Threshold-Based Level Set Method of Image Segmentation

Xu, Anping; Wang, Lijuan; Feng, Sha; Qu, Yanji

doi:10.1109/icinis.2010.181

Cited by 37 publications

(17 citation statements)

References 5 publications

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“…Results thus obtained outperform the Canny Operators results, because it performs edge detection and segmentation at the same time. Anping XU [4] proposed a threshold based segmentation and Fast marching method (FMM) for medical image segmentation [5]. The result of de-noising filter is passed to FMM for segmentation purpose with the help of threshold based level set technique.…”

Section: Threshold Based Segmentationmentioning

confidence: 99%

Review Study on Digital Image Processing and Segmentation

Shashi¹,

Suchithra²

2019

AJCST

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Image segmentation is in fact one of the most fundamental approach of digital image processing. In image processing, segmentation playa an important role. It may be defined as partitioning an image into meaning full regions or objects. In other words we can say that process of segmentation keeps on dividing an image into its constitute sub parts. The level to which the subdivision is carried on depends on type of problem to be solved by researchers. This segmentation process continues unless area of interest is isolated. Set of segment or set of contours that are extracted from the image is the main result of image segmentation. There are various application of image segmentation like locating tumors or other pathologies, measuring tissue volume, surgery aided by computer, treatment and planning, study of various anatomical structure, locating objects in satellite images, fingerprint. There are various types of generalized algorithm and methodology that are developed for image segmentation. Some common technique of image segmentation such as edge detection, thresh holding, region growing and clustering are taken for this study. In fact segmentation algorithm are based on two properties similarity and discontinuity. This paper concentrates on the various methods that are widely used to segment the image.

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Section: Threshold Based Segmentationmentioning

confidence: 99%

Review Study on Digital Image Processing and Segmentation

Shashi¹,

Suchithra²

2019

AJCST

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“…Anping XU [16] proposed a threshold-based level set approach comprising both threshold based segmentation and Fast Marching Method (FMM) for medical image segmentation. The result of de-noising filter is passed to FMM for segmentation purpose with the help of threshold based level set technique.…”

Section: ____________________________________________________________ 28mentioning

confidence: 99%

Medical Image Segmentation Techniques, a Literature Review, and Some Novel Trends

Mahmoud

El-Rabaie

Taha

et al. 2018

Menoufia Journal of Electronic Engineering Research

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“…These include traditional methods, like the histogram of oriented gradients (HOG) [23,24], the scale-invariant feature transform (SIFT) [25], and the features from accelerated segment test (FAST) [26]. Thresholding methods have also been widely used for grayscale image segmentation [27][28][29]. In addition, k-means [7] and support vector machines (SVMs) [30] have been employed to segment images.…”

Section: Introductionmentioning

confidence: 99%

FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network

Zhang

Men

Yang

et al. 2020

Biomed. Opt. Express

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A custom convolutional neural network (CNN) integrated with convolutional long short-term memory (LSTM) achieves accurate 3D (2D + time) segmentation in cross-sectional videos of the Drosophila heart acquired by an optical coherence microscopy (OCM) system. While our previous FlyNet 1.0 model utilized regular CNNs to extract 2D spatial information from individual video frames, convolutional LSTM, FlyNet 2.0, utilizes both spatial and temporal information to improve segmentation performance further. To train and test FlyNet 2.0, we used 100 datasets including 500,000 fly heart OCM images. OCM videos in three developmental stages and two heartbeat situations were segmented achieving an intersection over union (IOU) accuracy of 92%. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified.

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Threshold-Based Level Set Method of Image Segmentation

Cited by 37 publications

References 5 publications

Review Study on Digital Image Processing and Segmentation

Review Study on Digital Image Processing and Segmentation

Medical Image Segmentation Techniques, a Literature Review, and Some Novel Trends

FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network

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