Abstract
Based on analyzing the drawbacks existing in traditional watershed algorithms for bone removal from CTA (Computed Tomography Angiography, CTA), this paper presents an improved interactive watershed algorithm. The improved watershed algorithm is based on sorting and graded overflow of fast watershed algorithms, and the merging process of catchment basins is intervened by a merging threshold which is given by users so as to take an effect on segmentation results. The algorithm can record such basic information as the labels and ridge points of each basin in graded overflow basin marking, set merging threshold by means of user interaction for controlling merging process effectively, and suppresses over segmentation. At last, the improved algorithm is applied to bone removal from CTA Images, and three-dimensional rendering is taken for CTA Images after bone removal. The experimental results indicate that the improved algorithm prevents over segmentation in watershed transformation effectively and removes bone structure accurately.
Highlights
Medical image processing uses image segmentation to extract lesion area from a very complex background image for further analysis and processing, and the complexity of medical images makes medical image segmentation become a research hotspot and difficulty in medical image processing
Based on analyzing the drawbacks existing in traditional watershed algorithms for bone removal from Computerized Tomography Angiography (CTA) (Computed Tomography Angiography, CTA), this paper presents an improved interactive watershed algorithm
The improved algorithm is applied to bone removal from CTA Images, and three-dimensional rendering is taken for CTA Images after bone removal
Summary
Medical image processing uses image segmentation to extract lesion area from a very complex background image for further analysis and processing, and the complexity of medical images makes medical image segmentation become a research hotspot and difficulty in medical image processing. One of the most challenging problems is how to remove bone structure effectively from floods of Computerized Tomography Angiography (CTA) images so as to obtain vascular and internal organ information which is used to diagnose illness (Yongbum et al, 2005). Maximum Intensity projection (MIp) is one of the most commonly-used methods, but the high-brightness bone structure in CTA is the main obstacle in reappearing vascular structure by three dimensional visualization, so bone removal is necessary firstly (Abdalmajeid, 2001). Bone removal from CTA is a typical application in medical image segmentation area whose main problem is that the local characteristic of bone in images is very strong, and bone gray level is widely distributed to different parts.
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