Systematic mapping study on diagnosis of vulnerable plaque

Rezaei, Zahra; Selamat, Ali; Taki, Arash; Rahim, Mohd Shafry Mohd; Kadir, Mohammed Rafiq Abdul

doi:10.1007/s11042-019-7465-z

Cited by 6 publications

(2 citation statements)

References 85 publications

(158 reference statements)

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“…CTA finds the HRPC rate in CVD and provides the necessary data for the diagnosis process. CTA reduces the error rate and false identification rate in the HRPC analysis process [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Wang

Ren²,

2022

BioMed Research International

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Cardiovascular disease detection and analysis using ultrasonic imaging expels errors in manual clinical trials with precise outcomes. It requires a combination of smart computing systems and intelligent image processors. The disease characteristics are analyzed based on the configuration and precise tuning of the processing device. In this article, a characteristic extraction technique (CET) using knowledge learning (KL) is introduced to improve the analysis precision. The proposed method requires optimal selection of disease features and trained similar datasets for improving the characteristic extraction. The disease attributes and accuracy are identified using the standard knowledge update. The image and data features are segmented using the variable processor configuration to prevent false rates. The false rates due to unidentifiable plaque characteristics result in weak knowledge updates. Therefore, the segmentation and data extraction are unanimously performed to prevent feature misleads. The knowledge base is updated using the extracted and identified plaque characteristics for consecutive image analysis. The processor configurations are manageable using the updated knowledge and characteristics to improve precision. The proposed method is verified using precision, characteristic update, training rate, extraction ratio, and time factor.

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“…CTA finds the HRPC rate in CVD and provides the necessary data for the diagnosis process. CTA reduces the error rate and false identification rate in the HRPC analysis process [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Wang

Ren²,

2022

BioMed Research International

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“…The Level Set image segmentation algorithm is widely used in the field of image processing and is now frequently employed in image segmentation. It is one of the active contour models that can handle complex topologies and capture boundaries and is specially used in images with intensity inhomogeneity, such as medical images [25] [26]. Researchers often prefer this method because it is flexible, easy to understand, and easy to employ.…”

Section: B Segmentation 1) Level Set Algorithmmentioning

confidence: 99%

Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

Abaei,

Rezaei,

Mian

et al. 2023

IJACSA

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Mammography is a non-invasive method to study breast tissues for abnormalities. Computer-aided diagnosis (CAD) can automate the process of diagnosing malignant and benign tumors accurately. However, accurate results can be hampered by the presence of the pectoral muscle, which has a similar opacity to the breast tissue area. Detecting and removing pectoral muscles is not trivial due to various factors, and there are artifacts present near the pectoral muscle that can hamper proper segmentation. Given the significance of the topic, it is crucial to devise an accurate method for automatically detecting the muscle area in a mammography image and eliminating it from the rest of the image. This process of removing the pectoral muscle from the breast image can aid in precise segmentation and diagnosis of the tumor area, ultimately leading to faster diagnosis and better outcomes for patients. This study examined two segmentation algorithms, Level Set and Region Growing, for segmenting the pectoral muscle. An Improved Region Growingbased (IRG) algorithm was also proposed and showed promising results in automatically segmenting the pectoral muscle. All algorithms were tested on the MIAS dataset, and radiologists evaluated the results, showing an accuracy rating of up to 83% for IRG. The results indicated that IRG outperformed Level Set considerably due to many optimizations and modifications. IRG can be used as part of the preprocessing unit of an automated cancer diagnosis system.

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Spectral clustering based on extended deep ensemble auto encoder with eagle strategy

Gheytasi,

Yaghoubyan,

Rezaei

et al. 2023

Multimed Tools Appl

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Systematic mapping study on diagnosis of vulnerable plaque

Cited by 6 publications

References 85 publications

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Ultrasonic Imaging of Cardiovascular Disease Based on Image Processor Analysis of Hard Plaque Characteristics

Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

Spectral clustering based on extended deep ensemble auto encoder with eagle strategy

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