Daubechies wavelet‐based local feature descriptor for multimodal medical image registration

Abstract

A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining andencoding the Daubechies wavelet decomposed center–neighbour pixel relationshipin the local texture. RDbW features are applied in spatial alignment(registration) of multimodal medical images using a Procrustes analysis(PA)‐based affine transformation function and the registered images are furtherfused by employing a wavelet‐based fusion method. A significant amount ofexperiments is conducted and the registration and fusion accuracy of theproposed feature descriptor is compared with the prominent existing methods suchas local binary patterns (LBP), local tetra pattern (LTrP), local diagonalextrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP).Experimental results show the present registration method improves the averageregistration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP,and LBP, respectively. Further, the fusion results of the current approachexhibit an average improvement in entropy by 11%, standard deviation by 6% edgestrength by 12%, sharpness by 23%, and average gradient by 16% when comparedwith all other feature descriptors used for registering the images. Conceptspresented here can be used widely in analysing the combined information presentin multimodal medical images.

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

“…Ideas introduced here can be utilized broadly in nalyzing the consolidated data present in multimodal medicinal images. [4] Y. Tong and J. Chen Most imaging frameworks have a restricted profundity of-field in the sensor organizes that comprise of different visual sensors. Because of various protest separations, few out of every odd question can be obviously imaged by a solitary sensor.…”

“…Low pass sub band will be consolidated utilizing straightforward averaging tasks since they both contain approximations of the source images. 4. Backwards discrete wavelet changes: After chose the fused low recurrence and high recurrence bands, fused coefficient is reproduced utilizing the Converse quick discrete wavelet change to get the fused image which speak to the new image.…”

“…Image fusion mingles absolutely registered images from numerous sources to fabricate a high quality fused image with spatial and spectral information. So many image fusion methods have been developed from the past to now such as: the IHS [3], DCT [7], DWT [4], DT CWT [6] and PCA [3] methods etc. These methods functions under spatial domain & have proved to be flourishing in computer vision, robotics, satellite and medical image fusion applications.…”

New design of adaptive Gabor wavelet filter bank for medical image retrieval

“…Yelampalli et al . [11] used Daubechies wavelet‐based local feature descriptor for the registration of medical images. Same authors used a binary feature descriptor to discriminate normal and abnormal chest computed tomography (CT) images [12].…”

Forming optimal projection images from intra-retinal layers using curvelet-based image fusion method

“…[ 20 ] This transform with describing the precise detail of the image in horizontal, vertical, and diagonal axes helps to provide more detail of vessels – particularly those that are only observed in vertical layers and also other useful amplified information and those that are displayed in the final projection image. [ 26 ] Nonetheless, limitations of wavelet transform in representing image details in four subbands and three axes divest, creating a fully optimum image, while there are always vessels and details in the image which are out of the three maximum information axes. [ 27 ] As described later, Curvelet transform can demonstrate information of subbands and different axes (e.g., more angles).…”