Online fat detection and evaluation in modelling digital physiological fish

• Main Authors: Nian, Rui, Gao, Mingshan, Kong, Shuang, Yu, Junjie, Wang, Ruirui, Li, Xueshan, Zhang, Shichang, Hao, Baochen, Xu, Xiao, Che, Renzheng, Ai, Qinghui, Macq, Benoît
• Other Authors: UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley-Blackwell Publishing Ltd. 2020
• Subjects: Scophthalmus maximus; Turbot
• Online Access: http://hdl.handle.net/2078.1/245832

The accumulation of excess fat in fish might impair the health of fish in aquaculture. This paper introduces an online sequential extreme learning machine (OSâ€ELM) into regionâ€ofâ€interest (ROI) detection of adipose tissues in fish digitalized by means of magnetic resonance imaging (MRI). Three typical economic fish species, turbot (Scophthalmus maximus L.), large yellow croaker (Pseudosciaena crocea R.) and Japanese seabass (Lateolabrax japonicus), were selected to compose into digital physiological atlas. We manually labelled with ITKâ€SNAP discriminating adipose tissue regions as standard references. Then, singleâ€hiddenâ€layer feedforward neural networks (SLFNs) were established to deduce the potential mathematical criterion for fat detection via OSâ€ELM for each fish species. We further carried out classical adaptive segmentation to extract details in fat location and distribution of adipose tissues.