Adaptive Multilevel Thresholding Research Articles

Editorial

Dear Readers,  Welcome to the seventh issue in 2023. I am very pleased to announce the journal’s continued high Scopus CiteScore of 2.7 and Web of Science Impact Factor of 1.0 for 2022, indicating another scientifically successful year. On behalf of the J.UCS team, I would like to thank all the authors for their sound research contributions, the reviewers for their very helpful suggestions for improvements, and the consortium members for their financial support. Your commitment and dedicated work have contributed significantly to the long-lasting success of our journal.  As we want to secure the financial support also for the years to come, we are looking for institutions and libraries to financially support our diamond open access journal as consortium members, who will then benefit from the research community, international visibility, and the opportunity to manage special issues and focused topics within the journal. Please think about the possibility of such financial participation of your institution, we would be very grateful for any kind of support.  In this regular issue, I am very pleased to introduce six accepted papers from seven different countries and 20 involved authors.  Ana P. Allian, Leandro F. Silva, Edson OliveiraJr and Elisa Y. Nakagawa from Brazil present VMTools-RA, a reference architecture that encompasses the knowledge and practice for developing and evolving variability tools. In a collaboration between researchers from the UK and Estonia, Vimal Dwivedi, Mubashar Iqbal, Alex Norta and Raimundas Matulevičius are focusing their research on the evaluation of a legally binding smart-contract language for blockchain applications. Monika, Seema Verma, and Pardeep Kumar from India discuss an intelligent vision-based decision-making system for the exploration of past aviation accidents and incidents, which is based on a visual query-based model capable of analyzing the major factors including flight phases, human factors, weather conditions, and faulty components in particular aircraft models. Luis Eduardo Ordoñez Palacios, Víctor Bucheli Guerrero and Hugo Ordoñez from Colombia present their research on integrating satellite imagery and meteorological data to estimate solar radiation applying and evaluating five machine learning models. In a collaboration between researchers from Palestine and Egypt, Muath Sabha, Thaer Thaher, and Marwa M. Emam apply cooperative swarm intelligence algorithms to adaptive multilevel thresholding segmentation of COVID-19 CT scan images. Geovana Ramos Sousa Silva, Genaína Nunes Rodrigues and Edna Dias Canedo from Brazil introduce their work on a modeling strategy to design and verify chatbot conversational flows via the Uppaal model checking tool.  Enjoy Reading! Cordially, Christian Gütl, Managing Editor 

Novel QRS detection based on the Adaptive Improved Permutation Entropy

Detection of the QRS complex is the most important step in analyzing ECG signals for heart monitoring and diagnosis. There have been several QRS-peak detection methods reported in the literature. Most of these methods have low performance under noisy conditions. In this paper, we propose a novel QRS detection algorithm based on a new Permutation Entropy (PE) method that we developed and referred to as the Adaptive Improved Permutation Entropy (AIPE) method. The parameters of the AIPE method are determined based on the specific signal properties. Implementing the AIPE method leads to prominently preserving the QRS complex and eliminating noises of the ECG signal without smoothing the ECG signal. Our simulations show that the proposed QRS detection algorithm is effective and robust under noisy conditions. The algorithm is validated on the MIT-BIH Noise Stress Test Database for various SNR values. In addition, we examined the algorithm’s performance under motion noise conditions, mimicking a practical scenario. We used the metrics of sensitivity, positive predictive, and F1 score to evaluate the performance of our algorithm and compare it with several other algorithms explained in the literature. Our investigation shows that the proposed algorithm is efficient and effective. More importantly, it is robust under noisy conditions providing superior performance over other recent and popular QRS detection algorithms, including the popular Pan–Tompkins and the recent Advanced Adaptive Multilevel Thresholding (AAMT) algorithms.

A Novel method of QRS Detection Using Adaptive Multilevel Thresholding with Statistical False Peak Elimination

Abstract: Heart is the vital organ of a Human Body, because of its involvement in various works and processes in the entire body such as blood pumping etc., so recording a heart function is also a great thing, it is done through ECG signals. ECG signal records the electrical signals and activity of a Human Heart based on the electrical signals released by the Heart. ECG signal consists of PQRST waves, which are the reference points on an ECG signal. But, recording them is much easy than extracting and analyzing them, so, as to extract them, we are applying an advanced adaptive multi-level thresholding (AAMT) along with a selective statistical false peak elimination for the detection of QRS peaks of an ECG signal. Initially, median and moving average filters are applied for removing noise as well as terms. After AAMT is implemented on the complete dataset of ECG signals. Then selective statistical false peak elimination (SSFPE) is implemented for removing noise terms that might be missed out during filtering. At last, a search back stage will be implemented to search for low amplitude useful peaks.

An adaptive multilevel thresholding method with chaotically-enhanced Rao algorithm.

Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced Rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy.

A novel adaptive multilevel thresholding based algorithm for QRS detection

The heart is the single most important organ of the human body. By studying the ECG signal any abnormalities in the heart’s tempo can be identified. The QRS complex is the most prominent feature of an ECG signal. However, the detection of this feature is hampered by the presence of high and low-frequency noises in the ECG signal and abrupt changes in the signal’s morphology. This paper presents a new method of QRS detection using advanced adaptive multilevel thresholding (AAMT) with selective statistical false peak elimination (SSFPE). Firstly, a band-pass filter is used to filter out most of the unwanted noise and interference to aid the detection operation. Then, AAMT is applied to the entire ECG record to find the location and amplitudes of the pseudo peaks. Next, SSFPE is employed to eliminate false peaks resulting from electromyogram (EMG) and any other high-frequency noise that has not been eliminated in the filtering stage. Finally, after most of the peaks are correctly identified a search back stage is included to find any low amplitude true peaks that might have been missed in the peak detection stage. The proposed method is tested on the MIT-BIH arrhythmia and Fantasia databases and shows high accuracy in detection compared to many state-of-the-art QRS detection methods. The method yields high sensitivity, positive predictivity, and a low detection error rate of 99.85%, 99.91%, and 0.25%, respectively, for the MIT-BIH arrhythmia and 99.98%, 99.90%, and 0.12%, respectively for the Fantasia database.

Adaptive multilevel thresholding based on multiobjective artificial bee colony optimization for noisy image segmentation

Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perf

Pedometer method based on adaptive multilevel thresholding

Pedometer method based on adaptive multilevel thresholding

Efficient detection of obstacles on tramways using adaptive multilevel thresholding and region growing methods

With the rapid development of light-rail public transportation, video-based obstacle detection is becoming an essential and foregoing task in driver assistance systems. The system should be able to automatically survey the tramway using an onboard camera. However, the functioning of the system is challenging due to the presence of various ground types, different weather and illumination conditions, as well as varying time of acquisition. This article presents a real-time tramway detection method that deals efficiently with various challenging situations in real-world urban rail traffic scenarios. It first uses an adaptive multilevel thresholding method to segment the regions of interest of the tramway, in which the threshold parameters are estimated using a local accumulated histogram. The approach then adopts the region growing method to decrease the influence of environmental noise and to predict the trend of the tramway. The experiment validation of this study proves that the method is able to correctly detect tramways even in challenging scenarios and uses lesser computational time to meet the real-time demand.

Quantum genetic algorithm for adaptive image multi-thresholding segmentation

An adaptive image multilevel thresholding segmentation algorithm is presented in this paper. The proposed algorithm introduces a parallel quantum genetic algorithm PQGA for histogram-based image segmentation. Quantum genetic algorithm QGA has the advantages of fast convergence speed and strong global search capabilities. And PQGA can improve the computational efficiency of the QGA further. Without predetermining the number of the thresholds, the proposed algorithm that chooses the automatic thresholding criterion as its objective function can obtain the number of the thresholds and the corresponding thresholds accurately. The experimental results demonstrate good performance of the PQGA in solving adaptive multilevel thresholding segmentation problems by comparing with other methods for several test images.

Line Defect Detection in TFT-LCD Using Directional Filter Bank and Adaptive Multilevel Thresholding

Line Defect Detection in TFT-LCD Using Directional Filter Bank and Adaptive Multilevel Thresholding

IC 패키지 마킹검사를 위한 적응적 다단계 이진화와 정합단위의 동적 선택

머신비전을 이용한 IC 패키지 마킹검사 시스템은 입력영상으로부터 검사할 요소들의 위치를 식별하고, 추출된 요소들을 학습된 표준 패턴과 비교하여 마킹의 불량 여부를 판단한다. 본 논문에서는 검사 대상 IC 패키지의 위치 판별, 마킹문자 추출, 핀원딤플 검출과 같은 일련의 작업들에 적합한 적응적 다단계 이진화 방법과 마킹문자의 국소적인 오류검출은 물론 잡영에 강건한 정합단위의 동적 선택 방법을 제안한다. 제안하는 이진화 방법은 이진화 대상 영역과 명도 값의 범위를 제한하여 Otsu의 이진화 알고리즘을 적용함으로써 특정 응용에 적응적인 이진화가 가능하다. 정합단위의 동적 선택 방법은 문자추출 및 배치분석에 대한 결과에 따라 정합단위를 선택한다. 그러므로 문자추출 및 배치분석 과정에서 발생하는 예기치 못한 부적절한 상황에서도 가능한 범위내에서 최소의 정합단위를 선택할 수 있다. 제안된 방법을 구현하여 8종의 IC 패키지, 총 280개의 영상에 대하여 실험한 결과, IC 패키지와 핀원딤플의 검출율은 100%였으며, 마킹상태에 대한 판정은 98.8%의 정확도를 나타내어 제안된 방법이 효과적임을 확인할 수 있었다. IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.

Novel decision strategy for P-wave detection utilising nonlinearly synthesised ECG components and their enhanced pseudospectral resonances

A novel, robust, patient adaptive and potentially on-line P-wave detection decision strategy is presented. It is particularly well-suited to the noisy ECG environment, and highly effective with abnormal atrial activations which can occur simultaneously with the QRS complex or the T-wave. Essentially, it is based on the exploitation of the unique structural properties of the raw and nonlinearly synthesised high-resolution pseudospectral multi-resonance signatures of the P-wave (or the entire PR interval including the P-wave), QRS complex, and the combined S-T segment and the T-wave. Clinical trials involving 59 healthy volunteers, equivalent to 1600 hours of ECG recordings, sampled at 500 Hz, have confined that most of the atrium and ventricle depolarisation-wave spectral energies (approximately 90%) are confined to two overlapping clusters inscribed by the top of the principal pseudospectral peaks (PPPs) and centred at 12 and 15 Hz, respectively. The degree of overlapping varies from one person to another and can, to some extent, be reduced by using a temporal interleaving technique or sub-sampling. Normal sinus records obtained from the MIT/BIM database and sampled at a lower rate of 128 Hz have confirmed the above, thus facilitating an easy decision strategy for all the P-waves that bear relationships to the following QRS complexes. For the P-on-QRS and the P-on-T episodes, the linear and quadratic PPPs are used in the detection of independent P-waves, respectively. The results presented here include a representative of 59 cases of sinus rhythm and three cases of challenging arrhythmias. One observation of mechanical contraction of the atrium and using echocardiographic imaging coinciding with the P-wave is presented to demonstrate the clinical fall-outs resulting from detection of dependent as well as independent P-waves. In arrhythmias cases, the standard 12-lead or the 3-lead machines were used as appropriate. The multi-faceted detection routine employs temporal and frequency interleaving, the LMS-based cubic Volterra synthesiser in conjunction with Kaiser-filtered state-enhanced spectral MUSIC, and subsequent thresholding. An adaptive multi-level thresholding with first derivative calculations is only activated in suspected cases of several independent P-waves perturbing the QRS complex.