A5035610270- Medical Image Segmentation Techniques
- Brain Tumor Detection and Classification
- AI in cancer detection
- Image Retrieval and Classification Techniques
- Metaheuristic Optimization Algorithms Research
- Liver Disease Diagnosis and Treatment
- Hepatitis B Virus Studies
- Acute Myocardial Infarction Research
- Infrared Target Detection Methodologies
- Hepatitis C virus research
- Radiomics and Machine Learning in Medical Imaging
- Advanced Image and Video Retrieval Techniques
- Atrial Fibrillation Management and Outcomes
- Photoacoustic and Ultrasonic Imaging
- Medical Imaging and Analysis
- Mathematical Biology Tumor Growth
- Advanced Image Fusion Techniques
- Vehicle License Plate Recognition
- Coronary Interventions and Diagnostics
- Handwritten Text Recognition Techniques
Ministry of Health and Population
2022
Menoufia University
2019
Cairo University
2015-2017
Scientific Research Group in Egypt
2015-2017
The automated segmentation of the liver area is an essential phase in diagnosis from medical images. In this paper, we propose artificial bee colony (ABC) optimisation algorithm that used as a clustering technique to segment CT our algorithm, ABC calculates centroids clusters image together with region corresponding each cluster. Using mathematical morphological operations, then remove small and thin regions, which may represents flesh regions around area, sharp edges organs or lesions...
In this paper, it is intended to enhance the simple region growing technique (RG) extract liver from abdomen away other organs in CT images. Iterative K-means clustering used as a preprocessing step pass image and watershed segmentation techniques. The usage of preferred here for its simplicity low cost execution. proposed approach starts with cleaning annotation enhancing boundaries liver. This performed using texture filter ribs connection algorithm, followed by iterative K-means. removes...
Historical manuscript image binarization is a very important step towards full word spotting system. In this paper, we present novel algorithm based on artificial bee colony optimizer. The proposed approach contains two phases. first phase stretching the intensity level of by contrast filter and removing noise cleaning algorithm, second determining number clusters, iterations for starting Artificial Bee Colony (ABC) algorithm. tested set images collected from electronic Arabic manuscripts...
Previous trials remain inconsistent regarding the advantages and hazards related to intracoronary (IC) compared with intravenous (IV) administration of thrombolytics. We aimed evaluate safety effectiveness IC versus IV tirofiban in diabetic patients (DM) acute ST-segment elevation myocardial infarction (STEMI) during primary percutaneous coronary intervention (PCI).