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We propose a new neurally inspired algorithm to the problem of segmenting sampled medical image datasets. Studies from neurobiology and the oscillatory correlation theory suggest that objects in a visual scene may be represented by the temporal binding of activated neurons via their firing patterns. In oscillatory correlation, neural oscillators respond to object features with oscillatory behavior and group together by synchronization in their phases. Different groups representing different objects desynchronize from each other. Our algorithm is derived from LEGION dynamics for image segmentation because of its desirable properties of stimulus-dependent oscillations, rapid synchronization for forming oscillator groups, and rapid desynchronization via a global inhibitor for separating oscillator groups.
This paper takes a functional view of LEGION by analyzing its four key computational components: input stimulation that determines group participation, local excitatory coupling used to form groups of oscillators by synchronization, global inhibition used to separate oscillator groups representing different objects, and oscillator potential used to place noisy fragments into a background. Due to performance issues of direct computer simulation of the LEGION network on large image datasets, we derive a graph-based algorithm, called G-LEGION, that closely follows the four computational components. A graph formulation reveals that the formation of oscillator groups is akin to connected labeling of largest subgraphs. To segment grey level CT and MRI sampled datasets, we group grey level pixels based on their intensity contrasts and introduce an adaptive tolerance scheme to better identify structures of interest. Our results show that the LEGION approach is able to segment volume datasets, and with appropriate parameter settings produces results that are comparable to commonly used manual segmentation. Our software is currently used to segment structures from sampled medical datasets from various sources including the Visible Human Project dataset.
Various extensions to the G-LEGION algorithm can be explored for further improving segmentation results and reducing user involvements in parameter setting. The neighborhood kernels used for both the potential and coupling neighborhoods may be set in many ways. For example, a Gaussian kernel may be used as a coupling neighborhood, with the connection strength between two oscillators falling off exponentially. Such a Gaussian neighborhood can potentially alleviate unwanted region merging, or the flooding problem (see ). Other tolerance functions may also be used to better define pixel similarity. For example, a tolerance mapping using a Gaussian function may provide proper tolerance values for both darker and brighter pixels.
So far, segmentation is performed by a single network layer only. Another G-LEGION layer may be added to process the result of the first layer. Because the second layer deals with segmented regions, it is easy to make the second layer extract only major, say large, regions while putting other regions into a background. This can be implemented by choosing a larger potential neighborhood for the second layer while using the same coupling neighborhood. With the addition of this second layer, we expect that a majority of small segments in the segmentation results of Sect. V will be removed, and the number of final segments will be cut dramatically.
While G-LEGION captures major components of LEGION dynamics and leads to much faster implementation, it should be pointed out that our algorithm is not equivalent to LEGION dynamics. For example, LEGION dynamics exhibits a segmentation capacity - only a limited number of segments can be separated, whereas our algorithm can produce an arbitrary number of segments. The ability to naturally exhibit a segmentation capacity may be a very useful property for explaining psychophysical data concerning perceptual organization. Also, G-LEGION is an iterative algorithm in nature. The dynamical system of LEGION, on the other hand, is fully parallel, and does not require synchronous algorithmic operations.
To conclude, our results suggest that LEGION is an effective computational framework to tackle the image segmentation problem. The network has been used to perform image segmentation of other features, such as texture and motion. Layers of LEGION networks may be envisioned that are capable of grouping and segregation based on multiple features, which are required for general segmentation. The network architecture is amenable to VLSI chip implementation, which would make LEGION a plausible architecture for real-time segmentation tasks.
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