Unsupervised Image Segmentation Using a Hierarchical Clustering Selection Process
نویسندگان
چکیده
In this paper we present an unsupervised algorithm to select the most adequate grouping of regions in an image using a hierarchical clustering scheme. Then, we introduce an optimisation approach for the whole process. The grouping method presented is based on the maximisation of a measure that represents the perceptual decision. The whole strategy takes profit from a hierarchical clustering to find a maximum of the proposed criterion. The algorithm has been used to segment real images as well as multispectral images achieving very accurate results on this task.
منابع مشابه
Extraction and 3D Segmentation of Tumors-Based Unsupervised Clustering Techniques in Medical Images
Introduction The diagnosis and separation of cancerous tumors in medical images require accuracy, experience, and time, and it has always posed itself as a major challenge to the radiologists and physicians. Materials and Methods We Received 290 medical images composed of 120 mammographic images, LJPEG format, scanned in gray-scale with 50 microns size, 110 MRI images including of T1-Wighted, T...
متن کاملTree Structured Dirichlet Processes for Hierarchical Morphological Segmentation
This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process (HDP). Tree hierarchies are learned alon...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملSimple-to-Complex Discriminative Clustering for Hierarchical Image Segmentation
We propose a novel discriminative clustering algorithm with a hierarchical framework for solving unsupervised image segmentation problems. Our discriminative clustering process can be viewed as an EM algorithm, which alternates between the learning of image visual appearance models and the updates of cluster labels (i.e., segmentation outputs) for each image segment. In particular, we advance a...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کامل