Automatic Sediment Detection in Urine Micrograph
| dc.contributor.advisor | Assabie, Yaregal (PhD) | |
| dc.contributor.author | Worku, Ameha | |
| dc.date.accessioned | 2019-11-27T10:39:22Z | |
| dc.date.accessioned | 2023-11-04T12:22:46Z | |
| dc.date.available | 2019-11-27T10:39:22Z | |
| dc.date.available | 2023-11-04T12:22:46Z | |
| dc.date.issued | 10/3/2018 | |
| dc.description.abstract | Urine is one of the most complex fluid specimens found in our body. The concentration of urine sediments is an indication of various diseases. Invariably, medical facilities in our country employ manual approach to detect sediments under microscopes. However, medical results using manual approach are not always accurate. It could vary from person to person. Also, the approach is time consuming, and increases workloads of technicians. To mitigate these problems, scholars in the field recommend using automated detection of sediment in urine. However, ensuring accuracy from detection of se in urine remains challenging due to variations in urine color, irregular shape, and non-uniform illumination. Hence, in this research a better segmentation and feature extraction technique is proposed to detect urine sediments. Urine microscopic input image is improved by grayscale image, adaptive median filtering, and image adjustment which in turn yield uniform illumination for further analysis task. This study proposes a fusion of adaptive threshold, canny edge detection and morphological operations to isolate the background from the foreground and remove tiny objects. In this regard, a total of twenty-three features are extracted from shape, texture and color of urine to represent white blood cells, red blood cells, epithelia cells, and crystal in urine microscopic image. Finally, classification models are built using Neural Network and Multi Class Support Vector Machine. The performance of each model is compared using tenfold cross validation technique. Compared to other methods, this technique demonstrated acceptable detection performance with average sensitivity of 95.34%, specificity of 98.10%, precision of 90.22%, and accuracy of 95.93% using neural network classifier and an average sensitivity of 90.38%, specificity of 98.01%, precision of 91.68%, and accuracy of 97.40% using multiclass support vector machine for white blood cell(WBC), red blood cell(RBC), epithelial cell(EP) and Crystal, respectively. The performance of the proposed prototype is found to be effective for the identification of sediment in urine sample even in the context where sediment in urine have irregular shape, different color and poorly illuminated microscopic images. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/20272 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Multiclass Support Vector Machine | en_US |
| dc.subject | Neural Network | en_US |
| dc.subject | Urine Micrograph | en_US |
| dc.title | Automatic Sediment Detection in Urine Micrograph | en_US |
| dc.type | Thesis | en_US |