Karim Kalti

Software comparison dealing with bayesian networks

This paper presents a comparative study of tools dealing with Bayesian networks. Indeed, Bayesian networks are mathematical models now increasingly used in the field of decision support and artificial intelligence. Our study focuses on methods for inference and learning. It presents a state of the art in the field.

Image features extraction for masses classification in mammograms

Computer aided diagnosis of breast cancer is becoming increasingly a necessity given the exponential growth of performed mammograms. In particular, the breast mass diagnosis and classification arouse nowadays a great interest. Indeed, the complexity of processed mass shapes and the difficulty to distinguish between them require the use of appropriate descriptors. In this paper, suitable characterization methods for breast pathologies are proposed and the study of different classification methods is addressed. In order to analyze the mass shapes, a segmentation is performed manually. Once the images are segmented, a study of various descriptors proposed in the literature is conducted. In order to compare different approaches of characterization, a comparative study is performed. The descriptors commonly used in the breast cancer field are compared to test their ability to characterize the breast lesions. Obtained results show that statistical approaches of texture provides the best classification result.

Fuzzy generalized median graphs computation: Application to content-based document retrieval

Abstract Fuzzy median graph is an important new concept that can represent a set of fuzzy graphs by a representative fuzzy graph prototype. However, the computation of a fuzzy median graph remains a computationally expensive task. In this paper, we propose a new approximate algorithm for the computation of the Fuzzy Generalized Median Graph (FGMG) based on Fuzzy Attributed Relational Graph (FARG) embedding in a suitable vector space in order to capture the maximum information in graphs and to improve the accuracy and speed of document image retrieval processing. In this study, we focus on the application of FGMGs to the Content-based Document Retrieval (CBDR) problem. Experiments on real and synthetic databases containing a large number of FARGs with large sizes show that a CBDR using the FGMG as a dataset representative yields better results than an exhaustive and sequential retrieval in terms of gains in accuracy and time processing.

The threshold EM algorithm for parameter learning in bayesian network with incomplete data

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. In order to limit the search space and escape from local maxima produced by executing EM algorithm, this paper presents a learning parameter algorithm that is a fusion of EM and RBE algorithms. This algorithm incorporates the range of a parameter into the EM algorithm. This range is calculated by the first step of RBE algorithm allowing a regularization of each parameter in bayesian network after the maximization step of the EM algorithm. The threshold EM algorithm is applied in brain tumor diagnosis and show some advantages and disadvantages over the EM algorithm.

A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents

Recently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set.

Two-step evidential fusion approach for accurate breast region segmentation in mammograms

In mammograms, the breast skin line often appears ambiguous and poorly defined. This is mainly due to the breast organ compression during the image acquisition process along with the inherent low density of the tissue in that area. The accurate delimitation of the breast region becomes a challenging task to conventional segmentation techniques. In this study, the authors propose a new segmentation approach allowing to overcome this challenge. This approach is based on the application of two complementary segmentation techniques exploring each, respectively, the grey-scale intensities and the local-homogeneity domains. The knowledge resulting from each segmentation technique is considered as a knowledge source and is modelled using the belief functions formalism. The two considered knowledge sources are then fused using an iterative process. The obtained results show the efficiency of the proposed evidential approach especially in terms of ambiguity removal and decision quality improvement for accurate breast border delimitation (which is often under-segmented and assimilated to the background by most of the existing segmentation techniques).

A content-based digital mammography retrieval using inexact graph matching

Content-Based Image Retrieval (CBIR) is becoming one of the most vivid research area in computer vision. It is widely used in medical applications especially in computer aided diagnostic systems (CAD). CBIR systems in digital mammography take an important part of these works. The work presented in this paper aims to propose a CBIR approach based on inexact graph matching algorithm for mammographic images. To achieve this task, we represent a mammogram as an Attributed Relational Graph (ARG) based on ImageMap approach where each node of the graph represents a semantic object. Objects that are considered in mammogram are: Background, Breast, Pectoral Muscle, Masses and Calcifications. Then, for each node, we compute a signature that describes the selected object. In order to retrieve the most similar images to a query one, a graph matching technique is applied based on the Hungarian algorithm. To Evaluate our approach 100 mammographic images from the MIAS database were used and six metrics were computed. Experiments demonstrate that the proposed method using Hamming distance gives the most promising results.