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A Region Growing Method for Medical Images Segmentation

Alberto De Santis, Daniela Iacoviello


Diagnosis by medical images implies the expert ability of recognizing patterns of interest in terms of some features like gray (or color) level intensity, shape attributes, texture. The image segmentation algorithms constitute a valid support in the analysis of medical images by providing reliable computer tools able to separate the objects of interest from the background. There is a great deal of segmentation algorithms depending on the mathematical model adopted for the information to be retrieved from data. They span from very simple and fast threshold procedures, to local signal processing like edge detection, to sophisticated ones based on global optimization methods. This work describes a region growing algorithm that falls within the last framework; it is based on a novel image model. It is formulated in the discrete domain to deal directly with the image data without approximation schemes required by the formulation in the continuum domain, typical of the variational methods. The segmentation procedure is efficient and reliable, allowing a hierarchical processing also in term of the signal components. It can easily take into account a wide range of situations occurring in the medical environment, going from the analysis of angiographies to the analysis of CT scan images of human body organs.

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