Marcela Xavier Ribeiro

Improving the ranking quality of medical image retrieval using a genetic feature selection method

In this paper, we take advantage of single-valued functions that evaluate rankings to develop a family of feature selection methods based on the genetic algorithm approach, tailored to improve the accuracy of content-based image retrieval systems. Experiments on three image datasets, comprising images of breast and lung nodules, showed that developing functions to evaluate the ranking quality allows improving retrieval performance. This approach produces significantly better results than those of other fitness function approaches, such as the traditional wrapper and than filter feature selection algorithms.

An Association Rule-Based Method to Support Medical Image Diagnosis With Efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Mining Statistical Association Rules to Select the Most Relevant Medical Image Features

In this chapter we discuss how to take advantage of association rule mining to promote feature selection from low-level image features. Feature selection can significantly improve the precision of content-based queries in image databases by removing noisy and redundant features. A new algorithm named StARMiner is presented. StARMiner aims at finding association rules relating low-level image features to high-level knowledge about the images. Such rules are employed to select the most relevant features. We present a case study in order to highlight how the proposed algorithm performs in different situations, regarding its ability to select the most relevant features that properly distinguish the images. We compare the StARMiner algorithm with other well-known feature selection algorithms, showing that StARMiner reaches higher precision rates. The results obtained corroborate the assumption that association rule mining can effectively support dimensionality reduction in image databases.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems.

How to Improve Medical Image Diagnosis through Association Rules: The IDEA Method

In this paper we present a new method, called IDEA, which employs association rules to assist in medical image diagnosis. IDEA mines association rules, relating visual features with the knowledge gotten from specialists, and employs the associations to suggest possible diagnoses for a given medical image. IDEA incorporates two new algorithms called Omega and ACE. Omega performs simultaneously feature selection and data discretization very efficiently with linear cost on the number of feature values. ACE is a new associative classifier, which has the particular ability of suggesting multiple keywords to compose the diagnosis for a given medical image. The IDEA method has an important characteristic that makes it different from other CAD methods: it suggests multiple diagnosis hypotheses for an image and ranks them based on a measure of quality. The IDEA method was implemented in a prototype (IDEA system) for radiologists evaluate it. The radiologists showed enormous interest in employing the system to aid them in their daily work. The IDEA system was applied to real datasets and the results presented high accuracy (up to 96.7%). The results testify that association rules are well-suited to support the diagnosing task.

Effective shape-based retrieval and classification of mammograms

This paper presents a new approach to support Computer-aided Diagnosis (CAD) aiming at assisting the task of classification and similarity retrieval of mammographic mass lesions, based on shape content. We have tested classical algorithms for automatic segmentation of this kind of image, but usually they are not precise enough to generate accurate contours to allow lesion classification based on shape analyses. Thus, in this work, we have used Zernike moments for invariant pattern recognition within regions of interest (ROIs), without previous segmentation of images. A new data mining algorithm that generates statistical-based association rules is used to identify representative features that discriminate the disease classes of images. In order to minimize the computational effort, an algorithm based on fractal theory is applied to reduce the dimension of feature vectors. K-nearest neighbor retrieval was applied to a database containing images excerpted from previously classified digitalized mammograms presenting breast lesions. The results reveal that our approach allows fast and effective feature extraction and is robust and suitable for analyzing this kind of image.

Statistical Association Rules and Relevance Feedback: Powerful Allies to Improve the Retrieval of Medical Images

This work aims at developing an efficient support to improve the precision of medical image retrieval by content, introducing an approach that combines techniques of statistical association rule mining and relevance feedback. Low level features of shape and texture are extracted from images. Statistical association rules are used to select the most relevant features to discriminate the images, reducing the size of the feature vectors and eliminating noisy features that influence negatively the query results, making the whole process more efficient. Additionally, our approach uses a new relevance feedback technique to overcome the semantic gap that exists between low level features and the high level user interpretation of images. Experiments show that the combination of statistical association rule mining and the relevance feedback technique proposed here improve the precision of the query results up to 100%

A new algorithm for data discretization and feature selection

Data discretization and feature selection are two important tasks that can be performed prior to the learning phase of data mining algorithms and can significantly reduce the processing effort of the learning algorithm. In this paper, we present a new algorithm, called Omega, for data preprocessing. Our proposed algorithm performs simultaneously data discretization and feature selection. Some experiments were performed to validate the effects of the preprocessing performed by the Omega algorithm in the results of the C4.5 algorithm (a well-known decision tree-based classifier). The results indicates that the proposed algorithm Omega is well-suited to both, data discretization and feature selection, being appropriate for data pre-processing.

HEAD: The Human Encephalon Automatic Delimiter

In this paper we present HEAD, the Human Encephalon Automatic Delimiter, a new and efficient method for skull-stripping in T1-weighted MRI that combines an unique histogram analysis with binary mathematical morphology. In our experiments we use real images with highly variable noise ratios and intensity non-uniformity. We evaluate our results based on manually generated true masks and the well known Jaccard metric, achieving accuracy close to 99%. We compare our method with the popular Brain Extractor Surface algorithm (BSE), which in the same experiments achieved less than 95% of accuracy.

Feature Extraction and Selection for Decision Making

This chapter presents and discusses useful algorithms and techniques of feature extraction and selection as well as the relationship between the image features, their discretization and distance functions to maximize the image representativeness when executing similarity queries to improve medical image processing, mining, indexing and retrieval. In particular, we discuss the Omega algorithm combining both, feature selection and discretization, as well as the technique of association rule mining. In addition, we present the Image Diagnosis Enhancement through Associations (IDEA) framework as an example of a system developed to be part of a computer-aided diagnosis environment, which validates the approaches discussed here.