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1 Image analysis with EBImage. 2 Introduction EBImage Set of quantitative image processing tools –Image reading/writing/rendering –Image processing (linear.

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Presentation on theme: "1 Image analysis with EBImage. 2 Introduction EBImage Set of quantitative image processing tools –Image reading/writing/rendering –Image processing (linear."— Presentation transcript:

1 1 Image analysis with EBImage

2 2 Introduction EBImage Set of quantitative image processing tools –Image reading/writing/rendering –Image processing (linear operators, matrix algebra, morphology) –Object detection –Features extraction Goal –Extract cellular descriptors –Classify cells –Automatic phenotyping –Process/transform images

3 3 Image representation Image object –Array of intensity values –Lena: 512x512 matrix

4 4 Image representation Multi-dimensionnal array –2D: spatial dimensions –Other dimensions: replicate, time point, color, condition, z- slice Nuclei 4 replicates Lena 3 color channels r2r1r0 RGB r3

5 5 Image rendering Rendering dissociated from representation Nuclei 4 replicates Lena 3 color channels r2r1r0 RGB Color images Sequence of images

6 6 IO Functions readImage(), writeImage() –Reads an image, returns an array –Supports 80 formats (JPEG, TIFF, PNG…) –Supports HTTP, sequences of images Example: image format conversion –x = readImage('sample a.tif') –writeImage(x, 'sample a.jpeg', quality=95)

7 7 Display Function display() –GTK+ interactive: zoom, scroll, animate –Supports RGB color channels and sequence of images

8 8 Matrix algebra Adjust brightness, contrast and  -factor xx+0.53*x(x+0.2) 3

9 9 Spatial transformation Cropping, thresholding, rotation, resize x[45:56, 98:112]x > 0.5 resize(x, w=128)rotate(x, 30)

10 10 Thresholding Global thresholding Building block tool to segment cells xx > 0.3

11 11 Linear filter 2D convolution with filter2() Low-pass filter Smooth images, remove artefacts x x  1 1 1

12 12 Linear filter High-pass filter Detect cell edges x  x

13 13 Nuclei segmentation Global thesholding + labelling Function bwlabel() –Labels connected sets (objects) from a binary image –Every pixels of an object is set to an unique integer value –max(bwlabel(x)) gives the number of detected objects x x>0.2bwlabel(x>0.2)

14 14 Better segmentation Adaptive thresholding + mathematical opening + holes filling –xb = thresh(x[,,1], 10, 10, 0.05) –kern = makeBrush(5, shape='disc') –xb = dilate(erode(xb, kern), kern) –xl = bwlabel(fillHull(nuct2)) x xbxl

15 15 Cell segmentation Using Voronoi segmentation with propagate() –Image gradient-based metric

16 16 Cell segmentation Combining channels and segmentation masks

17 17 Feature extraction Function getFeatures() –Extracts features from image objects –Geometric, image moment based features –Texture based features (Zernike moments, Haralick features) g.x g.y g.s g.p g.pdm g.pdsd g.effr g.acirc [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,] [11,] [12,] [13,] [14,] [15,] [16,] [17,] [18,] [19,] [20,] features 76 cells

18 18 siRlucsiCLSPN siRNA screen 3 immunostains: DNA, Actin, Tubulin

19 19 Segmentation

20 20 Features Cell size Median cell size of 1024 um 2 (siRluc) and 1517 um 2 (siCLSPN) Wilcoxon rank sum test, P-value <


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