The poster presenting the paper entitled “Automatic Grade Classification of Barretts Esophagus through Feature Enhancement” presented in the SPIE Medical imaging Conference 2017 got the “Cum laude Poster Award” – Computer Aided Diagnosis.
Noha Ghatwary presented her accepted paper in SPIE Medical Imaging 2017 , Orlando, USA.
The paper title is “Automatic Grade Classification of Barretts Esophagus through Feature Enhancement”
Abstract— 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.
The conference paper entitled “Liver CT Enhancement using Fractional Differentiation and Integration” presented earlier in “World Congress on Engineering 2016“ got “Best Paper Award 2016″ well done and congratulations to Noha Ghatwary.
The conference paper entitled “Hierarchical Classification of Liver Tumor from CT Images Based on Difference-of-features (DOF) ” presented earlier in “World Congress on Engineering 2016“ got “Best Student Paper Award 2016″ well done and congratulations to Hussein Alahmer
Noha Ghatwary and Alyaa Amer attended the Medical Imaging Summer School that was held in Favignana, Sicily. They had the chance to engage with around 160 medical image researchers and share their knowledge through discussion and reading groups.
The school held several lectures that discussed different topics presented by different Lectures expert in that field. Also, Noha Ghatwary had the chance to present the accepted paper “Liver CT Enhancement using Fractional Differentiation and Integration” in the poster session and discuss it with the attendees.
The paper title is ” Liver CT Enhancement using Fractional Differentiation and Integration”
In this paper, a digital image filter is proposed to enhance the Liver CT image for improving the classification of tumors area in an infected Liver. 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, 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 15×15 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 as tumor 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.
Members of DCAPI have presented and showed their research work in the third Annual Showcase Event for the School of Computer Science, University of Lincoln. (6th and 7th May). The event accompanied by wide attendances from several companies (Google UK, Siemens, QinetiQ, mass, Artsgraphica, Heritage Lincolnshire, …).
Saddam also won the “Best Presentation” prize for his video similarity detection presentation.