Automatic segmentation of brain tumors in magnetic resonance images

Behzadfar, Neda; Soltanian‐Zadeh, Hamid

doi:10.1109/bhi.2012.6211580

Cited by 10 publications

(1 citation statement)

References 13 publications

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“…The author intended to develop an automatic segmentation system for detecting brain tumors [10]. The author used patient images of polymorphous brain tumors in this case.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification using Deep Learning Methods

Rehman

Masood

Khan

2022

ijisct

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A brain tumor is the most common and destructive disease which takes the patient to the end of life. Thus, treatment planning is a key stage to enhance the quality of patient life. There are various image techniques such as computed tomography, Magnetic Resonance Imaging (MRI), and ultrasound imaging used to measure the Tumor in the brain, lungs, liver, and so on. Classification of Tumor and non-tumor can be done, but it has some limitations, like only a limited number of images can be measured accurately and quantitatively. Therefore, an automatic classification scheme plays an important role in preventing the death rate of human beings, rendering us a challenging task. For this purpose, we take some MRI images to classify Tumor by examining the huge amount of data generated by MRI scan. In this work, brain tumor classification is proposed by extended Convolutional Neural Network (CNN); a deeper convolution layer is designed to improve the performance by using a small kernel size. Surgeons and radiologists can classify brain tumors more easily and effectively using this technique. Extended CNN achieves approximately 99% accuracy rate by showing the experimental results onward.

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“…The author intended to develop an automatic segmentation system for detecting brain tumors [10]. The author used patient images of polymorphous brain tumors in this case.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification using Deep Learning Methods

Rehman

Masood

Khan

2022

ijisct

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A new intelligent system for diagnosing tumors with MR images using type-2 fuzzy neural network (T2FNN)

Rezaie

Parnianifard

2021

Multimed Tools Appl

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A review of silhouette extraction algorithms for use within visual hull pipelines

Ascenso

Yap

Allen

et al. 2020

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Markerless motion capture would permit the study of human biomechanics in environments where marker-based systems are impractical, e.g. outdoors or underwater. The visual hull tool may enable such data to be recorded, but it requires the accurate detection of the silhouette of the object in multiple camera views. This paper reviews the top-performing algorithms available to date for silhouette extraction, with the visual hull in mind as the downstream application; the rationale is that higher-quality silhouettes would lead to higher-quality visual hulls, and consequently better measurement of movement. This paper is the first attempt in the literature to compare silhouette extraction algorithms that belong to different fields of Computer Vision, namely background subtraction, semantic segmentation, and multiview segmentation. It was found that several algorithms exist that would be substantial improvements over the silhouette extraction algorithms traditionally used in visual hull pipelines. In particular, FgSegNet v2 (a background subtraction algorithm), DeepLabv3+ JFT (a semantic segmentation algorithm), and Djelouah 2013 (a multi-view segmentation algorithm) are the most accurate and promising methods for the extraction of silhouettes from 2D images to date, and could seamlessly be integrated within a visual hull pipeline for studies of human movement or biomechanics.

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Automatic segmentation of brain tumors in magnetic resonance images

Cited by 10 publications

References 13 publications

Brain Tumor Classification using Deep Learning Methods

Brain Tumor Classification using Deep Learning Methods

A new intelligent system for diagnosing tumors with MR images using type-2 fuzzy neural network (T2FNN)

A review of silhouette extraction algorithms for use within visual hull pipelines

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