An algorithm for detecting brain tumors in MRI images

Badran, Ehab F.; Mahmoud, Esraa Galal; Hamdy, N.

doi:10.1109/icces.2010.5674887

Cited by 82 publications

(22 citation statements)

References 3 publications

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“…Indeed, the use of automatic or semiautomated methods for ROI determination has been implemented and developed. These algorithms significantly increase the accuracy of the localization of brain tumors and can achieve more stable extraction results . Moreover, these methods reduce the processing time, compared to manual methods.…”

Section: Background and Purposementioning

confidence: 99%

“…These algorithms significantly increase the accuracy of the localization of brain tumors and can achieve more stable extraction results. [27][28][29][30][31] Moreover, these methods reduce the processing time, compared to manual methods. The present study attempted to evaluate a semiautomated method for the ROI extraction on APTw images by comparing it with a manual segmentation by experts.…”

Section: Background and Purposementioning

confidence: 99%

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Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

Lee

Peng

et al. 2016

Journal of Neuroimaging

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Background and Purpose Multi-modality MRI can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multi-parametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semi-automated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method. Methods Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7 T animal scanner pre-radiation, and at day 1, day 4, and day 8 post-radiation. Two experts performed manual and semi-automated methods to extract tumor ROIs on APTw images. Multi-modality MRI signals of the tumors, including structural (T2 and T1), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively. Results The semi-automated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the post-radiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy. Conclusions The semi-automated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.

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Section: Background and Purposementioning

confidence: 99%

Section: Background and Purposementioning

confidence: 99%

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

Lee

Peng

et al. 2016

Journal of Neuroimaging

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“…Primary tumors originate in the brain while secondary tumors originate in varied parts of the body and then reach to the brain by spreading. Brain cancer can be detected using image segmentation techniques [5], image enhancement techniques or morphological techniques [6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Segmentation and Classifier Approach Based on HMM for Brain Cancer Detection

Sharma¹,

Rattan²

2019

TOBEJ

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Introduction: Brain tumors are fatal diseases that are spread worldwide and affect all types of age groups. Due to its direct impact on the central nervous system, if tumor cells prevail at certain locations in the brain, the overall functionality of the body is disturbed and chances of a person approaching death are high. Tumors can be cancerous or non-cancerous but in many cases, the chances of complete recovery are less and as a result death rate has increased all over the world despite recent advancements in technology, equipment and awareness. So the main concern is to detect brain related diseases at early stages so that they do not spread into vital parts of brain and disrupt body functions. Also, more precise and accurate technologies are required to serve as aid in the diagnosis, treatment and surgery of brain. Aims & Objectives: Therefore, its high alarming time to monitor mortality statistics and develop faster and accurate methods to curb the situation by simulating tissue deformation and locating cancerous nodes which is currently the prominent area of interest. Methods: A brain tumor is used to design the deformation model. Early stage detection of tumors is difficult from images. Moreover, the accuracy involved is low. Keeping all this into consideration, a machine learning approach has been developed for classification of cancerous and non-cancerous tissues so that the tissues having risk of future problem can also be recognized. The patient’s deformation model can be designed and brain tumor patterns are given as input on the basis of which tumor in the brain is marked. The proposed method of segmentation is based on a statistical model called Hidden Markov Model (HMM) which extricates the cancerous portion out of fed input MRI image along with the calculation of parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), fault rate dust detection and accuracy. Results &Discussion: The results obtained from parametric analysis show that HMM has performed better than the technique of Support Vector Regression (SVR) for brain cancer segmentation in terms of PSNR, MSE, fault rate dust detection and accuracy. So image processing is used in combination with Hidden Markov Model for classification and analysis to which MRI images are fed as input. Conclusion: In this way, integration of artificial intelligence techniques with image processing can serve as a good way for segmentation of tumors and for classification purposes with good accuracy.

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“…et al have classified brain tumor using SVM, where they have first converted RGB to gray, then applied median filtering and skull masking using morphological opening, region filling and power law transform. Then extracted features and after that classified using SVM [1].Siva Sankari S., et al have Segmented tumor part using k-mean clustering, and extracted features using GLCM and Gabor filter, where Analysis is done over 45 images [2].Ehab F. Badran, Esraa Galal Mahmoud, and Nadder Hamdy [3] have constructed GUI using MATLAB to test their proposed work. The methods proposed by them are: preprocessing by image edge expansion; segmentation using canny edge detection or adaptive thresholding; feature extraction using Harris method; classification using neural network.…”

Section: Introductionmentioning

confidence: 99%

An Efficacious Graphical User Interface Implementation for Automatic Classification of Brain Tumor from Magnetic Resonance Imaging Images Using Image Processing

Modi¹

2018

IJRASET

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Biomedical image processing is an emerging field, and is very useful for automatic disease diagnosis. Brain tumor classification is performed using image processing toolbox of MATLAB over here. For tumor classification: Image Preprocessing, Segmentation and feature Analysis are essential steps, these steps are described in the paper. First of all, images were filtered by Gaussian filter, then tumor part i.e. regions having high intensity are enhanced using power low transform in order to neglect insufficient data from image. Then enhanced part was extracted and denoising was performed to get tumor region only at output. By the method we have got only tumor part having high intensity and all other part of brain considered as background. After tumor segmentation, feature analysis has been made by which we can get idea that how they differs into normal and benign or malignant tumors. Then classified based on feature analysis of tumor region, all the processes have been done over dataset of 45 Brain Tumor images. Here overall Accuracy of correct classification is got to be 97.78%, where normal brain is classified with 100%, benign with 95% and malignant with 100% accuracy. A Graphical User Interface (GUI) is developed in MATLAB to perform all the task and to test proposed work. In future, everything would be going to be computer assisted. This is a step toward automatic disease diagnosis using Computer Aided Design (CAD).

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An algorithm for detecting brain tumors in MRI images

Cited by 82 publications

References 3 publications

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

An Improved Segmentation and Classifier Approach Based on HMM for Brain Cancer Detection

An Efficacious Graphical User Interface Implementation for Automatic Classification of Brain Tumor from Magnetic Resonance Imaging Images Using Image Processing

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