Comput Math Methods Med - A novel approach to segment and classify regional lymph nodes on computed tomography images.

Tópicos

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Resumo

Morphology of lymph nodal metastasis is critical for diagnosis and prognosis of cancer patients. However, accurate prediction of lymph node type based on morphological information is rarely available due to lack of pathological validation. To obtain correct morphological information, lymph nodes must be segmented from computed tomography (CT) image accurately. In this paper we described a novel approach to segment and predict the status of lymph nodes from CT images and confirmed the diagnostic performance by clinical pathological results. We firstly removed noise and preserved edge details using a revised nonlinear diffusion equation, and secondly we used a repulsive-force-based snake method to segment the lymph nodes. Morphological measurements for the characterization of the node status were obtained from the segmented node image. These measurements were further selected to derive a highly representative set of node status, called feature vector. Finally, classical classification scheme based on support vector machine model was employed to simulate the prediction of nodal status. Experiments on real clinical rectal cancer data showed that the prediction performance with the proposed framework is highly consistent with pathological results. Therefore, this novel algorithm is promising for status prediction of lymph nodes.

Resumo Limpo

morpholog lymph nodal metastasi critic diagnosi prognosi cancer patient howev accur predict lymph node type base morpholog inform rare avail due lack patholog valid obtain correct morpholog inform lymph node must segment comput tomographi ct imag accur paper describ novel approach segment predict status lymph node ct imag confirm diagnost perform clinic patholog result first remov nois preserv edg detail use revis nonlinear diffus equat second use repulsiveforcebas snake method segment lymph node morpholog measur character node status obtain segment node imag measur select deriv high repres set node status call featur vector final classic classif scheme base support vector machin model employ simul predict nodal status experi real clinic rectal cancer data show predict perform propos framework high consist patholog result therefor novel algorithm promis status predict lymph node

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