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A Didirot whitepaper based on the December 2009 NSF proposal
%!TEX root = paper.tex % \section{Image analysis} \label{sec:image} % provide rationale for why the language looks the way it does % e.g. why do we fundmentally need derivatives % talk about general structure of these algorithms An overall goal in image-analysis methods is to correlate the physical, biological, and anatomical structure of the objects being scanned with the mathematical structure that is accessible in the digital image. Quantitative analysis of the scanned objects is computed from their measured image representations. We propose to simplify the work of developing, implementing, and applying image-analysis algorithms by including fundamental image operations directly in a domain-specific language. One elementary operation in a variety of analysis algorithms is convolution-based reconstruction of a continuous image domain from the discretely sampled values, which usefully mimics the underlying continuity of the measured space. A related operation is the point-wise measurement of derivatives and other locally-determined image attributes, which can support a richer mathematical vocabulary for detecting and delineating image features of interest. Matching the increasing flexibility of newer image acquisitions, the image values involved may not be grayscale scalar values, but multivariate vector or tensor values. As we illustrate below, with the abstraction of the continuous image and its derived attributes in place, it is straightforward to describe a variety of analysis algorithms in terms of potentially parallel threads of computation. Based on our previous experience, our proposed initial work focuses on direct volume rendering, fiber tractography, and particle-based feature sampling. \subsection{Continuous reconstruction and derived attributes} At the scale of current imaging, the physical world is continuous, so it is appropriate to build analysis methods upon abstractions that represent the image as a continuous domain. The boundaries and structures in the scanned object are generally not aligned in any consistent way with the image sampling grid, so visualization and analysis methods tend to work in the continuous \emph{world space} associated with the scanned object, rather than the discrete {\em index space} associated with the raster ordering of the image samples. A rich literature on signal processing provides us with a machinery for reconstructing continuous domains from discrete data via convolution~\cite{GLK:unser00,GLK:Meijering2002}. Rather than inventing information where it is not known, established methods for convolution-based reconstruction start with the recognition that some amount of blurring is intrinsic to any image measurement process: each sampled value records an integration over space as governed by a \emph{point-spread function}~\cite{GLK:Gonzalez2002}. Subsequent convolution-based reconstruction can use the same point-spread function or some approximation of it to recover the continuous field from which the discrete samples were drawn. % %The definition of convolution-based reconstruction starts with %representing the sequence of sampled image values $v[i]$, %defined for some range of integral values $i$, in a %one-dimensional continuous domain %as $v(x) = \sum_i v[i] \delta(x-i)$, where $\delta(x)$ %is the Dirac delta function. % The convolution of one-dimensional discrete data $v[i]$ with the continuous reconstruction kernel $h(x)$ is defined as~\cite{GLK:Gonzalez2002} \begin{equation*} (v * h)(x) = \int v(\xi) h(x - \xi) d\xi % _{-\infty}^{+\infty} = \int \sum_i v[i] \delta(\xi-i) h(x - \xi) d\xi % _{-\infty}^{+\infty} = \sum_i v[i] h(x - i) , \end{equation*} % ------------------- \begin{figure}[t] \vspace*{-4em} \begin{center} \begin{tabular}{ccccc} \includegraphics[width=0.28\hackwidth]{pictures/convo/data} & \raisebox{0.072\hackwidth}{\scalebox{1.2}{*}} & \includegraphics[width=0.28\hackwidth]{pictures/convo/kern0-bspl} & \raisebox{0.08\hackwidth}{\scalebox{1.2}{=}} & \includegraphics[width=0.28\hackwidth]{pictures/convo/data0-bspl} \\ $v[i]$ && $h(x)$ && $f(x) = (v * h)(x)$ \end{tabular} \end{center}% \caption{Reconstructing a continuous $f(x)$ by convolving discrete data $v[i]$ with kernel $h(x)$} \label{fig:convo-demo} \end{figure}% % ------------------- which is illustrated in Figure~\ref{fig:convo-demo}. In practice, the bounds of the summation will be limited by the \emph{support} of $h(x)$: the range of positions for which $h(x)$ is nonzero. % The result is the same as adding together copies %of $h(-x)$ located at the integers $i$, and scaled by $v[i]$. Using {\em separable} convolution, a one-dimensional reconstruction kernel $h(x)$ generates a three-dimensional reconstruction kernel $h(x,y,z) = h(x)h(y)h(z)$. The three-dimensional separable convolution sums over three image indices of the sampled data $v[i,j,k]$: \begin{eqnarray*} (v * h)(x,y,z) &=& \sum_{i,j,k} v[i,j,k] h(x - i) h(y - j) h(z - k) \; . \end{eqnarray*}% Many traditional image-analysis methods rely on measurements of the local rates of changes in the image, commonly quantified with the first derivative or \emph{gradient} and the second derivative or {\em Hessian}, which are vector and second-order tensor quantities, respectively~\cite{GLK:Marsden1996}. Our work focuses on three dimension image domains, in which the gradient is a represented as a 3-vector, and the Hessian as a $3 \times 3$ symmetric matrix. \begin{equation*} \nabla f = \begin{bmatrix} \frac{\partial f}{\partial x} \\ \frac{\partial f}{\partial y} \\ \frac{\partial f}{\partial z} \end{bmatrix} ; \, \mathbf{H} f = \begin{bmatrix} \frac{\partial^2 f}{\partial x^2} & \frac{\partial^2 f}{\partial x \partial y} & \frac{\partial^2 f}{\partial x \partial z} \\ & \frac{\partial^2 f}{\partial y^2} & \frac{\partial^2 f}{\partial y \partial z} \\ (sym) & & \frac{\partial^2 f}{\partial z^2} \end{bmatrix} \end{equation*}% The gradient plays a basic role in many edge detection algorithms, and the Hessian figures in ridge and valley detection. The constituent partial derivatives are measured by convolving with the derivatives of a reconstruction kernel, due to the linearity of differentiation and convolution: \begin{eqnarray*} \frac{\partial(v * h)(x,y,z)}{\partial x} &=& \sum_{i,j,k} v[i,j,k] h'(x - i) h(y - j) h(z - k) \\ \frac{\partial^2(v * h)(x,y,z)}{\partial x \partial y} &=& \sum_{i,j,k} v[i,j,k] h'(x - i) h'(y - j) h(z - k) . \end{eqnarray*}% %\begin{equation*} %\left. \frac{d(v * h)(x)}{dx} \right|_{x = x_0} %= \left. \frac{d \sum_i v[i] h(x - i)}{dx}\right|_{x = x_0} %= \sum_i v[i] h'(x_0 - i) %= (v * h')(x_0) \; . %\end{equation*}% Finally, while the description above has focused in scalar-valued (grayscale) images, much of modern imaging is producing more complex and multi-variate images, such as vectors from particle image velocimetry~\cite{GLK:Raffel2002} and diffusion tensors estimated from diffusion-weighted MRI~\cite{GLK:Basser1994a}. In these situations, the analysis algorithms may directly use the multi-variate data values, but it is common to access the structure in the images through some scalar quantity computed from the data, which effectively condenses or simplifies the information. In a vector field, one may need only the vector magnitude (the speed) for finding features of interest, but in a tensor field, various scalar-valued tensor invariants may play a role in the analysis, such as the trace $\mathrm{tr}(\mathbf{D})$ or the determinant $\det(\mathbf{D})$ of a tensor $\mathbf{D}$. Diffusion tensor analysis typically involves a particular measure of the directional dependence of diffusivity, termed Fractional Anisotropy (FA), which is defined as \begin{equation*} \mathrm{FA}(\mathbf{F})(\mathbf{x}) = \sqrt{\frac{3 \, \mathrm{tr}(D D^T)}{\mathrm{tr}(\mathbf{D} \mathbf{D}^T)}} \in \mathbb{R} \end{equation*}% where $\mathbf{D} = \mathbf{F}(\mathbf{x})$ and $D = \mathbf{D} - \langle \mathbf{D} \rangle \mathbf{I} $. Note that both vector magnitude $|\mathbf{v}|$ and tensor fractional anisotropy $\mathrm{FA}(\mathbf{D})$ are non-linear (\ie{}, $|\mathbf{v} + \mathbf{u}| \neq |\mathbf{v}| + |\mathbf{u}|$ and $\mathrm{FA}(\mathbf{D} + \mathbf{E}) \neq \mathrm{FA}(\mathbf{D}) + \mathrm{FA}(\mathbf{E})$), which means that their spatial derivatives are non-trivial functions of the derivatives of the image values. The chain rule must be applied to determine the exact formulae, and computing these correctly is one challenge in implementing image-analysis methods that operate on vector or tensor fields~\cite{GLK:Kindlmann2007}. Having language-level support for evaluating these functions and their spatial derivatives would simplify the implementation of analysis algorithms on multi-variate images. \subsection{Applications} % isosurface % creases (ridges & valleys) % edges % an overview of the computational model of image analysis % local vs. global The initial application of our domain-specific language focuses on research areas in scientific visualization and image analysis, with which we have previous experience. In this section, we describe three of these types of algorithms. Volume rendering is included as a simple visualization method that showcases convolution-based measurements of values and derived attributes, a computational step that appears in the inner loop of the other two algorithms. Fiber tractography uses pointwise estimates of axonal direction to trace paths of possible neural connectivity in diffusion tensor fields. Particle-based methods use a combination of energy terms and feature constraints to compute uniform samplings of continuous image features. \subsection{Direct volume rendering} \label{sec:dvr} Direct volume rendering is a standard method for visually indicating the over-all structure in a volume dataset with a rendered image~\cite{GLK:drebin88,GLK:levoy88}. It sidesteps explicitly computing geometric (polygonal) models of features in the data, in contrast to methods like Marching Cubes that create a triangular mesh of an isosurface~\cite{GLK:Lorensen1987}. Volume rendering maps more directly from the image samples to the rendered image, by assigning colors and opacities to the data values via user-specified {\em transfer function}. % \begin{algorithm} \caption{Direct volume render continuous field $V$, which maps from position $\mathbf{x}$ to data values $V(\mathbf{x})$, with transfer function $F$, which maps from data values to color and opacity.} \label{alg:volrend} \begin{algorithmic} \FOR{samples $(i,j)$ \textbf{in} rendered image $m$} \STATE $\mathbf{x}_0 \leftarrow$ origin of ray through $m[i,j]$ \STATE $\mathbf{r} \leftarrow $ unit-length direction origin of ray through $m[i,j]$ \STATE $t \leftarrow 0$ \COMMENT{initialize ray parametrization} \STATE $\mathbf{C} \leftarrow \mathbf{C}_0$ \COMMENT{initialize color $\mathbf{C}$} \WHILE{$\mathbf{x}_0 + t \mathbf{r}$ within far clipping plane} \STATE $\mathbf{v} \leftarrow V(\mathbf{x}_0 + t \mathbf{r})$ \COMMENT{learn values and derived attributes at current ray position} \STATE $\mathbf{C} \leftarrow \mathrm{composite}(\mathbf{C}, F(\mathbf{v}))$ \COMMENT{apply transfer function and accumulate color} \STATE $t \leftarrow t + t_{step}$ \ENDWHILE \STATE $m[i,j] \leftarrow \mathbf{C}$ \ENDFOR \end{algorithmic} \end{algorithm}% % Algorithm~\ref{alg:volrend} gives pseudocode for a basic volume renderer. The inner loop depends on learning (via convolution-based reconstruction) data values and attributes in volume $V$ at position $\mathbf{x}_0 + t \mathbf{r}$. These are then mapped through the transfer function $F$, which determines the rendered appearance of the data values. GPU-based methods for direct volume rendering are increasingly common~\cite{GLK:Ikits2005}. The implementations typically involve hand-coded GPU routines designed around particular hardware architectures, with an eye towards performance rather than flexibility. Although the outcome of our research may not out-perform hand-coded volume-rendering implementations, we plan to use volume rendering as an informative and self-contained test-case for expressing high-quality convolution-based reconstruction in a domain-specific language. \subsection{Fiber tractography} \label{sec:fibers} Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) uses a tensor model of diffusion-weighted MRI to describe the directional structure of tissue \emph{in vivo}~\cite{GLK:Basser1994}. % The coherent organization of axons in the white matter of the brain tissue shapes the pattern of diffusion of water within it~\cite{GLK:Pierpaoli1996}. The directional dependence of the apparent diffusion coefficient is termed \emph{anisotropy}, and quantitative measurements of anisotropy and its orientation have enabled research on white matter organization in health and disease~\cite{GLK:Bihan2003}. % Empirical evidence has shown that in much of the white matter, the principal eigenvector $\mathbf{e}_1$ of the diffusion tensor $\mathbf{D}$ indicates the local direction of axon bundles~\cite{GLK:Beaulieu2002}. % Using integrals of the principal eigenvector, we can compute paths through a diffusion tensor field that approximate axonal connectivity~\cite{GLK:conturo99,GLK:BasserMRM00}. % The path integration algorithm, termed \emph{tractography}, is shown % \begin{algorithm} \caption{$\mathrm{tract}(\mathbf{p})$: integrate path everywhere tangent to principal eigenvector of tensor field $\mathbf{D}(\mathbf{x})$ starting at seed point $\mathbf{p}$} \label{alg:tracto} \begin{algorithmic} \STATE $T_- \leftarrow [] ;\, T_+ \leftarrow []$ \COMMENT{initialize forward and backward halves of path} \FOR{$d \in \{+1, -1\}$} \STATE $\mathbf{x} \leftarrow \mathbf{p}$ \STATE $\mathbf{v} \leftarrow d \mathbf{e}_1(\mathbf{D}(\mathbf{p}))$ \COMMENT{use $d$ to determine starting direction from $\mathbf{p}$} \REPEAT \STATE $\mathbf{x} \leftarrow \mathbf{x} + s \mathbf{v}$ \COMMENT{Euler integration step} \STATE $\mathbf{u} \leftarrow \mathbf{e}_1(\mathbf{D}(\mathbf{x}))$ \IF {$\mathbf{u} \cdot \mathbf{v} < 0$} \STATE $\mathbf{u} \leftarrow -\mathbf{u}$ \COMMENT{fix direction at new position to align with incoming direction} \ENDIF \STATE $\mathrm{append}(T_d, \mathbf{x})$ \STATE $\mathbf{v} \leftarrow \mathbf{u}$ \COMMENT{save new position to path so far} \UNTIL {$\mathrm{CL}(\mathbf{D}(\mathbf{x})) < \mathrm{CL}_{\mathrm{min}}$} \COMMENT{anisotropy measure CL quantifies numerical certainty in $\mathbf{e}_1$} \ENDFOR \RETURN $\mathrm{concat}(\mathrm{reverse}(T_-), [\mathbf{p}], T_+)$ \end{algorithmic} \end{algorithm} % in Algorithm~\ref{alg:tracto}. Euler integration is used here for illustration, but Runge-Kutta integration is also common~\cite{GLK:NR}. The termination condition for tractography is when the numerical certainty of the principle eigenvector $\mathbf{e}_1$ is too low to warrant further integration. The CL anisotropy measure is, like FA, a scalar-valued tensor invariant~\cite{GLK:Westin2002}. The result of tractography is a list of vertex locations along a polyline that roughly indicates the course of axon bundles through the brain. \subsection{Particle systems} \label{sec:particles} Some tasks can be naturally decomposed into computational units, which can be termed \emph{particles}, that individually implement some uniform behavior with respect to their environment so that their iterated collective action represents the numerical solution to an optimization or simulation. Such particle systems have a long history in computer graphics for, among other applications, the simulation and rendering of natural phenomenon~\cite{GLK:Reeves1983}, the organic generation of surface textures~\cite{GLK:Turk1991}, and the interactive sculpting and manipulation of surfaces in three-dimensions~\cite{GLK:Szeliski1992,GLK:Witkin1994}. Other recent work, described in more detail below, solves biomedical image-analysis tasks with data-driven particle systems, indicating their promise for a broader range of scientific applications. Particle systems represent a challenging but rewarding target for our proposed domain-specific language. While the rays and paths of volume rendering (\secref{sec:dvr}) and fiber tractography (\secref{sec:fibers}) do not interact with each other and thus can easily be computed in parallel, particle systems do require inter-particle interactions, which constrains the structure of parallel implementations. Particle systems recently developed for image analysis are designed to compute a uniform sampling of the spatial extent of an image feature, where the feature is defined as the set of locations satisfying some equation involving locally measured image attributes. For example, the isosurface $S_i$ (or implicit surface) of a scalar field $f(\mathbf{x})$ at isovalue $v$ is defined by $S_v = \{\mathbf{x} | f(\mathbf{x}) = v\}$. Particle systems for isosurface features have been studied in visualization~\cite{GLK:Crossno1997,GLK:Meyer2005,GLK:Meyer2007a}, and have been adapted in medical image analysis for group studies of shape variation of pre-segmented objects~\cite{GLK:Cates2006,GLK:Cates2007}. In this approach, the particle system is a combination of two components. First, particles are constrained to the isosurface by application of Algorithm~\ref{alg:iso-newton}, % \begin{algorithm} \caption{$\mathrm{constr}_v(\mathbf{x})$: move position $\mathbf{x}$ onto isosurface $S_v = \{\mathbf{x} | f(\mathbf{x}) = v_0\}$} \label{alg:iso-newton} \begin{algorithmic} \REPEAT \STATE $(v,\mathbf{g}) \leftarrow (f(\mathbf{x}) - v_0,\nabla f(\mathbf{x}))$ \STATE $\mathbf{s} \leftarrow v \frac{\mathbf{g}}{\mathbf{g}\cdot\mathbf{g}}$ \COMMENT{Newton-Raphson step} \STATE $\mathbf{x} \leftarrow \mathbf{x} - \mathbf{s}$ \UNTIL{$|\mathbf{s}| < \epsilon$} \RETURN $\mathbf{x}$ \end{algorithmic} \end{algorithm} % a numerical search computed independently for each particle. Second, particles are endowed with a radially decreasing potential energy function $\phi(r)$, so that the minimum energy configuration is a spatially uniform sampling of the surface (according to Algorithm~\ref{alg:particles} below). Our recent work has extended this methodology to a larger set of image features, ridges and valleys (both surfaces and lines), in order to detect and sample image features in unsegmented data, which is more computationally demanding because of the feature definition~\cite{GLK:Kindlmann2009}. Ridges and valleys of the scalar field $f(\mathbf{x})$ are defined in terms of the gradient $\nabla f$ and the eigenvectors $\{\mathbf{e}_1, \mathbf{e}_2, \mathbf{e}_3\}$ of the Hessian $\mathbf{H} f$ with corresponding eigenvalues $\lambda_1 \geq \lambda_2 \geq \lambda_3$ ($\mathbf{H} \mathbf{e}_i = \lambda_i \mathbf{e}_i$)~\cite{GLK:Eberly1996}. A ridge surface $S_r$, for example, is defined in terms of the gradient and the minor eigenvector $\mathbf{e}_3$ of the Hessian: $S_r = \{\mathbf{x} | \mathbf{e}_3(\mathbf{x}) \cdot \nabla f(\mathbf{x}) = 0 \}$. Intuitively $S_r$ is the set of locations where motion along $\mathbf{e}_3$ can not increase the height further. % \begin{algorithm} \caption{$\mathrm{constr}_r(\mathbf{x})$: move position $\mathbf{x}$ onto ridge surface $S_r = \{\mathbf{x} | \mathbf{e}_3(\mathbf{x}) \cdot \nabla f(\mathbf{x}) = 0 \}$} \label{alg:sridge} \begin{algorithmic} \REPEAT \STATE $(\mathbf{g},\mathbf{H}) \leftarrow (\nabla f(\mathbf{x}), \mathbf{H} f(\mathbf{x}))$ % \STATE $(\{\lambda_1,\lambda_2,\lambda_3\}, % \{\mathbf{e}_1, \mathbf{e}_2, \mathbf{e}_3\}) = \mathrm{eigensolve}(\mathbf{H})$ \STATE $\{\mathbf{e}_1, \mathbf{e}_2, \mathbf{e}_3\} = \mathrm{eigenvectors}(\mathbf{H})$ \STATE $\mathbf{P} = \mathbf{e}_3 \otimes \mathbf{e}_3$ \COMMENT{tangent to allowed motion} \STATE $\mathbf{n} = \mathbf{P} \mathbf{g} / |\mathbf{P} \mathbf{g}|$ \COMMENT{unit-length direction of constrained gradient ascent} \STATE $(f',f'') = (\mathbf{g} \cdot \mathbf{n}, \mathbf{n} \cdot \mathbf{H} \mathbf{n})$ \COMMENT{directional derivatives of $f$ along $\mathbf{n}$} \IF {$f'' \geq 0$} \STATE $s \leftarrow s_0 |\mathbf{P} \mathbf{g}|$ \COMMENT{regular gradient ascent if concave-up} \ELSE \STATE $s \leftarrow -f'/f''$ \COMMENT{aim for top of parabola (2nd-order fit) if concave-down} \ENDIF \STATE $\mathbf{x} \leftarrow \mathbf{x} + s \mathbf{n}$ \UNTIL{$s < \epsilon$} \RETURN $\mathbf{x}$ \end{algorithmic} \end{algorithm} % The numerical search in Algorithm~\ref{alg:sridge} to locate the ridge surface is more complex than Algorithm~\ref{alg:iso-newton} for isosurfaces, but it plays the same role in the overall particle system. After the definition of the feature constraint, the other major piece in the system is the per-particle energy function $\phi(r)$, which completely determines the particle system energy. $\phi(r)$ is maximal at $r=0$ and decreases to $\phi(r)=0$ beyond an $r_{\mathrm{max}}$, determining the radius of influence of a single particle. Particles move in the system only to minimize the energy due to their neighbors. % %\begin{algorithm} %\caption{$\mathrm{push}(\mathbf{p}, \mathcal{R})$: Sum energy $e$ and force $\mathbf{f}$ %at $\mathbf{p}$ from other particles in set $\mathcal{R}$ due to potential function $\phi(r)$} %\label{alg:push} %\begin{algorithmic} %\STATE $(e,\mathbf{f}) \leftarrow (0,\mathbf{0})$ %\FOR{ $\mathbf{r}$ \textbf{in} $\mathcal{R}$ } % \STATE $\mathbf{d} \leftarrow \mathbf{p} - \mathbf{r}$ % \STATE $e \leftarrow e + \phi(|\mathbf{d}|)$ % \STATE $\mathbf{f} \leftarrow \mathbf{f} - \phi'(|\mathbf{d}|)\frac{\mathbf{d}}{|\mathbf{d}|}$ \COMMENT{force is negative spatial gradient of energy} %\ENDFOR %\RETURN $(e,\mathbf{f})$ %\end{algorithmic} %\end{algorithm} % \begin{algorithm} \caption{Subject to constraint feature $\mathrm{constr}$, move particle set $\mathcal{P}$ to energy minima, given potential function $\phi(r)$, $\phi(r) = 0$ for $r > r_{\mathrm{max}}$} \label{alg:particles} \begin{algorithmic} \REPEAT \STATE $E \leftarrow 0$ \COMMENT{total system energy sum} \FOR{ $\mathbf{p}_i \in \mathcal{P}$} \STATE $\mathcal{R} \leftarrow \mathrm{neighb}(\mathbf{p}_i)$ \COMMENT{$\mathcal{R} \subset \mathcal{P}$ is all particles within $r_{\mathrm{max}}$ of $\mathbf{p}_i$} \STATE $e \leftarrow \sum_{\mathbf{r} \in \mathcal{R}}{\phi(|\mathbf{p} - \mathbf{r}|)}$ \COMMENT{sum energy contributions from neighbors} \STATE $\mathbf{f} \leftarrow - \sum_{\mathbf{r} \in \mathcal{R}}{\phi'(|\mathbf{p} - \mathbf{r}|)\frac{\mathbf{p} - \mathbf{r}}{|\mathbf{p} - \mathbf{r}|}}$ \COMMENT{force is negative spatial gradient of energy} \REPEAT \STATE $\mathbf{p}' \leftarrow \mathbf{p}_i + s_i \mathbf{f}$ \COMMENT{particle $\mathbf{p}_i$ maintains a gradient descent step size $s_i$} \STATE $\mathbf{p}' \leftarrow \mathrm{constr}(\mathbf{p}')$ \COMMENT{re-apply constraint for every test step} \STATE $e' \leftarrow \sum_{\mathbf{r} \in \mathrm{neighb}(\mathbf{p}')}{\phi(|\mathbf{p}' - \mathbf{r}|)}$ \IF {$e' > e$} \STATE $s_i \leftarrow s_i / 5$ \COMMENT{decrease step size if energy increased} \ENDIF \UNTIL {$e' < e$} \STATE $\mathbf{p}_i \leftarrow \mathbf{p}'$ \COMMENT{save new position at lower energy} \STATE $E \leftarrow E + e'$ \ENDFOR \UNTIL {$\Delta E < \epsilon$} \end{algorithmic} \end{algorithm} % Each iteration of Algorithm~\ref{alg:particles} moves particles individually (and asynchronously) to lower their own energy, ensuring that the total system energy is consistently reduced, until the system reaches a local minimum in its energy configuration. Experience has shown that the local minima found by this gradient descent approach are sufficiently optimal for the intended applications of the system. % \begin{figure}[ht] %\def\subfigcapskip{-1pt} % between subfig and its own caption %\def\subfigtopskip{4pt} % between subfig caption and next subfig %\def\subfigbottomskip{-4pt} % also between subfig caption and next subfig %\def\subfigcapmargin{0pt} % margin on subfig caption \centering{ \subfigure[Lung airway segmentation]{ \includegraphics[width=0.264\columnwidth]{pictures/lung-ccpick-iso} \label{fig:lung} } \hspace{0.02\columnwidth} \subfigure[Brain white matter skeleton]{ \includegraphics[width=0.6292\columnwidth]{pictures/wb} \label{fig:brain} } } \caption{Example results from particle systems in lung CT (a) and brain DTI (b).} \label{fig:lung-brain} \end{figure} % Figure~\ref{fig:lung-brain} shows some results with our current particle system~\cite{GLK:Kindlmann2009}. The valley lines of a CT scan, sampled and displayed in Fig.~\ref{fig:lung} successfully capture the branching airways of the lung (as compared to a previous segmentation method shown with a white semi-transparent surface). The ridge surfaces of FA in a brain DTI scan, displayed in Fig.~\ref{fig:brain}, capture the major pieces of white matter anatomy, and can serve as a structural skeleton for white matter analysis. Further improvement of the method is currently hindered by the complexity of its C implementation (in Teem~\cite{teem}), and the long execution times on a single CPU. \subsection{Future analysis tasks} \label{sec:future} Image analysis remains an active research area, and many experimental algorithms diverge from those shown above. Many research methods, however, conform to or build upon the basic structure of the algorithms show above, and our proposed language will facilitate their implementation and experimental application. For example, the fiber tractography in \secref{sec:fibers} computed paths through a pre-computed field of diffusion tensors, which were estimated per-sample from a larger collection of diffusion-weighted magnetic resonance images (DW-MRI). State-of-the-art tractography algorithms interpolate the DW-MRI values, and perform the per-point model-fitting and fiber orientation computations inside the path integration~\cite{GLK:Schultz2008,GLK:Qazi2009}. However, these advanced methods can also be expressed as a convolution-based reconstruction followed by computation of non-linear derived attributes, so the basic structure of the tractography Algorithm~\ref{alg:tracto} is preserved. Being able to rapidly create new tractography methods with different models for diffusion and fiber orientation would accelerate research in determining brain structure from diffusion imaging. Many other types of image features can conceivably be recovered with the types of particle systems described in Section~\ref{sec:particles} and Algorithm~\ref{alg:particles}. Essentially, implementing the routine for constraining particles to stay within a new feature type (analogous to Algorithms~\ref{alg:iso-newton} and~\ref{alg:sridge} for isosurfaces and ridges) is the hard part of creating a particle system to detect and sample the feature. One classical type of image feature is Canny edges, which are essentially ridge surfaces of gradient magnitude~\cite{GLK:Canny1986}. Although these are well-understood for two-dimensional images, they have been applied less frequently in three-dimensional medical images, and very rarely to derived attributes of multi-variate data. One could imagine, for example, a particle system that computes a brain white matter segmentation by finding Canny edges of the fractional anisotropy (FA) of a diffusion tensor field. The variety of attributes (and their spatial derivatives) available in modern multi-variate medical images suggests that there are many analysis algorithms that have yet to be explored because of the daunting implementation complexity and computation cost.
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