Posted in Barrett's Esophagus, Conference, Enhancement, Medical Image Processing

Conference paper presented in “SPIE Medical Imaging 2017”

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.

Congratulations and well done for Noha.

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Posted in Conference, dcapi, DCAPI blog, Medical Image Processing, PhD, Research

Medical Imaging Summer School “MISS’16”

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.

 

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Posted in Conference, dcapi, DCAPI blog, Enhancement, Medical Image Processing

Conference paper presented in “ World Congress on Engineering 2016”

Noha Ghatwary presented her accepted paper in the “World Congress on Engineering 2016” .

The paper title is ” Liver CT Enhancement using Fractional Differentiation and Integration”

Abstract:

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.

 

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Posted in Computer Vision, Conference, I-Frame, International, PhD, Research, Video Analysis, Video Matching

Conference paper presented in “ ICPR 2014”

Saddam Bekhet presented his accepted paper in  ICPR2014 , Stockholm, Sweden
The paper title is “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)

Abstract— This paper presents a novel technique for efficient and generic matching of compressed video shots, through compact signatures extracted directly without decompression. The compact signature is based on the Dominant Color Profile (DCP); a sequence of dominant colors extracted and arranged as a sequence of spikes in analogy to the human retinal representation of a scene. The proposed signature represents a given video shot with ~490 integer values, facilitating for real-time processing to retrieve a maximum set of matching videos. The technique is able to work directly on MPEG compressed videos, without full decompression, as it utilizes the DC-image as a base for extracting color features. The DC-image has a highly reduced size, while retaining most of visual aspects, and provides high performance compared to the full I-frame. The experiments and results on various standard datasets show the promising performance, both the accuracy and the efficient computation complexity, of the proposed technique.

Congratulations and well done for Saddam and Amr.

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Posted in Compressed Video, Computer Vision, Conference, Research, Video Analysis, Video Matching

New paper accepted in ICPR 2014 – “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)”

The paper “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)” has been accepted in the ICPR 2014 conference http://www.icpr2014.org/, and will be presented in August 2014, Stockholm, Sweden.

Abstract— This paper presents a novel technique for efficient and generic matching of compressed video shots, through compact signatures extracted directly without decompression. The compact signature is based on the Dominant Color Profile (DCP); a sequence of dominant colors extracted and arranged as a sequence of spikes in analogy to the human retinal representation of a scene. The proposed signature represents a given video shot with ~490 integer values, facilitating for real-time processing to retrieve a maximum set of matching videos. The technique is able to work directly on MPEG compressed videos, without full decompression, as it utilizes the DC-image as a base for extracting color features. The DC-image has a highly reduced size, while retaining most of visual aspects, and provides high performance compared to the full I-frame. The experiments and results on various standard datasets show the promising performance, both the accuracy and the efficient computation complexity, of the proposed technique.

Congratulations and well done for Saddam.

Posted in Computer Vision, Computer Visions, Conference, dcapi, DCAPI blog, PhD, Research, Uncategorized, Video Analysis, video information retrieval, Video search engine

Conference paper presented in “ World Congress on Engineering 2013”

Saddam Bekhet presented his accepted paper in “World Congress on Engineering 2013“.
The paper title is “Video Matching Using DC-image and Local Features ”

Abstract:

This paper presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin. There are also various optimisations that can be done to improve this computation complexity.

Well done and congratulations to Saddam Bekhet .
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