Artificial Life Models in Lung CTs

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1 Artificial Life Models in Lung CTs Marcello Castellano 1, Roberto Bellotti 2,3, Piergiorgio Cerello 4, Sorin Cristian Cheran 4,5, Gianfranco Gargano 2, Ernesto Lopez Torres 6, Sabina Tangaro 2 1 Politecnico di Bari 2 Istituto Nazionale di Fisica Nucleare, Sezione di Bari Universita degli Studi di Bari, Dipartimento di Fisica gianfranco.gargano@ba.infn.it,tangaro@ba.infn.it 3 Center of Innovative Technologies for Signal Detection and Processing (TIRES), Bari roberto.bellotti@ba.infn.it 4 Istituto Nazionale di Fisica Nucleare, Sezione di Torino cerello@to.infn.it 5 Universita degli Studi di Torino, Dipartimento di Informatica Associatione Sviluppo Piemonte cheran@to.infn.it 6 Ceaden, Havana, Cuba lopez@ceaden.cu Abstract. A new method for the analysis of 3D medical images is introduced. The algorithm is based on Biological Models of ants known as Artificial Life models. Test images (lung Computed Tomographies) undergo a 3D region growing procedure for the identification of the ribs cage. Active Contour Models are used in order to build a confined area where ants are deployed. The antbased approach, in which steps are allowed in any direction with different probability, allows a kind of tunneling effect for the successful identification of small 3D structures that are not clearly connected to the rest of the tree. The best approach is based on a gradient rule for the release of pheromone. A possible application, as component of a Computer Assisted Detection system for the identification of lung nodules, is the removal of the bronchial and vascular tree from lung CTs. 1 Introduction The use of Chest Computer Tomography (CT) dramatically improved the sensibility in the identification of lung cancer as well as the definition of its type, also reducing the number of benign nodules that were misidentified and removed. The output of such an exam is a series of 2D images that the radiologist must investigate and look upon and amounts to about 150 MB if reconstructed at high resolution. With the present paper we discuss a new Computer Assisted Detection (CAD) system that makes use of Artificial Life models and other supporting techniques. Region growing is used to reconstruct the ribs cage, whose boundaries are defined in detail with Active Contour models. Ants are released in this newly created confined volume, with the task of

2 reconstructing the bronchial and the vascular tree. The reconstructed trees are then removed and the modified image becomes the input for the search for nodules (Fig. 1). In section 2 some research work is introduced. The following section describes the Ant System and is followed by some conclusions and the discussion of future developments. Fig. 1: A simplified flow-chart of the algorithm 2 Artificial Life Artificial Life is the study of man-made systems which exhibit behaviors that are characteristic of natural living systems. In nature ants use stigmergic communication (by indirect interactions or by modifying the environment) using trails of pheromone [1]. These trails generally lead from the nest to different food-sources. When ants are trying to find a food source, generally the ant (trail C Figure 2) that finds the shortest path is quicker in reaching the nest and thus its path has most pheromone. Ants evolved the ability to cooperate, dividing their labor, such that all of them solve the tasks for which they are best suited in a robust manner. 2.1 Research work Among the pioneers of the field was Dorigo, that treated the Ant Colony Optimization problem [2] and showed how a group of ants can successfully find a close-tooptimal solution for the Traveling Salesman problem [3], Graph Coloring, Quadratic Assignment Problem [4] or the Job Shop scheduling.

3 The capability of ants to forage for food has been applied also in telecommunication routing: Ruud Schoonderwoerd, with the collaboration of a group of scientists, successfully created the first ant-based system for routing [5]. In 1998, Dorigo et al. proposed another routing algorithm that outperformed all the existing ones and also the standard internet routing protocol: Open Shortest Path First[6]. Fig.2: Communication in ant colonies Another pioneer of Collective Behaviour was James Kennedy that, in 1995, proposed Particle Swarm Optimization (PSO) [7]. More related to the approach we pursue is the work by Chialvo and Millonas [8]: they introduce one of the simplest and most efficient models of trail forming when ants are not moving in a closed boundary neither are suppressed with other behaviour rules. Chialvo and Millonas also compare the trail-leaving technique with the cognitive map patterns from brain science, the difference being that ants leave their trails in the environment while the mammalian cognitive maps lie inside the brain. Based on this paper, Ramos and Almeida [9] developed an extended model where ants are deployed in a digital habitat (image), such that the insects are able to move and perceive it. Their tests showed that ants are able to react to every type of digital habitat, achieving in the end a global perception of the image as the sum of the local perceptions of the entire colony. In [10] Mazouzi and Batouche introduce a Multi-Agent System (MAS) for 3D object recognition. Other work can also be found in [11] where a cognitive MAS for the surveillance of dynamic scenes is discussed. 2.2 Materials The Input of our algorithm are lung CT images, composed of a series of 2D files in DICOM format. The 2D image size is generally 512x512 pixels with a 16-bits depth.

4 The series is reconstructed as a 3D matrix with a voxel size in the 3 rd dimension which depends on the number of slices contained and is typically in the 1-5 mm range. The images for all the tests, provided by the MAGIC-5 Collaboration [12], were taken in the framework of the Regione Toscana (Italy) lung cancer screening program. 3. Ants Ants are to be created and released in a confined 3-D world. Different algorithms could be developed. In the present paper we discuss the wander approach in which ants are randomly released in the habitat. While they wander about according to some well defined rules, they leave behind different quantities of pheromone. In the end a map of pheromone that contains information about the reconstruction of the bronchial and vascular tree is created. Two kinds of ant individuals are present: the queen, that does not move and does not perceive the habitat, creates and manages all the reconstructors, that are aware of the habitat and live in it. A third type, the shaper, that will try to recognize the nodule-like structures, will soon be implemented. Our approach is based on an idea introduced by Ramos and Almeida in [7]. Let s suppose that at time t an ant is in voxel k. At time t+1 the ant is supposed to choose as next destination one of the 26 neighbours of k. The implementation is done as follows: for each neighbour i the probability P ik is calculated: P ik = # j / k W (" i )w(" i ) W (" j )w(" j ) (1) where w(" i ) is the probabilistic directional bias and $ " ' W (") = & 1+ ) % 1+ #" ( * (2) is a function that depends on the pheromone density, " being the osmotropotaxic sensitivity and " the sensory capacity. The directional bias is a probability associated to each neighbouring voxel and takes into account the initial direction of the ant (Fig. 3).

5 Fig.3. Two of the 4 possible cases of directional bias. The lighter the voxel grey scale the higher the probability associated to that voxel. As next destination an ant will choose the voxel using a roulette-wheel algorithm, according to P ik, and will leave behind in voxel k an amount of pheromone T: T = " + p# h (3) where " is a preset constant amount, p is a constant and " h is the function that relates the amount of pheromone to the information provided by the image. The deposited pheromone will evaporate with a standard rate [8] and it will not diffuse to neighbouring voxels. For this paper (if not specified otherwise) " h is defined as the gradient of the voxel intensity: " h = I i # I k (4) We used the gradient rule (difference in intensities between two voxels) in order to maximize the amount of released pheromone on the boundaries of vessel structures and therefore be able to optimize their definition. As a 2D image can be seen as a 3D image with just one slice, the first tests were performed on 2D images. In the examples shown in Fig. 4, " h was defined as described in (4).

6 Fig.4 First Image (A): Original structures extracted from a real CT. Second (B): the pheromone map after 50 cycles. Third (C): the position of ants after 50 cycles. The test of the algorithm is performed using as input data sub-images like that shown in Fig. 4A, which was obtained by individually extracting, from real CTs, structures that are characterized by particular morphological properties: lung fissures, branching points, round structures, branches intersections. The image resolution is 300x300 pixels. The number of ants to be deployed (30000) and the number of cycles were selected according to [8], such that a statistically significant amount of pheromone is released in the image. The choice of the structures shown in Fig. 4A enables us to understand the collective behaviour of ants when presented with different food sources in an environment. Fig. 4B shows the result of the dynamical collective behaviour of the ant colony by means of the pheromone trails. The results show that ants can easily find the objects boundaries and recognize the different objects. The image was obtained after only 50 collective movement of the colony (cycles). Fig. 4C shows the final position of ants: it can be seen that the majority of ants are attracted to the areas where food sources are present. The following testing step was to launch a colony of ants in a full 2D slice of a CT image. As the image is 512x512 pixels we defined the number of ants as The output is presented in Fig. 5: the different structures of the original image (Fig. 5A) can be correctly identified, as in the previous case. Not only the boundaries of the bronchial and vascular trees, but also the ribs, the pleura, the skin, the spinal cord and others are identified. The correct identification of all the former structures is a further test of the capability of the algorithm. Fig.5. Ants in full 2D CT image. Left image (A): Original CT. Right image (B): Pheromone Map for the CT slice. However, our goal is to be able to analyse a full 3D CT with a global approach. 3D

7 trials are still ongoing; we are trying to optimize the model parameters, for example by choosing different " h and optimizing the different rules for the ant movements. In some cases the rules that we selected made ants non-discriminative: with appropriate parameters or rules, the resulting reconstruction can contain the whole lung, as one can see in Fig. 6 (in this case with " h = k * I i ). It is possible to show that the antreconstructed image allows a better noise rejection. However, that approach is by far not optimal from the point of view of the execution time, since ants are pushed towards high-intensity voxels, which cover a large fraction of the 3D image. A gradientbased rule for the pheromone release, on the other hand, would generate a behaviour in which attraction points are just the boundaries of structures, corresponding to a much smaller fraction of the image and therefore allowing a statistically significant amount of pheromone in a smaller number of cycles. Fig. 6. Left: Zelous ants (reconstructing too much) So, in order to optimize the parameters for the 3D search of boundaries, the identification of small 3D structures of known shape was carried out using the gradient rule for " h, as shown in Fig. 7. Fig. 7. Artificial Structures. Branching point after 1 and 50 cycles, artificial sphere after 1 and 50 cycles In Fig. 8 the result a test on a 3D CT branch are shown: at the beginning (A) the distribution is almost flat and reproduces the random positions in which ants are released; after 50 cycles, the most relevant structures are identified, but the smaller ones are still missing; after 100 cycles the reconstruction is pretty satisfactory. The speed of the convergence process is not constant: once ants start to get attracted by the boundaries and therefore increase the amount of pheromone in those voxels, the directional bias makes them move mostly along the boundaries themselves.

8 Fig.8. Ants at work. Pheromone maps of a 3D lung branch obtained with a gradient rule after 1 cycle (A), 50 cycles (B), 100 cycles (C). 4 Conclusions Artificial life models (virtual ants) stand at the core of our algorithm. Among our goals is the study and understanding of the collective behaviour of ants in 3D environments (still unknown) and its use to extract useful information about the environment itself. From the application point of view, our interest is mostly in the use of ants for the analysis of biomedical 3D images, such as lung Computed Tomographies. The analysis of sub-samples of 3D CTs allowed the definition of the best rules for the deposition of pheromone (gradient rule) and for the ant movements (probabilistic directional bias). 3D structures (vessels of bronchi) can be identified and the convergence process accelerates with the number of cycles, taking advantage of the feedback effect generated by the coupling of the above-mentioned rules. Our future work will focus on the evaluation of the efficiency of our method on a full 3D CT, with respect to other approaches (e.g., 3D region growing). Also, in view of its use as a component of a lung CAD algorithm, a careful evaluation of the sensibility and specificity on a database of 3D lung CTs is mandatory. Acknowledgment This work was possible by our participation in the MAGIC-5 Collaboration, which we wish to thank. Reference 1. Grasse P: Termitologia, Tome II Fondation des Socie te s. Construction. Paris; Masson, Colorni A, Dorigo M, Maniezzo V : Distributed Optimization by ant colonies Proc.

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