Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.

PURPOSE: In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. METHODS: The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed points provided by recognition. The authors assess the performance of this method for both nonsparse (compact blob-like) and sparse (thin tubular) objects in the thorax. RESULTS: The results of eight thoracic organs on 30 real images are presented. Overall, the delineation accuracy in terms of mean false positive and false negative volume fractions is 0.34% and 4.02%, respectively, for nonsparse objects, and 0.16% and 12.6%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 1.31 and 2.28 mm, respectively. CONCLUSIONS: The hierarchical structure and location relation integrated into the model provide the initial pose for registration and make the recognition process efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both nonsparse and sparse organs. Tailoring the registration process for each organ by specialized similarity criteria and updating the organ intensity properties based on refined recognition improve the overall segmentation process.

Local morphology fitting active contour for automatic vascular segmentation.

In this paper, we propose an active contour model using local morphology fitting for automatic vascular segmentation on 2-D angiogram. The vessel and background are fitted to fuzzy morphology maximum and minimum opening, separately, using linear structuring element with adaptive scale and orientation. The minimization of the energy associated with the active contour model is implemented within a level set framework. As in the current local model, fitting the image to local region information makes the model robust against the inhomogeneous background. Moreover, selective local estimations for fitting that are precomputed instead of updated in each contour evolution makes the evolution of level set robust again initial location compared to the current local model. The results on synthetic image and real angiogram compared with other methods are presented. It is shown that the proposed method can achieve automatic and accurate segmentation of vascular angiogram.

Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram.

This paper presented an automatic morphological method to extract a vascular tree using an angiogram. Under the assumption that vessels are connected in a local linear pattern in a noisy environment, the algorithm decomposes the vessel extraction problem into several consecutive morphological operators, aiming to characterize and distinguish different patterns on the angiogram: background, approximate vessel region and the boundary. It started with a contrast enhancement and background suppression process implemented by subtracting the background from the original angiogram. The background was estimated using multiscale morphology opening operators by varying the size of structuring element on each pixel. Subsequently, the algorithm simplified the enhanced angiogram with a combined fuzzy morphological opening operation, with linear rotating structuring element, in order to fit the vessel pattern. This filtering process was then followed by simply setting a threshold to produce approximate vessel region. Finally, the vessel boundaries were detected using watershed techniques with the obtained approximate vessel centerline, thinned result of the obtained vessel region, as prior marker for vessel structure. Experimental results using clinical digitized vascular angiogram and some comparative performance of the proposed algorithm were reported.