Algorithms for leukaemia image edge detection technique/
The main objectives of this study are to detect the edges of leukaemia through filtering, sorting process, kernel multiphase convolution, binarization and transformation techniques; to employ Sobel, Prewitt and Robert edge detection to recognize the pattern of the detected leukaemia cells in the whi...
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| Format: | Thesis Software eBook |
| Language: | English |
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Perlis, Malaysia
Institute of Engineering Mathematics, Universiti Malaysia Perlis
2017.
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| Summary: | The main objectives of this study are to detect the edges of leukaemia through filtering, sorting process, kernel multiphase convolution, binarization and transformation techniques; to employ Sobel, Prewitt and Robert edge detection to recognize the pattern of the detected leukaemia cells in the white blood image; to utilize Ant Colony Optimization (ACO) algorithm to detect, extract and recognize the edge boundaries of the leukaemia cells from the white blood cell image; to compare the performance of the gradient edge detection methods (Sobel, Prewitt and Robert) with the ACO method to determine the optimal edges of the leukaemia cells; to describe the features of white and red blood cells based the detected edges through the visual comparison. |
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| Physical Description: | 1 CD-ROM 12 cm |
| Bibliography: | Includes bibliographical references. |