textbf{P}_i$, $\textbf{V}_i$ and $\textbf{C}_i$ are the point, vector and covariant
+vector $i$-th components while $\textbf{S}_i$ is the scaling factor along dimension
 $i-th$.  The following equation illustrates the effect of the scaling transform
 on a $3D$ point.
 
@@ -770,9 +770,9 @@ \subsection{QuaternionRigidTransform}
 \textbf{Restrictions} \\
 \hline\hline
 Represents a $3D$ rotation and a $3D$ translation. The rotation is specified as a
-quaternion, defined by a set of four numbers $\bf{q}$.  The relationship
-between quaternion and rotation about vector $\bf{n}$ by angle $\theta$ is as
-follows: \[ \bf{q} = (\bf{n}\sin(\theta/2), \cos(\theta/2))\] Note that if the
+quaternion, defined by a set of four numbers $\textbf{q}$.  The relationship
+between quaternion and rotation about vector $\textbf{n}$ by angle $\theta$ is as
+follows: \[ \textbf{q} = (\textbf{n}\sin(\theta/2), \cos(\theta/2))\] Note that if the
 quaternion is not of unit length, scaling will also result. &
 7 &
 The first four parameters defines the quaternion and the last three parameters