Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

2020 ◽  
Vol 20 (03) ◽  
pp. 2050018
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Seed Selection ◽  
Seeded Region Growing ◽  
Processing Strategies ◽  
Edge Based ◽  
Seed Points ◽  
Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

Medical image segmentation using 3D seeded region growing

1997 ◽  
Author(s):  
R. K. Justice ◽  
Ernest M. Stokely ◽  
John S. Strobel ◽  
Raymond E. Ideker ◽  
William M. Smith
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Region Growing ◽  
Medical Image Segmentation ◽  
Seeded Region Growing

scholarly journals Improving Parameters Selection of a Seeded Region Growing Method for Multiband Image Segmentation

2015 ◽  
Vol 13 (3) ◽  
pp. 843-849 ◽  
Author(s):  
Javier Sanchez Hernandez ◽  
Estibaliz Martinez Izquierdo ◽  
Agueda Arquero Hidalgo
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Seeded Region Growing ◽  
Parameters Selection ◽  
Selection Of

scholarly journals Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks

2021 ◽  
Vol 12 (1) ◽  
pp. 29-39
Author(s):  
Lars J. Isaksson ◽  
Paul Summers ◽  
Sara Raimondi ◽  
Sara Gandini ◽  
Abhir Bhalerao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Data Augmentation ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Surface Distance ◽  
Experienced Radiologist ◽  
Generalization Problem

Abstract Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.

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