Image-Based Quantitative Analysis of Gold Immunochromatographic Strip via Cellular Neural Network Approach

Zeng, Nianyin; Wang, Zidong; Zineddin, Bachar; Li, Yurong; Du, Min; Xiao, Liang; Liu, Xiaohui; Young, Terry

doi:10.1109/tmi.2014.2305394

Cited by 139 publications

(55 citation statements)

References 35 publications

(58 reference statements)

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“…Several studies in the literature explore image‐based quantitative analysis of biomaterials . Studies on automated analysis and validation of anti‐PTBP1 antibody are required due to several reasons as explained in Section 1.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies in the literature explore image-based quantitative analysis of biomaterials. [34][35][36] Studies on automated analysis and validation of anti-PTBP1 antibody are required due to several reasons as explained in Section 1. Therefore, the computerized system that allows automated image analysis to be used in routine neurosurgical biopsies was developed.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative validation of anti‐PTBP1 antibody for diagnostic neuropathology use: Image analysis approach

Göçeri

Goksel

Elder

et al. 2017

Numer Methods Biomed Eng

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Problem Diagnostic neuropathology traditionally relies on subjective interpretation of visual data obtained from a brightfield microscope. This results in high variability, inability to multiplex, and unsatisfactory reproducibility even among experts. These diagnostic problems may affect patient outcomes and confound clinical decision-making. Furthermore, standard histological processing of pathological specimens results in auto-fluorescence and other artifacts, which have nearly blocked the implementation of fluorescent microscopy in diagnostic pathology. Thus, generation of objective and quantitative methodologies would augment the toolbox available to neuropathologists, which would ultimately enhance clinical decision making. Objective To develop image analysis methods to quantitatively validate anti-PTBP1 antibody for use in diagnostic neuropathology. Method We propose a computerized image analysis method to validate anti-PTBP1 antibody. Images were obtained from standard neuropathological specimens stained with anti-PTBP1 antibody. First, the noise characteristics of the images were modeled and images are de-noised according to the noise model. Next, images are filtered with sigma-adaptive Gaussian filtering for local normalization, and cell nuclei are detected and segmented with a k-means based deterministic approach. Result Experiments on 29 data sets from three cases of brain tumor (recurrent glioma, primary resections of glioblastoma harboring the EGFRVIII mutation, pilocytic astrocytoma) and reactive gliosis show statistically significant differences between the number of positively stained nuclei in images stained with and without anti-PTBP1 antibody (p values, t-test, are 40×10−4, 33×10−4, 6×10−4 and 46×10−3, respectively). Conclusion The experimental of analysis of specimens from three different brain tumor groups and one reactive gliosis group indicate the feasibility of using anti-PTBP1 antibody in diagnostic neuropathology and computerized image analysis provides a systematic and quantitative approach to explore feasibility.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantitative validation of anti‐PTBP1 antibody for diagnostic neuropathology use: Image analysis approach

Göçeri

Goksel

Elder

et al. 2017

Numer Methods Biomed Eng

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“…Extensive research has been proposed and new research is still performed regarding the provision of solutions for malware detection systems [13,14]. For instance, in the case of known malware, content signatures-based methods that map samples of activities against known malware have been proposed [15,16]. Nevertheless, these methods have weaknesses when presented with obfuscated malware, metamorphic or polymorphic techniques to hide malware, and unknown malware.…”

Section: Related Workmentioning

confidence: 99%

Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

Bruce¹,

Kim

Kang

et al. 2019

Applied Sciences

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Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.

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“…Therefore, most dynamic analysis research works rely on real-time behavioral patterns. It is shown that patterns such as API-call sequence as well as control flow as major features that capture malware behavior [15,16]. However, dynamic analysis approaches are also imperfect.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble-Based Malware Detection Model Using Minimum Feature Set

Zelinka

Amer²

2019

mendel

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Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

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Image-Based Quantitative Analysis of Gold Immunochromatographic Strip via Cellular Neural Network Approach

Cited by 139 publications

References 35 publications

Quantitative validation of anti‐PTBP1 antibody for diagnostic neuropathology use: Image analysis approach

Quantitative validation of anti‐PTBP1 antibody for diagnostic neuropathology use: Image analysis approach

Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

An Ensemble-Based Malware Detection Model Using Minimum Feature Set

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