Segmenting Cellular Retinal Images by Optimizing Super-Pixels, Multi-Level Modularity, and Cell Boundary Representation

Autor(es)

Linares, Oscar Cuadros ^[1] ; Hamann, Bernd ^[2] ; Batista Neto, Joao ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 - USA

• Tipo de documento: Artigo Científico
• Fonte: IEEE Transactions on Image Processing; v. 29, p. 809-818, 2020.
• Citações Web of Science: 0

Resumo

We introduce an interactive method for retina layer segmentation in gray-level and RGB images based on super-pixels, multi-level optimization of modularity, and boundary erosion. Our method produces highly accurate segmentation results and can segment very large images. We have evaluated our method with two datasets of 2D confocal microscopy (CM) images of a mammalian retina. We have obtained average Jaccard index values of 0.948 and 0.942 respectively, confirming the high-quality segmentation performance of our method relative to a known ground truth segmentation. Average processing time was two seconds. (AU)

• Processo FAPESP: 18/06074-0 - Content-Based Image Retrieval using Selective Visual Attention
• Beneficiário: Oscar Alonso Cuadros Linares
• Linha de fomento: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 12/24036-1 - Segmentação Tri Dimensional do Crânio para o monitoramento de alterações ósseas aplicada à Odontologia
• Beneficiário: Oscar Alonso Cuadros Linares
• Linha de fomento: Bolsas no Brasil - Doutorado