Preferred Language
Articles
/
IRg0aZQBVTCNdQwCcRWQ
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
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
Sun Jul 02 2023
Journal Name
Iraqi Journal Of Science
Image Segmentation Using Superpixel Based Split and Merge Method
...Show More Authors

A super pixel can be defined as a group of pixels, which have similar characteristics, which can be very helpful for image segmentation. It is generally color based segmentation as well as other features like texture, statistics…etc .There are many algorithms available to segment super pixels like Simple Linear Iterative Clustering (SLIC) super pixels and Density-Based Spatial Clustering of Application with Noise (DBSCAN). SLIC algorithm essentially relay on choosing N random or regular seeds points covering the used image for segmentation. In this paper Split and Merge algorithm was used instead to overcome determination the seed point's location and numbers as well as other used parameters. The overall results were better from the SL

... Show More
View Publication Preview PDF
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
Fri Apr 12 2019
Journal Name
Journal Of Economics And Administrative Sciences
Split and Merge Regions of Satellite Images using the Non-Hierarchical Algorithm of Cluster Analysis
...Show More Authors

يعد التقطيع الصوري من الاهداف الرئيسة والضرورية في المعالجات الصورية للصور الرقمية، فهو يسعى الى تجزئة الصور المدروسة الى مناطق متعددة اكثر نفعاً تلخص فيها المناطق ذات الافادة لصور الاقمار الصناعية، وهي صور متعددة الاطياف ومجهزة من الاقمار الصناعية باستخدام مبدأ الاستشعار عن بعد والذي اصبح من المفاهيم المهمة التي تُعتمد تطبيقاته في اغلب ضروريات الحياة اليومية، وخاصة بعد التطورات المتسارعة التي شهد

... Show More
View Publication Preview PDF
Crossref
Publication Date
Thu May 10 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network
...Show More Authors

  Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead

... Show More
View Publication Preview PDF
Crossref (14)
Crossref
Publication Date
Mon Feb 22 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
MRI images series segmentation using the geodesic deformable model
...Show More Authors

View Publication
Scopus Crossref
Publication Date
Wed Nov 24 2021
Journal Name
Iraqi Journal Of Science
Comparison of Some Statistical Measurements Extracted From Benign, Malignant and Normal MRI Brain Images
...Show More Authors

People may believe that tissue of normal brain and brain with benign tumor
have the same statistical descriptive measurements that are significantly different
from the of brain with malignant tumor. Thirty brain tumor images were collected
from thirty patients with different complains (10 normal brain images, 10 images
with benign brain tumor and 10 images with malignant brain tumor). Pixel
intensities are significantly different for all three types of images and the F-test was
measured and found equal to 25.55 with p-value less than 0.0001. The means of
standard deviations and coefficients of variation showed that pixel intensities from
normal and benign tumors images are almost have the same behavior whereas the

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Aug 26 2019
Journal Name
Iraqi Journal Of Science
Medical Image Enhancement to Extract Brain Tumors from CT and MRI images
...Show More Authors

     Always MRI and CT Medical images are noisy so that preprocessing is necessary for enhance these images to assist clinicians and make accurate diagnosis. Firstly, in the proposed method uses two denoising filters (Median and Slantlet) are applied to images in parallel and the best enhanced image gained from both filters is voted by use PSNR and MSE as image quality measurements. Next, extraction of brain tumor from cleaned images is done by segmentation method based on k-mean.  The result shows that the proposed method is giving an optimal solution due to denoising method which is based on multiple filter types to obtain best clear images and that is leads to make the extraction of tumor more precision best.<

... Show More
View Publication Preview PDF
Scopus (5)
Crossref (2)
Scopus Crossref
Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
MR Brain Image Segmentation Using Spatial Fuzzy C- Means Clustering Algorithm
...Show More Authors

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

View Publication Preview PDF
Crossref
Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Automatic Segmentation and Identification of Abnormal Breast Region in Mammogram Images Based on Statistical Features
...Show More Authors

Breast cancer is one of the most common malignant diseases among women;
Mammography is at present one of the available method for early detection of
abnormalities which is related to breast cancer. There are different lesions that are
breast cancer characteristic such as masses and calcifications which can be detected
trough this technique. This paper proposes a computer aided diagnostic system for
the extraction of features like masses and calcifications lesions in mammograms for
early detection of breast cancer. The proposed technique is based on a two-step
procedure: (a) unsupervised segmentation method includes two stages performed
using the minimum distance (MD) criterion, (b) feature extraction based on Gray

... Show More
View Publication Preview PDF