No abstract

Nucleus segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we proposed a technique to segment images of the nucleus. We analyze a set of white-blood-cell-nucleus-based features using color fuzzy texture spectrum (Base 5). We applied artificial neural network for classification. We compared the results with moment based features. The classification performances are evaluated by class wise classification rates. The results show that the features using nucleus alone could be utilized to achieve a classification rate of 99.05% on the test sets.

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Magnetic Resonance Image plays a major role in medical diagnostics. Image segmentation is done to divide an image into meaningful structures. Image segmentation is the initial step in image analysis and pattern recognition. It becomes more important while dealing with medical images where presurgery and post-surgery decisions are required for the purpose of initiating and speeding up the recovery process. Manual segmentation of abnormal tissues cannot be compared with modern day's high speed computing machines. Segmentation is done to extract the features of the image that are used for analysis, interpretation, and understanding of images. Accuracy of the extracted features decides the accuracy of the algorithm. Selection of a suitable algorithm is highly based on the application. This paper highlights the various image segmentation algorithms, used in medical images.