A comprehensive review: Segmentation of MRI images-brain tumor

Saladi, Saritha; Prabha, N. Amutha

doi:10.1002/ima.22201

Cited by 34 publications

(20 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, primary brain cancer originates from glia or respective precursors and is commonly categorized as glioma. The metastatic brain cancer originates from systemic neoplasms and further develops in the interior of brain parenchyma 6 . Three notable types of CNS cancers are listed by WHO classification.…”

Section: Introductionmentioning

confidence: 99%

Nanotheranostics: Emerging Strategies for Early Diagnosis and Therapy of Brain Cancer

Sonali¹,

Viswanadh²,

Singh³

et al. 2018

Nanotheranostics

127

View full text Add to dashboard Cite

Nanotheranostics have demonstrated the development of advanced platforms that can diagnose brain cancer at early stages, initiate first-line therapy, monitor it, and if needed, rapidly start subsequent treatments. In brain nanotheranostics, therapeutic as well as diagnostic entities are loaded in a single nanoplatform, which can be further developed as a clinical formulation for targeting various modes of brain cancer. In the present review, we concerned about theranostic nanosystems established till now in the research field. These include gold nanoparticles, carbon nanotubes, magnetic nanoparticles, mesoporous silica nanoparticles, quantum dots, polymeric nanoparticles, upconversion nanoparticles, polymeric micelles, solid lipid nanoparticles and dendrimers for the advanced detection and treatment of brain cancer with advanced features. Also, we included the role of three-dimensional models of the BBB and cancer stem cell concept for the advanced characterization of nanotheranostic systems for the unification of diagnosis and treatment of brain cancer. In future, brain nanotheranostics will be able to provide personalized treatment which can make brain cancer even remediable or at least treatable at the primary stages.

show abstract

Section: Introductionmentioning

confidence: 99%

Nanotheranostics: Emerging Strategies for Early Diagnosis and Therapy of Brain Cancer

Sonali¹,

Viswanadh²,

Singh³

et al. 2018

Nanotheranostics

127

View full text Add to dashboard Cite

show abstract

“…In recent years, brain diseases have become one of the most important diseases that endanger the health of human beings. The segmentation of brain lesions from brain images can be a valuable reference for the follow-up treatment of patients [1]. In the diagnosis of brain diseases, magnetic resonance (MR) imaging is the most commonly used imaging modality.…”

Section: Introductionmentioning

confidence: 99%

Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation

Zhan

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The segmentation of brain lesions from a brain magnetic resonance (MR) image is of great significance for the clinical diagnosis and follow-up treatment. An automatic segmentation method for brain lesions is proposed based on the low-rank representation (LRR) and the sparse representation (SR) theory. The proposed method decomposes the brain image into the background part composed of brain tissue and the brain lesion part. Considering that each pixel in the brain tissue can be represented by the background dictionary, a low-rank representation that incorporates sparsity-inducing regularization term is adopted to model the part. Then, the linearized alternating direction method with adaptive penalty (LADMAP) was selected to solve the model, and the brain lesions can be obtained by the response of the residual matrix. The presented model not only reflects the global structure of the image but also preserves the local information of the pixels, thus improving the representation accuracy. The experimental results on the data of brain tumor patients and multiple sclerosis patients revealed that the proposed method is superior to several existing methods in terms of segmentation accuracy while realizing the segmentation automatically.

show abstract

“…Medical imageprocessing methods, used for fully automatic segmentation, are classified into four main categories. 1,2 The first category includes the threshold-based techniques, such as Otsu, Kapur and adaptive thresholding. 3 The second category covers region-based techniques, such as region growing 4 and watershed.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm

Hachemi

Chama

Alim-Ferhat

et al. 2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is widely used in the medical field, especially for detecting serious abnormalities affecting the organs of the human body, such as tumors. Automatic detection of tumors needs high-performance recognition techniques. In this paper, we have developed a new automatic method based on the multisegmentation of brain tumor region. We used an improved region-growing algorithm, which is based on quasi-Monte Carlo and expectation maximization methods to define the desired classes. Several metrics were calculated to evaluate the performance of our technique. The fully automatic multisegmentation approach, developed in this study, showed good performance, and it can offer a new option to replace conventional techniques used for tumor detection in MRI images.K E Y W O R D S brain tumor, expectation maximization, multisegmentation, quasi-Monte Carlo, region growing

show abstract

A comprehensive review: Segmentation of MRI images-brain tumor

Cited by 34 publications

References 57 publications

Nanotheranostics: Emerging Strategies for Early Diagnosis and Therapy of Brain Cancer

Nanotheranostics: Emerging Strategies for Early Diagnosis and Therapy of Brain Cancer

Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation

Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm

Contact Info

Product

Resources

About