Lamiche Chaabane

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With the development of acquisition image techniques, more and more image data from different sources of image become available. Multi-modality image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single modality. In medical imaging based application fields, image fusion has emerged as a(More)
Multiple sequence alignment (MSA) is an important tool in biological analysis. However, it is difficult to solve this class of problems due to their exponential time complexity when the number of sequences and their lengths increase. In this research paper, we present a new method for multiple sequence alignment problem, based on classical tabu search (TS)(More)
The paper presents a study and an evaluation of a novel unsupervised segmentation technique based aggregation approach and some of possibility theory concepts. Firstly, the MPFCM (Modified Possibilistic Fuzzy C-Means) algorithm is used to extract information from each of MR images modalities. In second step, an obtained data are combined with an operator in(More)
The paper presents the evaluation of the segmentation of MR images using the multispectral fusion approach in the possibility theory context. the process of fusion consists of three parts : (1) information extraction, (2) information aggregation, and (3) decision step. Information provided by T2-weighted and PDweighted images is extracted and modeled(More)
In this paper, we propose an automatic segmentation technique of multispectral magnetic resonance image (MRI) of the brain using three models based data fusion approach through the framework of the possibility theory. The fusion process is decomposed into three fundamental phases. Firstly, we modeling information extracted from the various images in a(More)
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