Geraldo L. B. Ramalho

On the evaluation of texture and color features for nondestructive corrosion detection

We present a methodology for automatic corrosion detection in digital images of carbon steel storage tanks and pipelines from a petroleum refinery. The database consists of optical digital images taken from equipments exposed to marine atmosphere during their operational life. This new approach focuses on color and texture descriptors to accomplish corroded and noncorroded surface area discrimination. The performance of the proposed corrosion descriptors is evaluated by using Fisher linear discriminant analysis (FLDA). This approach presents two main advantages: No refinery stoppages are required and potential-related catastrophes can be prevented.

Using Boosting to Improve Oil Spill Detection in SAR Images

Marine surveillance system which uses synthetic aperture radar (SAR) images to oil spill detection must minimize false alarms in order to improve its reliability. This paper presents an application that uses boosting method to minimize misclassification and yields better generalization. Different feature sets were applied to neural network classifiers and its performance compared do boosting methods. The experiments reached substantial improvement in the classification accuracy to discriminate oil spots from the look-alike ones

An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images

An unsupervised method to detect blood vessels in fundus images is proposed.The algorithm effectively tackles image distortions such as central vessel reflex.The two expert vessel identification images present significant differences.The average observer plays an important role in defining a neutral standard.Balanced accuracy is an alternative for performance evaluation of segmentation. Algorithms for retinal vessel segmentation are powerful tools in automatic tracking systems for early detection of ophthalmological and cardiovascular diseases, and for biometric identification. In order to create more robust and reliable systems, the algorithms need to be accurately evaluated to certify their ability to emulate specific human expertise. The main contribution of this paper is an unsupervised method to detect blood vessels in fundus images using a coarse-to-fine approach. Our methodology combines Gaussian smoothing, a morphological top-hat operator, and vessel contrast enhancement for background homogenization and noise reduction. Here, statistics of spatial dependency and probability are used to coarsely approximate the vessel map with an adaptive local thresholding scheme. The coarse segmentation is then refined through curvature analysis and morphological reconstruction to reduce pixel mislabeling and better estimate the retinal vessel tree. The method was evaluated in terms of its sensitivity, specificity and balanced accuracy. Extensive experiments have been conducted on DRIVE and STARE public retinal images databases. Comparisons with state-of-the-art methods revealed that our method outperformed most recent methods in terms of sensitivity and balanced accuracy with an average of 0.7819 and 0.8702, respectively. Also, the proposed method outperformed state-of-the-art methods when evaluating only pathological images that is a more challenging task. The method achieved for this set of images an average of 0.7842 and 0.8662 for sensitivity and balanced accuracy, respectively. Visual inspection also revealed that the proposed approach effectively addressed main image distortions by reducing mislabeling of central vessel reflex regions and false-positive detection of pathological patterns. These improvements indicate the ability of the method to accurately approximate the vessel tree with reduced visual interference of pathological patterns and vessel-like structures. Therefore, our method has the potential for supporting expert systems in screening, diagnosis and treatment of ophthalmological diseases, and furthermore for personal recognition based on retinal profile matching.

Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

The World Health Organization estimated that around 300 million people have asthma, and 210 million people are affected by Chronic Obstructive Pulmonary Disease (COPD). Also, it is estimated that the number of deaths from COPD increased

Combining features to improve oil spill classification in SAR images

30\%

Development of a single-phase anti-island analysis platform according to NBR-IEC 62116 standard

30% in 2015 and COPD will become the third major cause of death worldwide by 2030. These statistics about lung diseases get worse when one considers fibrosis, calcifications and other diseases. For the public health system, the early and accurate diagnosis of any pulmonary disease is mandatory for effective treatments and prevention of further deaths. In this sense, this work consists in using information from lung images to identify and classify lung diseases. Two steps are required to achieve these goals: automatically extraction of representative image features of the lungs and recognition of the possible disease using a computational classifier. As to the first step, this work proposes an approach that combines Spatial Interdependence Matrix (SIM) and Visual Information Fidelity (VIF). Concerning the second step, we propose to employ a Gaussian-based distance to be used together with the optimum-path forest (OPF) classifier to classify the lungs under study as normal or with fibrosis, or even affected by COPD. Moreover, to confirm the robustness of OPF in this classification problem, we also considered Support Vector Machines and a Multilayer Perceptron Neural Network for comparison purposes. Overall, the results confirmed the good performance of the OPF configured with the Gaussian distance when applied to SIM- and VIF-based features. The performance scores achieved by the OPF classifier were as follows: average accuracy of

Lung disease detection using feature extraction and extreme learning machine

98.2\%