Prenatal events such as intrauterine growth restriction have been shown to be associated with an increased thickness of
abdominal aorta in the fetus. Therefore the measurement of abdominal aortic intima-media thickness (aIMT) has been
recently considered a sensitive marker of artherosclerosis risk. To date measure of aortic diameter and of aIMT has been
performed manually on US fetal images, thus being susceptible to intra- and inter- operator variability. This work
introduces an automatic algorithm that identifies abdominal aorta and estimates its diameter and aIMT from videos
recorded during routine third trimester ultrasonographic fetal biometry.
Firstly, in each frame, the algorithm locates and segments the region corresponding to aorta by means of an active
contour driven by two different external forces: a static vector field convolution force and a dynamic pressure force.
Then, in each frame, the mean diameter of the vessel is computed, to reconstruct the cardiac cycle: in fact, we expect the
diameter to have a sinusoidal trend, according to the heart rate. From the obtained sinusoid, we identify the frames
corresponding to the end diastole and to the end systole. Finally, in these frames we assess the aIMT. According to its
definition, we consider as aIMT the distance between the leading edge of the blood-intima interface, and the leading
edge of the media-adventitia interface on the far wall of the vessel. The correlation between end-diastole and end-systole
aIMT automatic and manual measures is 0.90 and 0.84 respectively.
The automatic segmentation of brain tissues in magnetic resonance (MR) is usually performed on T1-weighted images,
due to their high spatial resolution. T1w sequence, however, has some major downsides when brain lesions are present:
the altered appearance of diseased tissues causes errors in tissues classification. In order to overcome these drawbacks,
we employed two different MR sequences: fluid attenuated inversion recovery (FLAIR) and double inversion recovery
(DIR). The former highlights both gray matter (GM) and white matter (WM), the latter highlights GM alone.
We propose here a supervised classification scheme that does not require any anatomical a priori information to identify
the 3 classes, "GM", "WM", and "background". Features are extracted by means of a local multi-scale texture analysis,
computed for each pixel of the DIR and FLAIR sequences. The 9 textures considered are average, standard deviation,
kurtosis, entropy, contrast, correlation, energy, homogeneity, and skewness, evaluated on a neighborhood of 3x3, 5x5,
and 7x7 pixels. Hence, the total number of features associated to a pixel is 56 (9 textures x3 scales x2 sequences +2
original pixel values). The classifier employed is a Support Vector Machine with Radial Basis Function as kernel.
From each of the 4 brain volumes evaluated, a DIR and a FLAIR slice have been selected and manually segmented by 2
expert neurologists, providing 1st and 2nd human reference observations which agree with an average accuracy of
99.03%. SVM performances have been assessed with a 4-fold cross-validation, yielding an average classification
accuracy of 98.79%.