In this article, we present an interactive algorithm segmenting white brain matter, visible as hyperechoic flaring areas in ultrasound (US) images of preterm infants with periventricular leukomalacia (PVL). The algorithm combines both the textural properties of pathological brain tissue and mathematical morphology operations. An initial flaring area estimate is derived from a multifeature multiclassifier tissue texture classifier. This area is refined based on the structural properties of the choroid plexus, a brain feature known to have characteristics similar to flaring. Subsequently, a combination of a morphological closing, gradient and opening by reconstruction operation determines the final flaring area boundaries. Experimental results are compared with a gold standard constructed from manual flaring area delineations of 12 medical experts. In addition, we compared our algorithm to an existing active contour method. The results show our technique agrees to the gold standard with statistical significance and outperforms the existing method in accuracy. Finally, using the flaring area as a criterion we improve the sensitivity of PVL detection up to 98% as compared with the state of the art. (E-mail:

Flaring, Mathematical morphology, Medical ultrasound, Periventricular leukomalacia, Texture,
Ultrasound in Medicine & Biology
Erasmus MC: University Medical Center Rotterdam

Vansteenkiste, E, Govaert, P, Conneman, N, Leguin, M, & Philips, W. (2009). Segmentation of White Matter Flaring Areas in Ultrasound Images of Very-Low-Birth-Weight Preterm Infants. Ultrasound in Medicine & Biology, 35(6), 991–1004. doi:10.1016/j.ultrasmedbio.2008.12.009