Stochastic watershed segmentation
نویسندگان
چکیده
This paper introduces a watershed-based stochastic segmentation methodology. The approach is based on using M realizations of N random markers to build a probability density function (pdf) of contours which is then segmented by volumic watershed for de ning the R most signi cant regions. It over-performs the standard watershed algorithms when the aim is to segment complex images into a few regions. Three variants of the random germs framework are discussed, according to the algorithm used to build the pdf: 1) uniform random germs on the same gradient, 2) regionalised random germs on the same gradient, and 3) uniform random germs on levelled-based gradient. The last algorithm is more complex but it yields the best results.
منابع مشابه
Bagging Stochastic Watershed on Natural Color Image Segmentation
The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochasti...
متن کاملRandom Germs and Stochastic Watershed for Unsupervised Multispectral Image Segmentation
This paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N rsandom markers), are used as a gradient for segmentation: a weighted marginal pdf a vectorial pdf and a probabilistic gradient. These gradien...
متن کاملLiver segmentation in MRI: A fully automatic method based on stochastic partitions
There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed tra...
متن کاملStatistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation
Stochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a gaussian model of image regions. In this framework, the ba...
متن کاملImproving the stochastic watershed
The stochastic watershed is an unsupervised segmentation tool recently proposed by Angulo and Jeulin. By repeated application of the seeded watershed with randomly placed markers, a probability density function for object boundaries is created. In a second step, the algorithm then generates a meaningful segmentation of the image using this probability density function. The method performs best ...
متن کامل