From 8bf574facf268ac213f08abcdeaff81486137f7d Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauldmccarthy@gmail.com>
Date: Fri, 6 Oct 2017 16:06:14 +0100
Subject: [PATCH] Adjusted atlas mask query routines to be less inaccurate
---
fsl/data/atlases.py | 53 +++++++++++++++++++++++++++------------------
1 file changed, 32 insertions(+), 21 deletions(-)
diff --git a/fsl/data/atlases.py b/fsl/data/atlases.py
index 3f5af0c0a..2cefdf25b 100644
--- a/fsl/data/atlases.py
+++ b/fsl/data/atlases.py
@@ -698,33 +698,43 @@ class LabelAtlas(Atlas):
- A sequence of all labels which are present in the mask
- A sequence containing the proportion, within the mask,
- of each present label.
+ of each present label. The proportions are returned as
+ values between 0 and 100.
+
"""
- # Make sure that the mask has the
- # same number of voxels as the
- # atlas image
- mask = mask.resample(self.shape[:3], order=1)
+ # Make sure that the mask has the same
+ # number of voxels as the atlas image.
+ # Use nearest neighbour interpolation
+ # for resampling, as it is most likely
+ # that the mask is binary.
+ mask = mask.resample(self.shape[:3], dtype=np.float32, order=0)[0]
boolmask = mask > 0
+ fslimage.Image(mask, xform=self.voxToWorldMat).save('blag.nii.gz')
+
# Extract the labels that are in
# the mask, and their corresponding
# mask weights
- vals = self[boolmask]
- weights = mask[boolmask]
- labels = np.unique(vals)
- props = []
+ vals = self[boolmask]
+ weights = mask[boolmask]
+ weightsum = weights.sum()
+ labels = np.unique(vals)
+ props = []
for label in labels:
# Figure out the number of all voxels
# in the mask with this label, weighted
- # by the mask
- prop = ((vals == label) * weights).sum()
+ # by the mask.
+ prop = weights[vals == label].sum()
# Normalise it to be a proportion
- # of all voxels in the mask
- props.append(prop / float(len(vals)))
+ # of all voxels in the mask. We
+ # multiply by 100 because the FSL
+ # probabilistic atlases store their
+ # probabilities as percentages.
+ props.append(100 * prop / weightsum)
return labels, props
@@ -817,7 +827,8 @@ class ProbabilisticAtlas(Atlas):
- A sequence of all labels which are present in the mask
- A sequence containing the proportion, within the mask,
- of each present label.
+ of each present label. The proportions are returned as
+ values between 0 and 100.
"""
labels = []
@@ -825,18 +836,18 @@ class ProbabilisticAtlas(Atlas):
# Make sure that the mask has the same
# number of voxels as the atlas image
- mask = mask.resample(self.shape[:3], dtype=np.float32, order=1)[0]
- boolmask = mask > 0
+ mask = mask.resample(self.shape[:3], dtype=np.float32, order=0)[0]
+ boolmask = mask > 0
+ weights = mask[boolmask]
+ weightsum = weights.sum()
for label in range(self.shape[3]):
- weights = mask[boolmask]
- vals = self[..., label]
- vals = vals[boolmask] * weights
- prop = vals.sum() / weights.sum()
+ vals = self[..., label]
+ vals = vals[boolmask] * weights
+ prop = vals.sum() / weightsum
if not np.isclose(prop, 0):
-
labels.append(label)
props .append(prop)
--
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