International Journal of Computational Intelligence and Applications

Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately.

Tumor segmentation from brain magnetic resonance image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting brain tumors in magnetic resonance images using Cellular Neural Networks (CNN). Learning CNN templates values are formulated as an optimization problem. The template coefficients (weights) of an CNN which will give a desired performance, can be derived by learning genetic algorithm and simulated annealing optimization techniques. The objective of this work is to compare the performance of genetic algorithm (GA) and simulated annealing (SA) for finding the optimum template values in the CNN which is used for segmenting the tumor region in the abnormal MR images. The method is applied on real data of MRI images of thirty patients with four different types of tumors. The results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of segmentation efficiency. From the analysis and performance measures like segmentation accuracy, it is inferred that the brain tumor segmentation is best done using CNN with genetic algorithm template optimization than CNN with simulated annealing template optimization. An average accuracy rate of above 95% was obtained using this segmentation algorithm.

To improve the understanding of the GA in dynamic environments we explore a set of test problems, the shaky ladder hyper-defined functions (sl-hdf), and extend these functions to create versions that are equivalent to many classical dynamic problems. We do this by constraining the space of all sl-hdfs to create representations of these classical functions. We have examined three classical problems, and compared sl-hdf versions of these problems with their standard representations. These results show that the sl-hdfs are representative of a larger class of problems, and can represent a larger class of test suite. Previous results on sl-hdf showed that GA performance is best in the Defined Cliffs variant of the sl-hdf. We build upon these results to improve GA performance in several classes of real world dynamic problems by modifying the problem representation. These results lend insight into dynamic problems where the GA will perform well.

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules are represented as binary string. Modified forms of crossover and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence of GA and accuracy of the classifier. The performance of the proposed approach is evaluated through development of fuzzy classifier for seven standard data sets. From the simulation study it is found that the proposed algorithm produces a fuzzy classifier with minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.

The extraction of information from tabular data is not a natural task for human beings, which is worse when dealing with high dimensional systems. On the other hand, graphical representations make the understanding easier by exploring the human capacity of processing visual information. Such representations can be used for many purposes, e.g., complex systems structuring which contributes to a better understanding of it. This paper constructs a cause-effect map relating the influence of each input process variable on the steam generated by a boiler. The real case study is based on the operations of a chemical recovery boiler of a Kraft pulp mill in Brazil. The map is obtained by two steps, namely the identification of a neural predictive model for the steam and a study of sensitivity analysis. The numerical results are then depicted in a graphical format using a cause-effect map. This representation highlights the relative importance of the predictor variables to the steam generation. The results, in agreement with the literature, show the higher contribution of the heat released during the fuel burning, and the lower influence of both the fuel temperature and the operating variables associated with the primary level of injection of the combustion air.