Brain MR image segmentation using NAMS in pseudo-color

Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong

doi:10.1080/24699322.2017.1389395

Cited by 14 publications

(3 citation statements)

References 18 publications

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“…A pseudo-colour translation is a method to augment the colour contrast of the input grey image, which can improve the accuracy of the brain segmentation performance (Li et al, 2017). The translation process is to obtain practical features and intensify the visual by mapping a single channel of the grey level pixel into Red, Green, and Blue channels (Afruz et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Isa¹,

Kipli²,

Jobli³

et al. 2021

JST

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Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

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Section: Methodsmentioning

confidence: 99%

Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Isa¹,

Kipli²,

Jobli³

et al. 2021

JST

View full text Add to dashboard Cite

show abstract

“…Traditional image segmentation algorithms are composed of segmentations, such as edgebased segmentation and area segmentation. However, as traditional image segmentation algorithms can only use information on the gray scale of an image to segment the target area and lack spatial information, such algorithms are more sensitive to noise in images (6,7). With the emergence and growth of deep learning (DL), new segmentation technology has achieved excellent results in terms of computer-vision tasks.…”

Section: Original Articlementioning

confidence: 99%

Deep-learning-based MRI in the diagnosis of cerebral infarction and its correlation with the neutrophil to lymphocyte ratio

Wei¹,

Ai²,

Xu³

2021

Ann Palliat Med

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Background: Dizziness is a common symptom in clinic, but there lacks an effective treatment method.This study sought to examine the efficiency of deep learning (DL)-based magnetic resonance imaging (MRI) in the diagnosis of cerebral infarction mainly manifesting as vertigo using the neutrophil to lymphocyte ratio (NLR) and other routine blood indexes.Methods: An improved multiscale U-Net [MS (U-Net)] model, based on the U-net model, was proposed and applied in the segmentation of MRI of the brain. One hundred and fifteen vertiginous cerebral infarction

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“…(Fu, Lei, Wang, Curran, Liu & Yang, 2021) (Fu, Lei, Wang, Curran, Liu & Yang, 2020) (Chen, Wang, Zhang, Fung, Thai, Moore et al, 2022) (Wang, Yang, Rong, Zhan & Xiao, 2019) (Li, Chen, Fang & Zhao, 2017) (Li, Jiang, Kambhamettu & Shatkay, 2018), however, two questions still remain open:…”

mentioning

confidence: 99%

An Ai Segmenter on Medical Imaging for Geomatics Applications Consisting of a Two-State Pipeline, SNNS Network and Watershed Algorithm

Barrile,

Cotroneo,

Genovese

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. As is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increasing precision and accuracy. In recent decades, the automation of this task has been the subject of intensive research. In particular, to improve the segmentation of such images, investigations have focused on the use of neural networks, and in particular convolutional neural networks (CNNs). However, most existing CNN-based methods can produce unsatisfactory segmentation masks without precise object boundaries (Wang, Chen, Ji, Fan & Ye Li, 2022); this is mainly due to the shadows and high noise in these images. To address the problem of automated image segmentation, this work proposes a pipeline technique with two stages (applied primarily to the echocardiographic domain): the first consisting of a Self-normalising Neural Networks (SNNs) performs image denoising, while the second applies a Watershed segmentation algorithm on the cleaned image. The latter is a technique successfully applied in geomatics and land surveying. The proposed methodology may be of interest both in the medical field and in the field of Geomatics where segmentation and classification operations are required in different application areas.

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Brain MR image segmentation using NAMS in pseudo-color

Cited by 14 publications

References 18 publications

Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Deep-learning-based MRI in the diagnosis of cerebral infarction and its correlation with the neutrophil to lymphocyte ratio

An Ai Segmenter on Medical Imaging for Geomatics Applications Consisting of a Two-State Pipeline, SNNS Network and Watershed Algorithm

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