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Analysis of dynamic object deformations such as cardiac motion is of great importance, especially when there is a necessity to visualize and compare the deformation behavior across subjects. However, there is a lack of effective techniques for comparative visualization and assessment of a collection of motion data due to its 4-dimensional nature, i.e., timely varying three-dimensional shapes. From the geometric point of view, the motion change can be considered as a function defined on the 2D manifold of the surface. This paper presents a novel classification and visualization method based on a medial surface shape space, in which two novel shape descriptors are defined, for discriminating normal and abnormal human heart deformations as well as localizing the abnormal motion regions. In our medial surface shape space, the geodesic distance connecting two points in the space measures the similarity between their corresponding medial surfaces, which can quantify the similarity and disparity of the 3D heart motions. Furthermore, the novel descriptors can effectively localize the inconsistently deforming myopathic regions on the left ventricle. An easy visualization of heart motion sequences on the projected space allows users to distinguish the deformation differences. Our experimental results on both synthetic and real imaging data show that this method can automatically classify the healthy and myopathic subjects and accurately detect myopathic regions on the left ventricle, which outperforms other conventional cardiac diagnostic methods.

Histological tissue images typically exhibit very sophisticated spatial color patterns. It is of great clinical importance to extract qualitative and quantitative information from these images. As an ad hoc solution, various unsupervised approaches address the object detection and segmentation problem which are suitable for limited classes of histology images. In this paper, we propose a general purpose localization and segmentation method which utilizes reshapable templates. The method combines both pixel- and object-level features for detecting regions of interest. Segmentation is carried out in two levels including both the coarse and fine ones. A set of simple-shaped templates is used for coarse segmentation. A content based template reshaping algorithm is proposed for fine segmentation of target objects. Experimentation was done using a publicly available image data set which contains 7931 manually labeled cells of heterogeneous histology images. The experiments have demonstrated acceptable level of detection and segmentation results for the proposed approach (precision=0.904, recall=0.870 and Zijdenbos similarity index=73%). Thus, the prototype software developed based on proposed method can be considered as a potential tool for pathologists in clinical process., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

A novel approach based on the shape space concept is proposed to classify quasi-conformal deformations of 3D models. A new metric on the quotient space of meshes is introduced to capture changes of the curvature at each vertex of a simplicial complex during deformation. Then, the deformation curve is obtained by calculating the geodesic curve connecting two shapes in the shape space manifold. In order to compare the deformations, the deformation curves are first transferred to the same part of shape space. And then, the Multi-Dimensional Scaling method is employed to eliminate the redundant dimensions facilitating easy comparison of the deformations. To evaluate our method, some synthetic datasets and 23 datasets of gated images of the left heart ventricle during one heartbeat have been examined. Our experiments show that the algorithm can effectively classify normal and abnormal left heart ventricle deformations in shape space.