Comput Math Methods Med - Nonrigid registration of lung CT images based on tissue features.

Tópicos

{ imag(1057) registr(996) error(939) }
{ can(774) often(719) complex(702) }
{ method(984) reconstruct(947) comput(926) }
{ imag(2830) propos(1344) filter(1198) }
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{ imag(2675) segment(2577) method(1081) }
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Resumo

Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.

Resumo Limpo

nonrigid imag registr prerequisit various medic imag process analysi applic much effort devot thorac imag registr due breath motion recent scaleinvari featur transform sift use medic imag registr obtain promis result howev sift apt detect blob featur blob key point general detect smooth area may contain diagnost point general diagnost point use medic imag often vessel cross point vascular endpoint tissu boundari point provid abund inform vessel can reflect motion lung accur point general high gradient oppos blob key point can detect harri work propos hybrid featur detect method can detect tissu featur lung effect base harri sift addit novel method can remov mismatch landmark also propos seri thorac ct imag test use propos algorithm quantit qualit evalu show method statist signific better convent sift method especi case larg deform lung respir

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