3-D curve matching using splines

A NOVEL ITERATIVE MATCHING SCHEME BASED ON HOMOGRAPHY METHOD FOR X-RAY IMAGE

Purpose anterior cervical decompression and fusion is a common surgical procedure. Traditionally, experienced doctors observe X-ray films regularly examined by patients to determine postoperative conditions by observing the tiny movements between the limited vertebral bodies. But it is not accurate. This may lead to error diagnostics and serious deterioration of the condition and secondary injury to the patient and will also put a greater financial burden on them. Doctors need a quantitative standard to determine small motion with limited vertebral landmarks after surgery. Computer vision technology is needed to match the over-extension and over-flexion cervical vertebral body to provide objective measurement data for further quantification of intervertebral activity. Based on conventional scheme, the point mean square error is used as the evaluation criterion of the matching effect, and the iterative matching scheme is proposed to improve the stability of the original scheme. The cervical X-ray films of patients from the China–Japan Friendship Hospital were collected as samples to verify the reliability of the scheme. Compared with the existing image matching schemes based on feature points, our scheme is superior in matching effect, matching speed and stability. This scheme can provide a solid foundation for further assisting doctors in the study of rehabilitation after anterior cervical fusion.

IOCD: INTENSITY ORDER CURVE DESCRIPTOR

Curve matching plays an important role in pattern recognition, computer vision and image understanding. In several past years, this problem has been studied mainly based on the curve contour, while only little progress has been made using the texture feature of the curve's neighborhood. This paper develops a novel texture-based curve matching method called IOCD, which consists of three steps: (1) Curve support region (CSR) without assigning a dominant orientation is first determined; (2) CSR is equally partitioned into several order bins according to the overall intensity order; (3) The feature vector is computed based on the local intensity order mapping. Experiments prove that the proposed IOCD performs robust to image rotation, viewpoint change, illumination change, blur, noise and JPEG compress. The application of image mosaic further identifies IOCD can achieve good matching performance.

PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

GOSSIP: a method for fast and accurate global alignment of protein structures

MOTIVATION

The database of known protein structures (PDB) is increasing rapidly. This results in a growing need for methods that can cope with the vast amount of structural data. To analyze the accumulating data, it is important to have a fast tool for identifying similar structures and clustering them by structural resemblance. Several excellent tools have been developed for the comparison of protein structures. These usually address the task of local structure alignment, an important yet computationally intensive problem due to its complexity. It is difficult to use such tools for comparing a large number of structures to each other at a reasonable time.

RESULTS

Here we present GOSSIP, a novel method for a global all-against-all alignment of any set of protein structures. The method detects similarities between structures down to a certain cutoff (a parameter of the program), hence allowing it to detect similar structures at a much higher speed than local structure alignment methods. GOSSIP compares many structures in times which are several orders of magnitude faster than well-known available structure alignment servers, and it is also faster than a database scanning method. We evaluate GOSSIP both on a dataset of short structural fragments and on two large sequence-diverse structural benchmarks. Our conclusions are that for a threshold of 0.6 and above, the speed of GOSSIP is obtained with no compromise of the accuracy of the alignments or of the number of detected global similarities.

AVAILABILITY

A server, as well as an executable for download, are available at http://bioinfo3d.cs.tau.ac.il/gossip/.

FlexProt: alignment of flexible protein structures without a predefinition of hinge regions

FlexProt is a novel technique for the alignment of flexible proteins. Unlike all previous algorithms designed to solve the problem of structural comparisons allowing hinge-bending motions, FlexProt does not require an a priori knowledge of the location of the hinge(s). FlexProt carries out the flexible alignment, superimposing the matching rigid subpart pairs, and detects the flexible hinge regions simultaneously. A large number of methods are available to handle rigid structural alignment. However, proteins are flexible molecules, which may appear in different conformations. Hence, protein structural analysis requires algorithms that can deal with molecular flexibility. Here, we present a method addressing specifically a flexible protein alignment task. First, the method efficiently detects maximal congruent rigid fragments in both molecules. Transforming the task into a graph theoretic problem, our method proceeds to calculate the optimal arrangement of previously detected maximal congruent rigid fragments. The fragment arrangement does not violate the protein sequence order. A clustering procedure is performed on fragment-pairs which have the same 3-D rigid transformation regardless of insertions and deletions (such as loops and turns) which separate them. Although the theoretical worst case complexity of the algorithm is O(n(6)), in practice FlexProt is highly efficient. It performs a structural comparison of a pair of proteins 300 amino acids long in about seven seconds on a standard desktop PC (400 MHz Pentium II processor with 256MB internal memory). We have performed extensive experiments with the algorithm. An assortment of these results is presented here. FlexProt can be accessed via WWW at bioinfo3d.cs.tau.ac.il/FlexProt/.

A scheme for automatically building three-dimensional morphometric anatomical atlases: application to a skull atlas

We present a general scheme for automatically building a morphometric anatomical atlas. We detail each stage of the method, including the non-rigid registration algorithm, three-dimensional line averaging and statistical processes. We apply the method to obtain a quantitative atlas of skull crest lines. Finally, we use the resulting atlas to study a craniofacial disease; we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a mandible deformation with the atlas.