MRI segmentation fusion for brain tumor detection

Cabria, I.; Gondra, Iker

doi:10.1016/j.inffus.2016.10.003

Cited by 95 publications

(31 citation statements)

References 11 publications

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“…Different methods are presented in literature for abnormality detection in medical images. In [26], an approach is presented for detection of the brain tumor using MRI segmentation fusion, namely potential field segmentation. The performance of this system is tested on a publicly available MRI benchmark, known as brain tumor image segmentation.…”

Section: Detection and Classification Of Abnormalitymentioning

confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval.

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Section: Detection and Classification Of Abnormalitymentioning

confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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“…In [21], potential field segmentation was employed to segment the MRI brain tumor images. This method achieved the standard deviation of 0.283, the average value of 0.517, and the median values of 0.644.…”

Section: Related Literaturementioning

confidence: 99%

“…A brief review of the literature on various clustering methods in image segmentation and their performances appears in Table 1. [21] Potential field clustering…”

Section: Related Literaturementioning

confidence: 99%

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Inbarani

Azar

Ganesan³

2020

Electronics

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Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.

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“…Cabria and Gondra [18] fused the segmented parts of a brain MR image to detect brain tumor in it. The fusion is achieved using intersection and union methods.…”

Section: Mri Related Workmentioning

confidence: 99%

A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

Srivastava

Purwar

2017

Applied Computational Intelligence and Soft Computing

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This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The twodimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.

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MRI segmentation fusion for brain tumor detection

Cited by 95 publications

References 11 publications

Medical Image Analysis using Convolutional Neural Networks: A Review

Medical Image Analysis using Convolutional Neural Networks: A Review

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

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