André G. R. Balan

Fractal analysis of image textures for indexing and retrieval by content

This paper proposes the use of fractal analysis as a means to discriminate textured segmented regions of medical images. We show that the use of the fractals can boost the representation level of traditional image features allowing high rates of precision when answering similarity queries over images employing a variance weighted Manhattan distance. The cost to compute the fractal measurements is linear on the image size, what makes their use a suitable choice for large sets of images.

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.

Content-based image retrieval using approximate shape of objects

This paper presents a new approach to retrieve images by content using a composition of relevant features regarding texture, shape and brightness distribution. The first step of the method is a segmentation process based on Markov random fields, which can be done automatically, having as parameter the number of desired classes. The regions obtained in the segmentation guide the extraction of measures from the original image producing a 30-dimensional feature vector used in the image retrieval. The experiments showed that the feature vector has high discrimination power and the time for retrieval operations are only fractions of seconds.

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.

SuGAR: A Framework to Support Mammogram Diagnosis

In this paper we present a framework based on association-rules to help diagnosis of mammogram abnormalities. Our framework - SuGAR - combines low-level features automatically extracted from images with high-level knowledge gotten from specialists to mine association rules, suggesting possible diagnoses. Our framework is optimized, in the sense that it combines, in a single step, feature selection and discretization, reducing the mining complexity. The framework was applied to real datasets and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that association rules can effectively aid in the diagnosing task.

The Visual Expression Process: Bridging Vision and Data Visualization

Visual data analysis follows a sequence of steps derived from perceptual faculties that emanate from the human vision system. Firstly, pre-attentive phenomena determine a map of potential interesting objectives. Then, attentive selection concentrates on one element of a vocabulary of visual perceptions. Lastly, perceptions in working memory combine to long-term domain knowledge to support cognition. Following this process, we present a model that joins vision theory and visual data analysis aiming at settling a comprehension of why graphical presentations expand the human intellect, making us smarter.

Comparing images with distance functions based on attribute interaction

This paper presents two new families of distance functions for image comparison through their feature vectors. These families concern with the effects of the interaction of attributes when two images are compared. Experiments were executed in order to corroborate the effectiveness of the new functions, leading to very promising results.