3D nonrigid medical image registration using a new information theoretic measure

Abstract

Institute of Physics and Engineering in Medicine

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

“…Also, JAD is identical to 0 when and only when all of the probability distributions are the same as each other. The proof has been reported in [15].…”

“…When D achieve the minimum value, the similarity of two volumes is maximized, resulting in the two volumes completely registered. We have employed JAD as similarity measure to register medical images in the presence of non-rigid transformation, in which a negative sign is assigned to the JAD to construct the dissimilarity measure [15]. In this paper, according to Proposition 2 in Section 2.3, a normalized dissimilarity measure based on JAD is introduced.…”

“…Hence, the continuous probability density function (pdf) needs to be estimated by Parzen-window method. The continuous marginal and joint pdfs of two images to be registered have been calculated [15]. The continuous expression of gradient distribution distance has been also provided in Section 2.4.…”

“…In this paper, simulated brain MR volumes, thoracic CT volumes and real 3D cardiac CT images were exploited as experimental data. The detailed descriptions of brain MR and 3D thoracic CT images have been reported in [15]. Additionally, non-rigid tests were also performed on twelve 4D cardiac CT sequences acquired from twelve patients.…”

“…Illuminated by the reference [11], a divergence measure based on Arimoto entropy was presented called the Jensen-Arimoto divergence (JAD) [14]. In [15], some properties, such as the concavity of Arimoto entropy and the boundedness of JAD, have been further investigated. However, the registration method based on JAD does not take the spatial information between voxels into account, which is of significance to medical image registration.…”