Automatic Individual Detection and Separation of Multiple Overlapped Nematode Worms Using Skeleton Analysis

Rizvandi, Nikzad Babaii; Pižurica, Aleksandra; Philips, Wilfried

doi:10.1007/978-3-540-69812-8_81

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…In segmentation the challenge was to handle fibre intersections, and to this end a rotationally invariant method was introduced. The accuracy obtained in segmentation using these techniques compared well with the present state of the art, see e.g., Rizvandi et al (2008). Note that the segmentation algorithm introduced does not try to guess fibre paths through intersection areas.…”

Section: Discussionsupporting

confidence: 56%

“…Therefore, we developed a method for connecting segments of intersecting nanotubes where the resulting structures were physically possible and visually realistic. The method introduced is similar to that in Rizvandi et al (2008), but we also used the integrated curvature of the fibres and the second derivative of their orientation to properly describe their shape near the intersection areas, in addition to the first derivative already used in Kärkkäinen et al (2009).…”

Section: Introductionmentioning

confidence: 99%

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A Stochastic Shape and Orientation Model for Fibres With an Application to Carbon Nanotubes

et al. 2012

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Methods are introduced for analysing the shape and orientation of planar fibres from greyscale images of fibrous systems. The sequence of image processing techniques needed for segmentation of fibres is described. The identified fibres were interpreted as deformed line segments for which two shape and two orientation parameters are estimated by the maximum likelihood method. The methods introduced are shown to perform quite well for simulated systems of deformed line segments with known properties. They were applied to TEM images of carbon nanotubes embedded in polycarbonate.

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Section: Discussionsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

A Stochastic Shape and Orientation Model for Fibres With an Application to Carbon Nanotubes

et al. 2012

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“…The shapes from the 99−silhoutte database [24] were used by Goh [13], Sebastian, Klein, and Kimia [26], among others, in their experiments. In our case, the simplification algorithm was tested in 10 images (Fig.…”

Section: Data and Resultsmentioning

confidence: 99%

“…Rizvandi, Pizurica and Philip [24], in their method for the detection of worms, decompose the skeleton into branches through the elimination of junction pixels (pixels with more than two neighbors), then calculate the angles for all branches and compare the angles of neighboring branches. Neighboring branches with an angle difference of less than a threshold are connected.…”

Section: Skeletonisationmentioning

confidence: 99%

Skeleton Simplification by Key Points Identification

Rojas-Albarracín¹,

Carbajal²,

Fernández-Caballero

et al. 2010

Advances in Pattern Recognition

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The current skeletonisation algorithms, based on thinning, extract the morphological features of an object in an image but the skeletonized objects are coarsely presented. This paper proposes an algorithm which goes beyond that approach by changing the coarse line segments into perfect "straight" line segments, obtaining points, angles, line segment size and proportions. Our technique is applied in the postprocessing phase of the skeleton, which improves it no matter which skeletonisation technique is used, as long as the structure is made with one-pixel width continuous line segments. This proposal is a first step towards human activity recognition through the analysis of human poses represented by their skeletons.

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“…In [100], a new method for detection and separation individual nematode worms is proposed. The first step is preprocessing, including image binarization, small hole filling, skeleton and pruning.…”

Section: Classification Based Methodsmentioning

confidence: 99%

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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Automatic Individual Detection and Separation of Multiple Overlapped Nematode Worms Using Skeleton Analysis

Cited by 9 publications

References 11 publications

A Stochastic Shape and Orientation Model for Fibres With an Application to Carbon Nanotubes

A Stochastic Shape and Orientation Model for Fibres With an Application to Carbon Nanotubes

Skeleton Simplification by Key Points Identification

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

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