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ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples.

Kudella PW, Moll K, Wahlgren M, Wixforth A, Westerhausen C - Malar. J. (2016)

Bottom Line: The obtained results are compared with standardized manual analysis.Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis.The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects.

View Article: PubMed Central - PubMed

Affiliation: Experimental Physics I, University of Augsburg, Universitätsstraße 1, Augsburg, Germany.

No MeSH data available.


Related in: MedlinePlus

Schematic cell boarder dilation. The single white pixel in image a gets dilated in two steps: b first in vertical direction by a factor of five (c) and this operation is applied to the dilated image in horizontal direction also with a factor of five. In d a typical segment of the binary detected edges image is shown, in e a vertical dilation of all pixels (factor five) and in f an horizontal dilation of all pixels (factor five). g The eroded white areas. The suggest outline from the top left image is now clearly visible
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Fig2: Schematic cell boarder dilation. The single white pixel in image a gets dilated in two steps: b first in vertical direction by a factor of five (c) and this operation is applied to the dilated image in horizontal direction also with a factor of five. In d a typical segment of the binary detected edges image is shown, in e a vertical dilation of all pixels (factor five) and in f an horizontal dilation of all pixels (factor five). g The eroded white areas. The suggest outline from the top left image is now clearly visible

Mentions: This algorithm detects object contours in an image through the local derivation of neighboring pixel values. Therefore, RGB-colored micrographs are transformed to 8-bit grey value images. As the standard edge detection procedure the algorithm uses the Prewitt filter with the Prewitt operator as the kernel of this filter. This operator determines the gradient in x-direction and y-direction of the image. With the original image A the vertical and horizontal operators are3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{x} = \left[ {\begin{array}{*{20}c} { - 1} & 0 & {{ + }1} \\ { - 1} & 0 & {{ + }1} \\ { - 1} & 0 & {{ + }1} \\ \end{array} } \right]*{\mathbf{A}} ,$$\end{document}Gx=-10+1-10+1-10+1∗A,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{y} = \left[ {\begin{array}{*{20}c} { + 1} & { + 1} & { + 1} \\ 0 & 0 & 0 \\ { - 1} & 1 & { - 1} \\ \end{array} } \right]*{\mathbf{A}}$$\end{document}Gy=+1+1+1000-11-1∗AThe operation * represents the 2D convolution of kernel k (the matrix in Eq. 3) and image A. Because the matrices are not continuous functions the discrete formulation of a 2D convolution is utilized:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{\mathbf{G}}_{i} \,\left( {x,y} \right) &= {\mathbf{k}}_{i} \,\left( {m,n} \right) \otimes {\mathbf{A}}\,\left( {x,y} \right)\\&=\sum_{m = - 1}^{1} \sum_{n = - 1}^{1} {\mathbf{k}}_{i} \;\left( {m,n} \right){\mathbf{A }}\;(x - m,y - n) \nonumber\end{aligned}$$\end{document}Gix,y=kim,n⊗Ax,y=∑m=-11∑n=-11kim,nA(x-m,y-n) For every pixel the algorithm calculates the gradient magnitude from both contributions in Eq. (3) as5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}} = \sqrt {{\mathbf{G}}_{x}^{2} + {\mathbf{G}}_{y}^{2} }$$\end{document}G=Gx2+Gy2The result is a matrix of derivative approximations for every pixel, where a threshold filter creates binary entries from the calculated magnitude values. The background is now black (0) and the found edges white (1). In Fig. 1(b) a typical result is shown. Detected edges are marked as points and stripes with a width of one pixel. A dilation of these white structures in horizontal and vertical direction, as depicted in Fig. 1c, connect the whole cell boundary (adjustable in the configuration file expansion factor for cell detection; default value: 5). In Fig. 2, the process is schematically displayed for a single point and for multiple lines. To fill enclosed areas within the cell wall outline the algorithm uses the MATLAB ® function imfill. The so detected and marked cell areas are bigger than indicated by the edge detection filter. A correction is applied by an erosion filter with a diamond-shaped structuring element of tunable size (adjustable in the configuration file factor for adjusting dilation in cell detection; default value: 2). This filter skims white pixels on the 2D-surface of the areas. The resulting detected objects render the cells in the original image, as shown clearly in Fig. 1f.Fig. 1


ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples.

Kudella PW, Moll K, Wahlgren M, Wixforth A, Westerhausen C - Malar. J. (2016)

Schematic cell boarder dilation. The single white pixel in image a gets dilated in two steps: b first in vertical direction by a factor of five (c) and this operation is applied to the dilated image in horizontal direction also with a factor of five. In d a typical segment of the binary detected edges image is shown, in e a vertical dilation of all pixels (factor five) and in f an horizontal dilation of all pixels (factor five). g The eroded white areas. The suggest outline from the top left image is now clearly visible
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Related In: Results  -  Collection

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Fig2: Schematic cell boarder dilation. The single white pixel in image a gets dilated in two steps: b first in vertical direction by a factor of five (c) and this operation is applied to the dilated image in horizontal direction also with a factor of five. In d a typical segment of the binary detected edges image is shown, in e a vertical dilation of all pixels (factor five) and in f an horizontal dilation of all pixels (factor five). g The eroded white areas. The suggest outline from the top left image is now clearly visible
Mentions: This algorithm detects object contours in an image through the local derivation of neighboring pixel values. Therefore, RGB-colored micrographs are transformed to 8-bit grey value images. As the standard edge detection procedure the algorithm uses the Prewitt filter with the Prewitt operator as the kernel of this filter. This operator determines the gradient in x-direction and y-direction of the image. With the original image A the vertical and horizontal operators are3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{x} = \left[ {\begin{array}{*{20}c} { - 1} & 0 & {{ + }1} \\ { - 1} & 0 & {{ + }1} \\ { - 1} & 0 & {{ + }1} \\ \end{array} } \right]*{\mathbf{A}} ,$$\end{document}Gx=-10+1-10+1-10+1∗A,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{y} = \left[ {\begin{array}{*{20}c} { + 1} & { + 1} & { + 1} \\ 0 & 0 & 0 \\ { - 1} & 1 & { - 1} \\ \end{array} } \right]*{\mathbf{A}}$$\end{document}Gy=+1+1+1000-11-1∗AThe operation * represents the 2D convolution of kernel k (the matrix in Eq. 3) and image A. Because the matrices are not continuous functions the discrete formulation of a 2D convolution is utilized:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{\mathbf{G}}_{i} \,\left( {x,y} \right) &= {\mathbf{k}}_{i} \,\left( {m,n} \right) \otimes {\mathbf{A}}\,\left( {x,y} \right)\\&=\sum_{m = - 1}^{1} \sum_{n = - 1}^{1} {\mathbf{k}}_{i} \;\left( {m,n} \right){\mathbf{A }}\;(x - m,y - n) \nonumber\end{aligned}$$\end{document}Gix,y=kim,n⊗Ax,y=∑m=-11∑n=-11kim,nA(x-m,y-n) For every pixel the algorithm calculates the gradient magnitude from both contributions in Eq. (3) as5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}} = \sqrt {{\mathbf{G}}_{x}^{2} + {\mathbf{G}}_{y}^{2} }$$\end{document}G=Gx2+Gy2The result is a matrix of derivative approximations for every pixel, where a threshold filter creates binary entries from the calculated magnitude values. The background is now black (0) and the found edges white (1). In Fig. 1(b) a typical result is shown. Detected edges are marked as points and stripes with a width of one pixel. A dilation of these white structures in horizontal and vertical direction, as depicted in Fig. 1c, connect the whole cell boundary (adjustable in the configuration file expansion factor for cell detection; default value: 5). In Fig. 2, the process is schematically displayed for a single point and for multiple lines. To fill enclosed areas within the cell wall outline the algorithm uses the MATLAB ® function imfill. The so detected and marked cell areas are bigger than indicated by the edge detection filter. A correction is applied by an erosion filter with a diamond-shaped structuring element of tunable size (adjustable in the configuration file factor for adjusting dilation in cell detection; default value: 2). This filter skims white pixels on the 2D-surface of the areas. The resulting detected objects render the cells in the original image, as shown clearly in Fig. 1f.Fig. 1

Bottom Line: The obtained results are compared with standardized manual analysis.Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis.The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects.

View Article: PubMed Central - PubMed

Affiliation: Experimental Physics I, University of Augsburg, Universitätsstraße 1, Augsburg, Germany.

No MeSH data available.


Related in: MedlinePlus