Medical images of a brain, acquired with computer tomography (CT) or magnetic resonance (MR) are widely used in medicine for patient diagnosis. Therefore, a task of pathology zone detection and its volume estimation develops in medical image analysis. To successfully solve this task several problems have to be considered: 3D visualization of medical images, image segmentation, pathology zone extraction and volume estimation of the extracted zone. Standard software for processing medical CT and MR images in many cases does not allow extraction of the three-dimensional pathology zone and its volume estimation. Often the detection of pathology zone and its volume estimation is so complex, that the physicians prefer to measure only the pathology zone’s maximum axial and coaxial diameters in two-dimensional slices of medical images, although it is clear, that precise volume estimation could be of great assistance to the physicians in patient diagnostics. In addition, the standard medical imaging software is very specific – it is usually installed only on one work station linked to medical hardware and that is not always convenient. The problems described above complicate the diagnosis of the patient for physicians. In this paper we propose several algorithms for 3D visualization of medical images, image segmentation and volume estimation of the extracted pathology zone to solve these problems. Based on the proposed algorithms new software is developed for processing medical images in DICOM format that are acquired with CT and MR
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