Ali El-Zaart

Dr. Ali El-Zaart

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Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2018 in Sustainable Cities and Society
Verified
Journal Article
Open access
MIMaysoun Ibrahim
Maysoun Ibrahim
AEAli El-Zaart
Ali El-Zaart
CACarl Adams
Carl Adams
1 January 2021 in Computational Intelligence and Neuroscience
Verified
Journal Article
Open access
PDF file
RSRachid Sammouda
Rachid Sammouda
AEAli El-Zaart
Ali El-Zaart
Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate‐specific membrane antigen (PSMA) expressed by type‐II is an extremely attractive style for imaging‐based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near‐infrared (NIR) prostate cancer images acquired by PSMA‐targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k‐means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters’ statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.
1 March 2002 in Pattern Recognition
Verified
Journal Article
Closed access
AEAli El-Zaart
Ali El-Zaart
DZDjemel Ziou
Djemel Ziou
SWShengrui Wang
Shengrui Wang
QJQingshan Jiang
Qingshan Jiang
1 March 2010 in Pattern Recognition and Image Analysis
Verified
Journal Article
Closed access
PDF file
AEAli El-Zaart
Ali El-Zaart
Image segmentation is a fundamental step in many applications of image processing. Many image segmentation techniques exist based on different methods such as classification-based methods, edge-based methods, region-based methods, and hybrid methods. The principal approach of segmentation is based on thresholding (classification) that is related to thresholds estimation problem. The ISODATA (Iterative Self-Organizing Data Analysis Technique) method is one of the classification-based methods in image segmentation. We assumed that the data in images is modeled by Gamma distribution. The objective of this paper is to explain a new method that combines Gamma distribution with the technique of ISODATA. The algorithm has two phases: splitting using Gamma distribution then merging which are done based on some predefined parameters. Experimental results showed good segmentation for artificial and real images.
1 May 2007 in International Journal of Remote Sensing
Verified
Journal Article
Closed access
AEAli El-Zaart
Ali El-Zaart
DZD Ziou
D Ziou
This paper discusses the statistical modelling of multimodal SAR image histograms. We use a generalized mixture of distributions with flexible shapes that are likely to fit the SAR image histogram. We then form a system called GGBL, composed of four parametric distributions (Gaussian, Gamma, Beta and Log‐Normal). Selection of a parametric distribution from the GGBL system for each mode of the multimodal SAR histogram is performed according to the location of the skewness and flatness coefficients in this space. We propose a distribution stability method for distribution selection, using the asymmetry and flatness coefficients, and a feature method for the estimation of the parameters of these distributions based on the characteristic points of the histogram. The GGBL system is applied to real histograms of RADARSAT and ERS SAR images with different numbers of looks. The results obtained are promising.
1 January 2010
Verified
Conference Paper
Closed access
HAHala Al-Shamlan
Hala Al-Shamlan
AEAli El-Zaart
Ali El-Zaart
Breast cancer is one of the most common cancers among woman of the developing countries in the world, and it has also become a major cause of death. Treatment of breast cancer is effective only if it is detected at an early stage. X-ray mammography is the most effective method for early detection but the mammography images are complex. Thus nowadays, image processing and image analysis techniques are use to assist radiologist for detecting tumors in mammography images. In this paper we specify and determined the important and significant Breast Cancer Feature Extraction. After that, we analyze Breast cancer mammography images using these significant features. However, the aim of this study is to determine the features extraction ranges values for Breast Cancer mammography image.