Graph Image Language Techniques Supporting Advanced Classification and Cognitive Interpretation of CT Coronary Vessel Visualizations - Computational Intelligence Paradigms in Advanced Pattern Classification - page 102

The graph structure created in this way will form elements of a graph language
defining the spatial topology of the heart muscle vascularisation including its pos-
sible morphological changes. Formulating a linguistic description for the purpose
of determining the semantics of the lesions searched for and identifying (locating)
pathological stenoses will support the computer analysis of the structure obtained
in order to automatically detect the number of stenoses, their location, type (con-
centric or eccentric) and extent. For IE graphs defined as above, in order to locate
the place where stenoses occur in the case of a balanced artery distribution, the
graph grammar may take the following form:
a) for the right coronary artery:

G R =( Σ , Δ , Γ , P, Z)

Σ = {ST, RCA, RM, RP_PI, RP, PI, C_Right, C_Right_post_int} is a finite, non-

empty set of node labels
Δ = {ST, RCA, RM, RP_PI, RP, PI} is a set of terminal node labels

Γ } is a finite, non-empty set of edge labels

Start graph Z and set of productions P are shown in fig. 9.

= {15 ι , 6 ο , 10 ο , 4 λ , 10 π , 12 ξ

Fig. 9 Start graph Z and set of productions for grammar G R

b) for the left coronary artery:

G L =( Σ , Δ , Γ , P, Z)

Σ = {ST, LCA, L_LAD, CX, L, LAD, C_Left, C_Left_lad_lat}

Δ = {ST, LCA, L_LAD, CX, L, LAD}

Γ = {7 κ , 2 ο , 12 ο , 14 ξ , 12 μ , 17 μ , 15 ν }