-
Notifications
You must be signed in to change notification settings - Fork 0
/
Analysis_ILM.nb
496 lines (469 loc) · 24.4 KB
/
Analysis_ILM.nb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
(* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Mathematica 10.1' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLength[ 24285, 488]
NotebookOptionsPosition[ 23252, 447]
NotebookOutlinePosition[ 23595, 462]
CellTagsIndexPosition[ 23552, 459]
WindowFrame->Normal*)
(* Beginning of Notebook Content *)
Notebook[{
Cell[CellGroupData[{
Cell["Analysis of Inverted Light Microscope Images", "Title",
CellChangeTimes->{{3.6738005655564547`*^9, 3.6738005731984253`*^9}}],
Cell["\<\
Last updated 06/01/16 by Leanne Friedrich
This notebook is for processing groups of images of printed lines, where \
particles are dark and the matrix and background are light.
These images should be sorted into folders, which the ILM interface should do \
on its own.
Images should start with ILM and end with .tif\
\>", "Text",
CellChangeTimes->{{3.6738005830842905`*^9, 3.673800616707615*^9}, {
3.6738006976558313`*^9, 3.6738007057361083`*^9}, {3.6738009032492647`*^9,
3.6738009273706446`*^9}, {3.6738039998024178`*^9, 3.673804017090432*^9}}],
Cell[CellGroupData[{
Cell["Initialization", "Chapter",
CellChangeTimes->{{3.6738009861235523`*^9, 3.6738009873987355`*^9}}],
Cell[BoxData[{
RowBox[{
RowBox[{"SetDirectory", "[",
RowBox[{"NotebookDirectory", "[", "]"}], "]"}],
";"}], "\[IndentingNewLine]",
RowBox[{
RowBox[{"<<", "Functions_ILM.wl"}], ";"}]}], "Input",
InitializationCell->True,
CellChangeTimes->{{3.6665527867793713`*^9, 3.666552796608882*^9}, {
3.666552832730974*^9, 3.666552837059948*^9}, {3.6683601516572647`*^9,
3.6683601536732*^9}, {3.6694796090679436`*^9, 3.6694796140376*^9}}],
Cell["\<\
Initialization will give you a variable called directories, which will be a \
list of sample folders in the directory where you saved this notebook. \
\>", "Text",
CellChangeTimes->{{3.673800981557944*^9, 3.6738010283122644`*^9}}],
Cell[BoxData[
RowBox[{"Grid", "[",
RowBox[{
RowBox[{"columnIndex", "[", "directories", "]"}], "[",
RowBox[{"[",
RowBox[{"400", ";;"}], "]"}], "]"}], "]"}]], "Input",
CellChangeTimes->{{3.6665530108088074`*^9, 3.6665530137466536`*^9}, {
3.666553065461135*^9, 3.6665530726495943`*^9}, 3.666565891985603*^9, {
3.669479672701685*^9, 3.6694796734517374`*^9}, {3.670013168597783*^9,
3.6700131688294907`*^9}, {3.6700218428935127`*^9, 3.670021843335638*^9}}]
}, Open ]],
Cell[CellGroupData[{
Cell["Checking for errors", "Chapter",
CellChangeTimes->{{3.673801034318999*^9, 3.6738010400342817`*^9}}],
Cell["\<\
Before running any analysis, you should check to see what your segmented \
images will look like. To do so, use ILMInterface. This will create an \
interface where you can input an index of a file in directories, and it will \
show all ILM images in that folder, and what they look like when the \
particles are segmented out. If you decide that a file is too bad to repair, \
use the buttons to delete it. The image will stay showing when you press the \
button, but you only need to press it once. For analysis, the program will \
only use the first 7 images. \
\>", "Text",
CellChangeTimes->{{3.6738012242259045`*^9, 3.6738013981998262`*^9}, {
3.673802934326291*^9, 3.6738029346374316`*^9}}],
Cell[BoxData["ILMInterface"], "Input",
CellChangeTimes->{{3.6665528509204445`*^9, 3.666552852373875*^9}}]
}, Open ]],
Cell[CellGroupData[{
Cell["Analysis", "Chapter",
CellChangeTimes->{{3.6738014203159947`*^9, 3.673801423737625*^9}}],
Cell["\<\
To analyse images, use getAllImageStats[file, critical intensity for \
segmenting pixels, critical intensity for segmenting neighboring pixels]
getAllImageStats will just print out the directory name. If you want to \
identify problems, you must use ILM interface to see what the segmented \
images look like. \
\>", "Text",
CellChangeTimes->{{3.673801438656913*^9, 3.6738014483963304`*^9}, {
3.6738015298334317`*^9, 3.6738015957329016`*^9}, {3.6738029435688634`*^9,
3.673802951947311*^9}, {3.6738031593551035`*^9, 3.6738031907600965`*^9}}],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"getAllImageStats", "[",
RowBox[{
RowBox[{"directories", "[",
RowBox[{"[", "477", "]"}], "]"}], ",", " ", "0.85", ",", " ", "0.3"}],
"]"}]], "Input",
CellChangeTimes->{{3.6683575084541707`*^9, 3.668357516931451*^9}, {
3.668358116874387*^9, 3.6683581170150175`*^9}, 3.6683623628399005`*^9, {
3.6694796985483475`*^9, 3.6694797024082856`*^9}}],
Cell[BoxData["\<\"LF59\\\\SiO7_A16_CSph120\\\\V0\\\\LF59_SiO7_A16_CSph120_V0_\
P1404_S5_t16_04_08_17_05_07\"\>"], "Print",
CellChangeTimes->{3.6683614128557844`*^9, 3.6683615333331933`*^9,
3.6683615922609563`*^9, 3.6683618714076877`*^9, 3.668362236700257*^9,
3.6683623152992744`*^9, 3.6694797027676454`*^9}]
}, Open ]],
Cell["\<\
If you want to know what the composite profile looks like for a sample, use \
getILMProfile[the name of the directory]\
\>", "Text",
CellChangeTimes->{{3.673803362692235*^9, 3.6738033689987345`*^9}, {
3.673803466283825*^9, 3.6738034766674614`*^9}}],
Cell[CellGroupData[{
Cell[BoxData[
RowBox[{"getILMProfile", "[",
RowBox[{"directories", "[",
RowBox[{"[", "2", "]"}], "]"}], "]"}]], "Input",
CellChangeTimes->{{3.6683617157554617`*^9, 3.6683617638327665`*^9},
3.6694807033152623`*^9, {3.6700137304673333`*^9, 3.67001374962178*^9}, {
3.6738032605386147`*^9, 3.673803288555097*^9}}],
Cell[BoxData[
TemplateBox[{GraphicsBox[{{}, {{{}, {}, {
Hue[0.67, 0.6, 0.6],
Directive[
PointSize[0.006944444444444445],
AbsoluteThickness[1.6],
GrayLevel[0]],
LineBox[CompressedData["
1:eJxdmnVYVF3Xxm0EA/WxC8VuRESx9jJRwG4xETAQ6W6GgQEGmEGxRbFbWhBB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"]]}}}, {}}, {DisplayFunction -> Identity, PlotRangePadding -> {{
Scaled[0.02],
Scaled[0.02]}, {
Scaled[0.02],
Scaled[0.05]}}, AxesOrigin -> {0, 0},
PlotRange -> {{0, 1492.2279792746112`}, {0, 0.9999999999999999}},
PlotRangeClipping -> True, ImagePadding -> All, DisplayFunction ->
Identity, AspectRatio -> 1, Axes -> {True, True},
AxesLabel -> {None, None}, AxesOrigin -> {0, 0}, DisplayFunction :>
Identity, Frame -> {{True, True}, {True, True}}, FrameLabel -> {{
FormBox["\"Intensity\"", TraditionalForm], None}, {
FormBox["\"Width (micron)\"", TraditionalForm], None}}, FrameStyle ->
GrayLevel[0],
FrameTicks -> {{Automatic, Automatic}, {Automatic, Automatic}},
GridLines -> {None, None}, GridLinesStyle -> Directive[
GrayLevel[0.5, 0.4]], ImageSize -> 300, LabelStyle -> {12,
GrayLevel[0]},
Method -> {"CoordinatesToolOptions" -> {"DisplayFunction" -> ({
(Part[{{Identity, Identity}, {Identity, Identity}}, 1, 2][#]& )[
Part[#, 1]],
(Part[{{Identity, Identity}, {Identity, Identity}}, 2, 2][#]& )[
Part[#, 2]]}& ), "CopiedValueFunction" -> ({
(Part[{{Identity, Identity}, {Identity, Identity}}, 1, 2][#]& )[
Part[#, 1]],
(Part[{{Identity, Identity}, {Identity, Identity}}, 2, 2][#]& )[
Part[#, 2]]}& )}},
PlotRange -> {{0, 1492.2279792746112`}, {0, 0.9999999999999999}},
PlotRangeClipping -> True, PlotRangePadding -> {{
Scaled[0.02],
Scaled[0.02]}, {
Scaled[0.02],
Scaled[0.05]}}, Ticks -> {Automatic, Automatic}}],"\"\\t\"",TagBox[
GridBox[{{
"\"\"", "\"Composite center (\[CapitalAHat]\[Micro]m)\"",
"\"Composite STDEV (\[CapitalAHat]\[Micro]m)\""}, {
"\"Image 1\"", "747.6938889607477`", "198.85971190404217`"}, {
"\"Image 2\"", "744.8504339410797`", "174.0024062228359`"}, {
"\"Image 3\"", "691.9898179926824`", "152.18960875114698`"}, {
"\"Image 4\"", "731.5055071450151`", "144.46040412202666`"}, {
"\"Image 5\"", "695.034379999788`", "171.59689416319696`"}, {
"\"Image 6\"", "690.5931357304154`", "160.47687053116542`"}, {
"\"Image 7\"", "678.0463209098742`", "147.69850299292543`"}, {
"\"MEAN\"", "711.3876406685147`", "164.18348552676278`"}}, AutoDelete ->
False, GridBoxItemSize -> {
"Columns" -> {{Automatic}}, "Rows" -> {{Automatic}}}], "Grid"]},
"RowDefault"]], "Output",
CellChangeTimes->{{3.6683617538785696`*^9, 3.668361798166522*^9}, {
3.668361893187891*^9, 3.668361918717857*^9}, 3.6683619610264883`*^9, {
3.6683620059631777`*^9, 3.6683620618910217`*^9}, {3.6738032981672707`*^9,
3.673803327922461*^9}, {3.67380370214742*^9, 3.673803721334837*^9}, {
3.6738037571871414`*^9, 3.673803783331686*^9}, 3.67380382251103*^9,
3.673803965669984*^9}]
}, Open ]]
}, Open ]]
}, Open ]]
},
WindowSize->{958, 1108},
WindowMargins->{{-7, Automatic}, {Automatic, 0}},
FrontEndVersion->"10.4 for Microsoft Windows (64-bit) (April 11, 2016)",
StyleDefinitions->"Default.nb"
]
(* End of Notebook Content *)
(* Internal cache information *)
(*CellTagsOutline
CellTagsIndex->{}
*)
(*CellTagsIndex
CellTagsIndex->{}
*)
(*NotebookFileOutline
Notebook[{
Cell[CellGroupData[{
Cell[580, 22, 131, 1, 101, "Title"],
Cell[714, 25, 563, 10, 87, "Text"],
Cell[CellGroupData[{
Cell[1302, 39, 103, 1, 72, "Chapter"],
Cell[1408, 42, 446, 10, 52, "Input",
InitializationCell->True],
Cell[1857, 54, 241, 4, 49, "Text"],
Cell[2101, 60, 477, 9, 31, "Input"]
}, Open ]],
Cell[CellGroupData[{
Cell[2615, 74, 106, 1, 72, "Chapter"],
Cell[2724, 77, 707, 11, 106, "Text"],
Cell[3434, 90, 106, 1, 31, "Input"]
}, Open ]],
Cell[CellGroupData[{
Cell[3577, 96, 95, 1, 72, "Chapter"],
Cell[3675, 99, 556, 9, 87, "Text"],
Cell[CellGroupData[{
Cell[4256, 112, 384, 8, 31, "Input"],
Cell[4643, 122, 314, 4, 23, "Print"]
}, Open ]],
Cell[4972, 129, 261, 5, 30, "Text"],
Cell[CellGroupData[{
Cell[5258, 138, 325, 6, 31, "Input"],
Cell[5586, 146, 17626, 296, 315, "Output"]
}, Open ]]
}, Open ]]
}, Open ]]
}
]
*)