Multi-Modal Rigid Image Registration and Segmentation Using Multi-Stage Forward Path Regenerative Genetic Algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250188" target="_blank" >RIV/61989100:27240/22:10250188 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2073-8994/14/8/1506" target="_blank" >https://www.mdpi.com/2073-8994/14/8/1506</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/sym14081506" target="_blank" >10.3390/sym14081506</a>

Result language

angličtina

Original language name

Multi-Modal Rigid Image Registration and Segmentation Using Multi-Stage Forward Path Regenerative Genetic Algorithm

Original language description

Medical image diagnosis and delineation of lesions in the human brain require information to combine from different imaging sensors. Image registration is considered to be an essential pre-processing technique of aligning images of different modalities. The brain is a naturally bilateral symmetrical organ, where the left half lobe resembles the right half lobe around the symmetrical axis. The identified symmetry axis in one MRI image can identify symmetry axes in multi-modal registered MRI images instantly. MRI sensors may induce different levels of noise and Intensity Non-Uniformity (INU) in images. These image degradations may cause difficulty in finding true transformation parameters for an optimization technique. We will be investigating the new variant of evolution strategy of genetic algorithm as an optimization technique that performs well even for the high level of noise and INU, compared to Nesterov, Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (LBFGS), Simulated Annealing (SA), and Single-Stage Genetic Algorithm (SSGA). The proposed new multi-modal image registration technique based on a genetic algorithm with increasing precision levels and decreasing search spaces in successive stages is called the Multi-Stage Forward Path Regenerative Genetic Algorithm (MFRGA). Our proposed algorithm is better in terms of overall registration error as compared to the standard genetic algorithm. MFRGA results in a mean registration error of 0.492 in case of the same level of noise (1-9)% and INU (0-40)% in both reference and template image, and 0.317 in case of a noise-free template and reference with noise levels (1-9)% and INU (0-40)%. Accurate registration results in good segmentation, and we apply registration transformations to segment normal brain structures for evaluating registration accuracy. The brain segmentation via registration with our proposed algorithm is better even in cases of high levels of noise and INU as compared to GA and LBFGS. The mean dice similarity coefficient of brain structures CSF, GM, and WM is 0.701, 0.792, and 0.913, respectively.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Symmetry

ISSN

2073-8994

e-ISSN

2073-8994

Volume of the periodical

14

Issue of the periodical within the volume

8

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

nestrankovano

UT code for WoS article

000845250600001

EID of the result in the Scopus database

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