Symmetry based indexing of diatoms in an image database

Fischer, Stefan; Binkert, M.; Bunke, Horst

doi:10.1109/icpr.2000.906218

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…This includes elliptic fourier descriptors of the contour [28], statistics of a gray level co-occurrence matrix (glmc) of the Roi [29,30], a directionality histogram [31] and different image moments [32]. A symmetry measurement [33], rotation invariant local binary patterns [34] and the extraction of fluorescent features was newly integrated into the system. All features are listed in more detail in Additional file 2.…”

Section: Methodsmentioning

confidence: 99%

PlanktoVision - an automated analysis system for the identification of phytoplankton

Schulze¹,

Tillich²,

Dandekar³

et al. 2013

BMC Bioinformatics

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BackgroundPhytoplankton communities are often used as a marker for the determination of fresh water quality. The routine analysis, however, is very time consuming and expensive as it is carried out manually by trained personnel. The goal of this work is to develop a system for an automated analysis.ResultsA novel open source system for the automated recognition of phytoplankton by the use of microscopy and image analysis was developed. It integrates the segmentation of the organisms from the background, the calculation of a large range of features, and a neural network for the classification of imaged organisms into different groups of plankton taxa. The analysis of samples containing 10 different taxa showed an average recognition rate of 94.7% and an average error rate of 5.5%. The presented system has a flexible framework which easily allows expanding it to include additional taxa in the future.ConclusionsThe implemented automated microscopy and the new open source image analysis system - PlanktoVision - showed classification results that were comparable or better than existing systems and the exclusion of non-plankton particles could be greatly improved. The software package is published as free software and is available to anyone to help make the analysis of water quality more reproducible and cost effective.

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Section: Methodsmentioning

confidence: 99%

PlanktoVision - an automated analysis system for the identification of phytoplankton

Schulze¹,

Tillich²,

Dandekar³

et al. 2013

BMC Bioinformatics

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“…Furthermore, for some diatoms the size and the shape of the valve changes during the live cycle, but the morphology of the valve keeps mostly the same. While in previous studies the variation of the shape and shape based identification have been studied ( [4], [5], [10], [11], [13], [17]), the objective of the present paper is the identification of diatoms based on the morphology of the valve.…”

Section: Introductionmentioning

confidence: 99%

Identification of Diatoms by Grid Graph Matching

Fischer

Gilomen

Bunke

2002

Lecture Notes in Computer Science

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Diatoms are unicellular algae found in water and other places wherever there is humidity and enough light for photo synthesis. In this paper a graph matching based identification approach for the retrieval of diatoms from an image database is presented. The retrieval is based on the matching of labeled grid graphs carrying texture information of the underlying diatom. A grid graph is a regular, rectangular arrangement of nodes overlaid on an image. Each node of the graph is labeled with texture features describing a rectangular sub-region of the object. Properties of gray level co-occurrence matrices as well as of Gabor wavelets are used as texture features. The method has been evaluated on a diatom database holding images of 188 different diatoms belonging to 38 classes. For the identification of these diatoms recognition rates of more than 90 percent were obtained. 1 In terms of biologists diatoms are hierarchically classified in genus, species, subspecies and so forth, but in this paper we'll use the term class in the pattern recognition sense.

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“…Adicionalmente, investigações relacionadas às características utilizadas, desde efeitos da seleção de características a novos tipos de características (tais como descritores de Fourier elípticos do contorno (Kuhl e Giardina, 1982), estatísticas de uma matriz de co-ocorrência de níveis de cinza do Roi (Haralick et al, 1973;Walker et al, 1995), medidas de simetria (Fischer et al, 2000), padrões binários locais invariantes à rotação (Ahonen et al, 2009) As classes dos datasets não correspondem precisamente a espécies diferentes de plâncton.…”

Section: Sugestões Para Pesquisas Futurasunclassified

Classificação de imagens de plâncton usando múltiplas segmentações

Fernandez¹

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Symmetry based indexing of diatoms in an image database

Cited by 5 publications

References 6 publications

PlanktoVision - an automated analysis system for the identification of phytoplankton

PlanktoVision - an automated analysis system for the identification of phytoplankton

Identification of Diatoms by Grid Graph Matching

Classificação de imagens de plâncton usando múltiplas segmentações

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