efg’s R Notes: EBImage: Shape Features
Basic | Haralick | Moment | Shape
The function computeFeatures.shape computes features that quantify object shape:
- s.area: area size [pixels^2]
- s.perimeter: perimeter [pixels]
- s.radius.mean: mean radius [pixels]
- s.radius.sd: standard deviation of the mean radius [pixels]
- s.radius.max: max radius [pixels]
- s.radius.min: min radius [pixels]
All shape features have a prefix “s.”.
library(EBImage)computeFeatures.shape(x, properties=FALSE, xs, ...)List properties of shape features
feature.properties <- computeFeatures.shape(properties=TRUE)
feature.properties name translation.invariant rotation.invariant
1 s.area TRUE TRUE
2 s.perimeter TRUE TRUE
3 s.radius.mean TRUE TRUE
4 s.radius.sd TRUE TRUE
5 s.radius.min TRUE TRUE
6 s.radius.max TRUE TRUELoad and segment nucleus
par(mfrow=c(1,2))
y <- readImage(system.file("images", "nuclei.tif", package="EBImage"))[,,1]
x <- thresh(y, 10, 10, 0.05)
x <- opening(x, makeBrush(5, shape='disc'))
x <- bwlabel(x)
display(y, title="Cell nuclei")
display(x, title="Segmented nuclei")
Compute shape features
shapeFeatures <- computeFeatures.shape(x)
str(shapeFeatures) num [1:76, 1:6] 75 286 211 321 502 423 379 330 488 204 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:76] "1" "2" "3" "4" ...
..$ : chr [1:6] "s.area" "s.perimeter" "s.radius.mean" "s.radius.sd" ...options(digits=4, width=100)
shapeFeatures s.area s.perimeter s.radius.mean s.radius.sd s.radius.min s.radius.max
1 75 40 5.385 2.5473 1.4631 9.599
2 286 95 11.914 6.2510 1.6595 22.734
3 211 61 8.464 2.8626 3.9931 12.369
4 321 56 9.782 1.6472 6.2745 12.270
5 502 80 13.183 4.1268 6.3982 19.601
6 423 70 11.193 2.0760 5.6671 14.562
7 379 62 10.641 1.6784 7.8951 13.529
8 330 61 9.993 1.7385 6.7248 13.442
9 488 81 12.307 3.3772 6.2079 18.125
10 204 60 7.874 2.8414 2.3778 12.207
11 44 19 3.332 0.4756 2.3140 4.096
12 311 67 9.494 3.0865 1.5623 13.654
13 44 19 3.306 0.3704 2.5004 3.865
14 420 67 11.123 1.2934 8.6474 13.258
15 389 62 10.703 1.1018 8.0866 12.705
16 621 87 13.992 2.7306 8.2129 18.504
17 361 75 10.111 3.3553 2.9636 14.539
18 357 64 10.244 1.8122 7.0147 13.038
19 435 69 11.306 1.1549 8.8330 13.835
20 129 36 5.981 0.9313 4.1970 7.498
21 298 58 9.361 2.0215 4.6040 12.788
22 193 55 8.005 2.7832 3.3462 13.494
23 518 77 12.851 1.7066 9.7697 16.372
24 375 66 10.605 1.6753 6.0521 13.708
25 296 60 9.569 2.7347 4.1163 13.240
26 293 59 9.326 2.1520 4.5921 12.679
27 319 59 9.593 1.5283 5.2950 11.936
28 298 53 9.331 0.8379 7.9463 11.175
29 41 24 3.426 1.3888 1.3077 5.631
30 420 71 11.239 2.0213 7.1682 15.389
31 426 71 11.499 3.2205 4.3185 16.412
32 527 78 12.594 2.7746 7.2046 17.093
33 320 65 9.969 3.3953 3.6403 14.971
34 394 63 10.876 1.6450 7.5136 13.660
35 207 46 8.142 2.1091 4.7038 11.354
36 225 49 8.117 1.8313 4.9459 11.163
37 397 60 10.974 1.5693 7.5465 13.719
38 136 36 6.158 0.7461 4.4466 7.641
39 348 65 10.110 2.8345 3.4815 13.568
40 281 54 9.075 1.5849 6.3138 11.906
41 376 70 11.072 2.7499 6.8124 16.162
42 75 26 4.487 0.7051 2.7847 5.608
43 429 72 11.360 2.5599 5.2519 15.312
44 436 74 11.583 3.0003 4.9134 16.382
45 111 56 7.362 3.6955 1.5642 13.581
46 464 70 11.964 2.3913 6.9747 15.853
47 485 76 12.036 2.2340 7.8180 15.559
48 315 60 9.620 1.9466 5.0364 12.745
49 320 56 9.733 1.2713 7.3639 11.878
50 368 70 10.637 2.4402 5.2174 14.981
51 390 79 11.034 3.5890 1.6093 15.952
52 390 93 10.406 4.0866 1.3027 16.319
53 319 64 9.871 2.7186 5.0637 14.533
54 339 59 9.941 0.9332 8.2735 11.655
55 536 77 12.688 2.3562 8.2421 16.448
56 342 66 10.050 2.9300 2.8931 14.155
57 507 81 12.527 3.2253 6.2176 17.870
58 248 53 8.553 1.7549 4.2935 11.441
59 345 69 10.566 3.4668 3.1461 15.804
60 351 65 10.169 2.1134 5.2726 13.820
61 421 73 11.224 3.0791 3.9369 16.264
62 358 60 10.230 0.7600 8.7452 11.632
63 364 63 10.598 2.2577 7.1605 15.150
64 207 49 7.727 1.1779 5.2081 10.220
65 380 82 11.864 5.1506 2.0894 19.864
66 582 85 13.019 2.5345 7.0552 16.636
67 309 58 9.501 1.4419 6.3927 11.828
68 408 87 11.064 4.1957 0.5892 16.865
69 273 54 8.890 1.4331 5.7807 11.056
70 315 75 8.612 3.1603 0.5259 12.310
71 352 82 10.428 4.4001 2.2640 17.451
72 400 66 11.139 2.3483 7.3767 15.325
73 262 53 8.669 0.9546 6.9193 10.277
74 345 68 10.415 3.3806 3.2596 15.656
75 321 74 10.504 3.7010 4.3515 17.612
76 16 12 1.833 0.6019 0.9718 2.635Correlations of shape features
correlationMatrix <- cor(shapeFeatures)
correlationMatrix## s.area s.perimeter s.radius.mean s.radius.sd s.radius.min s.radius.max
## s.area 1.0000 0.8528 0.9604 0.3465 0.5639 0.8064
## s.perimeter 0.8528 1.0000 0.9155 0.7239 0.1861 0.9530
## s.radius.mean 0.9604 0.9155 1.0000 0.4820 0.5157 0.9086
## s.radius.sd 0.3465 0.7239 0.4820 1.0000 -0.4342 0.7864
## s.radius.min 0.5639 0.1861 0.5157 -0.4342 1.0000 0.1876
## s.radius.max 0.8064 0.9530 0.9086 0.7864 0.1876 1.0000Heatmaps of correlation matrix
Color-coded display of correlation matrix
library(gplots) # heatmap.2
library(RColorBrewer) # brewer.palheatmap.2(correlationMatrix, dendrogram="none", Rowv=FALSE, symm=TRUE,
cellnote=format(round(correlationMatrix,2)),
main="Shape Features",
scale="none", col=brewer.pal(11,"RdBu"), margins=c(9,9), trace="none")
Dendrograms show relationships in correlation matrix
heatmap.2(correlationMatrix, key=FALSE,
cellnote=format(round(correlationMatrix,2)),
main="Shape Features",
scale="none", col=brewer.pal(11,"RdBu"), margins=c(9,9), trace="none")
A diverging palette from RColorBrewer is used for the heatmaps since correlations can range from -1 to +1. In the RdBu pallete, red is low and blue is high.
packageVersion("EBImage")## [1] '4.8.2'packageVersion("gplots")## [1] '2.16.0'packageVersion("RColorBrewer")## [1] '1.1.2'efg
2015-01-22 2315