MSCF: Multi-Scale Canny Filter to Recognize Cells in Microscopic Images

Almoutaz Mbaidin, Eva Cernadas (), Zakaria A. Al-Tarawneh, Manuel Fernández-Delgado, Rosario Domínguez-Petit, Sonia Rábade-Uberos and Ahmad Hassanat
Additional contact information
Almoutaz Mbaidin: Computer Science Department, Mutah University, Karak 61711, Jordan
Eva Cernadas: Centro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
Zakaria A. Al-Tarawneh: Computer Science Department, Mutah University, Karak 61711, Jordan
Manuel Fernández-Delgado: Centro Singular de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
Rosario Domínguez-Petit: Instituto Español de Oceanografía (IEO, CSIC), Centro Oceanográfico de Vigo, 36390 Vigo, Spain
Sonia Rábade-Uberos: Instituto de Investigaciones Marinas (IIM, CSIC), Calle Eduardo Cabello 6, 36208 Vigo, Spain
Ahmad Hassanat: Computer Science Department, Mutah University, Karak 61711, Jordan

Sustainability, 2023, vol. 15, issue 18, 1-16

Abstract: Fish fecundity is one of the most relevant parameters for the estimation of the reproductive potential of fish stocks, used to assess the stock status to guarantee sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is the most accurate technique to estimate fecundity using histological images of fish ovaries, in which matured oocytes must be measured and counted. A new segmentation technique, named the multi-scale Canny filter (MSCF), is proposed to recognize the boundaries of cells (oocytes), based on the Canny edge detector. Our results show the superior performance of MSCF on five fish species compared to five other state-of-the-art segmentation methods. It provides the highest F 1 score in four out of five fish species, with values between 70% and 80%, and the highest percentage of correctly recognized cells, between 52% and 64%. This type of research aids in the promotion of sustainable fisheries management and conservation efforts, decreases research’s environmental impact and gives important insights into the health of fish populations and marine ecosystems.

Keywords: image segmentation; microscopic image; fish gonad; cell recognition; histological images; Canny filter; clustering; graph cuts; meanshift; active contours; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/18/13693/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/18/13693/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:18:p:13693-:d:1239263

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().