TY - CHAP T1 - Liver segment approximation in CT data for surgical resection planning T2 - Medical Imaging 2004: Image Processing Y1 - 2004 AB - Surgical planning of liver tumor resections requires detailed three-dimensional (3D) understanding of the complex arrangement of vasculature, liver segments and tumors. Knowledge about location and sizes of liver segments is important for choosing an optimal surgical resection approach and predicting postoperative residual liver capacity. The aim of this work is to facilitate such surgical planning process by developing a robust method for portal vein tree segmentation. The work also investigates the impact of vessel segmentation on the approximation of liver segment volumes. For segment approximation, smaller portal vein branches are of importance. Small branches, however, are difficult to segment due to noise and partial volume effects. Our vessel segmentation is based on the original gray-values and on the result of a vessel enhancement filter. Validation of the developed portal vein segmentation method in computer generated phantoms shows that, compared to a conventional approach, more vessel branches can be segmented. Experiments with in vivo acquired liver CT data sets confirmed this result. The outcome of a Nearest Neighbor liver segment approximation method applied to phantom data demonstrates, that the proposed vessel segmentation approach translates into a more accurate segment partitioning. JF - Medical Imaging 2004: Image Processing PB - SPIE CY - Bellingham; WashingtonScheele, J., Anatomical and atypical liver resection (2001) Chirurg, 72 (2), pp. 113-124;Couinaud, C., (1957) Le Foie - Etudes Anatomiques et Chirurgicales, , Masson, Paris; Strunk, H., Stuckmann, G., Textor, J., Willinek, W., Limit N1 - ScopusID: 5644267870doi: 10.1117/12.535514 ER - TY - CHAP T1 - Multiple Sclerosis lesion quantification in MR images by using vectorial scale-based relative fuzzy connectedness T2 - Medical Imaging 2004: Image Processing Y1 - 2004 AB - This paper presents a methodology for segmenting PD- andT2-weighted brain magnetic resonance (MR) images of multiplesclerosis (MS) patients into white matter (WM), gray matter (GM),cerebrospinal fluid (CSF), and MS lesions. For a given vectorialimage (with PD- and T2-weighted components) to be segmented, weperform first intensity inhomogeneity correction andstandardization prior to segmentation. Absolute fuzzyconnectedness and certain morphological operations are utilized togenerate the brain intracranial mask. The optimum thresholdingmethod is applied to the product image (the image in which voxelvalues represent T2 value x PD value) to automaticallyrecognize potential MS lesion sites. Then, the recently developedtechnique -- vectorial scale-based relative fuzzy connectedness --is utilized to segment all voxels within the brain intracranialmask into WM, GM, CSF, and MS lesion regions. The number ofsegmented lesions and the volume of each lesion are finally outputas well as the volume of other tissue regions. The method has beentested on 10 clinical brain MRI data sets of MS patients. Anaccuracy of better than 96% has been achieved. The preliminaryresults indicate that its performance is better than that of thek-nearest neighbors (kNN) method. JF - Medical Imaging 2004: Image Processing PB - SPIE CY - Bellingham; Washington N1 - ScopusID: 5644264947doi: 10.1117/12.535655 ER - TY - CHAP T1 - Quantitative analysis of three-dimensional tubular tree structures T2 - Medical Imaging 2003 Y1 - 2003 JF - Medical Imaging 2003 PB - SPIE - The International Society for Optical Engineering CY - Bellingham; Washington UR - http://spie.org/x648.html?product_id=459268 N1 - doi: 10.1117/12.481127 ER - TY - CHAP T1 - A protocol-independent brain MRI segmentation method T2 - Medical Imaging 2002: Image Processing Y1 - 2002 AB - We present a segmentation method that combines the robust,accurate, and efficient techniques of fuzzy connectedness with standardized MRI intensities and fast algorithms. The result is a general segmentation framework that more efficiently utilizes the user input (for recognition) and the power of computer (for delineation). This same method has been applied to segment brain tissues from a variety of MRI protocols. Images were corrected for inhomogeneity and standardized to yield tissue-specific intensity values. All parameters for the fuzzy affinity relations were fixed for a specific input protocol. Scale-based fuzzy affinity was used to better capture fine structures. Brain tissues were segmented as 3D fuzzy-connected objects by using relative fuzzy connectedness. The user can specify seed points in about a minute and tracking the 3D fuzzy-connected objects takes about 20 seconds per object. All other computations were performed before any user interaction took place. Segmentation of brain tissues as 3D fuzzy-connected objects from MRI data is feasible at interactive speeds. Utilizing the robust fuzzy connectedness principles and fast algorithms, it is possible to interactively select fuzzy affinity, seed point, and threshold parameters and perform efficient, precise, and accurate segmentations. JF - Medical Imaging 2002: Image Processing PB - SPIE CY - Bellingham; Washington N1 - ScopusID: 0036030011doi: 10.1117/12.467128 ER -