re-start from old repository

.gitignore

@@ -2,8 +2,6 @@ DICOM
 software
 Documents
 References
-Ming
-/Projects/BolusShaping/*.lst
 LIB
 BIN
 DATA
@@ -13,10 +11,10 @@ test*
 tmp*
 CMakeFiles
 CMakeCache.txt
+.idea
 .DS_Store
 .Rproj.user
 .RData
-.idea
 *~
 *.nrrd
 *.dcm
@@ -37,12 +35,4 @@ CMakeCache.txt
 *.log
 *.csv
 ~*
-*.Rhistory
-*.xlsx
-*.RData
-*.pdf
-*.mexw64
-*.mexa64
-*.sqlite
-
-*.gitconfig
+.ruffus_history.sqlite*

README.md

@@ -0,0 +1,116 @@
+Radiomics tools
+===================
+
+Image processing tools and ruffus based workflow for radiomics feature analysis
+
+Install software
+--------------------------
+### Python 3.5 ###
+  - Required
+  - Script engine and useful modules
+
+  https://www.python.org/downloads/  
+
+  > Required modules - pandas, ruffus  
+  > Recommended module - scipy, numpy, ipython, matplotlib
+  >
+  > install_modules.sh or install_modules.cmd is available to install these modules.
+
+### Slicer 4.5 ###
+- Recommended  
+- Image viewer, contour editor, simple image processing tool  
+
+  http://download.slicer.org/
+
+
+### To build  ###
+- gcc or visual studio
+- cmake
+- ITK
+
+
+Tools
+-----
+### 1. DICOMTools ###
+1. DICOMTagReader - Display entire DICOM tags
+  > DICOMTagReader [DICOM directory]
+
+2. DICOM2NRRDConverter - DICOM to nrrd (Slicer file format)  
+  Simple recursive converting for single patient data  
+  > DICOM2NRRDConverter [DICOM directory] [nrrd directory]  
+
+  For large data  
+  > python DICOM2NRRDConverter.py [DICOM directory] [nrrd directory]
+
+3. DICOM-RT2NRRDConverter - DICOM-RT to nrrd
+
+
+### 2. ContourTools ###
+1. STAPLEComparison - variation comparison on multiple contours
+1. ExtractBoundary
+1. GTVs2ITV
+1. HoleGenerator
+1. ROIGenerator
+1. ROI2BinImage
+1. ROICropImage
+
+
+
+### 3. GrowCutSegmentation ###
+  NoduleSegmentation - Segment small nodular objects for solid nodule and GGO
+  > NoduleSegmentation [InputImageFile] [SeedPoint_x] [SeedPoint_y [SeedPoint_z] [NoduleSize_long] [NoduleSize_short] [OutputImageFile]  
+
+
+### 4. Feature Extraction ###
+  FeatureExtraction - Extract image features from the nodule segmentation
+  > FeatureExtraction [InputImage] [LabelImage] [FeatureFile] [Label={1}]
+
+
+### 5. Python Tools ###
+  1. metadata.py - for handling metadata in csv or xls
+  2. organize_features.py - for collecting feature data into a single csv file
+
+
+### 6. MATLAB Tools ###
+  1. NRRD4Matlab - for handing nrrd format in MATLAB
+  2. PET2SUV - for converting raw PET image to standardized uptake value(SUV)
+
+
+### 7. ETC ###
+  1. RegistrationSITK - simple registration code, required SimpleITK module for python
+  2. SlicerPythonExtensions - simple extensions for Slicer
+    1. InterpolateROIsEffect.py
+    2. LineProfile.py
+
+
+### 6. LASSO-SVM ###
+  TBD - modeling code for radiomics features
+
+
+Usage
+-----
+Radiomics feature extraction example for LUNGx dataset
+
+1. Download DICOM images  
+  https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM+Lung+CT+Challenge  
+
+  Download all DICOM​ images to './DATA'  
+  You can use the included metadata files for LUNGx (TrainingSet.csv and TestSet.csv)  
+
+2. Environmental parameters  
+  Set your parameters in run_CAD.py (recommend default setting).  
+  > experiment_set = 'TrainingSet'  
+  > \# experiment_set = 'TestSet'  
+  > output_path = 'output'  
+  > data_path = 'DATA'  
+  > dicom_path = data_path + '/DOI'  
+  > image_path = data_path + '/' + experiment_set  
+  > nodule_info_path = './' + experiment_set + '.csv'  
+
+3. Run workflow
+  > $ python run.py or ./run.py
+
+4. Analysis feature data
+  output files (intermediate images and feature data) will be generated in ./output  
+  > TrainingSet              feature_list_TrainingSet.csv
+  > TestSet                  feature_list_TestSet.csv

TestSet.csv

@@ -0,0 +1,74 @@
+No.,PID,LD,PD,Z,X,Y,malignancy
+1,LUNGx-CT001,20,5,142,135,303,
+2,LUNGx-CT002,20,5,205,330,348,
+3,LUNGx-CT002,20,5,150,364,212,
+4,LUNGx-CT003,20,5,146,374,374,
+5,LUNGx-CT004,20,5,41,197,290,
+6,LUNGx-CT004,20,5,80,328,242,
+7,LUNGx-CT005,20,5,42,333,356,
+8,LUNGx-CT005,20,5,138,164,420,
+9,LUNGx-CT006,20,5,50,381,284,
+10,LUNGx-CT007,20,5,188,170,355,
+11,LUNGx-CT008,20,5,139,172,208,
+12,LUNGx-CT009,20,5,164,175,209,
+13,LUNGx-CT010,20,5,139,164,163,
+14,LUNGx-CT011,20,5,56,179,335,
+15,LUNGx-CT012,20,5,88,131,360,
+16,LUNGx-CT013,20,5,91,373,240,
+17,LUNGx-CT014,20,5,204,163,212,
+18,LUNGx-CT015,20,5,124,353,413,
+19,LUNGx-CT016,20,5,62,344,358,
+20,LUNGx-CT016,20,5,55,204,295,
+21,LUNGx-CT017,20,5,178,354,355,
+22,LUNGx-CT017,20,5,153,376,283,
+23,LUNGx-CT018,20,5,137,116,242,
+24,LUNGx-CT019,20,5,131,128,362,
+25,LUNGx-CT020,20,5,179,379,285,
+26,LUNGx-CT021,20,5,76,296,267,
+27,LUNGx-CT022,20,5,68,159,355,
+28,LUNGx-CT023,20,5,61,332,277,
+29,LUNGx-CT024,20,5,197,107,284,
+30,LUNGx-CT025,20,5,214,134,300,
+31,LUNGx-CT026,20,5,138,427,316,
+32,LUNGx-CT027,20,5,97,357,250,
+33,LUNGx-CT028,20,5,135,117,312,
+34,LUNGx-CT029,20,5,73,179,369,
+35,LUNGx-CT029,20,5,79,353,277,
+36,LUNGx-CT030,20,5,104,118,310,
+37,LUNGx-CT031,20,5,70,130,230,
+38,LUNGx-CT032,20,5,239,392,341,
+39,LUNGx-CT033,20,5,113,188,312,
+40,LUNGx-CT034,20,5,150,128,274,
+41,LUNGx-CT035,20,5,268,154,355,
+42,LUNGx-CT035,20,5,215,454,300,
+43,LUNGx-CT036,20,5,220,150,392,
+44,LUNGx-CT037,20,5,62,374,315,
+45,LUNGx-CT038,20,5,197,184,249,
+46,LUNGx-CT039,20,5,242,149,380,
+47,LUNGx-CT040,20,5,189,336,353,
+48,LUNGx-CT041,20,5,172,107,292,
+49,LUNGx-CT042,20,5,178,326,391,
+50,LUNGx-CT043,20,5,177,394,162,
+51,LUNGx-CT043,20,5,230,440,289,
+52,LUNGx-CT044,20,5,90,136,298,
+53,LUNGx-CT045,20,5,148,314,216,
+54,LUNGx-CT046,20,5,61,309,211,
+55,LUNGx-CT047,20,5,53,199,306,
+56,LUNGx-CT047,20,5,67,236,276,
+57,LUNGx-CT048,20,5,115,368,312,
+58,LUNGx-CT049,20,5,71,398,296,
+59,LUNGx-CT050,20,5,41,385,316,
+60,LUNGx-CT051,20,5,77,303,297,
+61,LUNGx-CT052,20,5,124,135,202,
+62,LUNGx-CT052,20,5,50,342,341,
+63,LUNGx-CT053,20,5,213,195,303,
+64,LUNGx-CT054,20,5,180,196,336,
+65,LUNGx-CT054,20,5,149,174,208,
+66,LUNGx-CT055,20,5,125,223,193,
+67,LUNGx-CT056,20,5,56,209,308,
+68,LUNGx-CT056,20,5,98,374,218,
+69,LUNGx-CT057,20,5,169,396,353,
+70,LUNGx-CT058,20,5,157,377,234,
+71,LUNGx-CT059,20,5,235,75,288,
+72,LUNGx-CT059,20,5,258,371,207,
+73,LUNGx-CT060,20,5,133,192,298,

Tools/Makefile

@@ -1,5 +1,5 @@
 all:
-	@make -C ITKUtils 
+	@make -C ITKUtils
 	@make -C DICOMTools
 	@make -C ContourTools
 	@make -C FeatureExtraction
@@ -13,4 +13,3 @@ clean:
 	@make -C GrowCutSegmentation clean
 
 	@echo "Clean up all builds!!"
-

TrainingSet.csv

@@ -0,0 +1,11 @@
+No.,PID,LD,PD,Z,X,Y,malignancy
+1,CT-Training-LC001,20,5,135,120,325,1
+2,CT-Training-LC002,20,5,70,139,359,1
+3,CT-Training-LC003,20,5,70,375,323,1
+4,CT-Training-LC008,20,5,65,95,328,1
+5,CT-Training-LC009,20,5,63,145,299,1
+6,CT-Training-BE001,20,5,169,405,296,0
+7,CT-Training-BE002,20,5,117,184,268,0
+8,CT-Training-BE006,20,5,241,449,266,0
+9,CT-Training-BE007,20,5,194,385,206,0
+10,CT-Training-BE010,20,5,69,120,336,0

install_modules.cmd

@@ -0,0 +1,4 @@
+pip install pandas ruffus
+pip install scipy numpy ipython matplotlib
+REM pip install pyqt pyside
+REM pip install SimpleITK

install_modules.sh

@@ -0,0 +1,4 @@
+pip install pandas ruffus
+pip install scipy numpy ipython matplotlib
+#pip install pyqt pyside
+#pip install SimpleITK