Applying texture-spectrum features for lung-CAD purposes

In this paper we propose using Texture Spectrum (T-Spec) as the feature extractor method component for Computer-Aided Diagnosis systems, particularly regarding medical lung images. We claim that employing T-Spec for this purpose offers several advantages to represent texture at low computational cost. Moreover, when combined with literature approaches, T-Spec can increase the capability of representing texture for several domains. We tested our approach using two distinct datasets, by classifying their instances considering the T-Spec features along with the RandomForest classifier. Our results showed that T-Spec has achieved good accuracy and requires less computational time and resources. When combined with literature methods, T-Spec has achieved a higher accuracy rate for lung images considering previous approaches.FAPESPCNPqCAPE

Evaluation of Objective Similarity Measures for Selecting Similar Images of Mammographic Lesions

The purpose of this study was to investigate four objective similarity measures as an image retrieval tool for selecting lesions similar to unknown lesions on mammograms. Measures A and B were based on the Euclidean distance in feature space and the psychophysical similarity measure, respectively. Measure C was the sequential combination of B and A, whereas measure D was the sequential combination of A and B. In this study, we selected 100 lesions each for masses and clustered microcalcifications randomly from our database, and we selected five pairs of lesions from 4,950 pairs based on all combinations of the 100 lesions by use of each measure. In two observer studies for 20 mass pairs and 20 calcification pairs, six radiologists compared all combinations of 20 pairs by using a two-alternative forced-choice method to determine the subjective similarity ranking score which was obtained from the frequency with which a pair was considered as more similar than the other 19 pairs. In both mass and calcification pairs, pairs selected by use of measure D had the highest mean value of the average subjective similarity ranking scores. The difference between measures D and A (P = 0.008 and 0.024), as well as that between measures D and B (P = 0.018 and 0.028) were statistically significant for masses and microcalcifications, respectively. The sequential combination of the objective similarity measure based on the Euclidean distance and the psychophysical similarity measure would be useful in the selection of images similar to those of unknown lesions