Anca AndreicaBabeş-Bolyai University | UBB · Department of Computer Science
Anca Andreica
PhD
About
78
Publications
9,322
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
341
Citations
Citations since 2017
Publications
Publications (78)
Introduction:
Bladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a...
The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and malignant). The performance of the system is evaluated...
Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define th...
Purpose
Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cance...
SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical...
Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of...
The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining the region of interest (ROI) and segmenting the result imag...
Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that...
Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D,...
Complex Networks theory represents a powerful tool to model real-world systems as graphs with non-trivial topological features. Static by their definition, complex networks are limited to be the reflection or the snapshot of the dynamical systems they encode in a given moment. Frankly, studies show that the network preserves the characteristics of...
Extensive research has been performed in image processing to find the best edge detector, from the gradient-based operators to evolved Cellular Automata (CA). Some of these detectors have weak points, such as disconnected edges, the incapacity of detecting the branching edges or the need of a ground truth that is not always available. To overcome t...
The exploitation of the important features exhibited by the complex systems found in the surrounding natural and artificial space will improve computational model performance. Therefore, the purpose of the current paper is to use cellular automata as a tool simulating complexity, able to bring forth an interesting global behaviour based only on sim...
We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The...
Edge detection is an important component in many computer vision tasks since edges convey information about the objects in an image. This paper presents a comparative analysis of the proposed edge detector with respect to one of the state-of-the-art methods, the Canny edge detector. Our edge detection model involves the supervised optimization of a...
One significant and complex image processing task that may be used as a step in various complex processes is the edge detection. The mechanism of detecting the edges is called edge detector. The scope of this paper is to propose an edge detector based on evolved Cellular Automata (CA), for binary images. It was proved in many cases that CA may be s...
One important image processing task is the edge detection in intensity images. Some approaches were proposed based on computing the gradient of the signal, but not many approaches were proposed based on Cellular Automata (CA). In this paper, an edge detection method based on CA evolved by the means of a Genetic Algorithm (GA) for grayscale images i...
Properties of complex networks represent a powerful set of tools that can be used to study the complex behaviour of these systems of interconnections. They can vary from properties represented as simplistic metrics (number of edges and nodes) to properties that reflect complex information of the connection between entities part of the network (asso...
Cellular Automata (CA) can be successfully applied in various image processing tasks because they have a number of advantages over the traditional methods of computations: simplicity of implementation, the complexity of behaviour, parallelisation, extensibility, scalability, robustness. In this paper, an edge detection method for binary images, bas...
Complex networks are data structures with great importance in representing real world interactions which surrounds us. While their structures might look chaotic at a first glance, the focus of most on-going studies in this field is in understanding how their topological properties influence the dynamics of a complex network’s structure in order to...
This paper presents an edge detection method based
on Cellular Automata where the rules are evolved to optimize the
edge detection in binary images. This method divides the edge
detection problem into two sub–problems: on the one hand it
trains the rules to detect the edge pixels, on the other hand it
trains the rules to detect non–edge (background...
Cellular Automata have been considered for a series of ap-plications among which several image processing tasks. The goal of this paper is to investigate such existing methods, supporting the broader goal of identifying Cellular Automata rules able to automatically segment images. With the same broader goal in mind as future work, a detailed descri...
The majority of Cellular Automata (CA) described in the literature are binary or three-state. While several abstractions are possible to generalise to more than three states, only a negligible number of multi-state CA rules exist with concrete practical applications. This paper proposes a generic rule for multi-state CA. The rule allows for any num...
This paper presents the first results obtained by exploring different neighborhoods in two-dimensional Cellular Automata applied for the difficult task of automatic image segmentation. Numerical experiments have been performed on several real-world and synthetic images for which the ground truth is known, being therefore able to compute the algorit...
Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a diagnosis. We therefore...
Discovering Cellular Automata (CAs) rules able to generate a desired global behaviour is a highly challenging problem due
to the local nature of rules combined with the expected global effect. This article investigates the evolution and dynamics
of small-world networks for the density classification task in CAs. Both unweighted and node-weighted ne...
Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existing individuals in order to generate new potentially better fit offspring leading to more promising areas of the search space. In this...
Modelled as finite homogeneous Markov chains, probabilistic cellular automata with local transition probabilities in (0, 1) always posses a stationary distribution. This result alone is not very helpful when it comes to predicting the final configuration; one needs also a formula connecting the probabilities in the stationary distribution to some i...
Abstract Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchr...
The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community stru...
The density classification problem aims to find automata able to correctly classify the density of the initial configuration. This problem is highly challenging as the desired computation requires global coordination while Cellular Automata (CAs) rules rely on the local interaction of simple components. Instead of using the standard CA topology of...
Cellular Automata are important tools in the study of complex interactions and analysis of emergent behaviour. They have the ability to generate highly complex behaviour starting from a simple initial configuration and set of update rules. Finding rules that exhibit a high degree of self-organization is a challenging task of major importance in the...
Resource-Constrained Project Scheduling is an NP-hard problem very attractive for researchers due to its large area of applications. This paper concentrates on the evolutionary approaches to Resource-Constrained Project Scheduling based on permutation encoded individuals. A new recombination operator is proposed and a comparative analysis of severa...
Cellular automata are discrete dynamical systems having the ability to generate highly complex behaviour starting from a simple initial configuration and set of update rules. The discovery of rules exhibiting a high degree of global self-organization is of major importance in the study and understanding of complex systems. This task is not easily a...
The discovery and analysis of communities in networks is a topic of high interest in sociology, biology and computer science. Complex networks in nature and society range from the immune system and the brain to social, communication and transport networks. The key issue in the development of algorithms able to automatically detect communities in co...
Cellular Automata (CAs) represent useful and important tools in the study of complex systems and interactions. The problem of finding CA rules able to generate a desired global behavior is considered of great importance and highly challenging. Evolutionary computing offers promising models for addressing this inverse problem of global to local mapp...
The Travelling Salesman Problem (TSP) is one of the most widely studied optimization problems due to its many applications in domains such as logistics, planning, routing, and scheduling. Approximation algorithms to address this NP-hard problem include genetic algorithms, ant colony systems, and simulated annealing. This chapter concentrates on the...
A fast compression based technique is proposed, capable of detecting promising emergent space-time patterns of cellular automata (CA). This information can be used to automatically guide the evolutionary search toward more complex, better performing rules. Results are presented for the most widely studied CA computation problem, the Density Classif...
The discovery of community structures in complex networks is a challenging problem intensively studied in recent years. This paper investigates the performance of evolutionary algorithms for the task of detecting overlapping communities. This task is of great importance as the membership of a node to more than one group is naturally occuring in man...
The Travelling Salesman Problem (TSP) is one of the most widely studied optimization problems due to its many applications
in domains such as logistics, planning, routing and scheduling. Approximation algorithms to address this NP-hard problem include
genetic algorithms, ant colony systems and simulated annealing. This paper concentrates on the evo...
A collaborative evolutionary model is proposed to address the community structure detection problem in complex networks. The
discovery of commmunities or organization of nodes in clusters (with dense intra-connections and comparatively sparse inter-cluster
connections) is a hard problem of great importance in sociology, biology and computer science...
The problem of detecting community structures in social networks is a complex problem of great importance in sociology, biology and computer science. Communities are characterized by dense intra-connections and comparatively sparse inter-cluster connections. The community detection problem is empirically formulated from a game theoretic point of vi...
Complex systems and their important principles of emergence, auto-organization and adaptability are being intensively studied by re-searchers in a variety of fields including physics, biology, computer science, sociology and economics. The aim of this paper is to highlight potential interesting research directions lying at the intersection of natur...
An evolutionary algorithm based on the collaboration between individuals is successfully applied for identifying community structure and overlapping communities in complex networks. The main feature of the algorithm is the use of collaborative selection and recombination, where individuals exchange information in order to accelerate the search proc...
![Figure 2. The pattern growth in snowflakes modeled with 2D CA (from [96])](profile/Anca-Andreica/publication/50366281/figure/fig2/AS:305688816439313@1449893301964/The-pattern-growth-in-snowflakes-modeled-with-2D-CA-from-96_Q320.jpg)
![Figure 3. Space-time diagram of Rule 110 [91]](profile/Anca-Andreica/publication/50366281/figure/fig3/AS:305688816439314@1449893301992/Space-time-diagram-of-Rule-110-91_Q320.jpg)
Complex systems consist of a large number of interconnected and mutually interacting components. The real world is full of examples of complex adaptive systems from ancient and modern cultures, biological and social systems to economies and ecosystems. The study of complex systems is crucial for a constructive assessment and understanding of essent...
Efficient recombination and selection strategies in evolutionary search models have a great impact on the quality of detected solutions. Evolving populations of adaptive individuals can potentiallly trigger important results for the design of evolutionary models. The Geometric Collaborative Evolutionary (GCE) model takes this approach by integratin...
A circular evolutionary model is proposed to produce Cellular Automata (CA) rules for the computationally emergent task of density classification. The task refers to determining the initial density
most present in the initial cellular state of a one-dimensional cellular automaton within a number of update steps. This is
a challenging problem extens...
A Geometric Collaborative Evolutionary (GCE) model is presented and studied. An asynchronous search process is facilitated through a gradual propagation of the fittest individuals' genetic material into the population. Recombination is guided by the geometrical structure of the population. The GCE model specifies three strategies for recombination...
Object database fragmentation (horizontal fragmentation) deals with splitting the extension of classes into subsets according to some criteria. The resulting fragments are then used either in distributed database processing or in parallel data processing in order to spread the computation power over multiple nodes or to increase data locality featu...
The paper explores connections between population topology and individual interactions inducing autonomy, communication and
learning. A Collaborative Asynchronous Multi-Population Evolutionary (CAME) model is proposed. Each individual in the population acts as an autonomous agent with the goal of optimizing its fitness
being able to communicate and...
Evolutionary algorithms require efficient recombination and selection mechanisms in order to produce high-quality solutions. In order to guide recombination a geometrical structure of the population is introduced. The aim of this paper is to explore connections between population geometry and individual interactions inducing autonomy, communication...
The selection of mates in an evolutionary algorithm can significantly influence the exploration and the exploitation abilities of the search process. Currently there are several strategies to guide the mate selection or to restrict the mating pool. The aim of this paper is to analyze the behavior of a fitness guided mate selection strategy and of a...
Spatial distribution of individuals in evolutionary search combined with agent-based interactions within a population formed
of multiple societies can induce new powerful models for complex optimization problems. The proposed search model relies on
the distribution of individuals in a spatial environment, the collaboration and coevolution of indivi...
The problem of evolving network topologies for celular au-tomata has been approached by means of circular evolutionary algorithms. This application is based on Watts proposal to consider small-world topolo-gies for CAs. He has shown that small-world networks could give a better performance for problems like the density task, compared to the perfor-...
The distributed collaborative evolutionary model analyzed in this paper is characterized by structuring the population using a fitness guided topology and by assigning the individuals to three societies characterized by different mating strategies. The membership to societies for the offspring is decided in a probabilistic manner using a dominance...
Scientific researchers from computer science, communication and as well from sociology and epidemiology reveal a strong interest
in the study of networks. One important feature studied in complex network is the community structure. A new evolutionary
technique for community detection in complex networks is proposed in this paper. The new algorithm...
One of the most important decisions that influence the performance of evolutionary algorithms is the way individuals are selected for recombination. Two new selection operators that explore more promising regions of the search space are proposed in order to avoid the search becoming trapped into a local optimum. The first operator is a variant of t...
A new evolutionary algorithm for problems having potential solutions encoded as permutations is proposed. The introduced algorithm is based on the collaboration between individuals that exchange information in order to accelerate the search process. Numerical experiments prove the efficiency of the proposed technique.
Optimization problems whose solutions are encoded with discrete variables, such as Combinatorial Optimization Problems, have a practical as well as a theoretical importance. Evolutionary Computation provides good approximate methods for solving problems belonging to this class. A new recombination operator for permutation based encoding is proposed...
Modern companies dynamically change their departmental structure, type of activities and staff. Database management systems of such companies require adequate design and administration solutions. In such a system the initial estimations and predictions for performance characteristics are mandatory but not sufficient. The performance problems of dat...
A parallel search technique for improving evolutionary algorithms is proposed. The method is based on a new philosophy of applying search operators. Two search operators compete for being applied. One is a hybrid operator (recombination plus mutation) and the other is pure mutation operator. The aim of the proposed technique is to maintain a good e...
A new search model for evolutionary algorithms is proposed. This model is based on the simultaneously run of two dierent search opera- tors. It is a good way to attend equilibrium between the exploration and the exploitation of the search space. By means of a basic evolutionary algorithm, it is proved that the proposed method improves the solution...
The execution process of the queries in distributed databases require accurate estimations and predictions for performance characteristics. The problems of data allocation and query optimization done by means of mobile agents and evolutionary algorithms are considered. These problems still present a challenge because of the dynamic changes in numbe...
Genetic Chromodynamics is a strategy for preventing prema-ture convergence and detecting multiple optimal solutions. A new technique of applying genetic operators is proposed. The Parallel Mutation Based Ge-netic Chromodynamics (PMGC) improves the local search from the standard approach and combines it with the global search, by using an appropriat...
Genetic Algorithms are general purpose optimization/search techniques relying on a biological metaphor. They usually detect a unique optimum, but many real world prob-lems need multiple optima. Genetic Chromodynamics is a strategy for preventing premature convergence and detecting multiple optimal solutions. A new technique of applying genetic op-e...
R-Tree is a multi-dimensional index structure for spatial databases. Existing methods for splitting a node of the tree when its capacity is overflowed, has a lot to do with the efficiency of this structure. A generalization of the node splitting by using evolutionary computation is proposed. The special case of spatial objects naturally grouped by...
Cellular Automata are discrete dynamical systems having the ability to generate highly complex behavior starting from a simple
initial configuration and set of update rules. However, the discovery of rules exhibiting a high degree of global self-organization
for certain tasks is not easily achieved. In this paper, a fast compression based technique...
Within spatial databases it is important to have a multi- dimensional index structure. This structure has to be a dynamic one that does not require to be reorganized after a certain number of operations on the database. R-Tree represents such a structure. Existing methods for splitting a node of the tree when its capacity is over∞owed, has a lot to...
A new selection scheme for evolutionary algorithms is proposed. The introduced selection operator is based on the collaboration between in-dividuals that exchange information in order to accelerate the search process. The NP-hard Travelling Salesman Problem is considered for testing the pro-posed approach. Numerical experiments prove the efficiency...
Projects
Projects (3)
Interdisciplinary conference
Scholars, both junior and senior, from domains such as philosophy, linguistics, psychology, neuroscience, anthropology, and artificial intelligence/robotics are invited to present original theoretical and empirical research papers that discuss any aspect of Embodied Cognition, including but not limited to the following topics:
Philosophical basis of embodied cognition (e.g., phenomenology, pragmatism, etc.);
Post-cognitivist approaches in psychology (e.g., embodied cognition, situated cognition; distributed cognition, extended cognition, embedded cognition);
Phenomenology of embodiment;
Embodied language acquisition and processing;
Body and language/speaking in the psychoanalytic tradition;
The embodiment of abstract thinking;
Enactivist approaches in psychology, philosophy and other fields;
Skills, abilities and dispositions;
Performance studies in arts, sports and other areas;
Embodied learning;
Embodied emotions;
Artificial intelligence and embodiment (machine learning, robotics);
Multimodal approaches in linguistics and anthropology;
Gestures and language;
Dynamic systems approach;
Complex systems, collective intelligence;
Epistemology and methodology of research in embodied cognition;
Neurocentrism in the age of embodied cognition;
Other
The projects focuses on knowledge transfer from academics towards a non-banking financial partner, aiming to improve the credit default metrics and customer retention.
We use visual analytics and predictive analytics tools in order to build credit scoring, risk management and customer attrition models.
Computer-aided medical image analysis is used to reconstruct anatomical features from imaging modalities such as tomography (CT) scans. These are useful for diagnosis, monitoring growth of tumours, planning surgery or assessing the effects of radiotherapy. Medical image segmentation techniques are still far from being able to identify features such as tumours or even simple organs in scans. It is in this process that we propose to make use of Cellular Automata (CA) where, through appropriate choice of evolution rules and topologies, we can identify cells which belong together. The main objective of the project is to build an innovative suite of techniques for unsupervised medical image segmentation using CA.