Init

image_parameters/README.md

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+image_quality_stats
+----
+
+Script calculates image quality statistics ( Contrast Recovery Coefficient, Background Variability)
+for NEMA IEC phantom reconstruction saved as interfile.
+
+```
+python image_quality_stats.py -i assets/test.hv -o output
+```
+Showing image quality statistics for all hot ROI and  save image as png in current directory.

image_parameters/assets/test.hv

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+!INTERFILE  :=
+name of data file := test.v
+!GENERAL DATA :=
+!GENERAL IMAGE DATA :=
+!type of data := PET
+imagedata byte order := LITTLEENDIAN
+!PET STUDY (General) :=
+!PET data type := Image
+process status := Reconstructed
+!number format := float
+!number of bytes per pixel := 4
+number of dimensions := 3
+matrix axis label [1] := x
+!matrix size [1] := 161
+scaling factor (mm/pixel) [1] := 2.5
+matrix axis label [2] := y
+!matrix size [2] := 161
+scaling factor (mm/pixel) [2] := 2.5
+matrix axis label [3] := z
+!matrix size [3] := 5
+scaling factor (mm/pixel) [3] := 10.0
+first pixel offset (mm) [1] := -223.882
+first pixel offset (mm) [2] := -223.882
+first pixel offset (mm) [3] := 37.5
+number of time frames := 1
+!END OF INTERFILE :=

image_parameters/image_quality_stats.py

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+import re
+from os import path
+from math import ceil
+import logging
+
+import pylab
+import numpy as np
+from optparse import OptionParser
+
+logging.basicConfig(level=logging.INFO)
+
+BACKGROUD_SPHERE_CENTER = [(71.349, 50.031), (-0.0, 76.), (-71.349, 50.031), (-95.146, 22.169),
+                           (-108.574, -11.922), (-110.173, -48.527), (61., -84.), (93.787, -71.414),
+                           (109.732, -40.122), (0.0, -84.), (-50., -84.), (-84.409, -73.597)]
+# 'name': (x, y, z, r)
+ROI = {'sphere22in': (-28.6, -49.54, 37., 11.0),
+       'sphere17in': (-57.2, 0.0, 37., 8.5),
+       'sphere13in': (-28.6, 49.54, 37., 6.5),
+       'sphere10in': (28.6, 49.54, 37., 5.0)}
+
+parser = OptionParser()
+
+parser.add_option("-i", "--input",
+                  action="store", dest="input", metavar="PATH", type="string",
+                  help="Path out file png")
+parser.add_option("-o", "--out",
+                  action="store", dest="output", metavar="PATH", type="string",
+                  help="Path input header interfile")
+
+
+def interfile_parser(file_name):
+    """
+    Parse interfile header to dict, get image size, scaling factor (mm/pixel)...
+    :param file_name: srt interfile path
+    :return:
+    """
+    f = open(file_name, 'r')
+    param = {}
+    for line in f.readlines():
+        matchObj = re.match(r'(.*) := (.*)', line, re.M | re.I)
+        if matchObj:
+            param[matchObj.group(1)] = matchObj.group(2)
+    try:
+        param['size'] = (int(param['!matrix size [3]']), int(param['!matrix size [2]']),
+                         int(param['!matrix size [1]']))
+    except KeyError:
+        raise Exception("Bad parsing Matrix size")
+
+    if param['!number format'] == 'float':
+        if param['!number of bytes per pixel'] == '4':
+            param["type"] = np.float32
+        else:
+            raise Exception("Bad number format")
+    else:
+        raise Exception("Bad number format")
+    try:
+        param['scaling_factor_xy'] = float(param['scaling factor (mm/pixel) [1]'])
+        param['scaling_factor_z'] = float(param['scaling factor (mm/pixel) [3]'])
+    except:
+        raise Exception("Bad parsing scaling_factor")
+    try:
+        param['offset_z'] = float(param['first pixel offset (mm) [3]'])
+    except:
+        raise Exception("Bad parsing z offset")
+    param['path_to_data_file'] = path.join(path.dirname(file_name), param['name of data file'])
+    return param
+
+
+def interfile2array(param):
+    """
+    conveft interfile to numpy array
+    :param param: dict contens interfile poarametrs
+    :return:
+    """
+
+    f = open(param['path_to_data_file'], 'r')
+    v_list = np.fromfile(f, dtype=param['type'])
+    f.close()
+    resh_arr = np.asarray(v_list).reshape(param['size'])
+    return resh_arr[:, ::-1, ::-1]  # rotate
+
+
+def txt2array(file_names, pram_dict, filip=False):
+    """
+    conveft interfile to numpy array
+    :param param: dict contens interfile poarametrs
+    :return:
+    """
+    arr = np.empty((pram_dict["size_z"], pram_dict["size_xy"], pram_dict["size_xy"]))
+    i = 0
+    for file in file_names:
+        f = open(file, 'r')
+        arr[i, :, :] = np.loadtxt(f)
+        f.close()
+        i += 1
+    if flip:
+        arr = arr[:, ::-1, ::-1]
+    return arr
+
+
+def txt2interfile(file_names, pram_dict, ouput_name='test.v'):
+    """
+    Convert interfile to numpy array
+    :param param: dict contens interfile poarametrs
+    :return:
+    """
+    image = txt2array(file_names, pram_dict)
+    image.astype(np.float32).tofile(ouput_name)
+
+
+def pos2arry_index(pos, scaling_factor, size):
+    """
+    Convert position in cm to array index
+    """
+    return int((size / 2.0 + (pos * (1 / scaling_factor))))
+
+
+def get_sphere_measure(image, x0, y0, z0, r, scaling_factor_xy, scaling_factor_z, size_xy,
+                       size_z, debug=False):
+    """
+    Return mean value in  sphere, center (x0,y0,z0) with radius r
+    """
+
+    idx_x0 = pos2arry_index(x0, scaling_factor_xy, size_xy)
+    idx_y0 = pos2arry_index(y0, scaling_factor_xy, size_xy)
+    idx_z0 = pos2arry_index(z0, scaling_factor_z, size_z)
+
+
+    radius_xy = (r * (1 / scaling_factor_xy))
+    idx_radius = int(round(radius_xy))
+
+    # build metric matrix, value is distance from center of cell to cener of matrix
+    size = 2*int(round(radius_xy))+1 
+    metric_array = np.empty((size,size))
+    delta = int(size/2)
+    for i in range(size):
+        for j in range(size):
+            metric_array[i,j] = ((i-delta)**2+(j-delta)**2)
+    # mask
+    index = metric_array<radius_xy**2 
+
+    roi_values_list = image[idx_z0, idx_y0 - idx_radius:idx_y0 + idx_radius + 1,
+               idx_x0 - idx_radius:idx_x0 + idx_radius + 1][
+        index].flatten()
+
+    if debug:
+        # DEBUG draw ROI mask
+        logging.debug(" x:%f y:%f z:%f", x0, y0, z0)
+        logging.debug("   %d   %d   %d", idx_x0, idx_y0, idx_z0)
+        image[idx_z0, idx_y0 - idx_radius:idx_y0 + idx_radius + 1,
+        idx_x0 - idx_radius:idx_x0 + idx_radius + 1][index] = -0.01
+        logging.debug("numer of pixels:%d", len(roi_values_list))
+
+    return np.mean(roi_values_list)
+
+
+def show_image(_array, xy_lim, norm=1, zoom=1, title=None):
+    """Show image. If title exist, save figure to *title*.png"""
+    pylab.figure()
+    # linear normalize
+    _array = (_array - min(_array.flatten()))
+    _array = _array / norm
+
+    pylab.xlabel('X [mm]')
+    pylab.ylabel('Y [mm]')
+    x_size, y_size = np.shape(_array)
+    center_x = int(x_size / 2)
+    center_y = int(y_size / 2)
+    delta_x = int(center_x / (zoom))
+    delta_y = int(center_y / (zoom))
+    temp_array = _array[center_x - delta_x:center_x + delta_x,
+                 center_y - delta_y:center_y + delta_y]
+    flip_matrix = temp_array[::-1, :]  # rotate
+    pylab.imshow(flip_matrix, cmap="gray",
+                 extent=[-xy_lim / zoom, xy_lim / zoom, -xy_lim / zoom, xy_lim / zoom])
+    pylab.colorbar()
+    if title != None:
+        logging.debug("save image as %s", title)
+        pylab.savefig(title)
+    else:
+        pylab.show()
+
+
+def CRC(roi_mean, background_mean, activity_ratio=4):
+    """ Contrast Recovery Coefficient"""
+    return ((roi_mean / background_mean - 1) / (activity_ratio - 1))
+
+
+def BV(background_std, background_mean):
+    """Background Variability"""
+    return background_std / background_mean
+
+
+def CNR(roi_mean, background_mean, background_std):
+    """Contrast-to-Noise Ratio"""
+    return abs(roi_mean - background_mean) / background_std
+
+
+def measure(image, params, volume_name, title=None):
+    "Salculates NEMA statistics, save image as *title*"
+
+    parameters = params
+    x, y, z, r = ROI[volume_name]
+    z = z - params['offset_z']
+
+    scaling_factor_xy = parameters['scaling_factor_xy']
+    scaling_factor_z = parameters['scaling_factor_z']
+    size_xy = parameters['size'][1]
+    size_z = parameters['size'][0]
+
+    background_values = []
+    for delta_z in [-20, -10, 0, 10, 20]:  # in mm
+        for _x, _y in BACKGROUD_SPHERE_CENTER:
+            background_values.append(
+                get_sphere_measure(image, _x, _y, z + delta_z, r, scaling_factor_xy,
+                                   scaling_factor_z, size_xy, size_z))
+    background_mean = np.mean(background_values)
+    background_std = np.std(background_values)
+
+    roi_mean = get_sphere_measure(image, x, y, z, r, scaling_factor_xy, scaling_factor_z, size_xy,
+                                  size_z)
+
+    show_image(image[pos2arry_index(z, scaling_factor_z, size_z), :, :],
+               xy_lim=scaling_factor_xy * (size_xy / 2),
+               norm=background_mean, zoom=1, title=title)
+    logging.debug("bg_std : %.4f    bg_mean :%.4f    mean_roi %.4f", background_std,
+                  background_mean, roi_mean)
+    logging.debug("bv_std : %.4f    crc_:%.4f    cnr %.4f", BV(background_std, background_mean),
+                  CRC(roi_mean, background_mean), CNR(roi_mean, background_mean, background_std))
+    return BV(background_std, background_mean), CRC(roi_mean, background_mean)
+
+
+if __name__ == "__main__":
+    (options, args) = parser.parse_args()
+    interfile_header = options.input
+    if interfile_header:
+        params = interfile_parser(interfile_header)
+
+        arr = interfile2array(params)
+        print("ROI   BV    CRC")
+        for roi in ROI:
+            print(roi + " %.4f %.4f" % measure(arr, params, roi, title=options.output))
+    else:
+        print("Error select  input header interfile")