Diogo Borges Faria

Automated segmentation of the incus and malleus ossicles in conventional tri-dimensional computed tomography images

This article proposes a fully automated computational solution to segment the incus and malleus ear ossicles in conventional tri-dimensional X-ray computed tomography images. The solution uses a registration-based segmentation paradigm, followed by image segmentation refinement. It was tested against a dataset comprising 21 computed tomography volumetric images of the ear acquired using standard protocols and with resolutions varying from 0.162 × 0.162 × 0.6 to 0.166 × 0.166 × 1.0 mm3. The images used were randomly selected from subjects who had had a computed tomography examination of the ear due to ear-related pathologies. Dice’s coefficient and the Hausdorff distance were used to compare the results of the automated segmentation against those of a manual segmentation performed by two experts. The mean agreement between automated and manual segmentations was equal to 0.956 (Dice’s coefficient), and the mean Hausdorff distance among the shapes obtained was 1.14 mm, which is approximately equal to the maximum distance between the neighbouring voxels in the dataset tested. The results confirm that the automated segmentation of the incus and malleus ossicles in tri-dimensional images acquired from patients with ear-related pathologies, using conventional computed tomography scanners and standard protocols, is feasible, robust and accurate. Thus, the solution developed can be employed efficiently in computed tomography ear examinations to help radiologists and otolaryngologists in the evaluation of bi-dimensional slices by providing the related tri-dimensional model.

Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson’s disease based on [123I]FP-CIT SPECT images

PurposeThis work aimed to assess the potential of a set of features extracted from [123I]FP-CIT SPECT brain images to be used in the computer-aided “in vivo” confirmation of dopaminergic degeneration and therefore to assist clinical decision to diagnose Parkinson’s disease.MethodsSeven features were computed from each brain hemisphere: five standard features related to uptake ratios on the striatum and two features related to the estimated volume and length of the striatal region with normal uptake. The features were tested on a dataset of 652 [123I]FP-CIT SPECT brain images from the Parkinson’s Progression Markers Initiative. The discrimination capacities of each feature individually and groups of features were assessed using three different machine learning techniques: support vector machines (SVM), k-nearest neighbors and logistic regression.ResultsCross-validation results based on SVM have shown that, individually, the features that generated the highest accuracies were the length of the striatal region (96.5%), the putaminal binding potential (95.4%) and the striatal binding potential (93.9%) with no statistically significant differences among them. The highest classification accuracy was obtained using all features simultaneously (accuracy 97.9%, sensitivity 98% and specificity 97.6%). Generally, slightly better results were obtained using the SVM with no statistically significant difference to the other classifiers for most of the features.ConclusionsThe length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration “in vivo” as an aid to the diagnosis of Parkinson’s disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson’s disease.

Analysis of gated myocardial perfusion spect images based on computational image registration

Myocardial perfusion is commonly studied based on the evaluation of the left ventricular function using stress-rest gated myocardial perfusion single photon emission computed tomography (GSPECT), which provides a suitable identification of the myocardial region, facilitating the localization and characterization of perfusion abnormalities. The prevalence and clinical predictors of myocardial ischemia and infarct can be assessed from GSPECT images. Here, techniques of image analysis, namely image segmentation and registration, are integrated to automatically extract a set of features from myocardial perfusion SPECT images that are automatically classified as related to myocardial perfusion disorders or not. The solution implemented can be divided into two main parts: 1) building of a template image, segmentation of the template image and computation of its dimensions; 2) registration of the image under study with the template image previously built, extraction of the image features, statistical analysis and classification. It should be noted that the first step just needs to be performed once for a particular population. Hence, algorithms of image segmentation, registration and classification were used, specifically of k-means clustering, rigid and deformable registration and classification. The computational solution developed was tested using 180 3D images from 48 patients with healthy cardiac condition and 72 3D images from 12 patients with cardiac diseases, which were reconstructed using the filtered back projection algorithm and a low pass Butterworth filter or iterative algorithms. The images were classified into two classes: “abnormality present” and “abnormality not present”. The classification was assessed using five parameters: sensitivity, specificity, precision, accuracy and mean error rate. The results obtained shown that the solution is effective, both for female and male cardiac SPECT images that can have very different structural dimensions. Particularly, the solution demonstrated reasonable robustness against the two major difficulties in SPECT image analysis: image noise and low resolution. Furthermore, the classifier used demonstrated good specificity and accuracy, Table 1.