Comparison and Evaluation of Segmentation Techniques for Brain MRI using Gold Standard

Akhil, M Kurup; Aishwarya, R.; Lal, Vandhana; Mahesh, Shanthi

doi:10.17485/ijst/2016/v9i46/106495

Cited by 3 publications

(3 citation statements)

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“…In neuroscience research, segmenting a brain MRI into different structures is a widely used pre-processing step [1,2] . Even though the manual segmentation is considered the gold standard [3], it is operator-dependent, laborious, and time-consuming. Automated segmentation tools are used to segment the brain regions in a reasonable…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0

et al. 2019

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Section: Introductionmentioning

confidence: 99%

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0

et al. 2019

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“…Segmentation being an important area of research, determining its performance is also important [9]. By using K-means and Adaptive K-means algorithm we have segmented the brain gray color & color MRI image also Satellite image of a lake.…”

Section: Resultsmentioning

confidence: 99%

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Sahu¹,

Parvathi²,

Krishna³

2017

IJECE

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<p>Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation. </p>

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“…The area can be manually segmented by an expert. Although this method is not used in practice because of operator bias, error, and time-consuming process [4], it is known as the gold standard method [5,6].…”

mentioning

confidence: 99%

Lung Segmentation using Active Shape Model to Detect the Disease from Chest Radiography

et al. 2021

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Background: Some parametric models are used to diagnose problems of lung segmentation more easily and effectively.Objective: The present study aims to detect lung diseases (nodules and tuberculosis) better using an active shape model (ASM) from chest radiographs. Material and Methods:In this analytical study, six grouping methods, including three primary methods such as physicians, Dice similarity, and correlation coefficients) and also three secondary methods using SVM (Support Vector Machine) were used to classify the chest radiographs regarding diaphragm congestion and heart reshaping. The most effective method, based on the evaluation of the results by a radiologist, was found and used as input data for segmenting the images by active shape model (ASM). Several segmentation parameters were evaluated to calculate the accuracy of segmentation. This work was conducted on JSRT (Japanese Society of Radiological Technology) database images and tuberculosis database images were used for validation. Results:The results indicated that the ASM can detect 94.12 ± 2.34 % and 94.38 ± 3.74 % (mean± standard deviation) of pulmonary nodules in left and right lungs, respectively, from the JRST radiology datasets. Furthermore, the ASM model detected 88.33 ± 6.72 % and 90.37 ± 5.48 % of tuberculosis in left and right lungs, respectively. Conclusion:The ASM segmentation method combined with pre-segmentation grouping can be used as a preliminary step to identify areas with tuberculosis or pulmonary nodules. In addition, this presented approach can be used to measure the size and dimensions of the heart in future studies.

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Comparison and Evaluation of Segmentation Techniques for Brain MRI using Gold Standard

Cited by 3 publications

References 0 publications

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Lung Segmentation using Active Shape Model to Detect the Disease from Chest Radiography

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