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Color Segmentation

For many years, work focused on segmenting gray scale images, due primarily to the fact that, until recently, computer systems were not powerful enough to display and manipulate large, full-color data sets. Research focused primarily on two different approaches to the segmentation problem: region-based and edge-based methods. Region-based methods take the basic approach of dividing the image into regions and classifying pixels as inside, outside, or on the boundary of a structure based on its location and the surrounding 2D regions. On the other hand, the edge-based approach classifies pixels using a numerical test for a property such as image gradient or curvature [22]. In particular, there are model-based techniques like snakes [14] which start with a deformable boundary and try to align the boundary with the actual boundary using gradient features. Recently a hybrid methods combining the model-based and region-based techniques have been considered [12].

With the advent of more powerful and easily accessible hardware came a shift in the current of research toward the more widely-applicable and more complex problem of color segmentation. Indeed, the field has finally begun to witness the publication of a sizable body of research in the area of color image segmentation as opposed to gray scale [5] [16] [20]. Much of the work currently being pursued involves the extension of various gray scale methods to the realm of color images. These efforts represent no mean feat considering that working with color images requires one to address various difficult issues, such as conversions between
different color spaces and manipulation of larger volumes of data as opposed to gray scale.

We have proposed a novel method, described in details in [11], of a 2D color image segmentation based on previous work in gray scale segmentation. Bertin and Chassery have presented a gray scale region-based segmentation method for microscopic data which makes use of Voronoi diagrams to divide the image into regions [4]. In 2D, a Voronoi diagram is a structure which divides a plane, for a given set of input seed points, into regions called Voronoi regions which each contains all the points closer to its seed point than any other seed point [21]. The gray scale method involves testing each Voronoi region to determine if it is homogeneous, either inside or outside of the structure, or heterogeneous, i.e. on the boundary of the structure of interest. The boundary regions are further subdivided by adding more seed points and the classification is repeated until all regions are found to be homogeneous. Our innovations include applying the method to more general color images by developing new, efficient methods to visit the regions and by experimentally determining appropriate classification statistics for color human tissue. In particular, since it is all but impossible to differentiate between regions which are exterior to a particular structure and those which are on its boundary in a color anatomical image or other general color data, our algorithm uses classification statistics to identify regions as either interior or exterior. After all regions have been classified, exterior regions adjacent to interior regions are reclassified as boundary regions. The algorithm runs for a user-specified number of iterations or in another unique facet of our approach, the user can interactively reclassify regions in order to improve the results. We also make use of another concept from computational geometry, namely Delaunay triangulation, the dual graph of a Voronoi diagram, to connect the final boundary regions to form an outline.

We were successful with segmentation of the lung regions Figure1. We use in the algorithm an experimental statistics to describe the color and texture of the lung tissue. Our goal is to to provide a methodology for classification of all of the soft human tissue types which can be found in the color Visible Human data.

Our experimental results clearly indicate the efficacy of our method and point the way toward a number of future developments. More experiments must be carried out to determine classification statistics for a wider variety of anatomical structures so that a more rigorous mathematical description of such structures can be developed. In this regard, our experiments lead us to believe that saturation values will play an important part in distinguishing between structures. Also, as
mentioned earlier, our approach allows for easy extension of classification criteria to include measures of geometric location and other statistics which should be implemented and tested on the anatomical data.

Designing the best (semi)automated segmentation technique to process the VH data is very important, but we must also be aware that some anatomical structures will always require manual segmentation (e.g. separation of pelvic muscles from other adjacent muscles has to be done manually with a help of an anatomist [27]. To obtain a complete segmentation of all the "visible" structures of both the VH male and the VH female, a database containing every visible and segmented structure must be built, meaning that every pixel/voxel on every VH slice will have to be labeled with the innermost structure overlapping it. A query language defined for a 3D body map, in conjunction with a knowledge model (an ontology), can be used to derive a segmentation for any more complex anatomical structure or combination of anatomical structures.

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