Blood vessel feature description for detection of Alzheimers disease

We describe how image analysis can be used to detect the presence of Alzheimer’s disease. The data are images of brain tissue collected from subjects with and without Alzheimer’s disease. The analysis concentrates on the shape and structure of the blood vessels which are known to be affected by amyloid beta, whose drainage is affected by Alzheimer’s disease. The structure is analysed by a new approach which measures the influence of the blood vessels’ branching structures. Their density and tortuosity are analysed in conjunction with a boundary description derived using Fourier descriptors. These measures form a feature vector which is derived from the images of brain tissue, and the discrimination capability shows that it is possible to detect the presence of Alzheimer’s disease using these measures and in an automated way. These measures also show that shape information is influenced by the vessels’ branchingstructure, as known to be consistent with Alzheimer’s disease evolution

Texture analysis of MR images of patients with Mild Traumatic Brain Injury

<p>Abstract</p> <p>Background</p> <p>Our objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA).</p> <p>Methods</p> <p>We imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters.</p> <p>Results</p> <p>There were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls.</p> <p>Conclusions</p> <p>TA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis.</p

Non-Hodgkin lymphoma response evaluation with MRI texture classification

<p>Abstract</p> <p>Background</p> <p>To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.</p> <p>Methods</p> <p>A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.</p> <p>Results</p> <p>NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.</p> <p>Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.</p> <p>Conclusion</p> <p>Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.</p

Texture analysis of tumor tissue in lung CT data.

Cílem této práce je odhalení možnosti využití metod texturní analýzy k detekci a segmentaci nádorové tkáně v CT datech plic a klasifikace viabilních oblastí nádorové tkáně. Hlavním předpokladem práce jsou rozdíly texturních vlastností (příznaků) mezi nádorem a okolními tkáněmi a změny těchto příznaků během vývoje a léčby nemoci. Práce obsahuje přehled jednotlivých metod texturní analýzy. Dále se zabývá vytvořením vlastní metody, která je tvořena vybranými metodami texturní analýzy, jejichž výstupy společně vytváří vektor příznaků (pro každý voxel obrazu získáme vektor příznaků). Ten je následně dále zpracováván pomocí metody shlukové analýzy. Obsahem práce je teoretická rešerše dané problematiky, popis návrhu vlastní metody a statistické vyhodnocení výsledků. Návrh metody je zpracován v programovacím prostředí Matlab®.The aim of this work is the revelation of the possibility of the use of texture analysis methods to detection and segmentation tumor tissue in lung CT image data and classification viable areas of tumor tissue. The main assumption of this thesis are differences of textural features between tumor and surrounding tissues and changes of these properties during development and treatment of this disease. The thesis contains overview of texture analysis methods. It deals with the creation of own method which is composed of some methods of texture analysis that create vector of properties (for each voxel in the image we get vector of features). This vector is afterwards processed by methods of cluster analysis. Content of this work is theoretical research of this issue, description of own method and statistical evaluation of the results. The method is processed in programming environment Matlab®.

Clinical Applicability of MRI Texture Analysis

Radiologisten kuvien tulkinta on perinteisesti perustunut asiantuntijan näköhavaintoihin. Tietokoneavusteisten menetelmien käyttö lisääntyy radiologisessa diagnostiikassa. Tekstuuria eli kuviorakennetta on käytetty erottelevana ominaisuutena kudoksia luokiteltaessa ja karakterisoitaessa. Kuvan kuviorakenteen ominaisuuksia kuvaavia tekstuuriparametreja voidaan laskea erilaisilla matemaattisilla ja signaalinkäsittelymenetelmillä. Tekstuurianalyysi on antanut lupaavia tuloksia magneettikuvien tarkastelussa. Sen avulla on voitu määrittää sekä pieniä hajanaisia että suurempia paikallisia muutoksia. Menetelmällä on mahdollista havaita ihmissilmälle näkymättömiä sekä näkyviä muutoksia. Menetelmää tulisi tutkia edelleen, koska kliinisen menetelmän kehittämistä varten tarvitaan lisätietoa sen soveltuvuudesta erilaisille aineistoille sekä analyysimenetelmän eri vaiheiden optimoimisesta. Tämän väitöstutkimuksen tavoite oli selvittää magneettikuvauksen tekstuurianalyysin kliinistä käytettävyyttä eri kannoilta. Tutkimusaineisto koostui kolmesta potilasmateriaalista ja yhdestä terveiden urheilijoiden joukosta sekä heidän verrokeistaan. Aineisto kerättiin osina Tampereen yliopistollisessa sairaalassa toteutettuja laajempia tutkimusprojekteja, ja mukaan otettiin yhteensä 220 osallistujaa. Ensimmäisessä osatyössä tarkasteltiin pehmytkudoskuvantamista, non-Hodgkin-lymfooman hoitovasteen arviointia tekstuurianalyysilla. Kaksi seuraavaa osatyötä käsitteli keskushermoston kuvantamista: lieviä aivovammoja sekä MS-tautia. Viimeisessä osatyössä arvioitiin liikunnan vaikutusta urheilijoiden ja verrokkien reisiluun kaulan luurakenteeseen. Kudosten ja muutosten vertailuissa oli edustettuna sekä ympäröivästä kudoksesta visuaalisella tarkastelulla erottumattomia että selkeästi erottuvia rakenteita. Lisäksi tutkimuksessa selvitettiin mielenkiintoalueen käsityönä tehtävän rajaamisen ja magneettikuvaussekvenssin valinnan vaikutusta analyysiin. Yhteenvetona todetaan, että tekstuurimenetelmällä on mahdollista havaita ja karakterisoida tutkimukseen valikoidun aineiston edustamia etiologialtaan erilaisia muutoksia kliinisistä 1.5 Teslan magneettikuvista. Tutkimuksessa käsitellyt yksityiskohdat MRI-kuvasarjojen valinnasta sekä mielenkiintoalueiden piirtämisestä antavat pohjaa kliinisen protokollan kehittämiseen. Osa tutkimusaineistoista oli kokeellisia, ja niiden tulokset tulisi vahvistaa laajemmilla kliinisillä tutkimuksilla.The usage of computerised methods in radiological image interpretation is becoming more common. Texture analysis has shown promising results as an image analysis method for detecting non-visible and visible lesions, with a number of applications in magnetic resonance imaging (MRI). Although several recent studies have investigated this topic, there remains a need for further analyses incorporating different clinical materials and taking protocol planning for clinical analyses into account. The purpose of this thesis was to determine the clinical applicability of MRI texture analysis from different viewpoints. This study is based on three patient materials and one collection of healthy athletes and their referents. A total of 220 participants in wider on-going study projects at Tampere University Hospital were included in this thesis. The materials include a study on non-Hodgkin lymphoma, representing soft tissue imaging with malignant disease treatment monitoring; and two studies on central nervous system diseases, mild traumatic brain injury and multiple sclerosis. A musculoskeletal imaging study investigated load-associated physiological changes in healthy participants? bones. Furthermore, manual Region of Interest (ROI) definition methods and the selection of MRI sequences for analyses of visible and non-visible lesions were evaluated. In summary, this study showed that non-visible lesions and physiological changes as well as visible focal lesions of different aetiologies could be detected and characterised by texture analysis of routine clinical 1.5 T scans. The details of MRI sequence selection and ROI definition in this study may serve as guidelines for the development of clinical protocols. However, these studies are partly experimental and need to be validated with larger sample sizes

Morphological quantitation software in breast MRI: application to neoadjuvant chemotherapy patients

The work in this thesis examines the use of texture analysis techniques and shape descriptors to analyse MR images of the breast and their application as a potential quantitative tool for prognostic indication.Textural information is undoubtedly very heavily used in a radiologist’s decision making process. However, subtle variations in texture are often missed, thus by quantitatively analysing MR images the textural properties that would otherwise be impossible to discern by simply visually inspecting the image can be obtained. Texture analysis is commonly used in image classification of aerial and satellite photography, studies have also focussed on utilising texture in MRI especially in the brain. Recent research has focussed on other organs such as the breast wherein lesion morphology is known to be an important diagnostic and prognostic indicator. Recent work suggests benefits in assessing lesion texture in dynamic contrast-enhanced (DCE) images, especially with regards to changes during the initial enhancement and subsequent washout phases. The commonest form of analysis is the spatial grey-level dependence matrix method, but there is no direct evidence concerning the most appropriate pixel separation and number of grey levels to utilise in the required co-occurrence matrix calculations. The aim of this work is to systematically assess the efficacy of DCE-MRI based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients. In addition an attempt was made to use shape parameters in order to assess tumour surface irregularity, and as a predictor of response to chemotherapy.In further work this study aimed to texture map DCE MR images of breast patients utilising the co-occurrence method but on a pixel by pixel basis in order to determine threshold values for normal, benign and malignant tissue and ultimately creating functionality within the in house developed software to highlight hotspots outlining areas of interest (possible lesions). Benign and normal data was taken from MRI screening data and malignant data from patients referred with known malignancies.This work has highlighted that textural differences between groups (based on response, nodal status, triple negative and biopsy grade groupings) are apparent and appear to be most evident 1-3 minutes post-contrast administration. Whilst the large number of statistical tests undertaken necessitates a degree of caution in interpreting the results, the fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging.With regards to shape analysis this thesis has highlighted that some differences between groups were seen in shape descriptors but that shape may be limited as a prognostic indicator. Using textural analysis gave a higher proportion of significant differences whilst shape analysis results showed inconsistency across time points.With regards to the mapping this work successfully analysed the texture maps for each case and established lesion detection is possible. The study successfully highlighted hotspots in the breast patients data post texture mapping, and has demonstrated the relationship between sensitivity and false positive rate via hotspot thresholding

Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians