Automatic tissue segmentation algorithm for ultrasonic transmission tomography using active contours segmentation and unsupervised clustering

Jeong-Won Jeong; Dae C. Shin; Synho Do; Vasilis Z. Marmarelis

doi:10.1117/12.593758

12 April 2005 Automatic tissue segmentation algorithm for ultrasonic transmission tomography using active contours segmentation and unsupervised clustering

Jeong-Won Jeong, Dae C. Shin, Synho Do, Vasilis Z. Marmarelis

Author Affiliations +

Proceedings Volume 5750, Medical Imaging 2005: Ultrasonic Imaging and Signal Processing; (2005) https://doi.org/10.1117/12.593758
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

This paper presents an automatic tissue segmentation methodology for High-Resolution Ultrasonic Transmission Tomography (HUTT) imagery of biological organs. This method combines a recent segmentation approach: the L-level set active contours algorithm with unsupervised clustering using the agglomerative hierarchical k-means algorithm. The active contours algorithm has been recently explored as a powerful tool for image segmentation since it automatically decomposes a given image into 2L segment classes by utilizing L level set functions and finding the optimal boundaries of the 2L segment classes so that the pixel feature values of each segment are as homogeneous as possible. Unfortunately, the algorithm is often trapped at local minima due to the intrinsic non-convexity of the cost function, especially for noisy data. To overcome this problem, we introduce a multi-stage multi-resolution analysis that optimizes the active contours at successive resolutions of the image data. The resulting segments are then re-clustered by subsequent agglomerative hierarchical k-means clustering that seeks the optimal clusters yielding the minimum within-cluster distance in the feature space. The preliminary studies reported here indicate that this proposed methodology can enhance the accuracy of soft tissue segmentation and provide fully automatic tissue differentiation without any user intervention except for specifying the number of level set functions L.

Citation Download Citation

Jeong-Won Jeong, Dae C. Shin, Synho Do, and Vasilis Z. Marmarelis "Automatic tissue segmentation algorithm for ultrasonic transmission tomography using active contours segmentation and unsupervised clustering", Proc. SPIE 5750, Medical Imaging 2005: Ultrasonic Imaging and Signal Processing, (12 April 2005); https://doi.org/10.1117/12.593758

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