Angiogenesis is the process of formation of new blood vessels as outgrowths of pre-existing ones. It occurs naturally
during development, tissue repair, and abnormally in pathologic diseases such as cancer. It is associated with
proliferation of blood vessels/tubular sprouts that penetrate deep into tissues to supply nutrients and remove waste
products. The process starts with migration of endothelial cells. As the cells move towards the target area they form
small tubular sprouts recruited from the parent vessel. The sprouts grow in length due to migration, proliferation, and
recruitment of new endothelial cells and the process continues until the target area becomes fully vascular. Accurate
quantification of sprout formation is very important for evaluation of treatments for ischemia as well as angiogenesis
inhibitors and plays a key role in the battle against cancer. This paper presents a technique for automatic quantification of
newly formed blood vessels from Micro-CT volumes of tumor samples. A semiautomatic technique based on
interpolation of Bezier curves was used to segment out the cancerous growths. Small vessels as determined by their
diameter within the segmented tumors were enhanced and quantified with a multi-scale 3-D line detection filter. The
same technique can be easily extended for quantification of tubular structures in other 3-D medical imaging modalities.
Experimental results are presented and discussed.
3-D analysis of blood vessels from volumetric CT and MR datasets has many applications ranging from examination of pathologies such as aneurysm and calcification to measurement of cross-sections for therapy planning. Segmentation of the vascular structures followed by tracking is an important processing step towards automating the 3-D vessel analysis workflow. This paper demonstrates a fast and automated algorithm for tracking the major arterial structures that have been previously segmented. Our algorithm uses anatomical
knowledge to identify the start and end points in the vessel structure that allows automation. Voxel coding scheme is used to code every voxel in the vessel based on its geodesic distance from the start point. A shortest path based iterative region growing is used to extract the vessel tracks that are subsequently smoothed using an active contour method. The algorithm also has the ability to automatically detect bifurcation points of major arteries. Results are shown for tracking the major arteries such as the common carotid, internal carotid, vertebrals, and arteries coming off the Circle of Willis across multiple cases with various data related
and pathological challenges from 7 CTA cases and 2 MR Time of Flight (TOF) cases.
Recent trends in medical image processing involve computationally intensive processing techniques on large data sets, especially for 3D applications such as segmentation, registration, volume rendering etc. Multi-resolution image processing techniques have been used in order to speed-up these methods. However, all well-known techniques currently used in multi-resolution medical image processing rely on using Gaussain-based or other equivalent floating point representations that are lossy and irreversible. In this paper, we study the use of Integer Wavelet Transforms (IWT) to address the issue of lossless representation and reversible reconstruction for such medical image processing applications while still retaining all the benefits which floating-point transforms offer such as high speed and efficient memory usage. In particular, we consider three low-complexity reversible wavelet transforms namely the - Lazy-wavelet, the Haar wavelet or (1,1) and the S+P transform as against the Gaussian filter for multi-resolution speed-up of an automatic bone removal algorithm for abdomen CT Angiography. Perfect-reconstruction integer wavelet filters have the ability to perfectly recover the original data set at any step in the application. An additional advantage with the reversible wavelet representation is that it is suitable for lossless compression for purposes of storage, archiving and fast retrieval. Given the fact that even a slight loss of information in medical image processing can be detrimental to diagnostic accuracy, IWTs seem to be the ideal choice for multi-resolution based medical image segmentation algorithms. These could also be useful for other medical image processing methods.
Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical
complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: “proximal”, “middle”, and “distal”. The “proximal” and “distal” sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the “middle” partition that
remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified
visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing
the “proximal” and “distal” partitions. Complex methods are restricted to only the “middle” partition. The partitionenabled
segmentation has been successfully tested and results are shown from multiple cases.