A Statistical Texture Model of the Liver Based on Generalized N-Dimensional Principal Component Analysis (GND-PCA) and 3D Shape Normalization

We present a method based on generalized N-dimensional principal component analysis (GND-PCA) and a 3D shape normalization technique for statistical texture modeling of the liver. The 3D shape normalization technique is used for normalizing liver shapes in order to remove the liver shape variability and capture pure texture variations. The GND-PCA is used to overcome overfitting problems when the training samples are too much fewer than the dimension of the data. The preliminary results of leave-one-out experiments show that the statistical texture model of the liver built by our method can represent an untrained liver volume well, even though the mode is trained by fewer samples. We also demonstrate its potential application to classification of normal and abnormal (with tumors) livers

Evaluation of the potentials for optical coherence tomography (OCT) to detect early signs of retinal neurodegeneration

Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA). Prior to the application of SVM and PCA for the classification of human OCT images, evaluation of the classifiers was performed in the classification of optical phantoms, the accuracy of which was in the range of 82-100%. This was the first attempt to measure the textural properties of various polystyrene and silica beads optical phantoms. To identify optical changes that characterise early apoptosis, OCT imaging of axotomised retinal ganglion cells (RGCs) in ex vivo retinal murine explants was performed. Substantial optical alterations in RGC dendrites in the early stages of apoptosis (up to 2 hours) were detected. ML algorithms correctly classified the retinal texture of the inner plexiform layer (IPL) of transgenic AD mice in all cases, indicating the potential for further investigation in in vivo animal and human studies. Not only the optical signature but also the transparency of the dissected murine retinal explants was investigated. Moreover, ML classification of 3xTg mice IPL layer was studied in terms of optical changes due to the RGD dendritic atrophy. ML classifiers’ accuracy in the detection of early and neovascular AMD was 93-100% for the texture of retinal pigment epithelium, 69-67% for the outer nuclear layer, 70% for the inner segment and 60-90% for the outer segment of photoreceptors. Classification of AMD stages and comparison with the age-matched healthy controls was carried out in the outer retina and RPE. Grey-level co-occurrence, run-length matrices, local binary patterns features were extracted from the IPL of the macula to classify glaucoma OCT images. The accuracy of linear and non-linear SVMs, linear and quadratic discriminant analyses, decision tree and logistic regression was between 55-70%. Based on the classifiers’ precision, recall and F1-score, Gaussian SVM outperformed other ML techniques. In this study, the observation of early glaucomatous subtle optical changes of human IPL was conducted. Also, the significance of various supervised ML algorithms was investigated. Understanding the optical signature of cumulative inherent speckle of OCT scans arising from apoptotic retinal ganglion cells and photoreceptors may provide vital information for the prevention of retinal neurodegeneration