The proposed method permits the analysis of blood cells automatically via image processing techniques, and it represents a medical tool to avoid the numerous drawbacks associated with manual observation. This process could also be used for counting, as it provides excellent performance and allows for early diagnostic suspicion, which can then be confirmed by a haematologist through specialised techniques.
Automated Blood Cell Counting instruments are very important tools, daily used by haematologists and medical analysts to perform a Complete Blood Count (CBC). The results of the CBC may be complex to interpret but could lead to important decisions regarding the patient medical treatment. The main focus of this research is oriented to a CBC technique, named White Blood Cell Count (WBCC). Generally, the WBCC is performed by skilled medical operators on peripheral blood smears in order to make a correct count and to obtain useful information such as cell abnormalities or the physical status. The manual WBCC is associated with several challenges, in fact it is a time-consuming, labour intensive and expensive process. This paper introduces a reliable automated WBCC system based on image processing techniques. The main aims are to speed up the and to improve the accuracy of the WBCC process. The proposed automated system introduces a new approach to segment white blood cells taking into account the knowledge acquired from a training set formed of the three main classes elements, the white blood cells, the red blood cells and the plasma present in a blood smear image. The segmented regions containing only the white blood cells are subjected to a further step in which the count is performed using the circular Hough transform exploiting the grey level information. The method has been tested on three different public datasets, in order to highlight the accuracy of the segmentation approach with different colour images and illumination conditions. The experimental results obtained on these datasets demonstrate that the pro-
In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community's disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six wellknown databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification.
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