Fireworks Algorithm Based Image Registration

Bejinariu, Silviu-Ioan; Costin, Hariton; Rotaru, F.; Luca, Ramona; Niţă, Cristina Diana; Lazăr, Camelia

doi:10.1007/978-3-319-62521-8_44

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Bejinariu and Costin, 2018: In [ 38 ], the authors apply the Fireworks Algorithm (FWA) [ 1 , 39 ] to the problem of image registration. The similarity measure chosen for the study is NMI, which is also used as the fitness function.…”

Section: Articlesmentioning

confidence: 99%

Evolutionary Image Registration: A Review

Cocianu

Uscatu²,

Stan³

2023

Sensors

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Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.

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Section: Articlesmentioning

confidence: 99%

Evolutionary Image Registration: A Review

Cocianu

Uscatu²,

Stan³

2023

Sensors

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“…After extracting the features from the image to be examined, a match is made with features that are stored in a data-base of recognized objects. Additionally, localization, 3D mapping, recognition, scene modeling, and tracking may all be done using SIFT characteristics [19]- [22].…”

Section: ❖ Sift (Scale-invariant Feature Transform) Features Extractionmentioning

confidence: 99%

Diagnose Eyes Diseases Using Various Features Extraction Approaches and Machine Learning Algorithms

Najm Abed,

M Al-Bakry

2023

ijci

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Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm).

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“…Prominent applications in medical image processing are related to edge detection [33], image segmentation [19], [20], but also for image registration [36], [37], knowing that these procedures may be viewed as optimization tasks.…”

Section: Optimization Using Bfoamentioning

confidence: 99%

Biomedical Image Registration by means of Bacterial Foraging Paradigm

Costin

Bejinariu

Costin

2016

INT J COMPUT COMMUN

Self Cite

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Image registration (IR) is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene (unimodal registration), taken or not at different time slots, from different angles, and/or by different image acquisition systems (multimodal registration). Technically, image registration implies a complex optimization of different parameters, performed at local or/and global level. Local optimization methods often fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular, and global methods are required, at least at the beginning of the procedure. This paper presents a new evolutionary and bio-inspired robust approach for IR, Bacterial Foraging Optimization Algorithm (BFOA), which is adapted for PET-CT multimodal and magnetic resonance image rigid registration. Results of optimizing the normalized mutual information and normalized cross correlation similarity metrics validated the efficacy and precision of the proposed method by using a freely available medical image database. Keywords: medical imaging, image registration, soft computing, evolutionary strategies, bacterial foraging algorithm, global optimization. About Multimodal Image RegistrationImage registration (IR) is a fundamental task in computer vision used to find either a spatial transformation (e.g., rotation, translation, etc.) or a correspondence (matching of similar image entities) among two (or more) images taken under different conditions (at different times, using different sensors, from different viewpoints, or a combination of them), with the aim of overlaying such images into a common one [1], [2], [3], [4]. Over the years, IR has been applied to a broad range of situations from remote sensing to medical images or artificial vision and CAD systems, and different techniques have been independently studied resulting in a large body of research.IR methods can be classified in two groups according to the nature of images: pixel/voxelbased IR methods (also called intensity-based), where the whole image is considered for the registration process; and, on the other side, feature-based methods, which consider prominent information extracted from the images, being a reduced subset of them. The latter methods take advantage of the lesser amount of information managed in order to overcome the problems found in the former when the images present some inconsistences to deal with, for example,

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Fireworks Algorithm Based Image Registration

Cited by 9 publications

References 12 publications

Evolutionary Image Registration: A Review

Evolutionary Image Registration: A Review

Diagnose Eyes Diseases Using Various Features Extraction Approaches and Machine Learning Algorithms

Biomedical Image Registration by means of Bacterial Foraging Paradigm

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