Preferred Language
Articles
/
jih-1423
Brain Tumor Detection Method Using Unsupervised Classification Technique
...Show More Authors

Magnetic  Resonance  Imaging  (MRI)  is  one  of  the  most important diagnostic tool. There are many methods to segment the

tumor of human brain. One of these, the conventional method that uses pure image processing techniques that are not preferred because they need human interaction for accurate segmentation. But unsupervised methods do not require any human interference and can segment   the   brain   with   high   precision.   In   this   project,   the unsupervised  classification methods have been used in order to detect the tumor  disease from MRI images.    These methods involved K­ mean or Isodat, which were based on the digital value distribution. The results show the classification process was a powerful tool to identify the Tumor disease from MRI images.   All results were evaluated  by  using the ENVI Version 3.2 facility.

View Publication Preview PDF
Quick Preview PDF
Publication Date
Wed Apr 02 2014
Journal Name
Journal Of Theoretical And Applied Information Technology
TUMOR BRAIN DETECTION THROUGH MR IMAGES: A REVIEW OF LITERATURE
...Show More Authors

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

... Show More
Scopus (45)
Scopus
Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
...Show More Authors

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

... Show More
View Publication Preview PDF
Crossref (5)
Crossref
Publication Date
Sun Jun 12 2011
Journal Name
Baghdad Science Journal
Satellite Images Unsupervised Classification Using Two Methods Fast Otsu and K-means
...Show More Authors

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

View Publication Preview PDF
Crossref
Publication Date
Tue Dec 03 2013
Journal Name
Baghdad Science Journal
Satellite Images Unsupervised Classification Using Two Methods Fast Otsu and K-means
...Show More Authors

Publication Date
Fri May 05 2017
Journal Name
International Journal Of Science And Research (ijsr)
Automatic brain tumor segmentation from MRI images using region growing algorithm
...Show More Authors

LK Abood, RA Ali, M Maliki, International Journal of Science and Research, 2015 - Cited by 2

View Publication
Publication Date
Mon Oct 01 2018
Journal Name
Iraqi Journal Of Physics
Classification of brain tumors using the multilayer perceptron artificial neural network
...Show More Authors

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect

... Show More
View Publication Preview PDF
Crossref (3)
Crossref
Publication Date
Fri Jul 07 2017
Journal Name
International Journal Of Science And Research (ijsr)
Automatic brain tumor segmentation from MRI Images using superpixels based split and Merge algorithm
...Show More Authors

RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2

View Publication
Publication Date
Mon Jan 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
ON-Line MRI Image Selection and Tumor Classification using Artificial Neural Network
...Show More Authors

When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Mon Jan 01 2018
Journal Name
Matec Web Of Conferences
Brain Tumour Detection using Fine-Tuning Mechanism for Magnetic Resonance Imaging
...Show More Authors

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

View Publication
Scopus (1)
Scopus Crossref
Publication Date
Wed Jul 01 2015
Journal Name
Magnetic Resonance Imaging
Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images
...Show More Authors

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

View Publication
Scopus (28)
Crossref (23)
Scopus Clarivate Crossref