Changming Sun

Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques

This paper presents a fast and reliable stereo matching algorithm which produces a dense disparity map by using fast cross correlation, rectangular subregioning (RSR) and 3D maximum-surface techniques in a coarse-to-fine scheme. Fast correlation is achieved by using the box-filtering technique whose speed is invariant to the size of the correlation window and by segmenting the stereo images into rectangular subimages at different levels of the pyramid. By working with rectangular subimages, not only can the speed of the correlation be further increased, the intermediate memory storage requirement can also be reduced. The disparity map for the stereo images is found in the 3D correlation coefficient volume by obtaining the global 3D maximum-surface rather than simply choosing the position that gives the local maximum correlation coefficient value for each pixel. The 3D maximum-surface is obtained using our new two-stage dynamic programming (TSDP) technique. There are two original contributions in this paper: (1) development of the RSR technique for fast similarity measure; and (2) development of the TSDP technique for efficiently obtaining 3D maximum-surface in a 3D volume. Typical running time of our algorithm implemented in the C language on a 512 × 512 image is in the order of a few seconds on a 500 MHz PC. A variety of synthetic and real images have been tested, and good results have been obtained.

Splitting touching cells based on concave points and ellipse fitting

A new touching cells splitting algorithm based on concave points and ellipse fitting is proposed in this paper. The algorithm includes two parts: contour pre-processing and ellipse processing. The purpose of contour pre-processing is to smooth fluctuations of the contour, find concave points of the contour and divide the contour into different segments via the concave points. The purpose of ellipse processing is to process the different segments of the contour into possible single cells by using the properties of the fitted ellipses. Because concave points divide the whole contour of touching cells into different segments and different segments of one single cell have similar properties, the ellipse processing can separate the touching cells through ellipse fitting. This paper demonstrates a new way of using ellipse fitting to split the binary contour of touching cells. Experimental results show that our algorithm is efficient.

Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images.

We present a knowledge-based approach to segmentation and analysis of the lung boundaries in chest X-rays. Image edges are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard. Edges associated with the lung boundary are automatically identified and abnormal features are reported. In preliminary testing on 14 images for a set of 18 detectable abnormalities, the system showed a sensitivity of 88% and a specificity of 95% when compared with assessment by an experienced radiologist.

Circular shortest path in images

Abstract Shortest path algorithms have been used in a number of applications such as crack detection, road or linear feature extraction in images. There are applications where the starting and ending positions of the shortest path need to be constrained. In this paper, we present several new algorithms for the extraction of a circular shortest path in an image such that the starting and ending positions coincide. The new algorithms we developed include multiple search algorithm, image patching algorithm, multiple backtracking algorithm, the combination of image patching and multiple back-tracking algorithm, and approximate algorithm. The typical running time of our circular shortest path extraction algorithm on a 256×256 image is about 0.3 s on a rather slow 85 MHz Sun SPARC computer. A variety of real images for crack detection in borehole data and object boundary extraction have been tested and good results have been obtained.

Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision

Manual neuron tracing is a very labor‐intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA‐Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA‐Vision with those obtained using an established semi‐automated tracing solution (NeuronJ). The effect of the Sez‐6 deletion was characterized in detail. Sez‐6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCA‐Vision delivered considerable speed benefits and reliable traces.

Symmetry detection using gradient information

Abstract Symmetry detection is important in the area of computer vision. A simple and fast symmetry detection algorithm has been developed in this paper. The algorithm employs only the original image and the gradient information. The direction of the symmetry axis is obtained from the gradient orientation histogram; and the position of this symmetry axis is decided either by the center of gravity or by the profile of the image projection along the direction of the symmetry axis. This method works directly on the grey-scale image and does not require any prior segmentation of the input image. Both simulated and real images have been tested and the results are very convincing.

Fast linear feature detection using multiple directional non-maximum suppression

Linear feature detection is a very important issue in the areas of image analysis, computer vision, and pattern recognition. It has found applications in many diverse areas such as neurite outgrowth detection, compartment assay analysis, retinal vessel extraction, skin hair removal for malonoma detection, plant root analysis, and roads detection. We have developed a new algorithm for linear feature detection using multiple directional non-maximum suppression. The algorithm is very fast compared with methods in the literature. We also show a large number of application examples using our linear feature detection algorithm, and very good results have been obtained

Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femurs appearance.

Circular Shortest Paths by Branch and Bound

Shortest path algorithms are used for a large variety of optimisation problems in network and transportation analysis. They are also used in image analysis for object segmentation, disparity estimation, path finding and crack detection. Sometimes the topology of the problem demands that the path be circular. Such circular path constraints occur in polar object segmentation, disparity estimation for panoramic stereo images and in shortest paths around a cylinder. In this paper we present a new efficient algorithm for circular shortest path determination on a

HCA-Vision Automated Neurite Outgrowth Analysis

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