Deep Learning for Classification of Brain Tumor Histopathological Images

Histopathological image classification has been at the forefront of medical research. We evaluated several deep and non-deep learning models for brain tumor histopathological image classification. The challenges were characterized by an insufficient amount of training data and identical glioma features. We employed transfer learning to tackle these challenges. We also employed some state-of-the-art non-deep learning classifiers on histogram of gradient features extracted from our images, as well as features extracted using CNN activations. Data augmentation was utilized in our study. We obtained an 82% accuracy with DenseNet-201 as our best for the deep learning models and an 83.8% accuracy with ANN for the non-deep learning classifiers. The average of the diagonals of the confusion matrices for each model was calculated as their accuracy. The performance metrics criteria in this study are our model’s precision in classifying each class and their average classification accuracy. Our result emphasizes the significance of deep learning as an invaluable tool for histopathological image studies

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images

Importance of multifractal analysis of magnetic resonance imaging in assessment of responses to chemotherapy in primary bone neoplasms

Osteosarcom je najučestaliji primarni maligni koštani tumor, Ewing sarkom je drugi po učestalosti u populaciji mlađoj od dvadeset godina. Iako se kombinovanom terapijom u njihovom lečenju značajno povećao procenat preživljavanja kod lokalizovanih formi oboljenja i dalje najveći problem predstavlja rezistencija na hemioterapiju primarnih koštanih tumora. Otuda bi što ranija procena terapijskog odgovora bila od neprocenjivog značaja u izboru terapijskog pristupa. Patohistološki grading sistem koji se definiše nakon radikalne hirurške intervencije posle sprovedene indukcione terapije i dalje je „zlatni standard“ u proceni terapijskog odgovora u odnosu na radiološke vizuelizacione tehnike. Uvođenje i primena metoda digitalne obrade slike u pre i postterapijskoj analizi MR snimaka primarnih koštanih tumora, koja se zasniva na matematičkim modelima, mogla bi da ubrza, objektivizuje i olakša svakodnevni dijagnostički postupak. Cilj: Cilj ove studije bila je procena parametara multifraktalne analize snimaka magnetne rezonance osteosarcoma i Ewing sarcoma u predikciji odgovora na terapiju kako inicijalno pre sprovedene hemioterapije, tako i nakon nje, kao i njihove razlike. Takođe i korelacija dobijenih parametara sa patohistološkom dijagnozom stepena tumorske nekroze po Huvos grading sistemu u cilju procene značaja multifraktalne analize u predikciji hemioterapijskog odgovora. Metodologija: Studijom preseka u petogodišnjem periodu (2010-2014) obuhvaćeno je 88 pacijenata sa osteosarkomom i Ewing sarkomom na dugim cevastim kostima kod kojih je bila moguća naknadna precizna obrada MRI snimaka pre i nakon sprovedene indukcione hemioterapije. MR snimci su prevedeni u digitalnu sliku rezolucije 1400 x 1054 pixela sive skale, a naknadnom upotrebom crop tool-a, tj isecanjem delova slike izdvajan je regions of interest (ROIs) u skladu sa granicama svakog pojedinačnog tumora...Osteosarcoma is the most frequent primary malignant bone tumour while Ewing sarcoma is the second one judging by its incidence in the population below twenty years of age. Although the combined therapy in their treatment has significantly increased the percentage of survivals in localized forms of the diseases, the resistance of primary bone tumours to chemotherapy still poses the biggest problem. Hence as early assessment of a therapy response as possible would be of invaluable importance in the selection of the therapy approach. The pathohistological grading system that is defined after a radical surgery intervention after the employed induction therapy is still the ‘golden standard’ in the assessment of therapy response as compared to radiological visualization techniques. Introduction and application of the method of digital image processing in the pre- and post-therapy analysis of MRIs of primary bone tumours, which is based on mathematical models, could speed up, objectify, and facilitate the everyday diagnostic procedure. Objective: The goal of this study was assessment of parameters of multifractal analysis of magnetic resonance imaging of osteosarcoma and Ewing sarcoma in the prediction of responses to therapy both initially prior to employing chemotherapy, and after it, as well as of their differences. Also the correlation between the obtained parameters and the pathohistological diagnosis of the degree of necrosis in tumours according to the Huvos grading system for the purpose of assessment of the importance of the multifractal analysis in the prediction of the chemotherapy response. Methodology: The cross-sectional study in the five year period (2010-2014) covered 88 patients suffering from osteosarcoma and Ewing sarcoma on long tubular bones, in which cases it was possible to subsequently precisely processMRIs prior to and after employing the induction chemotherapy. MRIs were converted into digital images of the resolution of 1400 x 1054 grayscale pixels and, by subsequent use of a crop tool, i.e. by cutting out parts of an image, regions of interest (ROIs) were singled out in line with the borders of each individual tumour..