Identifying Lesions in Paediatric Epilepsy using Morphometric and Textural Analysis of Magnetic Resonance Images
Sancgeetha Kulaseharan, Azad Aminpour, Mehran Ebrahimi, and Elysa Widjaja
Focal Cortical Dysplasia (FCD) is a brain malformation that is frequently responsible for epilepsy in children and accounts for approximately 26% of intractable epilepsy in children (Wyllie et al., 1998). FCD is one of the main causes of Epilepsy in adults and children. FCD is one of the most challenging groups of lesions to detect on conventional MRI as the imaging features may be subtle and not infrequently missed. In studies conducted with adult cohorts, FCD detection was improved with computational techniques. In this research we developed a computer-aided diagnosis system to classify and detect FCD lesions in MRI images, applied to developing brains. We implemented a modified version of the 2-Step Bayesian classifier method to a pediatric cohort with medically intractable epilepsy with MRI-positive and MRI-negative epilepsy, and evaluated the performance of the algorithm trained on textural features derived from T1-weighted (T1-w), T2-weighted (T2-w), and FLAIR (Fluid Attenuated Inversion Recovery) sequences.
These Research was conducted on an Ubuntu operating system using R (v3.4.1), MATLAB (R2017a) in combination with SPASM (Statistical Parametric Mapping), and FreeSurfer software packages. FreeSurfer v5.3.0 (Dale, Fischl, & Sereno, 1999; Dale & Sereno, 1993) (Fischl & Dale, 2000) (Fischl, Sereno, & Dale, 1999) (Fischl et al., 2004) was employed for pre-processing T1-w images for all cases and controls in order to perform cortical reconstruction using Surface Based Morphometry (SBM), produce anatomical labeling and structural details. FreeSurfer can directly process T1-w acquisitions, as opposed to T2-w and FLAIR. In order to incorporate T2-w and FLAIR sequences into the analysis, the SPM package was used with MATLAB and FreeSurfer output for T2-w and FLAIR acquisitions to prepare the images for analysis.
Naive Bayes classification is a machine learning technique based on Bayes’ Theorem, and assumes that the value of a particular feature is independent of the value of any other features, given the class variable. The algorithm is called “naive” because it makes a naive assumption that each feature is independent of other features. Although it is a relatively simple idea, Naive Bayes can often outperform other more sophisticated algorithms and is extremely useful in common image classification applications. The algorithm allows us to predict a class, given a set of features using probabilities. By the class conditional independence assumption, all predictor variables contribute independently to class labels and any dependence between input variables is disregarded; this process has the advantage of being less computationally expensive.
This implementation performs a 2 Step Bayesian classification for Focal Cortical Dysplasia (FCD) Detection in Magnetic Resonance Imaging (MRI). It uses a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences.
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A text file* containing feature vectors for training instances
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A text file* containing feature vectors for test instances
*Data file headers: 'case','struct','class','V1','V2','V3','ASM','CONTRAST','ENTROPY','CORRELTN','HOMOG','VARIANCE','SUMMN','INERTIA','CLUSTSHADE','CLUSTTEND','MAXPROB','INVVAR','CORTICAL','GRADMAG'
- An Excel sheet including corresponding predicted class label for each instance.
A relating paper is available at: https://www.sciencedirect.com/science/article/pii/S2213158219300130