A comparison of non-parametric segmentation methods

National audienceIn image segmentation, level-set methods discriminating regions with Parzen estimates of their intensity distributions have proven useful in a broad variety of contexts. A number of area cost terms have been proposed to achieve this goal, such as log-likelihood, Bhattacharyya coefficient, Kullback-Leibler divergence and several others. In this work we compare the performance of the most widespread criterions and show that log-likelihood and assimilated methods have a clear advantage in terms of robustness. In particular, the other methods tested suffer from a boundary instability due to small region/small initialization/hard to distinguish regions. We also give some theoretical arguments supporting our experimental results on synthetic and real images.Pour la segmentation d'images, différentes méthodes ont été proposées pour segmenter une image à partir d'estimateurs de Parzen des distributions d'intensités, par exemple la distance de Bhattacharyya, de Kullback-Leibler, ou la log-vraissemblance. Nous comparons plusieurs méthodes couramment utilisées et montrons que les méthodes basées sur la log-vraissemblance sont les plus robustes, et en particulier sont exemptes de problèmes de bords rencontrés dans toutes les autres méthodes testées. Ces résultats donnent des indications claires sur quelles méthodes doivent être préférées et nous avançons quelques arguments théoriques dans cette direction

Vessel segmentation in high-frequency 2D/3D ultrasound images

International audienc

Segmentation of Skin Tumors in High-Frequency 3-D Ultrasound Images

International audienceHigh-frequency 3-D ultrasound imaging is an informative tool for diagnosis, surgery planning and skin lesion examination. The purpose of this article was to describe a semi-automated segmentation tool providing easy access to the extent, shape and volume of a lesion. We propose an adaptive log-likelihood level-set segmentation procedure using non-parametric estimates of the intensity distribution. The algorithm has a single parameter to control the smoothness of the contour, and we describe how a fixed value yields satisfactory segmentation results with an average Dice coefficient of D = 0.76. The algorithm is implemented on a grid, which increases the speed by a factor of 100 compared with a standard pixelwise segmentation. We compare the method with parametric methods making the hypothesis of Rayleigh or Nakagami distributed signals, and illustrate that our method has greater robustness with similar computational speed. Benchmarks are made on realistic synthetic ultrasound images and a data set of nine clinical 3-D images acquired with a 50-MHz imaging system. The proposed algorithm is suitable for use in a clinical context as a post-processing tool

Segmentation of High Frequency Ultrasound Skin Data

International audienc

Joint segmentation and characterization of the dermis in 50 MHz ultrasound 2D and 3D images of the skin

International audienceHighlights• The signal statistics in 50 MHz high frequency ultrasound images of the dermis are studied accurately layer by layer.• An automatic, accurate segmentation method is developed, which is highly resistant to artifacts thanks to the use of elaborate non-linear filters and a multiple loss level-set segmentation algorithm.• Based on fits to a Nakagami law, a score is computed for characterizing skin photo aging.• The joint segmentation and characterization algorithm is designed to be applied to 2D and 3D images.AbstractWe propose a novel joint segmentation and characterization algorithm for the assessment of skin aging using 50 MHz high-frequency ultrasound images. The proposed segmentation method allows a fine determination of the envelope signal's statistics in the dermis as a function of depth. The sequence of statistical estimates obtained is then combined into a single aging score. The segmentation is based on tailored recursive non-linear filters. The epidermis and the dermis are jointly segmented with a non-parametric active contour combining a texture criterion, an epidermis indicator map and the geometric constraint of horizontal continuity. The algorithm is designed to apply to 2D and 3D images as well. We evaluated skin photo-aging on ultrasound images with an experimental study on a cohort of 76 women separated into 2 groups of different ages. Two aging scores are computed from the images: local dermal contrast and skin roughness. We show that these scores are much better at identifying the two groups (p-value) than the previously used indicator (p-value 0.046). Moreover, we find that a combined score more reliably evaluates skin photo-aging, with 84% success, than a scoring of the ultrasound images by 4 experts