Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Advancing combined radiological and optical scanning for breast-conserving surgery margin guidance

Breast cancer is one of the most common types of cancer worldwide, and standard-of-care for early-stage disease typically involves a lumpectomy or breast-conserving surgery (BCS). BCS involves the local resection of cancerous tissue, while sparring as much healthy tissue as possible. State-of-the-art methods for intraoperatively evaluating BCS margins are limited. Approximately 20% of BCS cases result in a tissue resection with cancer at or near the resection surface (i.e., a positive margin). A two-fold increase in ipsilateral breast cancer recurrence is associated with the presence of one or more positive margins. Consequently, positive margins often necessitate costly re-excision procedures to achieve a curative outcome. X-ray micro-computed tomography (CT) is emerging as a powerful ex vivo specimen imaging technology, as it provides robust three-dimensional sensing of tumor morphology rapidly. However, X-ray attenuation lacks contrast between soft tissues that are important for surgical decision making during BCS. Optical structured light imaging, including spatial frequency domain imaging and active line scan imaging, can act as adjuvant tools to complement micro-CT, providing wide field-of-view, non-contact sensing of relevant breast tissue subtypes on resection margins that cannot be differentiated by micro-CT alone. This thesis is dedicated to multimodal imaging of BCS tissues to ultimately improve intraoperative BCS margin assessment, reducing the number of positive margins after initial surgeries and thereby reducing the need for costly follow-up procedures. Volumetric sensing of micro-CT is combined with surface-weighted, sub-diffuse optical reflectance derived from high spatial frequency structured light imaging. Sub-diffuse reflectance plays the key role of providing enhanced contrast to a suite of normal, abnormal benign, and malignant breast tissue subtypes. This finding is corroborated through clinical studies imaging BCS specimen slices post-operatively and is further investigated through an observational clinical trial focused on combined, intraoperative micro-CT and optical imaging of whole, freshly resected BCS tumors. The central thesis of this work is that combining volumetric X-ray imaging and sub-diffuse optical scanning provides a synergistic multimodal imaging solution to margin assessment, one that can be readily implemented or retrofitted in X-ray specimen imaging systems and that could meaningfully improve surgical guidance during initial BCS procedures

Design, development and use of a deformable breast phantom to assess the relationship between thickness and lesion visibility in full field digital mammography

Aim of research:This research aimed to design and develop a synthetic anthropomorphic breast phantom with cancer mimicking lesions and use this phantom to assess the relationship between lesion visibility and breast thickness in mammography. Due to the risk of cancer induction associated with the use of ionising radiation on breast tissues, experiments on human breast tissue was not practical. Therefore, a synthetic anthropomorphic breast phantom with cancer mimicking lesions was needed to be designed and developed in order to provide a safe platform to evaluate the relationship between lesion visibility and breast thickness in mammography. Method: As part of this research custom Polyvinyl alcohol (PVAL) breast phantoms with embedded PVAL lesions doped with contrast agent were fabricated and utilised. These breast phantoms exhibited mechanical and X-ray properties which were similar to female breast/breast cancer tissues. In order for this research to be useful for human studies, patient safety factors have constrained the extent of this research. These factors include compression force and radiation dose. After acquiring mammograms of phantoms with varying thicknesses, the image quality of the embedded lesions were evaluated both perceptually and mathematically.The two-alternative forced choice (2AFC) perceptual method was used to evaluate image quality of the lesions. For mathematical evaluation the following methods were utilised: line profile analysis, contrast-to noise ratio (CNR), signal-to noise ratio (SNR) and figure of merit (FOM).Results: The results of the visual perception analysis of the mammograms demonstrate that as breast compressed thickness reduces the image quality increases. Additionally, the results display a correlation in the reduction in the level of noise with the reduction in breast thickness. This noise reduction was also demonstrated in the profile plots of the lesions. The line profile analysis, in agreement with visual perception, shows improvement of sharpness of the lesion edge in relation to the reduction of the phantom thickness. The intraclass correlation coefficient (ICC) has shown a great consistency and agreement among the observers for visibility, sharpness, contrast and noise. The ICC results are not as conclusive for the size criterion. Mathematical evaluation results also show a correlation of improvement in the image quality with the reduction in breast thickness. The results show that for the measures CNR, SNR, and FOM, the increase in image quality has a threshold after which the image quality ceases to improve and instead begins to reduce. CNR and FOM dropped when the breast phantom thickness was reduced approximately 40% of its initial thickness. This consistently happened at the point where the filter changed from rhodium (Rh) to molybdenum (Mo). Conclusion: This breast phantom study successfully designed and developed an anthropomorphic compressible breast phantom with cancer mimicking lesions with mechanical and X-ray properties similar to human breast tissue. This study also demonstrates that as breast compressed thickness reduces the visibility of the perceived lesion increases. The radiation dose generally decreases up to the point that the filter changes from rhodium to molybdenum. After this point, the radiation dose increases regardless of the phantom thickness. The results from this thesis are likely to have implications for clinical practice, as they support the need for compression/thickness reduction to enhance lesion visibilit

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

Diagnostic Musculoskeletal Imaging: How Physical Therapists Utilize Imaging in Clinical Decision-Making

This qualitative study describes how physical therapist experts in musculoskeletal disorders evaluate and interpret imaging studies and how they employ imaging in clinical decision-making. The informants are physical therapists who are certified orthopedic clinical specialists (OCS) and/or fellows of the American Academy of Orthopaedic Manual Physical Therapists (AAOMPT). The study employed web conferencing to display patient cases, record screen-capture videos, and to conduct interviews. Informants were observed and their activity video-captured as they evaluated imaging studies and, afterwards, interviews were employed to explore the processes they utilized to evaluate and interpret the images and to discuss imaging-related clinical decision-making, including possible functional consequences of changes seen in the images, contraindications to treatment, and indications for referral. The interviews were transcribed and analyzed in the tradition of grounded theory. This study found that the informants’ evaluation of imaging studies was contextual and non-systematic, guided by the clinical presentation. The informants used imaging studies to provide a deeper understanding of clinical findings and widen perspectives, arriving at clinical decisions through the synthesis of imaging, clinical findings, and didactic knowledge. They tended to look for imaging evidence of interference with normal motion, rather than evidence of pathology. Overall, the informants expressed conservative views on the use of imaging, noting they would rather use clinical findings and treatment response than imaging findings as a basis for referral to other health care professionals. Using imaging studies to support clinical decision-making can provide physical therapists a wider perspective when planning treatment interventions. By showing physical therapists’ approach to interpreting imaging studies and how this relates to their clinical decision-making, the findings of this study could contribute to discussions of the place of imaging in physical therapist practice, as well as help set objectives for imaging curricula in professional-level and continuing education

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Progress toward a quantitative scale for describing radiodensity in mammographic images

A technique is presented for computing a normalised image in which each pixel directly describes the radiodensity of the underlying anatomy. Precisely, each pixel quantifies the equivalent thickness of reference material per unit traversal distance required to match the radiodensity of the breast tissues present within the traversal between the focal spot and the image receptor pixel. Measurements are computed using a model of image formation, which includes consideration of both the attenuation and scattering phenomena that occur. In view of the complexity of the underlying model, substantial computational optimisation has been made to yield clinically realistic execution times. Validation experiments are described using a purpose designed and manufactured tissue equivalent test object which allows both the assessment of the performance of the image normalisation, and a comparison with "ground truth". © 2008 Springer-Verlag Berlin Heidelberg