Título: Methodology for automatic detection of lung nodules in computerized tomography images
Autor(es): Sousa, João Rodrigo Ferreira da Silva; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Nunes, Rodolfo Acatauassú
Resumo: Lung cancer is a disease with significant prevalence in several countries around the world.
Its difficult treatment and rapid progression make the mortality rates among people affected
by this illness to be very high.
Aiming to offer a computational alternative for helping in detection of nodules, serving
as a second opinion to the specialists, this work proposes a totally automatic methodology
based on successive detection refining stages.
The automated lung nodules detection scheme consists of six stages: thorax extraction,
lung extraction, lung reconstruction, structures extraction, tubular structures elimination,
and false positive reduction. In the thorax extraction stage all the artifacts external to the
patient’s body are discarded. Lung extraction stage is responsible for the identification of
the lung parenchyma. The objective of the lung reconstruction stage is to prevent incorrect
elimination of portions belonging to the parenchyma. Structures extraction stage comprises
the selection of dense structures from inside the lung parenchyma. The next stage, tubular
structures elimination eliminates a great part of the pulmonary trees. Finally, the false
positive stage selects only structures with great probability to be nodule. Each of the several
stages has very specific objectives in detection of particular cases of lung nodules, ensuring
good matching rates even in difficult detection situations.
We use 33 exams with diversified diagnosis and slices numbers for validating the methodology.
We obtained a false positive per exam rate of 0.42 and false negative rate of 0.15. The
total classification sensitivity obtained, measured out of the nodule candidates,was 84.84%.
The specificity achieved was 96.15% and the total accuracy of the method was 95.21%.
Descrição: Também disponível em journal homepage: www.intl.elsevierhealth.com/journals/cmpb
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