State-of-the-Art of Level-Set Methods in Segmentation and Registration of Spectral Domain Optical Coherence Tomographic Retinal Images

Padmasini, N.; Umamaheswari, R.; Sikkandar, Mohamed Yacin

doi:10.1016/b978-0-12-813087-2.00009-9

Cited by 8 publications

(6 citation statements)

References 49 publications

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“…Image quality has impact on segmentation process for it has to do with feature extraction, model matching, and object recognition [72]. Rupal et al (2018) determined three soft tissues in normal brain using MRI technique, such as gray matter (GM), white matter, and CSF.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

“…Rigid is known as image coordinate transformation and only involves translation and rotation processes. Transformation maps parallel lines fixed with parallel lines is affine for map lines onto maps is projective and map lines on curves is curved or elastic [72].…”

Section: Medical Image Registrationmentioning

confidence: 99%

“…The most necessity to transmit from source to destination using appropriate method rely on; selecting modality into spatial alignment, and the fusion that is necessary for showing integrated data [72].…”

Section: Medical Image Registrationmentioning

confidence: 99%

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Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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Section: Medical Image Segmentationmentioning

confidence: 99%

Section: Medical Image Registrationmentioning

confidence: 99%

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Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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“…The Otsu method is one of a global adaptive binarization threshold-based image segmentation algorithm [6]. The method work by finding the best threshold value between pixel values 0 and 255 by calculating and evaluating their between-class variance (or within-class variance) [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Enhancement and Segmentation of Ziehl Neelson Sputum Slide Images using Contrast Enhancement and Otsu Threshold Technique

Yusoff

Raof²,

Mahrom³

et al. 2023

ARASET

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Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.

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“…To extract the contour on land/water boundary, the method relies on a global threshold value derived by means of Otsu method [54]. This is a key component of the method since determines its simplicity in comparison to locally adaptive thresholding algorithm as it does not require the partitioning of the whole image in sub-imaged or statistic examination of the intensity values of the local neighborhood of each pixel [55]. The method allows for a fully automatic detection of the coastlines in a short amount of time.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Spinosa

Ziemba

Saponieri

et al. 2021

JMSE

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Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

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State-of-the-Art of Level-Set Methods in Segmentation and Registration of Spectral Domain Optical Coherence Tomographic Retinal Images

Cited by 8 publications

References 49 publications

Review Research of Medical Image Analysis Using Deep Learning

Review Research of Medical Image Analysis Using Deep Learning

Enhancement and Segmentation of Ziehl Neelson Sputum Slide Images using Contrast Enhancement and Otsu Threshold Technique

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

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