A new denoising technique for ultrasound images using morphological properties of speckle combined with tissue classifying parameters

In this paper we introduce a new speckle suppression technique for medical ultrasound images that incorporates morphological properties of speckle as well as tissue classifying parameters. Each individual speckles is located, and, exploiting our prior knowledge on the tissue classification, it is determined whether this speckle is noise or a medically relevant detail. We apply the technique on images of neonatal brains affected by White Matter Damage (leukomalacia). The results show that applying an active contour on a processed image, in order to segment the affected areas, yields a segmentation much closer to that of an expert

A new filtering method for ultrasound images incorporating prior statistics concerning medical features

20 To 50 percent of the neonates with a very low birth weight (VLBW: < 1500 g) suffer from White Matter Damage (leukomalacia). Nowadays the diagnosis of WND is still solely dependent on the visual interpretation by an expert. A need for a (semi-) computerized way of segmenting the affected regions, in order to make quantitative measurements as an aid to the subjective diagnosis, is felt. Applying active contours for this purpose, is a classical approach. The performance of active contours for this purpose, however, is heavily deteriorated by the presence of speckle noise. In this paper a new filter, taking into account local statistics in the image, is proposed; it removes a significant amount of speckle noise in the healthy parts, while it makes the areas affected by WMD more uniform, thus severely improving the performance of the active contour. The results show that applying an active contour after the proposed technique yields a segmentation much closer to that of an expert

Half-automatische segmentatie van echografiebeelden met behulp van slangen /

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