An automated detection and segmentation of tumor in brain MRI using artificial intelligence

Bhanumurthy, Yaswanth; Anne, Koteswararao

doi:10.1109/iccic.2014.7238374

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…combination of region based and texture based methods for brain tumor detection and classification. Region growing method [22] correctly segments regions that have similar properties and produces connected region and its performance is better with noisy images. Drawback of Region growing method is that it involves a manual seed point selection.…”

Section: Survey Discussionmentioning

confidence: 99%

“…M.Y.Bhanumurthy [22] worked on "An Automated Detection and Segmentation of Tumor in Brain MRI using Artificial Intelligence" In which a fully automated technique is discussed that uses artificial intelligence to detect and segment abnormal tissues like tumor and atrophy in brain MRI images accurately. The extracted features like energy, entropy, homogeneity, contrast and correlation from the brain MRI images are applied as input to an artificial intelligence system that uses a Neurofuzzy classifier which classifies the images into normal or abnormal.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

2017

IJMTER

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Brain tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and characteristics and have different treatments. Detection of tumor in the earlier stages makes the treatment easier. Presently many medical imaging techniques such as positron emission tomography (PET), X-ray, computed tomography (CT), magnetic resonance imaging (MRI) for tumor detection are in use among which MRI imaging technique is more attractive because of its higher resolution and therefore most preferred technique for diagnosing tumor. MRI scan is safer than any other scan as it does not involve any radiations and gives high resolution of image. Paper gives a brief review of different segmentation and classification methods used for detection of tumor from Magnetic Resonance Images (MRI).

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Section: Survey Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

2017

IJMTER

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“…Segmentation is the process of extracting a specific region of interest from an image, and is one of the core problems in medical imaging. Image segmentation has huge applications in different fields, such as: detecting tumours for tracking their volume in response to therapy (or for example to help determine which type of tumour the patient has, since different brain tumours have different shapes [99]), segmenting blood cells (to then classify them for detecting any increase/decrease in a specific type of blood cells that could be an indicator of some disease), detecting calcification within the breast tissue and much more [100]. In recent years, deep learning has been able to achieve human-level performance in many image recognition tasks.…”

Section: Segmentationmentioning

confidence: 99%

Enterprise imaging and big data: A review from a medical physics perspective

et al. 2021

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In recent years enterprise imaging (EI) solutions have become a core component of healthcare initiatives, while a simultaneous rise in big data has opened up a number of possibilities in how we can analyze and derive insights from large amounts of medical data. Together they afford us a range of opportunities that can transform healthcare in many fields. This paper provides a review of recent developments in EI and big data in the context of medical physics. It summarizes the key aspects of EI and big data in practice, with discussion and consideration of the steps necessary to implement an EI strategy. It examines the benefits that a healthcare service can achieve through the implementation of an EI solution by looking at it through the lenses of: compliance, improving patient care, maximizing revenue, optimizing workflows, and applications of artificial intelligence that support enterprise imaging. It also addresses some of the key challenges in enterprise imaging, with discussion and examples presented for those in systems integration, governance, and data security and privacy.

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“…R. Burget et al [10] Presents an innovative algorithm combining the theory of artificial intelligence and knowledge of human eye anatomy. Bhanumurthy et al [11] proposed an ABC algorithm that gives an efficient fitness function that improves the segmentation quality. Clustering is done after the transition of the input data into a higher dimensional feature space.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Medical Volume Data Based on Improved K-Means Clustering and Segmentation Rules

Muad

Chen

2021

IEEE Access

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Accurate extraction and visualization of the target features are important in medical visualization, allowing the user to get a meaningful view of their targets. The traditional way to generate visualization results is 1D TF-based volume visualization, which uses a 1D -TF to determine the optical properties of each voxel. However, it is difficult to distinguish multiple targets by using the 1D TF-based volume visualization. It is also challenging to distinguish accurate targets when the objects' intensity values are similar in volume. Using the traditional transfer function usually fails to extract important target features and generates less accurate results. In this paper, we proposed extracting and segmentation techniques based on K-means algorithm that allows the users to segment and enhances single or multiple targets by a single point to view the features in 3D view. We have applied it to various univariate or multivariate volume datasets from the medical field to demonstrate its effectiveness. Moreover, we have performed both qualitative and quantitative experiments to compare its results against the results from two state-of-the-art techniques and the ground truths. The experimental results showed that our method is able to generate the closest results to the ground truth.

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An automated detection and segmentation of tumor in brain MRI using artificial intelligence

Cited by 17 publications

References 17 publications

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

Enterprise imaging and big data: A review from a medical physics perspective

Visualization of Medical Volume Data Based on Improved K-Means Clustering and Segmentation Rules

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