A Methodology for Evaluating Image Segmentation Algorithms

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors - precision (reproducibility), accuracy (agreement with truth), and efficiency (time taken) – need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit (FOM), repeat segmentation considering all sources of variation, and determine variations in FOM via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. To assess efficiency, both the computational and the user time required for algorithm and operator training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency are interdependent. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors. The weight given to each factor depends on application

CAVASS: A Computer-Assisted Visualization and Analysis Software System

The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance

Shape-based interpolation of multidimensional image data: Principles, algorithms, and their evaluation

Medical image data are typically three-dimensional or higher and are acquired or sampled at different levels of discretization in each of the three or more directions. Display and manipulation of these data, whether it be in a two dimensional slice-by-slice fashion or in three-dimensional surface or volume renditions, requires that the data be estimated at points other than those at which they were originally acquired. This estimation process is usually referred to as interpolation. In this thesis, we present a new interpolation paradigm that is an extension of binary, shape-based interpolation to grey images. The gist of this technique is to convert the original grey scene of n dimensions to a binary scene of $n{+}1$ dimensions by projecting the grey values as height in the additional dimension. Then we perform a distance transform on this binary scene to create an $n{+}1$ dimensional grey scene. Interpolation of this data set is then performed to arrive at the desired discretization. Then this interpolated data set is thresholded to convert it back into a binary scene. Then finally, we convert the $n{+}1$ dimensional projections into grey values. We present a detailed investigation of the algorithm itself and its performance on phantom and medical image data. To evaluate this new method, we present a task-independent investigation by comparing the new method with a variety of established methods using some objective criteria. To further evaluate the new method, we also present a task-dependent investigation for the specific task of Multiple Sclerosis lesion detection. The results of the task-independent evaluation demonstrate that the new method outperforms the established methods. The results of the task-dependent evaluation demonstrate that the new method performs similarly to or better than the established interpolation methods. We conclude that this new method of grey, shape-based interpolation is a viable, superior alternative to established interpolation methods commonly used in tomographic 3D imaging

Shape-based interpolation of multidimensional image data: Principles, algorithms, and their evaluation

Medical image data are typically three-dimensional or higher and are acquired or sampled at different levels of discretization in each of the three or more directions. Display and manipulation of these data, whether it be in a two dimensional slice-by-slice fashion or in three-dimensional surface or volume renditions, requires that the data be estimated at points other than those at which they were originally acquired. This estimation process is usually referred to as interpolation. In this thesis, we present a new interpolation paradigm that is an extension of binary, shape-based interpolation to grey images. The gist of this technique is to convert the original grey scene of n dimensions to a binary scene of $n{+}1$ dimensions by projecting the grey values as height in the additional dimension. Then we perform a distance transform on this binary scene to create an $n{+}1$ dimensional grey scene. Interpolation of this data set is then performed to arrive at the desired discretization. Then this interpolated data set is thresholded to convert it back into a binary scene. Then finally, we convert the $n{+}1$ dimensional projections into grey values. We present a detailed investigation of the algorithm itself and its performance on phantom and medical image data. To evaluate this new method, we present a task-independent investigation by comparing the new method with a variety of established methods using some objective criteria. To further evaluate the new method, we also present a task-dependent investigation for the specific task of Multiple Sclerosis lesion detection. The results of the task-independent evaluation demonstrate that the new method outperforms the established methods. The results of the task-dependent evaluation demonstrate that the new method performs similarly to or better than the established interpolation methods. We conclude that this new method of grey, shape-based interpolation is a viable, superior alternative to established interpolation methods commonly used in tomographic 3D imaging