Modified DBSCAN Algorithm for Microscopic Image Analysis of Wood

• Type: Article in International Conference Proceedings Book
• Date: 2019-09-22

Authors

Pissarra, J (Author) FCUP; Marcal, A.R.S. (Author) FCUP; Martins, A.L.R. (Author) FCUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 257-269

9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019

1 July 2019 through 4 July 2019

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00R-2EZ

DOI: 10.1007/978-3-030-31332-6_23

Abstract (EN): The analysis of the intern anatomy of wood samples for species identification is a complex task that only experts can perform accurately. Since there are not many experts in the world and their training can last decades, there is great interest in developing automatic processes to extract high-level information from microscopic wood images. The purpose of this work was to develop algorithms that could provide meaningful information for the classification process. The work focuses on hardwoods, which have a very diverse anatomy including many different features. The ray width is one of such features, with high diagnostic value, which is visible on the tangential section. A modified distance function for the DBSCAN algorithm was developed to identify clusters that represent rays, in order to count the number of cells in width. To test both the segmentation and the modified DBSCAN algorithms, 20 images were manually segmented, obtaining an average Jaccard index of 0.66 for the segmentation and an average index M=0.78 for the clustering task. The final ray count had an accuracy of 0.91. © 2019, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific