Visualization of Density Variation in Lung

We propose a method for visualize lung nodule, in order to emphasize their internal structures. This provides an additional tool for diagnosis, which is traditionally based on the analysis of shape, growth and location. The method is divided into three stages: the first stage is semi-automatic segmentation, using the regiongrowing algorithm; the second stage, based on the previous segmentation, identifies the internal structures of the nodule using moment theory to determine several thresholds; finally, in the third stage, to help visualizing these structures, a pseudocolor is associated to each threshold determined in the previous stage. The tool developed is under tests, undergoing clinical evaluation, and its initial results are promising

Algorithms for Assisted Diagnosis of Solitary Lung Nodules in Computerized Tomography Images

The contents of this report are the sole responsibility of the authors. O conteúdo do presente relatório é de única responsabilidade dos autores

Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

Abstract Background Age-related macular degeneration (AMD) is a degenerative ocular disease that develops by the formation of drusen in the macula region leading to blindness. This condition can be detected automatically by automated image processing techniques applied in spectral domain optical coherence tomography (SD-OCT) volumes. The most common approach is the individualized analysis of each slice (B-Scan) of the SD-OCT volumes. However, it ends up losing the correlation between pixels of neighboring slices. The retina representation by topographic maps reveals the similarity of these structures with geographic relief maps, which can be represented by geostatistical descriptors. In this paper, we present a methodology based on geostatistical functions for the automatic diagnosis of AMD in SD-OCT. Methods The proposed methodology is based on the construction of a topographic map of the macular region. Over the topographic map, we compute geostatistical features using semivariogram and semimadogram functions as texture descriptors. The extracted descriptors are then used as input for a Support Vector Machine classifier. Results For training of the classifier and tests, a database composed of 384 OCT exams (269 volumes of eyes exhibiting AMD and 115 control volumes) with layers segmented and validated by specialists were used. The best classification model, validated with cross-validation k-fold, achieved an accuracy of 95.2% and an AUROC of 0.989. Conclusion The presented methodology exclusively uses geostatistical descriptors for the diagnosis of AMD in SD-OCT images of the macular region. The results are promising and the methodology is competitive considering previous results published in literature

Correction to: Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine

After publication, it was highlighted that the original publication [1] contained a spelling mistake in the first name of Marcelo Gattas. This was incorrectly captured as Marelo Gattass in the original article which has since been updated