Relevant bibliographies by topics / Dice Similarity Coefficient

Academic literature on the topic 'Dice Similarity Coefficient'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Dice Similarity Coefficient"

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Rahman, Zahid, Altaf Hussain, Hussain Shah, and Muhammad Arshad. "Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (February 8, 2022): 381–99. http://dx.doi.org/10.14201/adcaij2021104381399.

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Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.
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Cahyapratama, Afrianda, Kelly Rosa Sungkono, and Riyanarto Sarno. "Gap analysis business process model by using structural similarity." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 124. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp124-134.

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<span>Gap analysis process model is a study that can help an institution to determine differences between business process models, such as a model of Standard Operating Procedure and a model of activities in an event log. Gap analysis is used for finding incomplete processes and can be obtained by using structural similarity. Structural similarity measures the similarity of activities and relationships depicting in the models. This research introduces a graph-matching algorithm as the structural similarity algorithm and compares it with dice coefficient algorithms. Graph-matching algorithm notices parallel relationships and invisible tasks, on the contrary dice coefficient algorithms only measure closeness between activities and relationships. The evaluation shows that the graph-matching algorithm produces 76.76 percent similarity between an SOP model and a process model generating from an event log; while, dice coefficient algorithms produces 70 percent similarity. The ability in detecting parallel relationships and invisible tasks causes the graph-matching algorithm produces a higher similarity value than dice coefficient algorithms.</span>
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Warrens, Matthijs J., and Alexandra de Raadt. "Ordering Properties of the First Eigenvector of Certain Similarity Matrices." Journal of Mathematics 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/582731.

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It is shown for coefficient matrices of Russell-Rao coefficients and two asymmetric Dice coefficients that ordinal information on a latent variable model can be obtained from the eigenvector corresponding to the largest eigenvalue.
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GAWRON, JEAN MARK, and KELLEN STEPHENS. "Sparsity and normalization in word similarity systems." Natural Language Engineering 22, no. 3 (August 19, 2015): 351–95. http://dx.doi.org/10.1017/s1351324915000261.

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AbstractWe investigate the problem of improving performance in distributional word similarity systems trained on sparse data, focusing on a family of similarity functions we call Dice-family functions (Dice 1945Ecology26(3): 297–302), including the similarity function introduced in Lin (1998Proceedings of the 15th International Conference on Machine Learning, 296–304), and Curran (2004 PhD thesis, University of Edinburgh. College of Science and Engineering. School of Informatics), as well as a generalized version of Dice Coefficient used in data mining applications (Strehl 2000, 55). We propose a generalization of the Dice-family functions which uses a weight parameter α to make the similarity functions asymmetric. We show that this generalized family of functions (α systems) all belong to the class of asymmetric models first proposed in Tversky (1977Psychological Review84: 327–352), and in a multi-task evaluation of ten word similarity systems, we show that α systems have the best performance across word ranks. In particular, we show that α-parameterization substantially improves the correlations of all Dice-family functions with human judgements on three words sets, including the Miller–Charles/Rubenstein Goodenough word set (Miller and Charles 1991Language and Cognitive Processes6(1): 1–28; Rubenstein and Goodenough 1965Communications of the ACM8: 627–633).
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Wang, Hao-Jen, Li-Wei Chen, Hsin-Ying Lee, Yu-Jung Chung, Yan-Ting Lin, Yi-Chieh Lee, Yi-Chang Chen, Chung-Ming Chen, and Mong-Wei Lin. "Correction: Wang et al. Automated 3D Segmentation of the Aorta and Pulmonary Artery on Non-Contrast-Enhanced Chest Computed Tomography Images in Lung Cancer Patients. Diagnostics 2022, 12, 967." Diagnostics 12, no. 8 (August 2, 2022): 1867. http://dx.doi.org/10.3390/diagnostics12081867.

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Febriansyah, Luke Michael, and Shinta Estri Wahyuningrum. "ANALYSIS WINNOWING ALGORITHM FOR TEXT PLAGIARISM DETECTION USING THREE METHOD SIMILARITY." Proxies : Jurnal Informatika 2, no. 2 (March 10, 2021): 42. http://dx.doi.org/10.24167/proxies.v2i2.3208.

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Cases of plagiarism in recent years has been an issues. Based on that issues, this research will create a system to detect similarity in a text. There is an aspect as reference of the research that is analyze the plagiarism algorithm. This research will analyze the accuracy one of plagiarism check algorithm, winnowing algorithm. Winnowing algorithm is a plagiarism detection algorithm based on document fingerprinting. To calculate percentage similarity of document fingerprinting in text, there are 3 methods to measure similarity that will be used in this research, which is jaccard similarity coefficient, sorensen dice similarity coefficient, and berg similarity coefficient.
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Chen, Wenjuan, Penggang Bai, Jianji Pan, Yuanji Xu, and Kaiqiang Chen. "Changes in Tumor Volumes and Spatial Locations Relative to Normal Tissues During Cervical Cancer Radiotherapy Assessed by Cone Beam Computed Tomography." Technology in Cancer Research & Treatment 16, no. 2 (January 4, 2017): 246–52. http://dx.doi.org/10.1177/1533034616685942.

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Purpose: To assess changes in the volumes and spatial locations of tumors and surrounding organs by cone beam computed tomography during treatment for cervical cancer. Materials and Methods: Sixteen patients with cervical cancer had intensity-modulated radiotherapy and off-line cone beam computed tomography during chemotherapy and/or radiation therapy. The gross tumor volume (GTV-T) and clinical target volumes (CTVs) were contoured on the planning computed tomography and weekly cone beam computed tomography image, and changes in volumes and spatial locations were evaluated using the volume difference method and Dice similarity coefficients. Results: The GTV-T was 79.62 cm3 at prior treatment (0f) and then 20.86 cm3 at the end of external-beam chemoradiation. The clinical target volume changed slightly from 672.59 cm3 to 608.26 cm3, and the uterine volume (CTV-T) changed slightly from 83.72 cm3 to 80.23 cm3. There were significant differences in GTV-T and CTV-T among the different groups ( P < .001), but the clinical target volume was not significantly different in volume ( P > .05). The mean percent volume changes ranged from 23.05% to 70.85% for GTV-T, 4.71% to 6.78% for CTV-T, and 5.84% to 9.59% for clinical target volume, and the groups were significantly different ( P < .05). The Dice similarity coefficient of GTV-T decreased during the course of radiation therapy ( P < .001). In addition, there were significant differences in GTV-T among different groups ( P < .001), and changes in GTV-T correlated with the radiotherapy ( P < .001). There was a negative correlation between volume change rate (DV) and Dice similarity coefficient in the GTV-T and organs at risk ( r < 0; P < .05). Conclusion: The volume, volume change rate, and Dice similarity coefficient of GTV-T were all correlated with increase in radiation treatment. Significant variations in tumor regression and spatial location occurred during radiotherapy for cervical cancer. Adaptive radiotherapy approaches are needed to improve the treatment accuracy for cervical cancer.
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Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and havasi Subramanian. "Brain Tumor Segmentation based on Red-Bellied Woodpecker Mating Optimization Algorithm." NeuroQuantology 20, no. 5 (May 18, 2022): 785–90. http://dx.doi.org/10.14704/nq.2022.20.5.nq22235.

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Earlier, many researchers proposed various segmentation algorithms to segment tumor from MRI Brain image. The method of a nature-inspired meta heuristic-based woodpecker characteristics approach is used to segment the tumored area of this proposed study. In this automated MRI brain tumor segmentation, the MRI brain image gets enhanced for improving the performance of the segmentation accompanied by the skull elimination phase to eliminate the morphological operations of all non-brain tissues. In the end, the RBWMOA (Red-Bellied Woodpecker Mating Optimization Algorithm) is suggested for the segmentation of tumor. An assessment of the experimental outcomes of the methodology suggested was focused on the coefficient of dice similarity, Hausdorff distance, Jaccard coefficient, Precision, Recall, Accuracy and F-measure. The experimental result of RBWMOA obtain better performance and shows0.845 Dice Similarity Coefficient, 7.231 Hausdorff distance in mm, 0.6981 Jaccard Coefficient, 95.67 % Precision, 94.72 % Recall, 98.29 % Accuracy and 95.19 % F-measure.
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Farzaneh, Negar, Craig A. Williamson, Cheng Jiang, Ashok Srinivasan, Jayapalli R. Bapuraj, Jonathan Gryak, Kayvan Najarian, and S. M. Reza Soroushmehr. "Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries." Diagnostics 10, no. 10 (September 30, 2020): 773. http://dx.doi.org/10.3390/diagnostics10100773.

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Detection and severity assessment of subdural hematoma is a major step in the evaluation of traumatic brain injuries. This is a retrospective study of 110 computed tomography (CT) scans from patients admitted to the Michigan Medicine Neurological Intensive Care Unit or Emergency Department. A machine learning pipeline was developed to segment and assess the severity of subdural hematoma. First, the probability of each point belonging to the hematoma region was determined using a combination of hand-crafted and deep features. This probability provided the initial state of the segmentation. Next, a 3D post-processing model was applied to evolve the initial state and delineate the hematoma. The recall, precision, and Dice similarity coefficient of the proposed segmentation method were 78.61%, 76.12%, and 75.35%, respectively, for the entire population. The Dice similarity coefficient was 79.97% for clinically significant hematomas, which compared favorably to an inter-rater Dice similarity coefficient. In volume-based severity analysis, the proposed model yielded an F1, recall, and specificity of 98.22%, 98.81%, and 92.31%, respectively, in detecting moderate and severe subdural hematomas based on hematoma volume. These results show that the combination of classical image processing and deep learning can outperform deep learning only methods to achieve greater average performance and robustness. Such a system can aid critical care physicians in reducing time to intervention and thereby improve long-term patient outcomes.
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YOKOYAMA, E., and M. UCHIMURA. "Optimal settings of fingerprint-type analysing computer software for the analysis of enterohaemorrhagic Escherichia coli pulsed-field gel electrophoresis patterns." Epidemiology and Infection 134, no. 5 (March 28, 2006): 1004–14. http://dx.doi.org/10.1017/s0950268806006145.

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Settings of fingerprint-type analysing computer software were optimized for analysis of enterohaemorrhagic Escherichia coli (EHEC) pulsed-field gel electrophoresis (PFGE) patterns. Under the lowest values of parameters, maximum value of similarities calculated using the Dice coefficient were obtained between PFGE patterns from one EHEC strain on the same gel when reference lanes for calibration of distortions during electrophoresis were set to every fourth lane. PFGE patterns of 15 EHEC strains on different gels were investigated. Similarity values calculated using the Pearson product-moment correlation coefficient (Pearson correlation) were significantly higher than those using the Dice coefficient with optimal values of parameters determined by the program (P<0·01). When PFGE patterns of 45 EHEC strains were analysed by the computer program, EHEC strains from one mass outbreak and three intra-family outbreaks were each clustered and the similarity values within the clusters were >90% using Pearson correlation.
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Dissertations / Theses on the topic "Dice Similarity Coefficient"

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Lewis, Robert R. "Similarity Estimation with Non-Transitive LSH." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162323979030229.

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Liao, Hanqing. "Textural features for bladder cancer definition on CT images." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7655.

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Genitourinary cancer refers to the presence of tumours in the genital or urinary organs such as bladder, kidney and prostate. In 2008 the worldwide incidence of bladder cancer was 382,600 with a mortality of 150,282. Radiotherapy is one of the main treatment choices for genitourinary cancer where accurate delineation of the gross tumour volume (GTV) on computed tomography (CT) images is crucial for the success of this treatment. Limited CT resolution and contrast in soft tissue organs make this difficult and has led to significant inter- and intra- clinical variability in defining the extent of the GTV, especially at the junctions of different organs. In addition the introduction of new imaging techniques and modalities has significantly increased the number of the medical images that require contouring. More advanced image processing is required to help reduce contouring variability and assist in handling the increased volume of data. In this thesis image analysis methodologies were used to extract low-level features such as entropy, moment and correlation from radiotherapy planning CT images. These distinctive features were identified and used for defining the GTV and to implement a fully-automatic contouring system. The first key contribution is to demonstrate that second-order statistics from co-occurrence matrices (GTSDM) give higher accuracy in classifying soft tissue regions of interest (ROIs) into GTV and non-GTV. Loadings of the principal components (PCs) of the GTSDM features were found to be consistent over different patients. Exhaustive feature selection suggested that entropies and correlations produced consistently larger areas under receiver operating characteristic (AUROC) curves than first-order features. The second significant contribution is to demonstrate that in the bladder-prostate junction, where the largest inter-clinical variability is observed, the second-order principal entropy from stationery wavelet denoised CT images (DPE) increased the saliency of the bladder prostate junction. As a result thresholding of the DPE produced good agreement between gold standard clinical contours and those produced by this approach with Dice coefficients. The third contribution is to implement a fully automatic and reproducible system for bladder cancer GTV auto-contouring based on classifying second-order statistics. The Dice similarity coefficients (DSCs) were employed to evaluate the automatic contours. It was found that in the mid-range of the bladder the automatic contours are accurate, but in the inferior and superior ends of bladder automatic contours were more likely to have small DSCs with clinical contours, which reconcile with the fact of clinical variability in defining GTVs. A novel male bladder probability atlas was constructed based on the clinical contours and volume estimation from the classification results. Registration of the classification results with this probabilistic atlas consistently increases the DSCs of the inferior slices.
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Book chapters on the topic "Dice Similarity Coefficient"

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Bouzidi, Dalenda, Fahmi Ghozzi, and Ahmed Fakhfakh. "Ant Colony Optimization with BrainSeg3D Protocol for Multiple Sclerosis Lesion Detection." In Lecture Notes in Computer Science, 234–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_19.

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AbstractMagnetic resonance imaging (MRI) has quickly established itself as the reference imaging tool for the management of patients suffering from multiple sclerosis (MS), both for the diagnosis and the follow-up of the evolution and evaluation of the impact of new therapies.The treatment of multiple sclerosis does not cure the disease, but it slows its progression and can help to space out attacks. In this paper, tumor segmentation is treated as a problem of classification using the Ant Colony optimization algorithm (ACO) combined with a proposed protocol based on BrainSeg3D tools. Many studies and many existing approaches tend the multiple sclerosis (MS) which is a chronic inflammatory anomaly of the central nervous system.The aim of this work is to evaluate and to verify the effectiveness of the proposed protocol on a public longitudinal database which contains 20 MS patients. This study is concerned with comparing these results against the ground truth performed by two experts and against other methods namely Dissimilarity Map (DM) creation and segmentation in terms of Dice Similarity Coefficient (DSC).
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Meyer, Philip, Dominik Müller, Iñaki Soto-Rey, and Frank Kramer. "COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210223.

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Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient.
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Sahu, Satya Praksh, and Bhawna Kamble. "A Hybrid Approach for 3D Lung Segmentation in CT Images Using Active Contour and Morphological Operation." In Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering, 163–75. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2120-5.ch009.

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Lung segmentation is the initial step for detection and diagnosis for lung-related abnormalities and disease. In CAD system for lung cancer, this step traces the boundary for the pulmonary region from thorax in CT images. It decreases the overhead for a further step in CAD system by reducing the space for finding the ROIs. The major issue and challenging task for the segmentation is the inclusion of juxtapleural nodules in the segmented lungs. The chapter attempts 3D lung segmentation of CT images using active contour and morphological operations. The major steps in the proposed approach contain: preprocessing through various techniques, Otsu's thresholding for the binarizing the image; morphological operations are applied for elimination of undesired region and, finally, active contour for the segmentation of the lungs in 3D. For experiment, 10 subjects are taken from the public dataset of LIDC-IDRI. The proposed method achieved accuracies 0.979 Jaccard's similarity index value, 0.989 Dice similarity coefficient, and 0.073 volume overlap error when compared to ground truth.
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Sahu, Satya Praksh, and Bhawna Kamble. "A Hybrid Approach for 3D Lung Segmentation in CT Images Using Active Contour and Morphological Operation." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 722–34. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch036.

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Lung segmentation is the initial step for detection and diagnosis for lung-related abnormalities and disease. In CAD system for lung cancer, this step traces the boundary for the pulmonary region from thorax in CT images. It decreases the overhead for a further step in CAD system by reducing the space for finding the ROIs. The major issue and challenging task for the segmentation is the inclusion of juxtapleural nodules in the segmented lungs. The chapter attempts 3D lung segmentation of CT images using active contour and morphological operations. The major steps in the proposed approach contain: preprocessing through various techniques, Otsu's thresholding for the binarizing the image; morphological operations are applied for elimination of undesired region and, finally, active contour for the segmentation of the lungs in 3D. For experiment, 10 subjects are taken from the public dataset of LIDC-IDRI. The proposed method achieved accuracies 0.979 Jaccard's similarity index value, 0.989 Dice similarity coefficient, and 0.073 volume overlap error when compared to ground truth.
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Khoulqi, Ichrak, Najlae Idrissi, and Muhammad Sarfraz. "Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, 722–38. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7136-4.ch038.

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Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.
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Banu, Syeda Furruka, Md Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Hatem A. Rashwan, and Domenec Puig. "WEU-Net: A Weight Excitation U-Net for Lung Nodule Segmentation." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210154.

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Lung cancer is a dangerous non-communicable disease attacking both women and men and every year it causes thousands of deaths worldwide. Accurate lung nodule segmentation in computed tomography (CT) images can help detect lung cancer early. Since there are different locations and indistinguishable shapes of lung nodules in CT images, the accuracy of the existing automated lung nodule segmentation methods still needs further enhancements. In an attempt towards overcoming the above-mentioned challenges, this paper presents WEU-Net; an end-to-end encoder-decoder deep learning approach to accurately segment lung nodules in CT images. Specifically, we use a U-Net network as a baseline and propose a weight excitation (WE) mechanism to encourage the deep learning network to learn lung nodule-relevant contextual features during the training stage. WEU-Net was trained and validated on a publicly available CT images dataset called LIDC-IDRI. The experimental results demonstrated that WEU-Net achieved a Dice score of 82.83% and a Jaccard similarity coefficient of 70.55%.
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Khoulqi, Ichrak, Najlae Idrissi, and Muhammad Sarfraz. "Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies, 162–77. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4444-0.ch009.

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Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.
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Mopuru, Bhargavi, and Yellamma Pachipala. "Distribution Diversity Method of Feature Optimization (DDMFO) to Defend the Intrusion Practices on IoT Networks." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220012.

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The new forms of networks labeled IoT are relatively new and which become buzz in this decade. The network architecture lets any smart device loosely connect to the Internet under internet protocol. However, the other dimension of this network facilitates intruders to access the network with no critical efforts. The context of intrusions has been delineated as intrusion practices of other devices connected to an IoT network that are connected to external networks through a gateway. Vice versa, the compromised IoT network intends to communicate with external devices or networks to perform intrusion practices. In this regard, intrusion detection through machine learning demands significant feature selection and optimization techniques. This manuscript endeavored to demonstrate the scope distribution diversity assessment methods of traditional statistical practices toward feature selection and optimization in this regard, the contribution “Distribution Diversity Method of Feature Optimization (DDMFO) to Protect Intrusion Practices on IoT Networks” of this paper uses the Dice Similarity Coefficient procedure to pick the optimum characteristics for the training of the classifier. The classifier that has been adopted in this contribution is Naïve Bayes, trained by the features selected by the proposal. The experimental research concludes the significance of the taxonomy, which demonstrates substantial accuracy and minimal false alarm.
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Conference papers on the topic "Dice Similarity Coefficient"

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Stephanie, Cecilia, and Riyanarto Sarno. "Detecting Business Process Anomaly Using Graph Similarity Based on Dice Coefficient, Vertex Ranking and Spearman Method." In 2018 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2018. http://dx.doi.org/10.1109/isemantic.2018.8549830.

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Vijay, Amishi, Jasleen Saini, and B. S. Saini. "A Review of Brain Tumor Image Segmentation of MR Images Using Deep Learning Methods." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.19.

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A significant analysis is routine for Brain Tumor patients and it depends on accurate segmentation of Region of Interest. In automatic segmentation, field deep learning algorithms are attaining interest after they have performed very well in various ImageNet competitions. This review focuses on state-of-the-art Deep Learning Algorithms which are applied to Brain Tumor Segmentation. First, we review the methods of brain tumor segmentation, next the different deep learning algorithms and their performance measures like sensitivity, specificity and Dice similarity Coefficient (DSC) are discussed and Finally, we discuss and summarize the current deep learning techniques and identify future scope and trends.
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Colleoni Couto, Julia, Olimar Teixeira Borges, and Duncan Dubugras Ruiz. "Automatized Bioinformatics Data Integration in a Hadoop-based Data Lake." In 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120912.

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When we work in a data lake, data integration is not easy, mainly because the data is usually stored in raw format. Manually performing data integration is a time-consuming task that requires the supervision of a specialist, which can make mistakes or not be able to see the optimal point for data integration among two or more datasets. This paper presents a model to perform heterogeneous in-memory data integration in a Hadoop-based data lake based on a top-k set similarity approach. Our main contribution is the process of ingesting, storing, processing, integrating, and visualizing the data integration points. The algorithm for data integration is based on the Overlap coefficient since it presented better results when compared with the set similarity metrics Jaccard, Sørensen-Dice, and the Tversky index. We tested our model applying it on eight bioinformatics-domain datasets. Our model presents better results when compared to an analysis of a specialist, and we expect our model can be reused for other domains of datasets.
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Botero, Ulbert J., David Koblah, Daniel E. Capecci, Fatemeh Ganji, Navid Asadizanjani, Damon L. Woodard, and Domenic Forte. "Automated Via Detection for PCB Reverse Engineering." In ISTFA 2020. ASM International, 2020. http://dx.doi.org/10.31399/asm.cp.istfa2020p0157.

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Abstract Reverse engineering (RE) is the only foolproof method of establishing trust and assurance in hardware. This is especially important in today's climate, where new threats are arising daily. A Printed Circuit Board (PCB) serves at the heart of virtually all electronic systems and, for that reason, a precious target amongst attackers. Therefore, it is increasingly necessary to validate and verify these hardware boards both accurately and efficiently. When discussing PCBs, the current state-of-the-art is non-destructive RE through X-ray Computed Tomography (CT); however, it remains a predominantly manual process. Our work in this paper aims at paving the way for future developments in the automation of PCB RE by presenting automatic detection of vias, a key component to every PCB design. We provide a via detection framework that utilizes the Hough circle transform for the initial detection, and is followed by an iterative false removal process developed specifically for detecting vias. We discuss the challenges of detecting vias, our proposed solution, and lastly, evaluate our methodology not only from an accuracy perspective but the insights gained through iteratively removing false-positive circles as well. We also compare our proposed methodology to an off-the-shelf implementation with minimal adjustments of Mask R-CNN; a fast object detection algorithm that, although is not optimized for our application, is a reasonable deep learning model to measure our work against. The Mask R-CNN we utilize is a network pretrained on MS COCO followed by fine tuning/training on prepared PCB via images. Finally, we evaluate our results on two datasets, one PCB designed in house and another commercial PCB, and achieve peak results of 0.886, 0.936, 0.973, for intersection over union (IoU), Dice Coefficient, and Structural Similarity Index. These results vastly outperform our tuned implementation of Mask R-CNN.
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Данилов, Вячеслав, Viacheslav Danilov, Игорь Скирневский, Igor Skirnevskiy, Роман Манаков, Roman Manakov, Дмитрий Колпащиков, Dmitrii Kolpashchikov, Ольга Гергет, and Olga Gerget. "Segmentation Algorithm Based on Square Blocks Propagation." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-148-154.

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Abstract:
This research is devoted to the segmentation of heart and brain anatomical structures. In the study, we present a segmentation algorithm based on the square blocks (superpixels) propagation. The square blocks propagation algorithm checks two criteria. For the first criteria, the current intensity of the pixel is compared to the average intensity of the segmented region. For the second criterion, the intensity difference of the pixels lying on the superpixel sides is compared to the threshold. Once these criteria are successfully checked, the algorithm merges homogeneous superpixels into one region. Then the following superpixels are attached to the final superpixel set. The last step of the proposed method is the spline generation. The spline delineates the borders of the region of interest. The main parameter of the algorithm is the size of a square block. The cardiac MRI dataset of the University of York and the brain tumor dataset of Southern Medical University were used to estimate the segmentation accuracy and processing time. The highest Dice similarity coefficients obtained by the presented algorithm for the left ventricle and the brain tumor are 0.93±0.03 and 0.89±0.07 respectively. One of the most important features of the border detection step is its scalability. It allows implementing different one-dimensional methods for border detection.
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