| Title: | Single Image Focus Level Assessment Using Support Vector Machines |
| Full text: |  |
| Authors: | Beijbom, Oscar |
| Year: | 2007 |
| Document Type: | Master Thesis |
| Status: | Published |
| Refereed: | No |
| BibTeX item: |  |
| Extra: | matlth, matlthmig |
| Abstract: | Differential white blood cell count is the process of counting and classifying white blood cells in blood smears. It is one of the most common clinical tests which is performed in order to make diagnoses in conjunction with medical examinations. These tests indicate deceases such as infections, allergies, and blood cancer and approximately 200-300 million are done yearly around the world. Cellavision AB has developed machines that automate this work and is the global leader in this market. The method developed in this thesis will replace and improve the auto focus routine in these machines. It makes it possible to capture a focused image in only two steps instead of using a iterative multi step algorithm like those used today in most auto focus systems, including the one currently used at Cellavision. In the proposed method a Support Vector Machine, SVM, is trained to assess quantatively, from a singel image, the level of defocus as well as the direction of defocus for that image. The SVM is trained on features that measure both the image contrast and the image content. High precision is made possible through extracting features from the different parts of the image as well as from the image as a whole. This require the image to be segmented and a method for doing this is proposed. Using this method 99.5% of the images in the test datas distances to focus were classified less or equal to 5 micrometer wrong while over 85 % were classified completely correctly. A 5 micrometer defocus is borderline to what the human eye perceives as defocused. Cellavision AB has applied for a patent to protect the method described in this thesis. |
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