Parkinson disease (PD) is a common neurodegenerative pathology, whose accurate diagnosis is still a challenge. PET imaging obtained with [18F]-fluorodeoxyglucose provides a metabolic pattern, highlighting the brain substructures related to PD, thus constituting a valuable diagnosis tool. Besides, it has been reported that incorporating MRI into the analysis enhances the performance of methods aiming to discriminate between healthy subjects and PD patients. In this research, a methodology is proposed that allows: to integrate structural and metabolic imaging information at specific substructures of interest; to spatially align both modalities; to normalize functional images and to extract the adequate biomarkers. Among structural parameters, compacity and tortuosity are proposed, while metabolic biomarkers are extracted from histogram analyses. The random forest algorithm is used for classification and feature selection tasks. The studied populations consisted of nine patients with PD diagnosis and 12 healthy controls. Structural biomarkers showed a small contribution to discriminate between groups, while metabolic biomarkers resulted in 85% (training) to 100% (final test) accuracies. The proposed methodology is promising to diagnose PD and can be extended to other movement disorders.
Ultrasound (US) images are necessary in obstetrics because they provide the most important clinical parameters for fetal health assessment during the second and third trimesters: head circumference, biparietal diameter, abdominal circumference and femur length. These fetometric indices are helpful for gestational age and fetal weight estimation; they are also helpful for obstetricians to diagnose fetal development abnormalities. However, these indices are obtained manually, which provokes high intra and interobserver variability and lack of repeatability. A fully automatic method to segment and measure femur’s length is presented in this paper. The proposed methodology incorporates texture information and introduces a novel curvature analysis to adequately detect the femur. It consists on pre–processing US images with an anisotropic diffusion filter, followed by morphological operations and thresholding to isolate femur–candidate regions. A normalized metric composed of intensity, length, centroid position and entropy is assigned to each region in order to select the most probable candidate to be femur. This selected region is afterwards thinned to a one–pixel line, whose curvature is analyzed with an angle threshold criterion to accurately locate femur’s extrema. The method was tested on 64 US images (20 taken on the second and 44 on the third trimester of pregnancy); a correlation coefficient of 0.984 and an error of 1.016±2.764 mm were achieved between expert–obtained manual measures and automatically calculated indices. Results are consistent, outperform those reported previously by other authors and show a high correlation with measures obtained by experts; therefore, the developed method is suitable to be adapted for clinical use.
Infective endocarditis (IE) is a difficult-to-diagnose pathology, since its manifestation in patients is highly variable. In this work, it was proposed a semiautomatic algorithm based on SPECT images digital processing for the detection of IE using a CT images volume as a spatial reference. The heart/lung rate was calculated using the SPECT images information. There were no statistically significant differences between the heart/lung rates values of a group of patients diagnosed with IE (2.62±0.47) and a group of healthy or control subjects (2.84±0.68). However, it is necessary to increase the study sample of both the individuals diagnosed with IE and the control group subjects, as well as to improve the images quality.
The quality of the information contents in echocardiographic images is often reduced by the presence of dropout, speckle, movement artifact, and far field attenuation, although ultrasound is suitable to assessing the dynamic aspect of heart. The aim of this work is to find a set of texture features that optimally characterize the cardiac chambers from echocardiographic images and to use the to segment the image. In this work, seventy-seven texture characteristics from echographic and borders map images were extracted. An optimal subset of them was selected by an automatic process based on a separateness criterion, classification rate criterion and sequential forward algorithm. As a result the optimal set of texture characteristics found was: {Echo, Homogeneity of the co-ocurrence matrix at 90°, Central moment 22 of original image and Central moment 22 of borders map}. The classification rate reached was of 76.4%.
The anatomical and functional cardiac cavities information obtained by Ultrasound images allows a qualitative and quantitative analysis to determine patient's health and detect possible pathologies. Several approaches have been proposed for semiautomatic or fully automatic segmentation. Texture based presegmentation combined with an active contour model have proven to be a promising way to extract cardiac structures from echographic images. In this work a novel procedure for 3D cardiac image segmentation is introduced. A robust pre-processing step that reduces noise and extracts an initial frontier of cardiac structures is combined with an Active Surface Model to obtain final 3D segmentation. Preprocessing is performed by the Mean Shift algorithm that integrates 3D edge confidence map and includes entropy, echoes intensity and spatial information as input features. This procedure locates adequately homogeneous regions in 3D echocardiographic images. The external energy terms included in the Active Surface Model are the 3D edge confidence map and the entropy component obtained by the Mean Shift pre-segmentation. The results demonstrate that the pre-processing provides homogeneous regions and a good initial frontier between blood and myocardium. The Active Surface Model adjusts the initial surface computed by the mean-shift algorithm to the cardiac border. Finally, the obtained results are compared with the experts' manual segmentation and the Tanimoto index between these segmentations is calculated.
A segmentation procedure using a radial basis function network (RBFN), coupled with an active contour (AC) model based on a cubic splines formulation is presented for the detection of the gray-white matter boundary in axial MMRI (T1, T2 and PD). A RBFN classifier has been previously introduced for MMRI segmentation, with good generalization at a rate of 10% misclassification over white and gray matter pixels on the validation set. The coupled RBFN and AC model system incorporates the posterior probability estimation map into the AC energy term as a restriction force. The RBFN output is also employed to provide an initial contour for the AC. Furthermore, an adaptation strategy for the network weights, guided by a feedback from the contour model adjustment at each iteration, is described. In order to compare the algorithm's performance, the segmentations using the adaptive, as well as the non-adaptive schemes were computed. It was observed that the major differences are located around deep circonvolutions, where the result of the adaptive process is superior than that obtained with the non-adaptive scheme, even in moderate noise conditions. In summary, the RBFN provides a good initial contour for the AC, the coupling of both processes keeps the final contour within the desired region and the adaptive strategy enhances the contour location.
Spatial quantification of relevant brain structures, is usually carried out through the analysis of a stack of magnetic resonance (MR) images by means of some image segmentation approach. In this paper, multispectral MR imaging segmentation based on a modified radial-basis function network is presented. Multispectral MR image sets are constructed by collecting data for the same anatomical structures under T1, T2 and FLAIR excitation sequences. Classification features for the network are extended beyond the normalized intensities in each band to also include the cylindrical coordinates of the image pixels. Such coordinates are determined within a reference image space upon which all targets are registered to. The network classifier was designed to differentiate three structures: gray matter, white matter and image background. The classification layer was also modified to accommodate the pixel cylindrical coordinates as inputs. With the designed network, background pixels are correctly classified for all cases, while gray and white matter pixels are misclassified for about 10% of the cases in the validation set. The source of these errors can be traced to smooth transitions in the output nodes for these two classes. Thresholding the outputs of these nodes to include a reject class reduces the misclassification error. The small and simple architecture of the network shows good generalization, and thus good segmentation over unseen stacks.
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