Medical image enhancement is an essential process for superior disease diagnosis as well as for detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However, speckle noise corrupts the CT images and makes the clinical data analysis ambiguous. Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using log transform in an optimization framework. In order to achieve optimization, a well-known meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal parameter settings for log transform. The performance of the proposed technique is studied on a low contrast CT image dataset. Besides this, the results clearly show that the CS based approach has superior convergence and fitness values compared to PSO as the CS converge faster that proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness of the proposed enhancement technique.

/pdf/computed-tomography-image-enhancement-using-cuckoo-search-a-4z9au1krxr.pdf

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Nondeterministic metaheuristic optimization and digital image processing are two very different research fields, both extremely active and applicable. They touch in a very limited area, but that narrow interaction opens new very promising applications for digital image processing and new and different deployment of metaheuristic optimization. Multilevel image thresholding is very important for image segmentation, which in turn is crucial for higher level image analysis. The problem includes exponential combinatorial optimization with complex objective functions which are solvable only by nondeterministic methods. This short review presents successful applications of the natureinspired metaheuristics to multilevel image thresholding.

/pdf/multilevel-image-thresholding-by-nature-inspired-algorithms-3kn0hcbgzh.pdf

Multilevel image thresholding by nature-inspired algorithms - A short review.

Design of digital IIR filter: A research survey

The performance of fingerprint comparison algorithms depends on the reliability and accuracy of the features extracted from the fingerprints. The accuracy of the feature extraction algorithms is assumed to depend on the quality of the fingerprint images. Especially, low-quality images can be challenging for feature extraction algorithms. Image enhancement may allow to extract features more accurately. There is a lack of extensive and quantitative evaluation of image enhancement methods. This study investigates the impact of seven typical image enhancement methods on biometric sample quality and on biometric performance. The interrelation of image quality and biometric performance is investigated on 14 datasets. Biometric quality measures are estimated based on image quality metrics NFIQ1 and NFIQ2.0. Biometric performance is tested using MINDTCT and FingerJetFX for feature extraction and BOZORTH3 for biometric comparison. This work shows that the biometric performance can be improved by image enhancement. The significance of improvements depends on both the quality of the datasets and the feature extraction. Thus, there is no single best improvement algorithm. A correlation of changes in scores and image qualities can only be found on the level of entire datasets. No significant correlation can be found for single biometric comparisons.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-bmt.2016.0088

Survey on the impact of fingerprint image enhancement

There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques—image segmentation—with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu’s and Kapur’s thresholding methods are two well-known approaches, both of which maximize the between-class variance and the entropy measure, respectively, in a gray image histogram. Both techniques were developed for bi-level thresholding. However, these techniques can be extended to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. However, various optimization techniques have been used to overcome this drawback. In this study, a hybrid firefly and particle swarm optimization algorithm has been applied to yield optimum threshold values in multilevel image thresholding. The proposed method has been assessed by comparing it with four well-known optimization algorithms. The comprehensive experiments reveal that the proposed method achieves better results in term of fitness value, PSNR, SSIM, FSIM, and SD.

A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

In this work we have assessed the capability of a new optimization algorithm – the Cuckoo Search algorithm in tuning the image enhancement functions for peak performance. The assessment has been conducted in comparison to two of the old optimization algorithm aided enhancement, namely, Genetic Algorithms and Particle Swarm Optimization and previous enhancement techniques Histogram Equalization and Linear Contrast Stretch techniques. Results have been assimilated in this paper and conclusions have been drawn keeping the fitness of image and number of edgels in enhanced image as the benchmark. The results have illustrated the capability of Cuckoo search algorithm in optimizing the enhancement functions.

Gray Level Image Enhancement Using Cuckoo Search Algorithm

Frequency domain filtering is a conventional method in eliminating periodic noise in an image. Conventional methods include filtering via bandpass filter and notch filter for detecting both periodic and quasi-periodic noise in an image. In this paper, RFPCM clustering has been used to detect periodic noise spikes in Fourier amplitude domain. Qualitative and comparative analysis has been presented in this paper for clustered mask method and the conventional method as well. PSNR values of the resultant images are taken as filtering quality factor and comparison analysis is taken with respect to this value.

http://ieeexplore.ieee.org/iel7/6881541/6892507/06892633.pdf

Periodic noise recognition and elimination using RFPCM clustering

The entropy image thresholding technique is much in demand today for image segmentation. Furthermore, population algorithm aided thresholding techniques have been proven previously to be extremely effective in producing better results. In this work, we have concentrated on the minimum cross-entropy criterion for image segmentation. The objective of this work is to demonstrate the capability of Cuckoo Search Optimization-based Minimum Cross-Entropy Technique. The algorithm has been compared against old algorithms GA and PSO. Results have been assimilated in this work. The results have clearly demonstrated the competence of Cuckoo Search Optimization algorithm in assisting Cross Entropy-based thresholding procedure.

Comparative Analysis of Cuckoo Search Optimization-Based Multilevel Image Thresholding

Optimization algorithms has been used to solve many design problems in science and engineering; notable in image processing, construction design, path planning and many other application. In this paper the method of designing a linear phase symmetric FIR (Finite Impulse Response) filter with the help of Cuckoo Search Optimization (CSO) Algorithm has been highlighted. Since evolutionary optimization algorithms are more powerful than its linear counterpart, CSO has been deployed in order to find out the coefficients of the required filter for a given problem requirement. Even order linear phase symmetric FIR filter are best suitable for this kind of design procedure. The best nests selected by the CSO denotes the particular coefficient set for a given particular even order filter. A low pass filter is shown as a design example. Comparisonal analysis with respect to other strong evolutionary algorithm is also presented in this paper.

Cuckoo Search Optimization based design of linear phase FIR filters: A comparison approach

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Papers

Gray Level Image Enhancement Using Cuckoo Search Algorithm

Periodic noise recognition and elimination using RFPCM clustering

Comparative Analysis of Cuckoo Search Optimization-Based Multilevel Image Thresholding

Cuckoo Search Optimization based design of linear phase FIR filters: A comparison approach