Computer-Aided Diagnosis System for Medical Laboratory Investigation
Abdel Razak, M., Al Said, A., Mokhtar, M., Bader El Din, A., Gaber, A., Ahmed, O., Abdel Tawab, A., Ahmed, G. (2016). Computer-Aided Diagnosis System for Medical Laboratory Investigation. EKB Journal Management System, 1(First International Undergraduate Research Conference, IUGRC), 63-63. doi: 10.21608/iugrc.2016.91172
Mustafa Abdel Razak; Abdel Fatah Al Said; Mustafa Mokhtar; Abdel Rahman Bader El Din; Ahmed Gaber; Osama Ahmed; Amr Abdel Tawab; Ghada Ahmed. "Computer-Aided Diagnosis System for Medical Laboratory Investigation". EKB Journal Management System, 1, First International Undergraduate Research Conference, IUGRC, 2016, 63-63. doi: 10.21608/iugrc.2016.91172
Abdel Razak, M., Al Said, A., Mokhtar, M., Bader El Din, A., Gaber, A., Ahmed, O., Abdel Tawab, A., Ahmed, G. (2016). 'Computer-Aided Diagnosis System for Medical Laboratory Investigation', EKB Journal Management System, 1(First International Undergraduate Research Conference, IUGRC), pp. 63-63. doi: 10.21608/iugrc.2016.91172
Abdel Razak, M., Al Said, A., Mokhtar, M., Bader El Din, A., Gaber, A., Ahmed, O., Abdel Tawab, A., Ahmed, G. Computer-Aided Diagnosis System for Medical Laboratory Investigation. EKB Journal Management System, 2016; 1(First International Undergraduate Research Conference, IUGRC): 63-63. doi: 10.21608/iugrc.2016.91172

Computer-Aided Diagnosis System for Medical Laboratory Investigation

The International Undergraduate Research Conference
Article 27, Volume 1, First International Undergraduate Research Conference, IUGRC, 2016, Page 63-63  XML PDF (424.28 K)
Document Type: Original Article
DOI: 10.21608/iugrc.2016.91172
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Authors
Mustafa Abdel Razak1; Abdel Fatah Al Said1; Mustafa Mokhtar1; Abdel Rahman Bader El Din1; Ahmed Gaber1; Osama Ahmed1; Amr Abdel Tawab1; Ghada Ahmed2
1Helwan University, Egypt.
2PhD, Assistant Professor, Computer Science Dept. Faculty of Computers and Information,Egypt.
Abstract
This project applies automatic methods for classification and recognition of urine analysis microscopic images. We found that, it is necessary to apply automatization in the field of microscopic analyses of urine solution as detecting particles in the microscopic image is repeated and time consuming. Furthermore, Particles in many medical laboratory analyses have irregular shapes and blur edges. This project applies medical image processing algorithms and pattern recognition to microscopic images. It is composed of three stages: first, original urinary sediment microscopic images are transformed into binary image by image preprocessing including median filtering, color image conversion to gray scale image and image segmentation. Second, we select and extract some objects from images. Third, we classify the extracted images using SVM to recognize recognizes four kinds of urine sediment components: red blood cells, white blood cells, cast, calcium oxalate.
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