The effectiveness of brain magnetic resonance imaging (MRI) as a useful evaluation tool strongly depends on the performed segmentation of associated tissues or anatomical structures. We introduce an enhanced brain segmentation approach of Bayesian label fusion that includes the construction of adaptive target-specific probabilistic priors using atlases ranked by kernel-based similarity metrics to deal with the anatomical variability of collected MRI data. In particular, the developed segmentation approach appraises patch-based voxel representation to enhance the voxel embedding in spaces with increased tissue discrimination, as well as the construction of a neighborhood-dependent model that addresses the label assignment of each region with a different patch complexity. To measure the similarity between the target and training atlases, we propose a tensor-based kernel metric that also includes the training labeling set. We evaluate the proposed approach, adaptive Bayesian label fusion using kernel-based similarity metrics, in the specific case of hippocampus segmentation of five benchmark MRI collections, including ADNI dataset, resulting in an increased performance (assessed through the Dice index) as compared to other recent works.
Heartbeat characterization is an important issue in cardiac assistance diagnosis systems. In particular, wide sets of features are commonly used in long term electrocardiographic signals. Then, if such a feature space does not represent properly the arrhythmias to be grouped, classification or clustering process may fail. In this work a suitable feature set for different heartbeat types is studied, involving morphology, representation and time-frequency features. To determine what kind of features generate better clusters, feature selection procedure is used and assessed by means clustering validity measures. Then the feature subset is shown to produce fine clustering that yields into high sensitivity and specificity values for a broad range of heartbeat types.
Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.
Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection.
It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those
flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest
(ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology
for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform
heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in
In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is
robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian
scale analysis and local edge detection. In this methodology local correlation between image and Gaussian
window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well
modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using
multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size.
Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide
a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the
other dedicate algorithms proposed in the state of art.
Image quality assessment is indispensable for image-based applications. The approaches towards image quality
assessment fall into two main categories: subjective and objective methods. Subjective assessment has been
widely used. However, careful subjective assessments are experimentally difficult and lengthy, and the results
obtained may vary depending on the test conditions. On the other hand, objective image quality assessment would
not only alleviate the difficulties described above but would also help to expand the application field. Therefore,
several works have been developed for quantifying the distortion presented on a image achieving goodness of fit
between subjective and objective scores up to 92%. Nevertheless, current methodologies are designed assuming
that the nature of the distortion is known. Generally, this is a limiting assumption for practical applications, since
in a majority of cases the distortions in the image are unknown. Therefore, we believe that the current methods of
image quality assessment should be adapted in order to identify and quantify the distortion of images at the same
time. That combination can improve processes such as enhancement, restoration, compression, transmission,
among others. We present an approach based on the power of the experimental design and the joint localization
of the Gabor filters for studying the influence of the spatial/frequencies on image quality assessment. Therefore,
we achieve a correct identification and quantification of the distortion affecting images. This method provides
accurate scores and differentiability between distortions.
Conference Committee Involvement (1)
Tenth International Symposium on Medical Information Processing and Analysis