Les outils d’aide au diagnostic sont de nos jours au cœur de plusieurs protocoles cliniques car ils améliorent la qualité du diagnostic posé et des soins médicaux. Ce travail de recherche met en avant une architecture hiérarchique pour la conception d'un outil d'aide à la détection du cancer du sein robuste et performant. Il s’intéresse à la réduction des fausses alarmes en identifiant les régions potentiellement cancérogènes. La gamme dynamique des niveaux de gris des zones sombres est étirée pour améliorer le contraste entre la région du sein et l'arrière plan et permettre une meilleure extraction de celle-ci. Toutefois, le muscle pectoral demeure incrusté dans la région du sein et interfère avec l'analyse des tissus. Son extraction est à la fois difficile et complexe à mettre en œuvre à cause de son chevauchement avec les tissus denses du sein. Dans ces conditions, même en exploitant l'information spatiale pendant la clusterisation par un algorithme de fuzzy C-means ne produit pas toujours des résultats de segmentation pertinents. Pour s'affranchir de cette difficulté, une étape de validation suivie d'un ajustement de contour est mise sur pied pour détecter et corriger les imperfections de segmentation. La seconde étape est consacrée à la caractérisation de la densité des tissus. Pour faire face au problème de variabilité des distributions de niveaux de gris dans les classes de densités, nous introduisons une modification de contraste basée sur un transport optimisé de niveaux de gris. Grâce à cette technique, la surface relative de tissus denses estimée par simple segmentation est très fortement corrélée aux classes de densités issues d’un jeu de données étiquetées. / Computer-aided diagnosis systems are currently at the heart of many clinical protocols since they significantly improve diagnosis making and therefore medical care. This research work therefore puts forward a hierarchical architecture for the design of a robust and efficient CAD tool for breast cancer detection. More precisely, it focuses on the reduction of false alarms rate through the identification of image regions of foremost interest i.e potential cancerous areas. The dynamic range of gray level intensities in dark regions is, first of all stretched to enhance the contrast between tissues and background and thus favors accurate breast region extraction. A second segmentation follows since pectoral muscle which regularly tampers breast tissue analysis remains inlaid in the foreground region. Extracting pectoral muscle tissues is both hard and challenging due to its overlap with dense tissues. In such conditions, even exploiting spatial information during the clustering process of the fuzzy C-means algorithm does not always produce a relevant segmentation. To overcome this difficulty, a new validation process followed by a refinement strategy is proposed to detect and correct the segmentation imperfections. The second macro-step is devoted to breast tissue density analysis. To address the variability in gray levels distributions with of mammographic density classes, we introduce an optimized gray level transport map for mammographic image contrast standardization. Thanks to this technique, dense region areas computed using simple thresholding are highly correlated to density classes from an annotated dataset.

A técnica de agrupamento de dados é amplamente utilizada em análise exploratória, a qual é frequentemente necessária em diversas áreas de pesquisa tais como medicina, biologia e estatística, para avaliar potenciais hipóteses a serem utilizadas em estudos subseqüentes. Em bases de dados reais, a ocorrência de dados incompletos, nos quais os valores de um ou mais atributos do dado são desconhecidos, é bastante comum. Este trabalho apresenta um método capaz de identificar o número de grupos presentes em bases de dados incompletas, utilizando a combinação das técnicas de agrupamentos nebulosos e reamostragem bootstrap. A qualidade da classificação é baseada em medidas de comparação tradicionais como F1, Classificação Cruzada, Hubert e outras. Os estudos foram feitos em oito bases de dados. As quatro primeiras são bases de dados artificiais, a quinta e a sexta são a wine e íris. A sétima e oitava bases são formadas por uma coleção brasileira de 119 estirpes de Bradyrhizobium. Para avaliar toda informação sem introduzir estimativas, fez-se a modificação do algoritmo Fuzzy C-Means (FCM) utilizando-se um vetor de índices de atributos, os quais indicam onde o valor de um atributo é observado ou não, modificando-se ento, os cálculos do centro e distância ao centro. As simulações foram feitas de 2 até 8 grupos utilizando-se 100 sub-amostras. Os percentuais de valores faltando utilizados foram 2%, 5%, 10%, 20% e 30%. Os resultados deste trabalho demonstraram que nosso método é capaz de identificar participações relevantes, até em presença de altos índices de dados incompletos, sem a necessidade de se fazer nenhuma suposição sobre a base de dados. As medidas Hubert e índice randômico ajustado encontraram os melhores resultados experimentais. / Clustering in exploratory data analysis is often necessary in several areas of the survey such as medicine, biology and statistics, to evaluate potential hypotheses for subsequent studies. In real datasets the occurrence of incompleteness, where the values of some of the attributes are unknown, is very common. This work presents a method capable to identifying the number of clusters present in incomplete datasets, using a combination of the fuzzy clustering and resampling (bootstrapping). The quality of classification is based on the traditional measures, like F1, Cross-Classification, Hubert and others. The studies were made on eigth datasets. The first four are artificial datasets, the fifth and sixth are the wine and iris datasets. The seventh and eighth databases are composed of the brazilian collection of 119 Bradyrhizobium strains. To evaluate all information without introducing estimates, a modification of the Fuzzy C-Means (FCM) algorithm was developed using an index vector of attributes, which indicates whether an attribute value is observed or not, and changing the center and distance calculations. The simulations were made from 2 to 8 clusters using 100 sub-samples. The percentages of the missing values used were 2%, 5%, 10%, 20% and 30%. Even lacking data and with no special requirements of the database, the results of this work demonstrate that the proposed method is capable to identifying relevant partitions. The best experimental results were found using Hubert and corrected randomness measures.

A técnica de agrupamento de dados é amplamente utilizada em análise exploratória, a qual é frequentemente necessária em diversas áreas de pesquisa tais como medicina, biologia e estatística, para avaliar potenciais hipóteses a serem utilizadas em estudos subseqüentes. Em bases de dados reais, a ocorrência de dados incompletos, nos quais os valores de um ou mais atributos do dado são desconhecidos, é bastante comum. Este trabalho apresenta um método capaz de identificar o número de grupos presentes em bases de dados incompletas, utilizando a combinação das técnicas de agrupamentos nebulosos e reamostragem bootstrap. A qualidade da classificação é baseada em medidas de comparação tradicionais como F1, Classificação Cruzada, Hubert e outras. Os estudos foram feitos em oito bases de dados. As quatro primeiras são bases de dados artificiais, a quinta e a sexta são a wine e íris. A sétima e oitava bases são formadas por uma coleção brasileira de 119 estirpes de Bradyrhizobium. Para avaliar toda informação sem introduzir estimativas, fez-se a modificação do algoritmo Fuzzy C-Means (FCM) utilizando-se um vetor de índices de atributos, os quais indicam onde o valor de um atributo é observado ou não, modificando-se ento, os cálculos do centro e distância ao centro. As simulações foram feitas de 2 até 8 grupos utilizando-se 100 sub-amostras. Os percentuais de valores faltando utilizados foram 2%, 5%, 10%, 20% e 30%. Os resultados deste trabalho demonstraram que nosso método é capaz de identificar participações relevantes, até em presença de altos índices de dados incompletos, sem a necessidade de se fazer nenhuma suposição sobre a base de dados. As medidas Hubert e índice randômico ajustado encontraram os melhores resultados experimentais. / Clustering in exploratory data analysis is often necessary in several areas of the survey such as medicine, biology and statistics, to evaluate potential hypotheses for subsequent studies. In real datasets the occurrence of incompleteness, where the values of some of the attributes are unknown, is very common. This work presents a method capable to identifying the number of clusters present in incomplete datasets, using a combination of the fuzzy clustering and resampling (bootstrapping). The quality of classification is based on the traditional measures, like F1, Cross-Classification, Hubert and others. The studies were made on eigth datasets. The first four are artificial datasets, the fifth and sixth are the wine and iris datasets. The seventh and eighth databases are composed of the brazilian collection of 119 Bradyrhizobium strains. To evaluate all information without introducing estimates, a modification of the Fuzzy C-Means (FCM) algorithm was developed using an index vector of attributes, which indicates whether an attribute value is observed or not, and changing the center and distance calculations. The simulations were made from 2 to 8 clusters using 100 sub-samples. The percentages of the missing values used were 2%, 5%, 10%, 20% and 30%. Even lacking data and with no special requirements of the database, the results of this work demonstrate that the proposed method is capable to identifying relevant partitions. The best experimental results were found using Hubert and corrected randomness measures.

Computer aided diagnostic tool can aid the radiologist in the early detection of breast cancer. Even though mammography is considered to be the gold standard for breast cancer detection, it is limited by the spatial superposition of tissue. This limitation is the result of a using a two dimensional, (2D), representation of a three dimensional, (3D), structure. The limitation contributes to and results in misclassification of breast cancers. Tomosynthesis is a limited-angle 3D imaging device that overcomes this limitation by representing the breast structure with 3D volumetric data.This research, on tomosynthesis imaging, was a critical module of a larger research endeavor for the detection of breast cancer. Tomosynthesis is an emerging state-of-the-art 3D imaging technology. The purpose of this research was to develop a tomosynthesis based, efficient suspicious region segmentation, procedure for the breast to enhance the detection and diagnosis of breast cancer. The 3D breast volume is constructed to visualize the 3D structure of the breast region. Advanced image processing and analysis algorithms were developed to remove out-of-plane artifacts and increase the Signal Difference to Noise Ratio, (SDNR), of tomosynthetic images. Suspicious regions are extracted from the breast volume using efficient and robust clustering algorithms.A partial differential equation based non-linear diffusion method was modified to include the anisotropic nature of tomosynthesis data in order to filter out the out-of-plane artifacts, which are termed "tomosynthetic noise", and to smooth the in-plane noise. Fuzzy clustering algorithms were modified to include spatial domain information to segment suspicious regions. A significant improvement was observed, both qualitatively and quantitatively, in segmentation of the filtered data over the non-filtered data. The 3D segmentation system is robust enough to be used for statistical analysis of huge databases.

This research extends fuzzy methods to consider the fuzzy validation of fuzzy land cover data at the sub-pixel level. The study analyses the relationships between fuzzy memberships generated by field survey and those generated from the classification of remotely sensed data. In so doing it examines the variations in the relationship between observed and predicted fuzzy land cover classes. This research applies three land cover classification techniques: Fuzzy sets, Fuzzy c-means and Boolean classification, and develops three models to determine fuzzy land cover change. The first model is dependent on fuzzy object change. The second model depends on the sub-pixel change through a fuzzy change matrix, for both fuzzy sets and fuzzy c-means, to compute the fuzzy change, fuzzy loss and fuzzy gain. The third model is a Boolean change model which evaluates change on a pixel-by-pixel basis. The results show that using a fuzzy change analysis presents a subtle way of mapping a heterogeneous area with common mixed pixels. Furthermore, the results show that the fuzzy change matrix gives more detail and information about land cover change and is more appropriate than fuzzy object change because it deals with sub-pixel change. Finally the research has found that a fuzzy error matrix is more suitable than an error matrix for soft classification validation because it can compare the membership from the field with the classified image. From this research there arise some important points: • Fuzzy methodologies have the ability to define the uncertainties associated with describing the phenomenon itself and the ability to take into consideration the effect of mixed pixels. • This research compared fuzzy sets and fuzzy c-means, and found the fuzzy set is more suit-able than fuzzy c-means, because the latter suffers from some disadvantages, chiefly that the sum of membership values of a data point in all the clusters must be one, so the algorithm has difficulty in handling outlying points. • This research validates fuzzy classifications by determining the fuzzy memberships in the field and comparing them with the memberships derived from the classified image.

Dimploma thesis deals with the optimization of operation distribution in company Vodárenská akciová společnost, a.s. Aim of the thesis is to determine achievement standards of companies branches and to state the capacity reserves for every one of them. Next, using methods of cluster analysis and graph theory, proposing recommendation leading to effective utilization of operation capacity. This is done by optimizing total number of business branches and subsequent creation of new regions. Thesis consists of theoretical and practical part. In this papers theoretical part, hierarchical and nonhierarchical clustering algorithms, minimal spanning tree and water management sector are described. Practical part addresses optimization of layout of company operation distribution. Based on comparison of outputs of chosen methods, recommendations for company, will be proposed.

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Clustering large data sets has become very important as the amount of available unlabeled data increases. Single Pass Fuzzy C-Means (SPFCM) is useful when memory is too limited to load the whole data set. The main idea is to divide dataset into several chunks and to apply fuzzy c-means (FCM) to each chunk. SPFCM uses the weighted cluster centers of the previous chunk in the next data chunks. However, when the number of chunks is increased, the algorithm shows sensitivity to the order in which the data is processed. Hence, we improved SPFCM by recognizing boundary and noisy data in each chunk and using it to influence clustering in the next chunks. The proposed approach transfers the boundary and noisy data as well as the weighted cluster centers to the next chunks. We show that our proposed approach is significantly less sensitive to the order in which the data is loaded in each chunk.

In this thesis a new framework is proposed for obtaining the spongy bone, cortical bone, muscle and adipose tissue from MRI data. The method focuses on the accurate extraction of the edges of the target tissues, which is the main drawback of previous works. In this framework six new methods, as listed in section 1.3, are utilized together for improving the result of the segmentation by detecting the relational position of the tissues, acquiring the best possible contribution from the operator in terms of time and efficiency, forward and backward transfer of the segmented tissues at the seed slice and using the newly proposed Deformable Kernel Fuzzy-C Mean (DKFCM) method for improving the result of segmentation on the edges. This method first limits the searching area for the voxels of the target tissue from the whole data to a small strip around the edges of the target tissue. Then, it applies a very accurate segmentation on the searching area, using a deformable kernel, which is capable of adapting itself with the shape of the edge. Comparing the results of this work with some popular MRI segmentation methods like active contour, watershed, FCM and also some heuristic methods, which are proposed in literature for segmenting the MRI to four tissues, demonstrates the superiority of the proposed method especially on the edges.

In this master thesis I recapitulated several methods for clustering input data. Two well known clustering algorithms, concretely K-means algorithm and Fuzzy C-means (FCM) algorithm, were described in the submitted work. I presented several methods, which could help estimate the optimal number of clusters. Further, I described Kohonen maps and two models of Kohonen's maps with dynamically changing structure, namely Kohonen map with growing grid and the model of growing neural gas. At last I described quite new model of radial basis function neural networks. I presented several learning algorithms for this model of neural networks. In the end of this work I made some clustering experiments with real data. This data describes the international trade among states of the whole world.