Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound
images. However, due to their poor resolution and strong speckle noise, segmenting objects from this imaging
modality remains a challenging task and may not be satisfactory with traditional image segmentation methods.
To this end, this paper presents a simple, reliable, and conceptually different segmentation technique to locate
and extract bone contours from ultrasound images. Instead of considering a new elaborate (texture) segmentation
model specifically adapted for the ultrasound images, our technique proposes to fuse (i.e. efficiently combine)
several segmentation maps associated with simpler segmentation models in order to get a final reliable and
accurate segmentation result. More precisely, our segmentation model aims at fusing several K-means clustering
results, each one exploiting, as simple cues, a set of complementary textural features, either spatial or frequential.
Eligible models include the gray-level co-occurrence matrix, the re-quantized histogram, the Gabor filter bank,
and local DCT coefficients. The experiments reported in this paper demonstrate the efficiency and illustrate all
the potential of this segmentation approach.