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The paper title is Grade Classification of Barretts Esophagus through Feature Enhancement Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

Noha Ghatwary and AlyaaAmerattended the Medical Imaging Summer Schoolthat was held in Favignana,Sicily. They had the chance to engage witharound 160 medical image researchersand share their knowledge through discussion and reading groups.

The school held several lectures that discusseddifferent topics presentedby different Lectures expert in that field. Also, Noha Ghatwary had the chance to present the accepted paper CT Enhancement using Fractional Differentiation and Integration in the poster session and discussit with the attendees.

Congratulations to Noha Ghatwary , She passed her MPhil transfer and she is now officially a PhD researcher , wish Noha all the Luck and success in her life. The enhancement process is based on improving the main features within the image by utilizing the Fractional Differential and Integral in the wavelet sub bands of an image. After enhancement, different features were extracted such as GLCM, GRLM,
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and LBP, among others. Then, the areas/cells are classified into tumor or non tumor, using different models of classifiers to compare our proposed model with the original image and various established filters. Each image is divided into 15 non overlapping blocks, to extract the desired features. The SVM, Random Forest, J48 and Simple Cart were trained on a supplied dataset, different from the test dataset. Finally, the block cells are identified whether they are classified astumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of enhancement in the proposed technique.

Examiners have commended Saddam and contributions. They also emphasized how well written the thesis is.

A well deserved achievement Saddam, well done.

And all the best for your future career.2016 ISBI AIDA E Barrett Challenge Winner

Posted on April 12, 2016 by Noha GhatwaryThe proposed model was the winner of the challenge. Congratulations everyone!!!
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PGRs Showcase Event 2015: Well done DCAPI members

Posted on May 13, 2015 by Saddam Bekhet

Members of DCAPI have presented and showed their research work in the thirdAnnual Showcase Event for the School of Computer Science, University of Lincoln. (6th and 7th May).