Open AccessSegmentation of fish chromosomes in microscopy images: A new dataset
Author(s) -
Rodrigo Rodrigues,
Rubens Pasa,
Karine Frehner Kavalco,
JeanFrançois Mari
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2020.13481
Subject(s) - artificial intelligence , pattern recognition (psychology) , segmentation , computer science , image segmentation , chromosome , karyotype , computer vision , image (mathematics) , noise (video) , fish <actinopterygii> , biology , genetics , gene , fishery
The chromosome segmentation is the most important step in automatic karyotype assembling. In this work, we presented a brand new chromosome image dataset and proposed methods for segmenting the chromosomes. Chromosome images are usually low quality, especially fish chromosomes. In order to overcome this issue, we tested three filters to reduce noise and improve image quality. After filtering, we applied adaptive threshold segmentation combined with mathematical morphology and supervised classification methods. Support Vector Machine and k-nearest neighbors were applied to discriminate between chromosomes and image background. The proposed method was applied to segment chromosomes in a new dataset. To enable measure the performance of the methods all chromosomes were manually delineated. The results are evaluated considering the Hausdorff distance and normalized sum of distances between segmented and reference images.