Recent nature-Inspired algorithms for medical image segmentation based on tsallis statistics

Wachs-Lopes, Guilherme Alberto; Santos, Ricardo Morello; Saito, Nobuki; Rodrigues, Paulo S.

doi:10.1016/j.cnsns.2020.105256

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…Agrawal et al [48] proposed a hybrid adaptive cuckoo search-squirrel search algorithm to analyze Brain MRI scans by obtaining optimal multi-level thresholds using maximization of the edge magnitude information. Wachs-Lopes et al [49] discusses seven recent bio-inspired algorithms over multi-thresholding segmentation of medical images. The algorithms were tested for a range of values of non-extensivity parameter ('q'), which is an essential parameter for Tsallis entropy.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

et al. 2021

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A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

et al. 2021

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“…Considering limitations relating to a higher instance of higher false-negative or false-positive cortex segmentation in a subset of cases, future work will expand upon nature-inspired algorithms [ 39 ] to determine improved thresholding parameters. Another direction of future work will explore the usage of unsupervised deep learning for renal compartment segmentation, especially in light of limited ground-truth data.…”

Section: Discussionmentioning

confidence: 99%

Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function

Asaturyan

Villarini

Sarao

et al. 2021

Sensors

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There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time–intensity curves of the cortex and medulla for determining obstruction in the urinary tract. This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial–temporal information coupled with boundary-preserving fully convolutional dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively.

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“…In future, MMFO can be used along other entropy mechanism and also hybridized with other algorithms to solve the same 36 Minimum Cross Entropy- Self Adaptive Differential Evolution (MCE-SADE) Minimum Cross-Entropy Aranguren et al ( 2021 ) Magnetic Resonance Brain images (MRBI): Medical Images Proposed method is compared with SADE, GWO and ICA PSNR, FSIM and SSIM The proposed MCE-SADE is better in terms of consistency and its quality when compared to GWO and ICA 37 Fuzzy-Entropy and Image Fusion Based method Fuzzy-Entropy Singh et al ( 2020 ) Magnetic Resonance Brain Images (MRBI): Medical Images Proposed method is compared with multilevel, adaptive threshold method, K-means clustering algorithm and fuzzy c-means algorithm PSNR and JSC The proposed method outdoes the existing method. However, method is checked using only MRIs of brain tumors and in future it can be applied to other types of MRIs 38 Hybrid Slime Mould Algorithm with Whale Optimization Algorithm (HSMA_WOA) Kapur’s entropy Abdel-Basset et al ( 2020a ) X-Ray Images Proposed method is compared with LShade, WOA, FFA, HHA, SSA, and SMA PSNR, SSIM, Fitness values, CPU time and UQI The proposed HSMA_WOA outperforms SMA for all the metrics used 39 Study on recent Nature-Inspired Algorithms Tsallis entropy Wachs-Lopes et al ( 2020 ) Medical Images Proposed method is compared with FFA, CSL, GOA, KHA, GWO, EHO and WOA PSNR, J Index, and DC Experimental findings projects that all algorithms taken in account in the study performs likewise when quality of segmentations is the factor taken into consideration. Nevertheless, KHA generates worst results.…”

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Recent nature-Inspired algorithms for medical image segmentation based on tsallis statistics

Cited by 9 publications

References 40 publications

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

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