3D watershed transform combined with a probabilistic atlas for medical image segmentation

• Autorzy: Straka M. , La Cruz A. , Köchl A. , Šrámek M. , Gröller E. , Fleischmann D.
• Języki publikacji: EN

Abstrakty

EN

Recent advances in medical imaging technology using multiple detector-row computed tomography (CT) provide volumetric datasets with unprecedented spatial resolution. This has allowed for CT to evolve into an excellent non-invasive vascular imaging technology, commonly referred to as CT-angiography. Visualisation of vascular structures from CT datasets is demanding, however, and identification of anatomic objects in CT-datasets is highly desirable. Density and/or gradient operators have been used most commonly to classify CT data. In CT angiography, simple density/gradient operators do not allow precise and reliable classification of tissues due to the fact that different tissues (e.g. bones and vessels) possess the same density range and may lie in close spatial vicinity. We think, that anatomic classification can be achieved more accurately, if both spatial location and density properties of volume data are taken into account. We present a combination of two well-known methods for volume data processing to obtain accurate tissue classification. 3D watershed transform is used to partition the volume data in morphologically consistent blocks and a probabilistic anatomic atlas is used to distinguish between different kinds of tissues based on their density.

Słowa kluczowe

EN

CT angiography; knowledge based segmentation; probabilistic atlas; thin-plate-spline; histogram classification

PL

angiografia tomografii komputerowej; segmentacja oparta na wiedzy; probabilistyczny atlas; kategoria histogramu

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2003
• Tom: Vol. 6
• Strony: IT69--78
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Straka M.

• Commission for Scientific Visualization, Austrian Academy of Sciences, Vienna, Austria

autor

La Cruz A.

autor

Köchl A.

autor

Šrámek M.

autor

Gröller E.

autor

Fleischmann D.

Bibliografia

• [1] BANKMAN I.N., editor, Handbook of Medical Imaging, Processing and Analysis. Academic Press, 2000.
• [2] BAUM, S., PENTECOST, M.J., Abrams' angiography: vascular and interventional radiology. Little Brown, Boston, 1997
• [3] GONZALES R.C., WOODS R., Digital Image Processing. Addison Wesley, 1992.
• [4] STRAKA M., ŠRÁMEK M., Bone Segmentation in CT-Angiography Data Using a Probabilistic Atlas (Submitted paper). Vision, Modeling and Visualisation Workshop, Munich, Germany, 2003
• [5] ŠRÁMEK M., ISEG - A system for interactive segmentation of 3D tomographic data sets. In J. Rozman, editor, Proc. Of 12-th international conference Biosignal'94, pages 48-51, Czech Republic, 1994. Technical University Brno.
• [6] SAAMEK M., DIMITROV L.I., Segmentation of tomographic data by hierarchical watershed transform. Journal of Medical Informatics and Technologies, 3, pages MI-161-MI-169, 2002
• [7] VINCENT L., SOILLE P., Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence. 13:583-598, 1991.
• [8] Visualisierungsmethoden für die periphere CT-Angiographie (AngioVis), Project P15217, FWF Austria