AUTOMATED HYBRID ALGORITHM FOR BLOOD CELL SEGMENTATION, RBC & WBC DETECTION
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Abstract
Detection and diagnosis of a disease, at the appropriate early stage and time is crucial to save is in the field of medicine. The time in sever as emergency cased for non-automated tests such as blood tests can load a significant increase in mortality rates. As imaging techniques are widely used in the field of medical sciences for the detection of various diseases, classification of famous cancers, etc.
Also the existing system used by pathologists for the identification and classification of blood parameters is expensive and time-consuming. As the main power included in non-transformed festivals is high, they tend to be costly and are not affordable for an average patient.
Therefore, in this thesis we propose a noval image processing technique that can be automated by identifying the blood parameters such as red blood cell count, white blood cell count and other parameters with precision compared to other existing techniques. The white blood cell count, two algorithms we use each and its weighted average according to the user's performance is treated as final result.
Red blood cell count is achieved by Hough's circular transformation and segmented red blood cell count. Similarly, the white blood cell count is achieved using the k-media pool and the segmented blood count in the blue channel with the area response. The proposed technique also helps segregate blood cells into different categories
Also the existing system used by pathologists for the identification and classification of blood parameters is expensive and time-consuming. As the main power included in non-transformed festivals is high, they tend to be costly and are not affordable for an average patient.
Therefore, in this thesis we propose a noval image processing technique that can be automated by identifying the blood parameters such as red blood cell count, white blood cell count and other parameters with precision compared to other existing techniques. The white blood cell count, two algorithms we use each and its weighted average according to the user's performance is treated as final result.
Red blood cell count is achieved by Hough's circular transformation and segmented red blood cell count. Similarly, the white blood cell count is achieved using the k-media pool and the segmented blood count in the blue channel with the area response. The proposed technique also helps segregate blood cells into different categories
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