Automated advanced image processing for micromodel flow experiments; an application using labVIEW

Mahmoodi, Maziyar; James, Lesley; Johansen, T. H.

doi:10.1016/j.petrol.2018.02.031

Cited by 28 publications

(15 citation statements)

References 19 publications

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“…(1) IF yellow tint to skin or eyes=Yes THEN Disease="sick The laboratory virtual instrument engineering workbench (LabVIEW17) is a platform for system design and a development environment that utilizes a visual programming language [15]; this environment was utilized in the present work. Figure 5 shows the block diagram for symptom-based diagnosis.…”

Section: Symptom-based Diagnosismentioning

confidence: 99%

A Fuzzy Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions

Kamel

El-Mougi

2020

IJFIS

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Decision support systems improve medical diagnosis and minimize diagnostic errors. Existing diagnostic systems are often complex and exhibit limited performance on liver diseases, particularly the liver cancer. This paper presents a fuzzy decision support system for helping students diagnose some human liver diseases in educational medical institutions. The proposed system aims to improve real medical diagnosis processes. The approach has three basic steps: 1) symptoms-based diagnosis, 2) liver function-based diagnosis, and 3) image processingbased diagnosis. The proposed system employs two artificial intelligence techniques: fuzzy logic and image processing. The first is used for diagnosing liver diseases based on the liver function tests, while the second is used for diagnosing liver diseases such as the liver cancer, hepatitis, liver cirrhosis, liver fibrosis, and fatty liver. The proposed system combines two methods: the Mamdani inference and simulation method used in the MATLAB17 fuzzy logic toolbox, and the gray level co-occurrence matrix, for extracting the features of the secondorder statistical texture of images acquired using computed tomography, magnetic resonance imaging, or ultrasound, for various liver diseases. Our results reveal a very good agreement between expert-made and system-made diagnoses, suggesting high accuracy.

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Section: Symptom-based Diagnosismentioning

confidence: 99%

A Fuzzy Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions

Kamel

El-Mougi

2020

IJFIS

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“…(1) IF E_B = Yes ^ W_L = Yes ^ L_A = Yes ^ D_B = Yes THEN Disease = "Lung_cancer" Programming rules in laboratory virtual instrument engineering workbench (LabVIEW17), which is a system design platform and development environment for a visual programming language from national instruments (NI) [22]. Figure 5 shows sample of cases for the block diagram of the proposed system KB.…”

Section: Diagnosis Via Knowledge Basementioning

confidence: 99%

A Novel Intelligent System for Diagnosing some of Humans' Respiratory System Diseases

E.¹,

E.²,

A³

2019

IJCA

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This paper presents a novel intelligent system for diagnosing some of humans' respiratory diseases. The proposed system aims to simulate the real medical diagnosing processes. It was adopted in diagnosis on two main parts; knowledge base (KB) and image processing (IP). This paper combines two diagnostic methods; the decision tree (DT) method which used J48 algorithm in WEKA 3.7, and the gray level cooccurrence matrix (GLCM) method for extracting second order statistical texture features of chest x-ray images. The weighted euclidean distance (WED) algorithm was used for feature matching. The final decision calculated by probability measure method for independent events, which is depending on "multiplication rule". The proposed system implemented via visual studio.net 2017; used for designing the main graphical user interface (GUI), MATLAB17; used for image processing diagnoses, and LabVIEW17; used for knowledge base diagnoses. The obtained results show that there is a good agreement between expert's diagnoses and proposed system diagnoses with a high accuracy.

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“…In this paper, the OCR function in the visual development module of LabVIEW software [10] is used to train and recognize traffic signs. And the machine vision program created by OCR function is used to complete recognition.…”

Section: Ocr Training For Recognitionmentioning

confidence: 99%

Research of Traffic Sign Recognition Technology Based on Machine Vision

Li¹,

Chen²,

Li³

et al. 2019

dteees

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Automated advanced image processing for micromodel flow experiments; an application using labVIEW

Cited by 28 publications

References 19 publications

A Fuzzy Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions

A Fuzzy Decision Support System for Diagnosis of Some Liver Diseases in Educational Medical Institutions

A Novel Intelligent System for Diagnosing some of Humans' Respiratory System Diseases

Research of Traffic Sign Recognition Technology Based on Machine Vision

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