Cristina Diana Nita

Bio: Cristina Diana Nita is an academic researcher from Romanian Academy. The author has contributed to research in topics: Optimization problem & Image restoration. The author has an hindex of 6, co-authored 26 publications receiving 91 citations.

Parallel Processing and Bio-inspired Computing for Biomedical Image Registration.

Abstract: Image Registration (IR) is an optimization problem computing optimal parameters of a geometric transform used to overlay one or more source images to a given model by maximizing a similarity measure In this paper the use of bio-inspired optimization algorithms in image registration is analyzed Results obtained by means of three different algorithms are compared: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and Clonal Selection Algorithm (CSA) Depending on the images type, the registration may be: area based, which is slow but more precise, and features based, which is faster In this paper a feature based approach based on the Scale Invariant Feature Transform (SIFT) is proposed Finally, results obtained using sequential and parallel implementations on multi-core systems for area based and features based image registration are compared

Automatic multi-threshold image segmentation using metaheuristic algorithms

Abstract: In this paper is presented an automatic segmentation approach for gray level images based on usage of metaheuristic swarming algorithms for multiple thresholds computing The multi-threshold segmentation is an optimization problem while the thresholds must be determined and applied to the source image by minimizing an error measure Because the number of possible solution may be very large in case of multiple thresholds, we used four metaheuristic swarming algorithms to obtain faster the optimal solution of the segmentation problem: Bacterial Foraging, Particle Swarming, Multi Swarm and Firefly optimization As optimization criteria, root mean square error, peak signal-to-noise ratio and structural similarity index are used Each optimization algorithm allows obtaining the optimal solution in a reasonable number of iterations and the obtained results were compared

Image enhancement by multiobjective optimization and bio-inspired heuristics

Abstract: In image analysis and computer vision applications the results precision and correctness depend on the quality of processed images. The most common parameter which subjectively defines the image quality is its contrast. In this paper an image contrast enhancement procedure based on multiobjective optimization is proposed. The contrast gain which has to be maximized and tone distortion which has to be minimized are used as optimization criteria. Because the histogram optimization is a high-dimensional problem, as optimization algorithm the usage of nature-inspired heuristics is proposed. Particularly, in the experiments presented in this paper, the Particle Swarming Optimization algorithm is used. Our contrast enhancement method proves to be superior to traditional techniques like histogram equalization in terms of contrast gain and tone distortion, both criteria being optimized.

Image processing by means of some bio-inspired optimization algorithms

Abstract: Image processing problems often require optimization algorithms to be applied. In this paper some aspects concerning the behavior of Bat and Cuckoo Search optimization algorithms are presented. The obtained accuracy and the processing time depend on the input images characteristics, chosen optimization criteria, dimension of the search space and, last but not least, on the chosen optimization algorithm and its parameters. The two nature inspired optimization algorithms were studied first in case of some mathematical functions minimization and then in case of bio-medical image registration.

New optic disc localization method for retinal images

Abstract: The paper proposes an improved optic disc localisation method in color retinal images. First, the optic disc area in retinal images of any dimensions is identified. Then the method iteratively extracts the optic disc edges and obtains a circular optic disc boundary approximation by a Hough transform. However this is a first step of a retinal image analysis project which will be completed later with other tasks. The final goal is to detect in early stages signs of ophthalmic pathologies by successive analysis of ophthalmoscopy images.

A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization

Abstract: This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation Maximization (EM) algorithm, the ABC based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithms ability to perform automatic multi threshold selection yet showing interesting advantages by comparison to other well known algorithms.

Artificial intelligence in cyber security: research advances, challenges, and opportunities

Abstract: In recent times, there have been attempts to leverage artificial intelligence (AI) techniques in a broad range of cyber security applications. Therefore, this paper surveys the existing literature (comprising 54 papers mainly published between 2016 and 2020) on the applications of AI in user access authentication, network situation awareness, dangerous behavior monitoring, and abnormal traffic identification. This paper also identifies a number of limitations and challenges, and based on the findings, a conceptual human-in-the-loop intelligence cyber security model is presented.

A Graph Cut Approach to Artery/Vein Classification in Ultra-Widefield Scanning Laser Ophthalmoscopy

Abstract: The classification of blood vessels into arterioles and venules is a fundamental step in the automatic investigation of retinal biomarkers for systemic diseases. In this paper, we present a novel technique for vessel classification on ultra-wide-field-of-view images of the retinal fundus acquired with a scanning laser ophthalmoscope. To the best of our knowledge, this is the first time that a fully automated artery/vein classification technique for this type of retinal imaging with no manual intervention has been presented. The proposed method exploits hand-crafted features based on local vessel intensity and vascular morphology to formulate a graph representation from which a globally optimal separation between the arterial and venular networks is computed by graph cut approach. The technique was tested on three different data sets (one publicly available and two local) and achieved an average classification accuracy of 0.883 in the largest data set.

Novel multi-focus image fusion based on PCNN and random walks

Abstract: The purpose of multi-focus image fusion is to acquire an image where all the objects are focused by fusing the source images which have different focus points. A novel multi-focus image fusion method is proposed in this paper, which is based on PCNN and random walks. PCNN is consistent with people’s visual perception. And the random walks model has been proven to have enormous potential to fuse image in recent years. The proposed method first employs PCNN to measure the sharpness of source images. Then, an original fusion map is constructed. Next, the method of random walks is employed to improve the accuracy of the fused regions detection. Finally, the fused image is generated according to the probability computed by random walks. The experiments demonstrate that our method outperforms many existing methods of multi-focus image fusion in visual perception and objective criteria. To assess the performance of our method in practical application, some examples are given at the end of paper.

Firefly algorithm for optimized nonrigid demons registration

Abstract: Videos have vital applications in numerous real-time aspects such as teaching, learning, communication, computer vision, and medicine. Typically, video registration is required to describe a part of the scene/object in the video frame or to localize an object in the frame relative to a fixed reference system. The semilocal transformation generated by the B-splines registration was solved using demons algorithm. Thus, the current study is concerned with demons algorithm–based image registration for a fully local transformation model. The demons registration is optimized using the firefly algorithm (FA) to optimize the velocity-smoothing kernels of the demons registration considering the correlation coefficient as a fitness function. Afterward, the proposed system performance using demons algorithm–based FA is compared to the particle swarm optimization (PSO). The experimental results proved that the proposed system-based FA achieved a correlation value of 0.6108 compared to demons registration with default parameters that provided 0.4468. Additionally, the FA-based optimization framework was more stable and produced superior results than the PSO-based optimization framework. In addition, the FA algorithm converged faster than the PSO one.