Chia Yung Han

Review: A comparative study of deformable contour methods on medical image segmentation

A comparative study to review eight different deformable contour methods (DCMs) of snakes and level set methods applied to the medical image segmentation is presented. These DCMs are now applied extensively in industrial and medical image applications. The segmentation task that is required for biomedical applications is usually not simple. Critical issues for any practical application of DCMs include complex procedures, multiple parameter selection, and sensitive initial contour location. Guidance on the usage of these methods will be helpful for users, especially those unfamiliar with DCMs, to select suitable approaches in different conditions. This study is to provide such guidance by addressing the critical considerations on a common image test set. The test set of selected images offers different and typical difficult problems encountered in biomedical image segmentation. The studied DCMs are compared using both qualitative and quantitative measures and the comparative results highlight both the strengths and limitations of these methods. The lessons learned from this medical segmentation comparison can also be translated to other image segmentation domains.

Knowledge-based image analysis for automated boundary extraction of transesophageal echocardiographic left-ventricular images

A system for automatically determining the contour of the left ventricle (LV) and its bounded area, from transesophageal echocardiographic (TEE) images is presented. It uses knowledge of both heart anatomy and echocardiographic imaging to guide the selection of image processing methodologies for thresholding, edge detection, and contour following and the center-based boundary-finding technique to extract the contour of the LV region. To speed up the processing a rectangular region of interest from a TEE picture is first isolated and then reduced to a coarse version, one-ninth original size. All processing steps, except the final contour edge extraction, are performed on this reduced image. New methods developed for automatic threshold selection, region segmentation, noise removal, and region center determination are described.

On surface reconstruction: A priority driven approach

To reconstruct an object surface from a set of surface points, a fast, practical, and efficient priority driven algorithm is presented. The key idea of the method is to consider the shape changes of an object at the boundary of the mesh growing area and to create a priority queue to the advancing front of the mesh area according to the changes. The mesh growing process is then driven by the priority queue for efficient surface reconstruction. New and practical triangulation criteria are also developed to support the priority driven strategy and to construct a new triangle at each step of mesh growing in real time. The quality and correctness of the created triangles will be guaranteed by the triangulation criteria and topological operations. The algorithm can reconstruct an object surface from unorganized surface points in a fast and reliable manner. Moreover, it can successfully construct the surface of the objects with complex geometry or topology. The efficiency and robustness of the proposed algorithm is validated by extensive experiments.

Graph matching for object recognition and recovery

Abstract A robust skeleton-based graph matching method for object recognition and recovery applications is presented. The object model uses both a skeleton model and contour segment models, for object recognition and recovery. The presented skeleton-based shape matching method uses a combination of both structural and statistical methods that are applied in a sequential manner, which largely reduce the matching space when compared with previous works. This also provides a good alternate means to alleviate difficulties encountered in segmentation problems. Experiments of object recovery using real biomedical image samples have shown satisfactory results.

Object Recognition and Recovery by Skeleton Graph Matching

This paper presents a robust and efficient skeleton-based graph matching method for object recognition and recovery applications. The novel feature is to unify both object recognition and recovery components into an image understanding system architecture, in which a complementary feedback structure can be incorporated to alleviate processing difficulties of each component alone. The idea is firstly to recognize the preliminary extracted object from a set of models using the new skeleton graph matching method, then to apply the a priori shape information of the identified model for accurate object recovery. The output of the system is the recognized and segmented object. The skeleton graph matching method is illustrated by recognizing a set of tool and animal silhouette examples with the presence of geometric transformations (translation, rotation, scaling, reflection), shape deformations and noise. Experiments of object recovery using MR knee images, have shown satisfactory results

Web-based distributed image processing system

This paper presents a framework for developing a Web-based distributed image processing application system that is flexible, convenient, and scalable. The system uses the existing Web-based technology and image processing methodologies to implement this capability in a distributed computing environment that may include powerful machines to process complex and large images. The system consists of browser, server, service registry and task scheduler, image data storage and management, and knowledge-based image processing services. A server-side application considers the user’s request from a client side. The server host identifies the request and the necessary resources and schedules the computing resources and image processing services. Based on the instructions of the developer’s side (the service provider) a proper knowledge-based on-line assistance is given to the client to select the right algorithm, set up proper parameter values in order to maximize the usage. Developers can modify and upgrade the services at their own site and publish the workable version, its interface, and required resources to the server. The server enables remote invocation of the algorithm by providing a seamless and efficient linkage mechanism. An application for segmentation operation using deformable contour methods for complex images is provided as an example.

The status of vision expert systems for material inspection

As economic constraints and quality issues increasingly preoccupy materials producers, the topic of nondestructive product inspection is taking on increasing importance. Most promising is the potential of vision expert systems, which not only collect in-depth data, but use such advanced capabilities as expert knowledge and inference mechanisms to analyze part quality and immediately identify problems. As these intelligent processing systems are developed and applied, manufacturers should be able to realize reduced costs and improved quality.

A 3D Virtual Learning System for STEM Education

A recent boom has been seen in 3D virtual worlds for entertainment, and this in turn has led to a surge of interest in their educational applications. Although booming development has been seen, most of them only strengthen the traditional teaching methods using a new platform without changing the nature of how to teach and learn. Modern computer science technology should be applied in STEM education for the purpose of rising learning efficiency and interests. In this paper, we focus on the reasoning, design, and implementation of a 3D virtual learning system that merges STEM experiments into virtual laboratory and brings entertainment to knowledge learning. An advanced hand gesture interface was introduced to enable flexible manipulation on virtual objects with two hands. The recognition ability of single hand grasping-moving-rotating activity SH-GMR allows single hand to move and rotate a virtual object at the same time. We implemented several virtual experiments in the VR environment to demonstrate to the public that the proposed system is a powerful tool for STEM education. The benefits of this system are evaluated followed by two virtual experiments in STEM field.

A skeleton based shape matching and recovery approach

A robust skeleton-based shape matching method for model-based shape recovery applications is presented. The object model consists of both a skeleton model and a contour segments model, which are used in tandem and in a complementary manner. Initially, the skeleton of the contour, provided by a deformable contour method (DCM), is matched against a set of object skeleton models to select a candidate model and determine the corresponding landmarks on the contours. Segments obtained from these landmarks are then matched against the detected model segments for errors. For any large segment mismatch error, a fine-tuning process, which is formulated as a maximization of a posteriori probability, given the contour segments model and image features, is performed for the final result. The skeleton matching algorithm is illustrated by using a set of animal profile examples. Experimental results of shape recovery from practical applications, such as an MR knee image, are very encouraging.

A New Wavelet Based Image Denoising Method

This paper proposes a new wavelet based image denoising method by using linear elementary parameterized denoising functions in the form of derivatives of Gaussian of a set of estimated wavelet coefficients. These coefficients are derived from an improved context modeling procedure in terms of mean square error estimation combining inter- and intra-sub band data. The denoising method results in a two-step denoising effort which outperforms the state-of-the-art non-redundant methods. This method is also extended to the over complete wavelet expansion by applying cycle spinning, which provides additional denoising performance and yields significantly better results than the orthogonal transform.