Unsupervised segmentation of ultrasound images by fusion of spatio-frequential textural features

S. Benameur; M. Mignotte; F. Lavoie M.D.

doi:10.1117/12.871172

14 March 2011 Unsupervised segmentation of ultrasound images by fusion of spatio-frequential textural features

S. Benameur, M. Mignotte, F. Lavoie M.D.

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796239 (2011) https://doi.org/10.1117/12.871172
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

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.

Citation Download Citation

S. Benameur, M. Mignotte, and F. Lavoie M.D. "Unsupervised segmentation of ultrasound images by fusion of spatio-frequential textural features", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796239 (14 March 2011); https://doi.org/10.1117/12.871172

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