Evolutionary Image Registration: A Review

Cocianu, Cătălina; Uscatu, Cristian; Stan, Alexandru

doi:10.3390/s23020967

Cited by 7 publications

(8 citation statements)

References 108 publications

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“…Numerous well-established EAs have been enlisted to tackle the optimization process of IR. Comprehensive re-views of the application of this family of optimization techniques to IR dealing with 3D modeling and medical imaging scenarios can be found in [4], [6], [15].…”

Section: B Softcomputing and Rirmentioning

confidence: 99%

“…Then, this section is devoted to present a brief revision of the most pertinent IR methods within the field. Refer to the subsequent contributions for a more in-depth overview conducted up to the year 2021 [4]- [6], [15]. In addition, new emerging approaches within the field of DL is accordingly introduced below.…”

Section: Brief Overviewmentioning

confidence: 99%

“…IR is then routed to determine either a geometric transformation or messaging (matching feature points) resulting in optimal overlap of the images considered. Its use extends to a wide range of realworld applications, where medical imaging and remote sensing are the most important areas that are widely explored in the State-of-The-Art (SoTA) [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, evolutionary algorithms (EAs), such as genetic algorithms (GAs), have proven successful in treating the problem of IR. A large number of styles in this category have been published, especially in the last two decades, with many widely and comprehensively studied works found [4]- [6], [15].…”

Section: Introductionmentioning

confidence: 99%

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Testing the Robustness of JAYA Optimization on 3D Surface Alignment of Range Images: A Revised Computational Study

Santamaría

2024

IEEE Access

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The further development of image registration (IR) as automated image alignment techniques is a well-known concern in the field of computer vision (CV). These techniques have been implemented in many real-world scenarios: from remote sensing to medical imaging to artificial vision and computeraided design. There is great interest in applying original optimization algorithms to overcome the challenges associated with early IR methods (e.g. the ICP algorithm). On the other hand, algorithms rooted in evolutionary theories in nature-inspired computational models such as Evolutionary Computing (EC), have gained in importance over the past two decades. In addition, other algorithms with wide applicability fall into this category of methods, e.g. metaheuristics, swarmming, etc. Most of these methods have been widely adopted to address the IR problem, and are used as reliable alternatives for optimization goals. The aim of this paper is to address the following two main research challenges: (i) Bridging the gap in the revision of solutions proposed in recent years from the latter optimization model and from those ones from a new model based on deep learning (DL); and ii) introducing a new non-metaphor-based IR approach, called JAYA, to solve specific problems of aligning 3D surfaces of range images, also known as range image registration (RIR). In fact, as far as is known, this is the first time JAYA has been suggested for the above RIR problem. In particular, a new RIR method using the JAYA algorithm have been introduced and its performance has been accordingly compared against a wide set of methods from the SoTA. More than a dozen Softcomputingbased RIR methods have been included in the experimentation, making it the largest comparative study ever carried out in this category. In particular, range image datasets belonging to the SAMPL repository have been used, which has been widely adopted by many authors in the SoTA.

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Section: B Softcomputing and Rirmentioning

confidence: 99%

Section: Brief Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Testing the Robustness of JAYA Optimization on 3D Surface Alignment of Range Images: A Revised Computational Study

Santamaría

2024

IEEE Access

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“…According to the characteristics of our data, we performed image transformation by applying both rigid and non-rigid registration methods (26). Rigid transform is a type of transformation that preserves the shape of an image by including translation, rotation, and scaling.…”

Section: Image Registrationmentioning

confidence: 99%

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage

Yang

Chen

et al. 2023

Front. Neurol.

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IntroductionStroke-associated pneumonia (SAP) is a common complication of stroke that can increase the mortality rate of patients and the burden on their families. In contrast to prior clinical scoring models that rely on baseline data, we propose constructing models based on brain CT scans due to their accessibility and clinical universality.MethodsOur study aims to explore the mechanism behind the distribution and lesion areas of intracerebral hemorrhage (ICH) in relation to pneumonia, we utilized an MRI atlas that could present brain structures and a registration method in our program to extract features that may represent this relationship. We developed three machine learning models to predict the occurrence of SAP using these features. Ten-fold cross-validation was applied to evaluate the performance of models. Additionally, we constructed a probability map through statistical analysis that could display which brain regions are more frequently impacted by hematoma in patients with SAP based on four types of pneumonia.ResultsOur study included a cohort of 244 patients, and we extracted 35 features that captured the invasion of ICH to different brain regions for model development. We evaluated the performance of three machine learning models, namely, logistic regression, support vector machine, and random forest, in predicting SAP, and the AUCs for these models ranged from 0.77 to 0.82. The probability map revealed that the distribution of ICH varied between the left and right brain hemispheres in patients with moderate and severe SAP, and we identified several brain structures, including the left-choroid-plexus, right-choroid-plexus, right-hippocampus, and left-hippocampus, that were more closely related to SAP based on feature selection. Additionally, we observed that some statistical indicators of ICH volume, such as mean and maximum values, were proportional to the severity of SAP.DiscussionOur findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.

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