Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms

Abstract

Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented; the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation

Performance of a New Method of Multicomponent Images Segmentation in the Presence of Noise

“…The classification of "colours" is carried out in two steps [3]: the learning step and the decision step. The learning step is a hierarchical decomposition of populations in the compact n-D histogram.…”

“…In this work, we first make a brief presentation of our segmentation algorithm, for more details refer to the article [3]. Then we present here the additive noises that are likely considered Gaussian, uniform and so correlated.…”

“…The difficulty can be overcome by using a compact multidimensional histogram [7][8][9][10][11][12]. We have recently proposed a segmentation method [3] based on the analysis of compact nD histogram. We report here how this algorithm proves to be robust to additional noise considered as a harmful corrupting signal with the extraction of the useful signal.…”

“…Segmenting the images according to their radiometric attributes can be achieved by analyzing multidimensional histogram [2,3]. However, to manipulate a nD histogram (n > = 3) is not easy task because it requires a large memory [4][5][6].…”

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

“…Indeed, the approach of vectorial morphological segmentation by analysis of multidimensional (nD) compact histograms [14] that we propose in this paper is an extension of the segmentation by classification. It is based on a multi-thresholding of the monodimensional compact histograms resulting from the modal components of the multivariate image and a morphological order of the attributes vectors of its nD compact histogram with respect to constructed threshold vectors resulting from the multi-thresholding.…”