Superpixel Classification for Image Segmentation
series DFKI Documents, volume 14-07, pages 2, 2014. DFKI GmbH.
Aortic calcification is one of the main causes for aortic valve stenosis which can lead to a limited function
of the aortic valve. In a minimal invasive surgery (MIS) a catheter is inserted into the aorta and the aortic
valve is replaced by an artificial valve. Although MIS is considered safer than open surgery, it is still a risky
operation as the whole environment in which the catheter is applied is not directly visible. Risk prone areas
like branches, calcium deposits, aortic aneurysm and aortic valve stenosis need to be avoided. Especially
calcium deposit bears the risk of interrupting the oxygen supply if detached by the catheter.
The presented approach applies a Support Vector Machine (SVM) to learn the representation of aortic
calcification in computer tomography images which are pre-operative data. Two feature vector extraction
methods are presented and compared to each other in terms of prediction results and applicability. In
the pixel-wise method the feature vectors are extracted per pixel by windowing over the whole image and
computing statistical properties of intensity values. Every pixel is then classified separately. In the segmentwise
method an over-segmentation algorithm is first applied to the whole image and features are extracted
per segment. The properties of the intensity distribution per segment is used as feature by computing a
32-bin histogram of the intensity values within the segment. Consequently, the classification is carried out
on the segments instead of pixels resulting in less training examples needed to train the classifier.
It is exemplarily shown that the relationship of neighbouring pixels covered by the segmentation can reach
similar results compared to the windowing approach covering only a small neighbourhood of a pixel, and
that it can even outperform the pixel-wise approach on some false negatives. Additionally, the segment-wise
approach reduces the amount of training data by a factor of more than 1
1354 compared with the pixel-wise
The CT images were thankfully provided by Herbert De Praetere, MD from Katholieke Universiteit Leuven.