Supun Samarasekera

Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

Images are by nature fuzzy. Approaches to object information extraction from images should attempt to use this fact and retain fuzziness as realistically as possible. In past image segmentation research, the notion of “hanging togetherness” of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects forn-dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically, leading us to practical algorithms for fuzzy object extraction. We present algorithms for extracting a specified fuzzy object and for identifying all fuzzy objects present in the image data. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples all drawn from medical imaging.

Multiple sclerosis lesion quantification using fuzzy-connectedness principles

Multiple sclerosis (MS) is a disease of the white matter. Magnetic resonance imaging (MRI) is proven to be a sensitive method of monitoring the progression of this disease and of its changes due to treatment protocols. Quantification of the severity of the disease through estimation of MS lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This paper presents a novel methodology and a system that can be routinely used for segmenting and estimating the volume of MS lesions via dual-echo fast spin-echo MR imagery. A recently developed concept of fuzzy objects forms the basis of this methodology. An operator indicates a few points in the images by pointing to the white matter, the grey matter, and the cerebrospinal fluid (CSF). Each of these objects is then detected as a fuzzy connected set. The holes in the union of these objects correspond to potential lesion sites which are utilized to detect each potential lesion as a three-dimensional (3-D) fuzzy connected object. These objects are presented to the operator who indicates acceptance/rejection through the click of a mouse button. The number and volume of accepted lesions is then computed and output. Based on several evaluation studies, the authors conclude that the methodology is highly reliable and consistent, with a coefficient of variation (due to subjective operator actions) of 0.9% (based on 20 patient studies, three operators, and two trials) for volume and a mean false-negative volume fraction of 1.3%, with a 95% confidence interval of 0%-2.8% (based on ten patient studies).

3DVIEWNIX: an open, transportable multidimensional, multimodality, multiparametric imaging software system

Three-dimensional-VIEWNIX is a data-, machine-, and application-independent software system, developed and maintained on an ongoing basis by the Medical Imaging Processing Group. It is aimed at serving the needs of biomedical visualization researchers as well as biomedical end users. Three-dimensional-VIEWNIX is not designed around a fixed methodology or a set of methods packaged in a fixed fashion for a fixed application. Instead, we have identified and incorporated in 3DVIEWNIX a set of basic imaging transforms that are required in most visualization, manipulation, and analysis methods. In addition to visualization, it incorporates a variety of multidimensional structure manipulation and analysis methods. We have tried to make its design as much as possible image-dimensionality- independent to make it just as convenient to process 2D and 3D data as it is to process 4D data. It is distributed with source code in an open fashion. A single source code version is installed on a variety of computing platforms. It is currently in use worldwide.

Boundary detection via dynamic programming

This paper reports a new method for detecting optimal boundaries in multidimensional scene data via dynamic programming (DP). In its current form the algorithm detects 2-D contours on slices and differs from other reported DP-based algorithms in an essential way in that it allows freedom in 2-D for finding optimal contour paths (as opposed to a single degree of freedom in the published methods). The method is being successfully used in segmenting object boundaries in a variety of medical applications including orbital volume from CT images (for craniofacial surgical planning), segmenting bone in MR images for kinematic analysis of the joints of the foot, segmenting the surface of the brain from the inner surface of the cranial vault, segmenting pituitary gland tumor for following the effect of a drug on the tumor, segmenting the boundaries of the heart in MR images, and segmenting the olfactory bulb for verifying hypotheses related to the size of this bulb in certain disease states.

3DVIEWNIX: an open, transportable software system for the visualization and analysis of multidimensional, multimodality, multiparametric images

3DVIEWNIX, is a data-, machine-, and application-independent software system, developed and maintained on an ongoing basis by the Medical Image Processing Group. It is aimed at serving the needs of biomedical visualization researchers as well as biomedical end users. 3DVIEWNIX is not designed around a fixed methodology or a set of methods packaged in a fixed fashion for a fixed application. Instead, we have identified and incorporated in 3DVIEWNIX a set of basic imaging transforms that are required in most visualization, manipulation, and analysis methods. The result is a powerful exploratory environment that provides not only the commonly used standard tools but also an immense variety of others. In addition to visualization, it incorporates a variety of multidimensional structure manipulation and analysis methods. We have tried to make its design as much as possible image- dimensionality-independent to make it just as convenient to process 2D and 3D data as it is to process 4D data. It is based on UNIX, C, X-Window and our own multidimensional generalization of the 2D ACR-NEMA standards for image data representation.

Detection and quantification of MS lesions using fuzzy topological principles

Quantification of the severity of the multiple sclerosis (MS) disease through estimation of lesion volume via MR imaging is vital for understanding and monitoring the disease and its treatment. This paper presents a novel methodology and a system that can be routinely used for segmenting and estimating the volume of MS lesions via dual-echo spin-echo MR imagery. An operator indicates a few points in the images by pointing to the white matter, the gray matter, and the CSF. Each of these objects is then detected as a fuzzy connected set. The holes in the union of these objects correspond to potential lesion sites which are utilized to detect each potential lesion as a fuzzy connected object. These 3D objects are presented to the operator who indicates acceptance/rejection through the click of a mouse button. The volume of accepted lesions is then computed and output. Based on several evaluation studies and over 300 3D data sets that were processed, we conclude that the methodology is highly reliable and consistent, with a coefficient of variation (due to subjective operator actions) of less than 1.0% for volume.

Joint kinematics via three-dimensional MR imaging

The methodology reported here enables us to mathematically model and quantify the motion of each component bone, relative motion of bones, the contact surface of bones and their change during motion for complex joints, from a time sequence of MR image volumes. Additionally, since we model the bone surfaces, we are able to display in vivo joint motion. Through a variety of new rendering techniques we are able to create realistic displays of bones from MR images and to combine these displays with the motion parameters.

User-steered image boundary segmentation

In multidimensional imaging, there are, and will continue to be, situations wherein automatic image segmentation methods fail and extensive user assistance in the process is needed. For such situations, we introduce a novel user-steered image boundary segmentation paradigm under two new methods, live-wire and live-lane. The methods are designed to reduce the time spent by the user in the segmentation process providing tight user control while the process is being executed. The strategy to reach this goal is to exploit the synergy between the superior abilities of human observers (compared to computer algorithms) in boundary recognition and of computer algorithms (compared to human observers) in boundary delineation. We describe evaluation studied to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large on-going application. We conclude that the new methods are more repeatable and on the average two timers faster than manual tracing. Live-wire and live-lane operate slice-by-slice in their present form. Their 3D and 4D extensions, which we are currently developing, can further reduce the total segmentation time significantly.

Design and performance of a prototype analog neural computer

Abstract A prototype programmable analog neural computer and selected applications are described. The machine is assembled from over 100 custom VLSI modules containing neurons, synapses, routing switches and programmable synaptic time constants. Connection symmetry and modular construction allow expansion to arbitrary size. The network runs in real time analog mode, however connection architecture as well as neuron and synapse parameters are controlled by a digital host that monitors also the network performance through an A/D interface. Programming and monitoring software has been developed and several application examples including the dynamic decomposition of acoustical patterns are described. The machine is intended for real time, real world computations including ATR. In current configuration its maximal speed is equivalent to that of a digital machine capable of more than 1011 flops. A much larger machine is currently under development.

3DVIEWNIX: A machine-independent software system for the visualization and analysis of multidimensional biomedical images

3DVIEWNIX is a data-, machine-, and application-independent software system for the visualization and analysis of multimodality biomedical images. It is based on UNIX, C, X-Window, and our own multidimensional generalization the ACR-NEMA standards for image representation. Its design is image dimensionality independent to make it just as convenient to process 2D and 3D data as it is for higher-dimensional data. Its design is not tied to any specific approach, machine or application. It supports a large variety of visualization and analysis methods that run on from super graphics workstations to PCs for a variety of applications. It is an open, user expandable software system intended to promote cooperative research.