Software for Interactive and Automatic Medical Images Segmentation and Masking in Phlebology

Student: Iardukhin Georgii

Educational Programme: Software Engineering (Bachelor)

Year of Graduation: 2021

Segmentation and masking of medical images help phlebologists to predict the disease at an early stage and manage it successfully. Big amount of medical image data creates a new problem for specialists because sometimes it is impossible to determine the pathology among thousands of images. The reason for this is the inconvenience and not adapted for phlebology tasks viewers of medical images, since small objects such as a vein are very difficult to see on a large fuzzy image. Automating the segmentation process will help phlebologists save time on manual work and focus on image analysis. This paper will be described an application that will be helping phlebologists interactive segment and mask images. Also, algorithms for automatic segmentation will be described. Keywords: web application; supervised learning; dicom; medical image segmentation; medical image masking; computer vision algorithms

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