Medical Image Segmentation Using Active Contours and a Level Set Model: Application to Pulmonary Embolism (PE) Segmentation

Ebrahimdoost, Yousef; Dehmeshki, Jamshid; Ellis, Tim; Firoozbakht, Mohsen; Youannic, A.; Qanadli, Salah D.

doi:10.1109/icds.2010.64

Cited by 14 publications

(5 citation statements)

References 11 publications

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Section: Related Workmentioning

confidence: 99%

“…Therefore, Ebrahimdoost et al (2010) presented a medical image segmentation approach to deal with improper algorithm. Ebrahimdoost et al (2010) used active contour and a level set model to design a thresholding approach for pulmonary embolism segmentation. Since level-set segmentation is not pragmatic with low contrast image, they attempted to resolve the problem by taking the advantages of hybrid speed function that is formulated based on image gradient and intensity.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Heidari¹,

Abdullah²,

Abdullah³

2015

Journal of Computer Science

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Abstract:In automated pulmonary nodules extraction and lung disease diagnosis by image processing techniques, image segmentation is utilized as a primary and the most essential step of lung tumor analysis. But due to extensive similarity between pulmonary vessels, bronchus and arteries in lung region and the low contrast of the ComputedTomography (CT) image the accuracy of lung tumor diagnosis is highly dependent on the precision of segmentation. Therefore, precise lung CT image segmentation has become a challenging preprocessing task for every lung disease pathological application.In this study, a novel FourDirectional Thresholding (FDT) technique is introduced. This propounded technique segments the pulmonary parenchyma in Computed-Tomography (CT) images using the Similarity-Based Segmentation (SBS). The proposed technique aims to augment the precision of the CT image thresholding by implementing an advanced thresholding approach from four different directions in which the determination of pixels' value as being either on foreground or background is highly dependent on its adjacent pixel's intensity value and the final decision is made based on all four directions' thresholding results. In this study the importance of neighbor pixels in precision of thresholding with FDT technique is demonstrated and the effectiveness of FDT method has been evaluated on different CT images. Eventually the result of segmentation using FDT method is compared by other precursors techniques, which corroborates the high exactitude of proposed technique.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Heidari¹,

Abdullah²,

Abdullah³

2015

Journal of Computer Science

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show abstract

“…It is not able to detect objects in images with low-contrast boundaries [5]. The geometric active contour models are classified into edge-based and region-based models.…”

Section: Image Segmentation Based On Level Setmentioning

confidence: 99%

Biometric Foetal Contour Extraction using Hybrid Level Set

Jaiswal¹,

Satarkar²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

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“…The detection sensitivity of 177 clinical cases was 81%. Nowadays, the neural network method has achieved much attention in PE recognition [114][115][116]. Scott et al proved that radiologists can improve their interpretations of PE diagnosis by incorporating computer output in formulating diagnostic prediction [117].…”

Section: Pulmonary Embolism (Pe)mentioning

confidence: 99%

Survey on deep learning for pulmonary medical imaging

Song

Tian

et al. 2019

Front. Med.

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As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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Medical Image Segmentation Using Active Contours and a Level Set Model: Application to Pulmonary Embolism (PE) Segmentation

Cited by 14 publications

References 11 publications

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Biometric Foetal Contour Extraction using Hybrid Level Set

Survey on deep learning for pulmonary medical imaging

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