Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

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

Entwicklung der multifrequenten Magnetresonanz-Elastographie zur Quantifizierung der biophysikalischen Eigenschaften von menschlichem Hirngewebe

Magnetic resonance elastography (MRE) is an emerging technique for the quantitative imaging of the biophysical properties of soft tissues in humans. Following its successful clinical application in detecting and characterizing liver fibrosis, the scientific community is investigating the use of viscoelasticity as a biomarker for neurological diseases. Clinical implementation requires a thorough understanding of brain tissue mechanics in conjunction with innovative techniques in new research areas. Therefore, three in vivo studies were conducted to analyze the inherent stiffness dispersion of brain tissue over a wide frequency range, to investigate real-time MRE in monitoring the viscoelastic response of brain tissue during the Valsalva maneuver (VM), and to study mechanical alterations of small lesions in multiple sclerosis (MS). Ultra-low frequency MRE with profile-based wave analysis was developed in 14 healthy subjects to determine large-scale brain stiffness, from pulsation-induced shear waves (1 Hz) to ultra-low frequencies (5 – 10 Hz) to the conventional range (20 – 40 Hz). Furthermore, multifrequency real-time MRE with a frame rate of 5.4 Hz was introduced to analyze stiffness and fluidity changes in response to respiratory challenges and cerebral autoregulation in 17 healthy subjects. 2D and 3D wavenumber-based stiffness reconstruction of the brain was established for conventional MRE in 12 MS patients. MS lesions were analyzed in terms of mechanical contrast with surrounding tissue in relation to white matter (WM) heterogeneity. We found superviscous properties of brain tissue at large scales with a strong stiffness dispersion and a relatively high model-based viscosity of η = 6.6 ± 0.3 Pa∙s. The brain’s viscoelasticity was affected by perfusion changes during VM, which was associated with an increase in brain stiffness of 6.7% ± 4.1% (p<.001), whereas fluidity decreased by -2.1 ± 1.4% (p<.001). In the diseased brain, the analysis of 147 MS lesions revealed 46% of lesions to be softer and 54% of lesions to be stiffer than surrounding tissue. However, due to the heterogeneity of WM stiffness, the results provide no significant evidence for a systematic pattern of mechanical variations in MS. Nevertheless, the results may explain, for the first time, the gap between static ex vivo and dynamic in vivo methods. Fluidity-induced dispersion provides rich information on the structure of tissue compartments. Moreover, viscoelasticity is affected by perfusion during cerebral autoregulation and thus may be sensitive to intracranial pressure modulation. The overall heterogeneity of stiffness obscures changes in MS lesions, and MS may not exhibit sclerosis as a mechanical signature. In summary, this thesis contributes to the field of human brain MRE by presenting new methods developed in studies conducted in new research areas using state-of-the-art technology. The results advance clinical applications and open exciting possibilities for future in vivo studies of human brain tissue.Die Magnetresonanz-Elastographie (MRE) ist ein Verfahren zur quantitativen Darstellung der viskoelastischen Eigenschaften von Weichgewebe. Nach der erfolgreichen klinischen Anwendung in der Leberdiagnostik wird versucht, Viskoelastizität als Biomarker für neurologische Krankheiten zu nutzen. Hierzu bedarf es einer genauen Analyse der Gewebemechanik und innovativen Anwendungsgebieten. Daher, wurden drei Studien durchgeführt, um die Steifigkeitsdispersion von Hirngewebe zu analysieren, das viskoelastische Verhalten während des Valsalva Manövers (VM) abzubilden, und die mechanischen Veränderungen in Läsionen bei Multipler Sklerose (MS) zu untersuchen. Niedrigfrequenz-MRE mit profilbasierter Wellenanalyse wurde in 14 Probanden entwickelt, um die Steifigkeit des Gesamthirns von pulsationsinduzierten Scherwellen (1 Hz) über ultraniedrige Frequenzen (5 – 10 Hz) bis hin zum konventionellen Bereich (20 – 40 Hz) zu bestimmen. Außerdem wurde die multifrequente Echtzeit-MRE mit einer Bildfrequenz von 6.4 Hz eingeführt, um die viskoelastische Antwort des Gehirns auf respiratorische Herausforderungen bei 17 gesunden Probanden zu untersuchen. Neue 2D- und 3D-Wellenzahl-basierte Steifigkeitsrekonstruktionen für das Gehirn wurden in 12 MS Patienten und konventioneller MRE entwickelt. Die Steifigkeitsänderungen in MS-Läsionen wurden mit umliegender weißer Substanz und dessen Heterogenität verglichen. Wir fanden superviskose Eigenschaften des Hirngewebes mit einer starken Dispersion und relativ hohen, modellbasierten Viskosität von η = 6,6 ± 0,3 Pa∙s. Die mechanischen Gewebeeigenschaften wurden durch Perfusionsänderungen während VM beeinflusst und die Hirnsteifigkeit erhöhte sich um 6,7 ± 4,1% (p<.001) wobei sich die Fluidität um -2,1 ± 1,4% (p<.001) verringerte. Die Analyse von 147 MS-Läsionen ergab, dass 54% bzw. 46% der Läsionen steifer bzw. weicher sind als das umgebende Gewebe. Aufgrund der Heterogenität der WM-Steifigkeit konnte jedoch kein Hinweis auf ein systematisches Muster mechanischer Veränderungen in MS-Läsionen gefunden werden. Die Ergebnisse können zum ersten Mal die Lücke zwischen statischen ex vivo und dynamischen in vivo Methoden erklären. Die fluiditätsinduzierte Dispersion liefert interessante Informationen über die zugrundeliegende Gewebestruktur. Darüber hinaus wird die Viskoelastizität durch die Perfusion während der zerebralen Autoregulation beeinflusst und kann daher empfindlich auf intrakranielle Druckschwankungen reagieren. Die allgemeine Heterogenität der Steifigkeit überschattet die Veränderungen in MS-Läsionen, und somit ist Sklerose möglicherweise kein prominentes Merkmal von MS. Zusammenfassend lässt sich festhalten, dass diese Dissertation einen Beitrag zum Gebiet der MRE leistet, indem neue Methoden und Anwendungen in neuen Forschungsgebieten mit modernster Technologie dargestellt werden. Hierdurch wird die klinische Translation gefördert und spannende Möglichkeiten für zukünftige Studien eröffnet

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models