Rodrigo de Luis-García

A multidimensional segmentation evaluation for medical image data

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

Texture and color segmentation based on the combined use of the structure tensor and the image components

In this paper, we propose a novel segmentation scheme for textured gray-level and color images based on the combined use of the local structure tensor and the original image components. The structure tensor is a well-established tool for image segmentation and has been successfully employed for unsupervised segmentation of textured gray-level and color images. The original image components can also provide very useful information. Therefore, a combined segmentation approach has been designed that combines both elements within a common energy minimization framework. Besides, an original method is proposed to dynamically adapt the relative weight of these two pieces of information. Quantitative experimental results on a large number of gray-level and color images show the improved performance of the proposed approach, in comparison to several related approaches in recent studies. Experiments have also been carried out on real world images in order to validate the proposed method.

Tensor processing for texture and colour segmentation

In this paper, we propose an original approach for texture and colour segmentation based on the tensor processing of the nonlinear structure tensor. While the tensor structure is a well established tool for image segmentation, its advantages were only partly used because of the vector processing of that information. In this work, we use more appropriate definitions of tensor distance grounded in concepts from information theory and compare their performance on a large number of images. We clearly show that the traditional Frobenius norm-based tensor distance is not the most appropriate one. Symmetrized KL divergence and Riemannian distance intrinsic to the manifold of the symmetric positive definite matrices are tested and compared. Adding to that, the extended structure tensor and the compact structure tensor are two new concepts that we present to incorporate gray or colour information without losing the tensor properties. The performance and the superiority of the Riemannian based approach over some recent studies are demonstrated on a large number of gray-level and colour data sets as well as real images.

Gaussian Mixtures on Tensor Fields for Segmentation: Applications to Medical Imaging

In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results.

Attention deficit/hyperactivity disorder and medication with stimulants in young children: A DTI study

The relationship between attention deficit/hyperactivity disorder (ADHD) and white matter connectivity has not been well established yet, specially for children under 10 years of age. In addition, the effects of treatment on brain structure have not been sufficiently explored from a Diffusion Tensor Imaging (DTI) perspective. In this study, the influence of treatment with methylphenidate in the white matter of children with ADHD was investigated using two different and complementary DTI analysis methods: Tract-Based Spatial Statistics (TBSS) and a robust tractography selection method. No significant differences were found in Fractional Anisotropy (FA) between medicated, drug-naïve patients and healthy controls, but a reduced Mean Diffusivity (MD) was found in ADHD patients under treatment with respect to both healthy controls and drug-naïve ADHD patients. Also, correlations were found between MD increases and performance indicators of ADHD. These findings may help elucidate the nature of white matter alterations in ADHD, their relationship with symptoms and the effects of treatment with psychostimulants.

Impact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach

Acquisition parameters play a crucial role in Diffusion Tensor Imaging (DTI), as they have a major impact on the values of scalar measures such as Fractional Anisotropy (FA) or Mean Diffusivity (MD) that are usually the focus of clinical studies based on white matter analysis. This paper presents an analysis on the impact of the variation of several acquisition parameters on these scalar measures with a novel double focus. First, a tractography-based approach is employed, motivated by the significant number of clinical studies that are carried out using this technique. Second, the consequences of simultaneous changes in multiple parameters are analyzed: number of gradient directions, b-value and voxel resolution. Results indicate that the FA is most affected by changes in the number of gradients and voxel resolution, while MD is specially influenced by variations in the b-value. Even if the choice of a tractography algorithm has an effect on the numerical values of the final scalar measures, the evolution of these measures when acquisition parameters are modified is parallel.

Alterations in prefrontal connectivity in schizophrenia assessed using diffusion magnetic resonance imaging

Background: Spatial and biological characteristics of structural frontal disconnectivity in schizophrenia remain incompletely understood. Simultaneous streamline count (SC) and fractional anisotropy (FA) analyses may yield relevant complementary information to this end. Methods: Using 3T diffusion magnetic resonance imaging both SC and FA were calculated for the tracts linking lateral and medial subregions of prefrontal cortex (PFC) to cingulate, hippocampus, caudate and thalamus in 27 schizophrenia patients (14 first‐episodes) and 27 controls. Relationships of these parameters with cognition, symptoms, treatment doses and illness duration were assessed where significant between‐groups differences were detected. Results: Patients showed lower SC and FA in the tracts linking lateral and medial PFC to thalamus (likely corresponding to anterior thalamic peduncle) and lower FA in those linking PFC to caudate (likely through internal capsule), right caudal anterior cingulate and left hippocampus (likely corresponding to hippocampal‐prefrontal pathway). Moreover, patients showed greater SC values for the tracts linking medial PFC and left caudal anterior cingulate. SC and FA values for the tracts linking PFC and caudal anterior cingulate were positively related to motor speed, executive function, problem solving and completed categories in WCST. FA for the tract linking right lateral PFC and caudate was directly related to positive symptoms and FA for the tract linking left medial PFC and left thalamus was inversely related to negative symptoms. Treatment doses were not associated with SC or FA values in any tract. Illness duration was negatively associated with SC and FA in the tracts linking PFC and subcortical areas. Conclusions: Widespread alterations in frontal structural connectivity of PFC can be found in schizophrenia, and are related to cognition, symptoms and illness duration. HighlightsWe assessed fractional anisotropy and streamline counts for the tracts linking prefrontal cortex with relevant regions.Lower fractional anisotropy were found in schizophrenia patients in most of these anatomical connections.Streamline counts were lower in the patients for the right prefrontal cortico‐thalamic tract.These alterations were related with cognition and symptoms.FA and SC were inversely associated to illness duration, but not to treatment doses.

Mixtures of Gaussians on tensor fields for DT-MRI segmentation

In this paper, an original approach for the segmentation of tensor fields is proposed. Based on the modeling of the data by means of Gaussian mixtures directly in the tensor domain, this technique presents a wide range of applications in medical image processing, particularly for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). The performance of the segmentation method proposed is shown through the segmentation of the corpus callosum from a dataset of 32 DT-MRI volumes. Comparison with a recent and related segmentation approach is favorable to our method, showing its capability for the automatic extraction of anatomical structures in the white matter.

Geometrical constraints for robust tractography selection.

Tract-based analysis from DTI has become a widely employed procedure to study the white matter of the brain and its alterations in neurological and neurosurgical pathologies. Automatic tractography selection methods, where a subset of detected tracts corresponding to a specific white matter structure are selected, are a key component of the DTI processing pipeline. Using automatic tractography selection, repeatable results free of intra and inter-expert variability can be obtained rapidly, without the need for cumbersome manual segmentation. Many of the current approaches for automatic tractography selection rely on a previous registration procedure using an atlas; hence, these methods are likely very sensitive to the accuracy of the registration. In this paper we show that the performance of the registration step is critical to the overall result. This effect can in turn affect the calculation of scalar parameters derived subsequently from the selected tracts and often used in clinical practice; we show that such errors may be comparable in magnitude to the subtle differences found in clinical studies to differentiate between healthy and pathological. As an alternative, we propose a tractography selection method based on the use of geometrical constraints specific for each fiber bundle. Our experimental results show that the approach proposed performs with increased robustness and accuracy with respect to other approaches in the literature, particularly in the presence of imperfect registration.

Segmentation of Tensor Fields: Recent Advances and Perspectives

The segmentation of tensor-valued images or 3D volumes is a relatively recent issue in image processing, but a significant effort has been made in the last years. Most of this effort has been focused on the segmentation of anatomical structures from DT-MRI (Diffusion Tensor Magnetic Resonance Imaging), and some contributions have also been made for the segmentation of 2D textured images using the Local Structure Tensor (LST). In this chapter, we carefully review the state of the art in the segmentation of tensor fields. We will discuss the main approaches that have been proposed in the literature, with particular emphasis on the importance of the different tensor dissimilarity measures. Also, we will highlight the key limitations of the segmentation techniques proposed so far, and will provide some insight on the directions of current research.