Mounir Sayadi

Color image segmentation using automatic thresholding and the fuzzy C-means techniques

In this paper, a color image segmentation approach based on automatic histogram thresholding and the fuzzy C-means (FCM) techniques is presented. The originality of this work remains in using thresholding and clustering techniques together for color image segmentation. The histogram considers the occurrence of the gray levels among pixels. In a first stage, the thresholding histogram is used for finding all major homogenous areas. In order to reduce the computational burden required by the fuzzy C-means, the coarse-fine concept methodology is used. The thresholding technique is used for the coarsely segmentation. After the coarse step, and in order to refine further the segmentation of the assigned pixels which remain unclassified, the fuzzy C-means technique is then applied. The experimental results show that the proposed approach can find homogeneous areas effectively, and can solve the problem of discriminating shading in color images to some extent.

A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features

Gabor filtering is a widely applied approach for texture analysis. This technique shows a strong dependence on certain number of parameters. Unfortunately, each variation of values of any parameter may affect the texture characterization performance. Moreover, Gabor filters are unable to extract micro-texture features which also have a negative effect on the clustering task. This paper, deals with a new descriptor which avoids the drawbacks mentioned above. The novel texture descriptor combines grating cell operator outputs derived from a designed Gabor filters bank, and local binary pattern features. For the clustering task, an extended version of fuzzy type 2 clustering algorithm is also proposed. The effectiveness of the proposed segmentation approach on a variety of synthetic and textured images is highlighted. Several experimental results on a set of textured images show the superiority of the proposed approach in terms of segmentation accuracy with respect to quantitative and qualitative comparisons.

Automatic segmentation of clustered breast cancer cells using watershed and concave vertex graph

Automatic segmentation of stained breast tissue images helps pathologists to discover the cancer disease earlier. Separation of touching cells presents many difficulties to the traditional segmentation algorithms. In this paper, we propose a new automatic method to segment clustered cancer cells. In the first step, we detect cell regions using a modified geometric active contour based on Chan-Vese energy functional. Then, touching cell regions are extracted from the pre-segmented image by detecting high concavity points along the cell contours. A gradient-weighted distance transform is used in the watershed algorithm in order to get the most significant inner edges. To solve the problem of over-segmentation, which is the major drawback of the watershed method, a combination of three techniques is presented as a post-processing step. First, the nearest end points to concave vertices are detected in the inner edges in order to get the initial separating curve candidates. Second, a concave vertex graph is constructed from the end points and the separating curves. Finally, Dijkstra algorithm is applied to find the shortest path that separates the touching cells. The proposed algorithm is tested on several breast cancer cell images and its compared with the classical watershed algorithm and a recent marker-controlled watershed method. The experimental results show the performance of the presented approach.

An LMS adaptive second-order Volterra filter with a zeroth-order term: steady-state performance analysis in a time-varying environment

This article studies the steady-state performance of the least mean square (LMS) adaptive second-order Volterra filter (SOVF) with a zeroth-order term for Gaussian inputs. The mean-square-error (MSE) criterion is evaluated first. Then, SOV LMS algorithm-based updating equations are derived. Next, the steady-state performance of the recursions is analyzed for a random walk model for the unknown system parameters, and the steady-state excess MSE is evaluated. Finally, the theoretical performance predictions are shown to be in good agreement with simulation results, especially for small step sizes.

Interest of the multi-resolution analysis based on the co-occurrence matrix for texture classification

Wavelet transform provides several important characteristics which can be used in a texture analysis and classification. In this work, an efficient texture classification method, which combines concepts from wavelet and co-occurrence matrices, is presented. An Euclidian distance classifier is used to evaluate the various methods of classification. A comparative study is essential to determine the ideal method. Using this conjecture, we developed a novel feature set for texture classification and demonstrate its effectiveness.

Color image segmentation based on Dempster-Shafer evidence theory

In this paper, a color image segmentation approach based on Dempster-Shafer evidence theory is presented. The basic technique consists in combining information coming from three independent information sources for the same image. These sources correspond to the three component images R (red), G (green) and B (blue). The Dempster-Shafer theory of evidence is applied in order to fuse the information from these three sources. This method shows the spectacular ability of the evidence theory to handling uncertain, imprecise and incomplete information. The Results on cell images are presented in order to demonstrate the effectiveness of the proposed method.

A fast M-D Chandrasekhar algorithm for second order Volterra adaptive filtering

This paper presents a fast method for nonlinear filtering based on multichannel Chandrasekhar equations. By assuming that the adaptive second order Volterra filter may be transformed in a multichannel input linear filter, we present a new form of the second order Volterra filtering based a the fast multichannel Chandrasekhar algorithm. This method has a computational complexity of 3.N/sup 3/ multiplications per time instant, where N represents the memory span in number of samples of the nonlinear system model. This compares with 7.N/sup 3/ multiplications required for application of the fast Kalman filter with the same approach. A direct implementation of the RLS algorithm has a computational complexity of N/sup 6/. The adaptive filter is successfully used in a second order Volterra system identification in a stationary environment.

Estimation of the mass function in the Dempster-Shafer’s evidence theory using automatic thresholding for color image segmentation

In this paper we propose a color image segmentation method based on Demspter-Shaferpsilas theory. The salient aspects of the proposed method are at two levels. Firstly, the mass distributions of the Dempster-Shafer theory are directly derived from the image histogram. Secondly, the fusion of information coming from three different sources for the same image. A new strategy based on an automatic histogram thresholding is proposed to define the mass distributions in the combined framework. The proposed algorithm has been applied to the biomedical images.

AI tools in medical image analysis: efficacy of ANN for oestrogen receptor status assessment in immunohistochemical staining of breast cancer

Evaluating oestrogen receptor status in immunohistochemical staining of breast cancer is so complicated. This process is done subjectively and is so much time consuming. In fact, the studied images present many characteristics such as the non uniformity in the intensity of the organic tissue and the cells, and also the variability of the size and the form of cells which make their processing so difficult. So, given all these complexities, conventional methods are unable to solve the problem. In this work, we study the ability of artificial intelligence as a modern and an unconventional technique to automatically classify breast cancer cells. This step is considered as primary in assessing oestrogen receptor status. Three intelligent techniques are presented, applied and compared: fuzzy c-means (FCM), artificial neural network (ANN) and genetic algorithm (GA). The statistical analysis demonstrates the efficacy of the artificial neural network by recording an average rate of sensitivity, specificity and acc...

Image watermarking based on the hessenberg transform

Watermarking is now considered as an efficient means for assuring copyright protection and data owner identification. Watermark embedding techniques depend on the representation domain of the image (spatial, frequency, and multiresolution). Every domain has its specific advantages and limitations. Moreover, each technique in a chosen domain is found to be robust to specific sets of attack types. So we need to propose more robust domains to defeat these limitations and respect all the watermarking criterions (capacity, invisibility and robustness). In this paper, a new watermarking method is presented using a new domain for the image representation and the watermark embedding: the mathematical Hessenberg transformation. This domain is found to be robust against a wide range of STIRMARK attacks such as JPEG compression, convolution filtering and noise adding. The robustness of the new technique in preserving and extracting the embedded watermark is proved after various attacks types. It is also improved when compared with other methods in use. In addition, the proposed method is blind and the use of the host image is not needed in the watermark detection process.