Magnetic Resonance Imaging (MRI) has become a widely-used method of high quality medical imaging, especially in brain imaging where MRI's soft tissue contrast and non-invasiveness are clear advantages. An important use of MRI data is tracking the size of brain tumor as it responds (or doesn't) to treatment [1,2]. Therefore, an automatic and reliable method for segmenting tumor would be a useful tool [3]. Currently, however, there is no method widely accepted in clinical practice for quantitating tumor volumes from MR images [4]. The Eastern Cooperative Oncology group [5] uses an approximation of tumor area in the single MR slice with the largest contiguous, well-defined tumor. Significant variability across observers can be found in these estimations, however, and such an approach can miss tumor growth/shrinkage trends [6,2].
Computer-based brain tumor segmentation has remained largely experimental work. Many efforts have exploited MRI's multi-dimensional data capability through multi-spectral analysis [7,8,9,10,11,12]. Artificial neural networks have also been explored [13,14,15]. Others have introduced knowledge-based techniques to make more intelligent classification and segmentation decisions, such as in [16,17] where fuzzy rules are applied to make initial classification decisions, then clustering (initialized by the fuzzy rules) is used to classify the remaining pixels. More explicit knowledge has been used in the form of frames [18] or tissue models [19,20]. Our efforts in [21,22] showed that a combination of knowledge-based techniques and multi-spectral analysis (in the form of unsupervised fuzzy clustering) could effectively detect pathology and label normal transaxial slices intersecting the ventricles. In [23], we expanded this system to detect pathology and label normal brain tissues in partial brain volumes located above the ventricles.
Most reports on MR segmentation [24], however, have either dealt with normal data sets, or with neuro-psychiatric disorders with MR distribution characteristics similar to normals. In this paper, we describe a system that addresses the more difficult task of extracting tumor from transaxial MR images over a period of time during which the tumor is treated. Each slice is classified as abnormal by our system described in [23]. Of the tumor types that are found in the brain, glioblastoma-multiformes (Grade IV Gliomas) are the focus of this work. This tumor type was addressed first because of its relative compactness and tendency to enhance well with paramagnetic substances, such as gadolinium.
Using knowledge gained during ``pre-processing'' by our system in [23], extra-cranial tissues (air, bone, skin, fat, muscles, etc.) are first removed based on the segmentation created by a fuzzy c-means clustering algorithm [25,26]. The remaining pixels (really voxels since they have thickness) form an intra-cranial mask. An expert system uses information from multi-spectral and local statistical analysis to first separate suspected tumor from the rest of the intra-cranial mask, then refine the segmentation into a final set of regions containing tumor. A rule-based expert system shell, CLIPS [27,28], is used to organize the system. Low level modules for image processing and high level modules for image analysis are all written in C and called as actions from the right hand sides of the rules.
The system described in this paper provides a completely automatic (no human intervention on a per volume basis) segmentation and labeling of tumor after a rule set was built from a set of ``training images''. For the purposes of tumor volume tracking, segmentations from contiguous slices (within the same volume) are merged to calculate total tumor size in 3D. The tumor segmentation matches well with radiologist-labeled ``ground truth'' images and is comparable to results generated by a supervised segmentation technique.
The remainder of the paper is divided into four sections. Section 2 discusses the slices processed and gives a brief overview of the system. Section 3 details the system's the major processing stages and the knowledge used at each stage. The last two sections present the experimental results, an analysis of them, and future directions for this work.