Contour Detection of Leukocyte Cell Nucleus Using Morphological Image

Supriyanti, Retno; Satrio, G P; Ramadhani, Yogi; Siswandari, Wahyu

doi:10.1088/1742-6596/824/1/012069

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…The closing operation is the opposite of opening, it removes the small pixels representing the background from the foreground objects (9). This operation allows the removal of the image imperfections of the yarns created during the process of adaptive thresholding and is sometimes enhanced by the opening operation [33], [34]. The results obtained using both methods described are shown in Figures 10 and 11.…”

Section: Yarn Breaks Detection Programmentioning

confidence: 99%

Real-Time Yarn Breakage Detection in the Warping Machine

Idzik

Rybicki

2023

IEEE Access

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In the warping process, one of the most crucial elements determining production efficiency are break detectors. Due to the high warping speeds, they must ensure not only reliability but also very low response times. Currently, the most commonly used detectors are mechanical systems that detect the lack of yarn tension, and with the development of electronic systems, it has also become possible to measure the tension directly in the tensioners themselves. To significantly reduce the costs of such systems, a method has been proposed that will allow for the analysis of the recorded image and, on this basis, to determine not only the fact of breaking but also to indicate the exact location of the broken ends of the yarn, which will provide the operator or robotic systems in the future with a completely new, unobtainable earlier information. In this interdisciplinary project, the goal was to achieve the highest possible speed of real-time analysis. To achieve this, the techniques of filtration, adaptive thresholding, morphological transformations were used, and the thinning process, which had been used for years, was improved. During the experimental work, the possibility of detecting breaks for different types of yarns, both with different linear densities and colors, was examined. The results showed that this method worked well, but more importantly, it also tested and identified its limitations. The final results also showed that this method allows not only the installation of such solutions on new machines but also the modernization of old ones.

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Section: Yarn Breaks Detection Programmentioning

confidence: 99%

Real-Time Yarn Breakage Detection in the Warping Machine

Idzik

Rybicki

2023

IEEE Access

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“…Images data have different characteristics (Supriyanti et al, 2017). It is used to test the extent to which this system can work.…”

Section: Designing Contour Detection Systemmentioning

confidence: 99%

Probabilistic Ant Colony Optimization for Contour Detection of Psoriasis

Nugroho

Permadi

Hanifa

2020

icse

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Psoriasis is characterized by hyperkeratosis and thickening of the epidermal layer followed by an increase in vascularity and infiltration of inflammatory cells to the dermis, as a result of this process the scales appear, erythema and induration. In the field of health, identification and image analysis can be used as a conclusion to support expert decisions such as identification of tumors, cancers or other diseases including Psoriasis. Ant Colony Optimization (ACO) algorithms are the most successful and widely recognized algorithmic techniques based on ant behaviours. These algorithms have been applied to numerous problems; moreover, for many problems ACO algorithms are among the current high performing algorithms. The goal of this research is to develop a detection emphasize on the contour detection of Psoriasis using ACO. By using the parameters of the Pheromone intensity control values (α), the visibility (β), Evaporation coefficient of pheromone intensity (ρ) and the constant Q the results show that this method is can detect the contour an image of psoriasis.

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“…Our main contribution is the optimization of the use of simple methods but can provide accurate results in leukocyte identification based on the image of white blood cell smears photographed without illumination settings. We have done some preliminary research [16][17][18][19][20][21]. Salmam [22] researched facial recognition into a form of emotion using artificial neural networks.…”

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confidence: 99%

Preliminary process in blast cell morphology identification based on image segmentation methods

Supriyanti

Wibowo

Firmanda

et al. 2020

IJECE

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The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.

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Contour Detection of Leukocyte Cell Nucleus Using Morphological Image

Cited by 10 publications

References 12 publications

Real-Time Yarn Breakage Detection in the Warping Machine

Real-Time Yarn Breakage Detection in the Warping Machine

Probabilistic Ant Colony Optimization for Contour Detection of Psoriasis

Preliminary process in blast cell morphology identification based on image segmentation methods

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