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Lung Imaging and Computer Aided Diagnosis (Paperback)
Loot Price: R3,292
Discovery Miles 32 920
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Lung Imaging and Computer Aided Diagnosis (Paperback)
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Lung cancer remains the leading cause of cancer-related deaths
worldwide. Early diagnosis can improve the effectiveness of
treatment and increase a patient's chances of survival. Thus, there
is an urgent need for new technology to diagnose small, malignant
lung nodules early as well as large nodules located away from large
diameter airways because the current technology-namely, needle
biopsy and bronchoscopy-fail to diagnose those cases. However, the
analysis of small, indeterminate lung masses is fraught with many
technical difficulties. Often patients must be followed for years
with serial CT scans in order to establish a diagnosis, but
inter-scan variability, slice selection artifacts, differences in
degree of inspiration, and scan angles can make comparing serial
scans unreliable. Lung Imaging and Computer Aided Diagnosis brings
together researchers in pulmonary image analysis to present
state-of-the-art image processing techniques for detecting and
diagnosing lung cancer at an early stage. The book addresses
variables and discrepancies in scans and proposes ways of
evaluating small lung masses more consistently to allow for more
accurate measurement of growth rates and analysis of shape and
appearance of the detected lung nodules. Dealing with all aspects
of image analysis of the data, this book examines: Lung
segmentation Nodule segmentation Vessels segmentation Airways
segmentation Lung registration Detection of lung nodules Diagnosis
of detected lung nodules Shape and appearance analysis of lung
nodules Contributors also explore the effective use of these
methodologies for diagnosis and therapy in clinical applications.
Arguably the first book of its kind to address and evaluate
image-based diagnostic approaches for the early diagnosis of lung
cancer, Lung Imaging and Computer Aided Diagnosis constitutes a
valuable resource for biomedical engineers, researchers, and
clinicians in lung disease imaging.
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