Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

Danilov, Alexander; Pryamonosov, Roman; Yurova, Alexandra

doi:10.3390/computation4030035

Cited by 8 publications

(8 citation statements)

References 45 publications

(67 reference statements)

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“…In [46], several image processing techniques are examined for cardiovascular biomedical applications. Gray level analysis is also employed taking into account anatomical peculiarities of the patient.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

“…where Ph C p is the intensity's level probability distribution and ω C k is the occurrence probability of each threshold T k . As already described in the previous section, an entropy-based segmentation in gray scale, for cardiovascular biomedical applications is also presented in [46]. The statistical analysis followed in [32] for skin disease diagnosis is also based on the entropy of the image.…”

Section: Segmentation Methodsmentioning

confidence: 99%

“…The datasets used in these references are publicly available image libraries like digital database for screening mammography (DDSM) and the mammographic image analysis society (mini-MIAS). Cardiovascular disease detection is also another domain where image processing techniques are applied on coaxial tomography (CT) scans [46]. Available image datasets for cardiovascular diseases can be retrieved by Osiris DICOM (http://www.osirix-viewer.…”

Section: Introductionmentioning

confidence: 99%

“…Available image datasets for cardiovascular diseases can be retrieved by Osiris DICOM (http://www.osirix-viewer. com/datasets) [46]. Carotid arteries also offer significant information about cardiovascular diseases and ultrasound imaging can also be employed in this case [47].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.

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Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

Section: Segmentation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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“…Machine learning techniques can be considered a helpful tool, offering a first step in extracting useful and valuable information from healthcare data and gaining insights into the prediction of patient's LoS and on the major factors and elements which affect it. To this end, research focused on the application of machine learning on patients' data for the development of accurate and intelligent Decision Support Systems (DSS) [3][4][5][6][7][8][9]. Specifically, an academic DSS is a knowledge-based information system which captures, handles, and analyzes information which affects or is intended to affect decision making performed by people in the scope of a professional task appointed by a user [10].…”

Section: Introductionmentioning

confidence: 99%

Decision Support Software for Forecasting Patient’s Length of Stay

et al. 2018

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Length of stay of hospitalized patients is generally considered to be a significant and critical factor for healthcare policy planning which consequently affects the hospital management plan and resources. Its reliable prediction in the preadmission stage could further assist in identifying abnormality or potential medical risks to trigger additional attention for individual cases. Recently, data mining and machine learning constitute significant tools in the healthcare domain. In this work, we introduce a new decision support software for the accurate prediction of hospitalized patients’ length of stay which incorporates a novel two-level classification algorithm. Our numerical experiments indicate that the proposed algorithm exhibits better classification performance than any examined single learning algorithm. The proposed software was developed to provide assistance to the hospital management and strengthen the service system by offering customized assistance according to patients’ predicted hospitalization time.

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Automated segmentation of abdominal organs from contrast‐enhanced computed tomography using analysis of texture features

Danilov

Yurova

2020

Numer Methods Biomed Eng

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Generation of three-dimensional personalized geometric models of anatomical structures is an important process for many practical tasks: computer-aided diagnosis, treatment planning and numerical modeling in biomedical applications. Despite many efforts done by different research groups, automatic segmentation of organs still does not have any general solution. The main difficulties are caused by peculiarities of different medical imaging modalities, image variability (for the same modality) resulting from the wide range of imaging devices, noise and artifacts, large patient anatomical variability and overlapping of intensity ranges of neighboring anatomical structures. In this article, we propose segmentation method based on analysis of texture features and developed specially for segmentation of abdominal organs. Its main advantage is robustness to interpatient gray level and anatomical variability.The proposed method was validated on the patient data. The method implementation was accelerated using graphics processing unit (GPU). K E Y W O R D Sabdomen segmentation, personalized anatomical models, texture analysis

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Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

Cited by 8 publications

References 45 publications

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Decision Support Software for Forecasting Patient’s Length of Stay

Automated segmentation of abdominal organs from contrast‐enhanced computed tomography using analysis of texture features

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