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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Symmetry

ISSN

2073-8994

e-ISSN

2073-8994

Svazek periodika

14

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

17

Strana od-do

nestrankovano

Kód UT WoS článku

000845250600001

EID výsledku v databázi Scopus

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