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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

Gradient based algorithm. a The original micrograph with two rosettes, one single pRBC and multiple healthy RBC. b The Prewitt filter is applied and the threshold of the resulting gradient map displayed. c The edges from b are dilated like shown in Fig. 2. d Capsuled parts of the cells are filled. e The diamond shaped erosion filter shrinks the outline of the marked areas to a size comparable to the before detected outermost edges. f The edges are taken as outlines and plotted onto the original image for better visualization
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Fig1: Gradient based algorithm. a The original micrograph with two rosettes, one single pRBC and multiple healthy RBC. b The Prewitt filter is applied and the threshold of the resulting gradient map displayed. c The edges from b are dilated like shown in Fig. 2. d Capsuled parts of the cells are filled. e The diamond shaped erosion filter shrinks the outline of the marked areas to a size comparable to the before detected outermost edges. f The edges are taken as outlines and plotted onto the original image for better visualization

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)

Gradient based algorithm. a The original micrograph with two rosettes, one single pRBC and multiple healthy RBC. b The Prewitt filter is applied and the threshold of the resulting gradient map displayed. c The edges from b are dilated like shown in Fig. 2. d Capsuled parts of the cells are filled. e The diamond shaped erosion filter shrinks the outline of the marked areas to a size comparable to the before detected outermost edges. f The edges are taken as outlines and plotted onto the original image for better visualization
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4835829&req=5

Fig1: Gradient based algorithm. a The original micrograph with two rosettes, one single pRBC and multiple healthy RBC. b The Prewitt filter is applied and the threshold of the resulting gradient map displayed. c The edges from b are dilated like shown in Fig. 2. d Capsuled parts of the cells are filled. e The diamond shaped erosion filter shrinks the outline of the marked areas to a size comparable to the before detected outermost edges. f The edges are taken as outlines and plotted onto the original image for better visualization
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