Papers by soumaya louhichi
Skin Lesion Segmentation Using Multiple Density Clustering Algorithm MDCUT And Region Growing
2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018
Skin lesion segmentation is a key step in a diagnosis system based on dermoscopic images. This pa... more Skin lesion segmentation is a key step in a diagnosis system based on dermoscopic images. This paper proposes a method to detect the skin lesion accurately. The images are first cleansed to remove noise. Then, pertinent features are extracted from RGB, HSV and XYZ color spaces. Cluster analysis is used for segmentation. We take advantage of the multiple density clustering algorithm MDCUT [1] to solve the problem of image segmentation using region growing. We demonstrate how MDCUT algorithm is used to automatically determine the needed parameters for region growing image segmentation. Experiments on medical skin lesion image and comparison with the ground truth segmentation results demonstrate the validity of our method.
Distributed and Parallel Databases, 2018
Despite their adoption in many applications, density-based clustering algorithms perform ineffici... more Despite their adoption in many applications, density-based clustering algorithms perform inefficiently when dealing with data with varied density, imbricated and/or adjacent clusters. Clusters of lower density may be classified as outliers, and adjacent and imbricated clusters with varied density may be aggregated. To handle this inefficiency, the MDCUT algorithm (Multiple Density ClUsTering) (Louhichi et al. in Pattern Recogn Lett 93:48-57, 2017) detects multiple local density parameters to handle density variation in the data. MDCUT extracts density local levels by analyzing mathematically the interpolated k-nearest neighbors function. A clustering Sub-routine is lunched for each density level to form the clusters of that level. Compared to well-known density based clustering algorithms, MDCUT recorded good results on artificial datasets. The main drawback of MDCUT is its sensitivity to the parameter p of the used interpolation technique and the parameter k for the number of nearest neighbors. In this paper, we propose a new extension of the MDCUT algorithm to detect automatically pairs of values (k i ,ε i) to characterize the density levels in the data, where k i and ε i stand respectively for the number of neighbors and the radius threshold for the ith density level. We study the performance of the MDCUT 2 algo-B Soumaya Louhichi
Pattern Recognition Letters, 2017
Service Component Architecture specification (SCA) is an emerging and promising technology for th... more Service Component Architecture specification (SCA) is an emerging and promising technology for the development, deployment and integration of Internet applications. This technology supports the management of dynamic availability and treats the heterogeneity between the components of distributed applications. However, this technology is not able to solve all problems. Currently, software systems are evolving. This factor makes development and maintenance of systems more complex than before. One solution to remedy this was the use of the Model Driven Engineering (MDE) approach in the development process. The aim of this paper is to apply an MDE automation type ensuring the passage from an UML 2.0 model to SCA model. To achieve this, we study two metamodels: the UML 2.0 component metamodel and the SCA meta-model. To ensure traceability between these two meta-models, we have defined transformation rules in ATL language.
A density based algorithm for discovering clusters with varied density
2014 World Congress on Computer Applications and Information Systems (WCCAIS), 2014
Clustering is a well studied problem in data analysis and data mining. It has many areas of appli... more Clustering is a well studied problem in data analysis and data mining. It has many areas of applications and it is used as a preprocessing step before other data mining tasks such as classification and association analysis. Discovering clusters of arbitrary shapes is a challenging task. Even though density based clustering algorithms manage to detect clusters with different shapes and sizes in large data bases with the presence of noise, they fail in handling local density variation within the data. In this paper, we propose a new algorithm based on the well known density based clustering algorithm DBSCAN. Our algorithm approximates the k nearest neighbors curve by spline interpolation and uses mathematic properties of functions to detect automatically points where the function changes concavity. Some of these points corresponds to the different levels of density within the data set. Experimental results on synthetic data sets show the efficiency of the proposed approach.
MDE approach for the generation and verification of SCA model
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services - iiWAS '11, 2011
Service Component Architecture specification (SCA) is an emerging and promising technology for th... more Service Component Architecture specification (SCA) is an emerging and promising technology for the development, deployment and integration of Internet applications. This technology supports the management of dynamic availability and treats the heterogeneity between the components of distributed applications. However, this technology is not able to solve all problems. Currently, software systems are evolving. This factor makes development, verification and maintenance of systems more complex than before. One solution to remedy this was the use of the Model Driven Engineering (MDE) approach in the development and verification process. The purpose of this paper is to apply an approach MDE to obtain SCA models and to verify the properties of these models. To reach our purpose, we applied two transformations: The first one to obtain SCA models using UML 2.0 metamodel and the second transformation to ensure the verification of the properties of these models using event-B metamodel. To achieve this, we study the UML 2.0 component metamodel, the SCA metamodel and the event-B metamodel. We have defined transformation rules in ATL language.
ATL Transformation for the Generation of SCA Model
2011 Seventh International Conference on Semantics, Knowledge and Grids, 2011
Abstract: Service Component Architecture specification (SCA) is an emerging and promising technol... more Abstract: Service Component Architecture specification (SCA) is an emerging and promising technology for the development, deployment and integration of Internet applications. This technology supports the management of dynamic availability and treats the heterogeneity ...
2014 World Congress on Computer Applications and Information Systems (WCCAIS), 2014
Clustering algorithms are attractive for the task of class identification in spatial databases. H... more Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by factor of more than 100 in terms of efficiency.
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Papers by soumaya louhichi