The fibrous structure of thein silicotissue reconstruction was determined by segmenting the tissue using an edge detection algorithm. These edges must completely encase a fibrous struc- ture, which enables the distinction of two adjacent bundles that are not connected by a bridge, as illustrated inFig 21. Edge detectors which rely on a local differencing operator, such as the Canny edge detector, are not suitable for this kind of differentiation, since they tend to form incomplete boundaries [24]. To overcome this problem, a variant of watershed segmentation is used, as described in the following sections. Watershed algorithms have the general property that any boundary is continuous [25], and will therefore enable the distinction between bun- dles to be absolute. Segmentation of fibrous structure was performed in three dimensions to enable better representation of the fibrous structure in thein silicoreconstruction.
Segmentation in two dimensions was performed to measure bundle widths, which is used for comparison between registered and unregistered slides. The techniques used for segmenta- tion in two dimensions differ from those used in three dimensions, since the nature of the direction vectors is fundamentally different between the two cases: in two dimensions the directional data take the form of two-dimensional planar angles and vertical points, whereas in three dimensions all directions are represented by a three-dimensional vector. The general for- mulation of the algorithm in both cases is the same, however. Initially, a grey image or volume is generated from the directional data using a form of Gaussian smoothing. This grey image is used to perform the watershed segmentation, which generates the bundle edges.
Watershed segmentation with merging. A watershed algorithm uses the grey values of
an image to determine boundaries between local gradient minima [25]. The watershed algo- rithm used in the following determines these separating lines as follows. Each pointpin the
Fig 20. Isotropy within a region. Within a sufficiently small volume the nuclei in smooth muscle tissue have similar orientation. A: A histological image of smooth muscle tissue. B: The orientation of the nuclei within the slide plane from A. Scale bars represent 50μm.
image is sorted in order of increasing gradient values, to give the listfpig
n 1
i¼0. This list is subse- quently separated into sets of points, which shall be referred to as pools. This separation is per- formed sequentially fromp0using the following steps. For eachpi, consider all pointspj
adjacent topi. Ifi<jfor allj, then none of the pointspjhave been placed into a pool, and sopi
is placed into a new pool. If all pointspjwithj<iare contained in the same pool, thenpiis
added to that pool. Otherwise,piis on the boundary between two or more pools, and therefore
is not placed into any pools. Such unassigned points form the lines separating the features, and the set of all these points is referred to as the watershed.
The following sections outline a method of generating greyscale images that represent local anisotropy in two and three dimensions. Areas of high anisotropy are more likely to corre- spond to areas within fibrous structures, while areas of low anisotropy are more likely to corre- spond to points on the boundary between fibrous structures. For this reason, the watershed segmentation algorithm sorts points in order of decreasing local anisotropy, rather than increasing gradient. As will become apparent in the following sections, two local maxima in the greyscale anisotropy image do not necessarily correspond to two separate features. If the above watershed algorithm were performed on such an image, this would lead to erroneous boundaries forming, a phenomenon known as over-segmentation [26]. For this reason, an additional step of merging pools was employed. For two local maximapandq, a comparison functionf(p,q) is used which represents how well the local directions aboutpandqare matched. The specific formulation offis dependent on the dimensionality of the problem, and shall therefore be described separately in the following sections. This function can be used to modify the watershed algorithm in the following manner. Consider a pointpiwhich has been
found to lie on the watershed boundary of the two pools, and letpandqbe the local maxima in each of these pools. The pointpiis considered a watershed point if
fðp;qÞ<fmin;
wherefminis a constant threshold. This means that if the direction fields aboutpandqare suf-
ficiently well-matched, no watershed line is drawn between them. The modification can be extended to the case wherepilies on the boundary of more than two pools. In this case, all Fig 21. Different forms of adjacent bundles. A: The two tubes shown here represent fibrous structures. They are close together, but not communicating. At resolutions comparable to the size of the gap, these structures may appear to touch. This is a problem when the aim is to resolve individual structures and to determine conductivity in the tissue. B: A bridge structure (blue) has been added. In this situation the bundles are connected by a filamentous structure and therefore there should be a connection in the digital representation. Even though the two situations are similar, they have substantially different electroconductive properties, and therefore need to be differentiated.
such pairs of local maximapandqare compared, andpiis deemed a watershed point if any of
the pairs satisfy the above inequality.
This watershed-and-merging algorithm was employed to segment directional images in two and three dimensions. The following sections describe in detail how the grey image or vol- ume is generated in order to perform the watershed algorithm and how the comparison func- tionfis formulated.