A New Method for the Estimation of Mass Functions in the Dempster–Shafer’s Evidence Theory: Application to Colour Image Segmentation

Abstract

In this paper, the problem of colour image segmentation is addressed using the Dempster-Shafer (DS) theory. Examples are provided showing that this theory is able to take into account a large variety of special situations that occur and which are not well solved using classical approaches. Modelling both uncertainty and imprecision, and computing the conflict between images and introducing a priori information are the main features of this theory. Consequently, the performance of such a segmentation scheme is largely conditioned by the appropriate estimation of mass functions in the DS evidence theory. In this paper, a new method of automatically determining the mass function for colour-image segmentation problems is presented. The mass function of each pixel is determined by applying possibilistic c-means (PCM) clustering to the grey levels of the three primitive colours. A reliability criterion, associ- Circuits Syst Signal Process (2011) 30: 55-71 ated with each pixel and the mass functions of its neighbouring pixels, is used into a fuzzy based reasoning system in order to decide on the appropriate segmentation. Experimental segmentation results on medical and textured colour images highlight the effectiveness of the proposed method.

Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

“…The selection of the best and representative component images is based on the segmentation sensitivity criterion [25]. The best component images used in our application are the R, H, Y, X, A, and L components for the input image expressed in the {RGB, HIS, YIQ, XYZ, LAB, and LUV} color spaces, respectively.…”

“…In fact, this method may be seen to be a straightforward complement to the work proposed by Ben Chaabane et al [4,24,25]. The objective is to rebuild each cell from a series of six images represented in different color spaces.…”

“…Most recent studies in color image segmentation [21,24,25] have used the DS evidence theory to fuse oneby-one the pixels coming from the three components (Red, Green, and Blue) of original image, in order to increase the quality of information and to obtain an optimal segmented image.…”

“…To overcome this limitation, the authors have reformulated the fuzzy clustering problem so that the clustering method can be used to generate memberships with typical interpretation. This method called PCMDS [25] is based on the Possibilistic C-Means (PCM) algorithm [26].…”

“…The estimation of mass functions in the DS evidence theory is based on the assumption of GD [16,24], or fuzzy sets [4,22,25]. In principle, only one image is considered for each application, whereas many realizations of the same image fused together may be very helpful for the segmentation process.…”