Automatic segmentation of the prostate zones has great potential of improving the accuracy of lesion detection during
the image-guided prostate interventions. In this paper, we present a novel compact method to segment the prostate and
its zones using multi-parametric magnetic resonance imaging (MRI) and the anatomical priors. The proposed method
comprises of a prostate tissue representation using Gaussian mixture model (GMM), a prostate localization using the
mean shift with the kernel of the prostate atlas and a prostate partition using the probabilistic valley between zones. The
proposed method was tested on four sets of multi-parametric MRIs. The average Dice coefficient resulted from the
segmentation of the prostate is 0.80 ± 0.03, the central zone 0.83 ± 0.04, and the peripheral zone 0.52 ± 0.09. The
average computing time of the online segmentation is 1 min and 10 s per datasets on a PC with 2.4 GHz and 4.0 GB
RAM. The proposed method is fast and has the potential to be used in clinical practices.
Computer aided liver tumor detection and diagnosis can assist radiologists to interpret abnormal features in liver CT
scans. In this paper, a general frame work is proposed to automatically detect liver focal mass lesions, conduct
differential diagnosis of liver focal mass lesions based on multiphase CT scans, and provide visually similar case
samples for comparisons. The proposed method first detects liver abnormalities by eliminating the normal tissue/organ
from the liver region, and in the second step it ranks these abnormalities with respect to spherical symmetry,
compactness and size using a tumoroid measure to facilitate fast location of liver focal mass lesions. To differentiate
liver focal mass lesions, content-based image retrieval technique is used to query a CT model database with known
diagnosis. Multiple-phase encoded texture features are proposed to represent the focal mass lesions. A hypercube
indexing structure based method is adopted as the retrieval strategy and the similarity score is calculated to rank the
retrieval results. Good performances are obtained from eight clinical CT scans. With the proposed method, the clinician
is expected to improve the accuracy of differential diagnosis.
We propose a new deformable model Deformable Associate Net (DAN). It is represented by a set of nodes which are associated by deformation constrains such as topology association, inter-part association, intra-part association, and geometry to atlas association. Each node in the model is given a priority, and hence DAN is a hierarchical model in which each layer is decided by nodes with same priority. Directional edges and dynamic generated local atlases are used in energy function to incorporate knowledge about tissue and image acquisition. A fast digital topology based method is designed to check whether topology of the model is changed under deformation. The deformation procedure hierarchically combines global and local deformations. Layers with high priority deform first. Once a higher layer is deformed to its target position in an image, the nodes in this layer are fixed, and then used as reference to help lower layers deform to their initial positions. At a particular layer, the model is first deformed by using global affine transformation to fit the image roughly, and then is warped by using a local deformation to fit the image better. The proposed method has been used to segment chest CT images for thoracic surgical planning, and it is also promising for other medical applications, such as model based image registration, and model-based 3D modeling.