Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

BACKGROUND: Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. RESULT: Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. CONCLUSIONS: The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented

How automated image analysis techniques help scientists in species identification and classification?

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre­ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef­forts on identification of species include specimens’ image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179–193

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

Marine Ecosystem Challenges & Opportunities (MECOS 3)

The Marine Biological Association of India (MBAI), established in 1958, is proud to gear up for MECOS3, the third symposium on Marine Ecosystems- Challenges and Opportunities during 7-10 January, 2020. The MBAI besides organising MECOS1 (2009) and MECOS2 (2014) has inculcated active interest and participation among its members by handling several national and international symposia/seminars, since its formation. The MBAI has 794 life members and 20 institutional members. The mandate of the MBAI is promotion of scientific research in the field of marine biology and allied sciences

Exploring the Causes of Red Vent Syndrome in Wild Atlantic Salmon (salmo salar) From Coastal Waters Around Scotland

In 2005, Atlantic salmon (Salmo salar L.) migrating to the United Kingdom exhibited swollen, haemorrhagic vents, symptoms not previously recorded. The condition was latterly termed Red Vent Syndrome (RVS), and subsequently observed across the North Atlantic. RVS has been pathognomonically associated with one of the most abundant parasites within the marine environment, the ascaridoid nematode Anisakis simplex, which also causes Anisakiasis in humans. Although A. simplex is commonly found in Atlantic salmon, heavy infestation of the vent region is novel, and the expression of RVS has not been prevalent in other fish species. Red Vent Syndrome has been well studied, however, the causes of the condition, and the reasons driving the novel site of infestation exhibited by A. simplex, have not been clarified. The aim of this PhD therefore, is to provide new information regarding the underlying factors of the infestation of the vent region by A. simplex, and the emergence of RVS. This study therefore: i) assessed the relationship between nematode burdens within the viscera and musculature, in comparison to the vent in 117 adult Atlantic salmon; ii) compared the genetic structure of A. simplex present in the vent region and the viscera using the entire nuclear internal transcribed spacer (ITS) region; iii) investigated migratory route and feeding ground of Scottish salmon populations using stable isotope analysis of dorsal muscle tissue and parasite component communities and, iv) assessed the expression of the cytokine TNF-?1 within vent muscle tissue using (q)RT-PCR, in relation to RVS severity. Phylogenetic analyses have shown that it is A. simplex sensu stricto infesting the vent region. The results show that there is a significant positive relationship between the nematodue burden in the body (viscera and musculature) and in the vent region. Isotopic signatures of salmon populations showed no significant differences, however, A. simplex intensities between populations on the East and North coasts of Scotland suggest geographical differences in A. simplex transmission pathways. Finally, the expression of TNF-?1 is not significantly different between RVS severity, and nematode burden. Out of the four studied factors, increasing nematode intensities in Atlantic salmon populations, and the significant positive relationship of nematode intensities between the body (viscera and musculature) and the vent, are likely to explain the infestation of the vent by A. simplex. The underlying causes of RVS however remain uncertain and require further research. With incidences of RVS observed across a number of populations over a large spatial area, regional and global effectors such as warming sea surface temperatures, and the North Atlantic Oscillation are expected to play key roles in its aetiology

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition