Candidate Selection

As previously mentioned, the shape features are used to describe the SRs of retinal images. In the proposed hierarchical matching structure, we use these features to filter possible candidates in each stage of this structure. The sequence of employed features for filtering the candidates is relied on their abilities and efficiencies. In other words, the more complex and detailed features are applied in the higher steps to compare the SR query with the SR of candidates. In this module, we compare the query SR with the enrolled SR in database using only the region-based shape features.

As illustrated in Fig. 7, suppose be a selected SR of the query image as the input of our matching structure. We investigate the similarity of in multiple steps with computing Euclidean distance between and the candidate SRs in database. In each step, the most similar candidates are selected to investigate with the query SR based on the next feature in the next phase. Hence, the sequence of employed features in the candidate selection module in Fig. 7 is considered from more general and simpler features through more detailed and complex features.

In each step of the candidate selection module, a number of samples of the most similar candidate SRs among database are selected to feed the next step in this module. Therefore, in each step, we eliminate a constant percentage of remaining most similar candidates from possible search space so that in final step M number of most similar SR candidates will remain for corner matching module.

Corner Matching

The goal of corner matching module is matching a point on the boundary of the query SR with its corresponding point on the boundary of the selected candidate SRs by the candidate selection module. For this purpose, we apply angle of SR corner and centroid distance features as it is described in the following.

Assume be the query SR and be one of selected candidates in the previous module which exhibit the least distance from the query. Therefore, based on the SR corner angle feature, and would be sets of the extracted corner angles of query SR and enrolled candidate SR, respectively. To match a corner point on ’s boundary with its correspondence on , we propose an algorithm based on the least value of centroid distance and corner angles as follows:

for each candidate

for

for

if

end if

end for

if

Eliminate corresponding candidate

else

Specify the coordinates on the boundary belonging to matched corners in the query SR and its correspondence in the enrolled candidate ,

end if

end

end

where are the extracted centroid distance of query SR and enrolled candidate SR while their beginning points are and , respectively and function calculates Euclidean distance between two centroid distance vectors. is a constant that is set to 30 degrees in our application. With applying this algorithm, a pair of points on two boundaries of the query SR and candidate SR are determined as matched corners. As described in our proposed corner point algorithm, the number of candidates for further module can be reduced if the matching between the corners of the query SR and candidate SR does not happen.

SR Matching

The main goal of SR matching module is matching a SR among remaining candidates with query SR or rejecting the query SR. In Fig. 7, among remaining candidates (utmost SRs), we choose the most similar SR to query based on the similarity of differential tangent angle feature where their beginning points are the matched corner points obtained from corner matching module as,

(12)

where and are differential tangent angle feature vectors of the query SR and enrolled candidate, respectively and is the maximum number of remaining candidate SRs.

Now, the most similar enrolled SR in database with the query SR and also their matched corner points have been determined. In this step, we put a threshold to accept or reject the last candidate using weighted corner angle feature while the matched corner point is used to extract the feature from the query as follows,

(13)

where , are the extracted weighted corner angle feature of query SR and enrolled SR and is a threshold to reject or accept the last remaining candidate.

Date: 2016-04-22; view: 763