Computer-aided diagnosis (CAD) tools using MR images have been largely developed for disease burden quantification, patient diagnosis and follow-up. Newer CAD tools, based on machine learning techniques, often require large and heterogeneous data-sets to provide accurate and generalizable results. Commonly multi-center MR imaging data-sets are used. Typically, collection of these data-sets require adherence to an appropriate experimental protocol in order to assure that findings are due to a pathology and not due to variability in image quality or acquisition parameters across scanners and/or imaging centers. We compared different experimental training protocols used with a representative CAD tool (in this work, designed to identify Alzheimer’s disease (AD) patients from normal control (NC) subjects) using public multi-center data-sets. We examined: 1) subsets of the data-set that were acquired on the same scanner (simulating a single site homogeneous data-set), 2) a traditional cross validation framework (i.e., randomly splitting the data-set into training and testing sets irrespective of centre), and 3) a site-wise cross validation framework, in which training and testing data were differentiated by center using a leave one center out per iteration method. Results achieved with the homogeneous data-set, traditional cross-validation and site-wise cross validation differed (p = 0.0005): 100.0% (i.e., no misclassifications), 99.6% and 97.3% accuracy rates, respectively, even when the same image data-set, features and classifier were used. The lowest accuracy was observed with site-wise cross validation, the only protocol with no site-wise contamination between training and testing samples.
The Corpus Callosum (CC) is the largest white matter structure in the brain and subject of many relevant studies. In order to properly analyze this structure in 2D studies, the midsagittal plane (MSP) determination of the CC is required. Usually, this computation is done on structural MR images and transformed to diffusion space when necessary. Furthermore, most existing methods take into account the whole brain structure instead of only the object of study. Differently, our work proposes a plane computation based on the structure of interest, directly on Diffusion Tensor Images (DTI), through the DTI-based divergence map.1 Since our plane is computed in the diffusion domain, the method explores the high organization of the fibers in the CC to establish a reference system that can be used to perform 2D CC studies, while most existing MSP computation algorithms are based on structural characteristics of the brain, such as shape symmetry and inter-hemispheric fissure location. Experiments showed that the proposed method is reliable regarding repeatability and parameters choices. Results also indicate that the callosal fiber convergence plane (CFCP) found by our method is similar to MSP in most subjects. Nevertheless, when the CC is not well aligned with the brain intercommissural fissure, CFCP and MSP presented significant differences.
Lesions in the brain white matter are among the most frequently observed incidental findings on MR images. This paper presents a 3D texture-based classification to distinguish normal appearing white matter from white matter containing lesions, and compares it with the 2D approach. Texture analysis were based on 55 texture attributes extracted from gray-level histogram, gray-level co-occurrence matrix, run-length matrix and gradient. The results show that the 3D approach achieves an accuracy rate of 99.28%, against 97.41% of the 2D approach by using a support vector machine classifier. Furthermore, the most discriminating texture attributes on both 2D and 3D cases were obtained from the image histogram and co-occurrence matrix.
Medical imaging research depends basically on the availability of large image collections, image processing and analysis algorithms, hardware and a multidisciplinary research team. It has to be reproducible, free of errors, fast, accessible through a large variety of devices spread around research centers and conducted simultaneously by a multidisciplinary team. Therefore, we propose a collaborative research environment, named Adessowiki, where tools and datasets are integrated and readily available in the Internet through a web browser. Moreover, processing history and all intermediate results are stored and displayed in automatic generated web pages for each object in the research project or clinical study. It requires no installation or configuration from the client side and offers centralized tools and specialized hardware resources, since processing takes place in the cloud.
Brain white matter lesions found upon magnetic resonance imaging are often observed in psychiatric or neurological patients. Individuals with these lesions present a more significant cognitive impairment when compared with individuals without them. We propose a computerized method to distinguish tissue containing white matter lesions of different etiologies (e.g., demyelinating or ischemic) using texture-based classifiers. Texture attributes were extracted from manually selected regions of interest and used to train and test supervised classifiers. Experiments were conducted to evaluate texture attribute discrimination and classifiers’ performances. The most discriminating texture attributes were obtained from the gray-level histogram and from the co-occurrence matrix. The best classifier was the support vector machine, which achieved an accuracy of 87.9% in distinguishing lesions with different etiologies and an accuracy of 99.29% in distinguishing normal white matter from white matter lesions.
The brain white matter is responsible for the transmission of electrical signals through the central nervous system.
Lesions in the brain white matter, called white matter hyperintensity (WMH), can cause a significant functional deficit.
WMH are commonly seen in normal aging, but also in a number of neurological and psychiatric disorders. We propose
here an automatic method for WHM analysis in order to distinguish regions of interest between normal and non-normal
white matter (identification task) and also to distinguish different types of lesions based on their etiology: demyelinating
or ischemic (classification task). The method combines texture analysis with the use of classifiers, such as Support
Vector Machine (SVM), Nearst Neighboor (1NN), Linear Discriminant Analysis (LDA) and Optimum Path Forest
(OPF). Experiments with real brain MRI data showed that the proposed method is suitable to identify and classify the