Chanho Jung

Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization

In this letter, we present a novel watershed-based method for segmentation of cervical and breast cell images. We formulate the segmentation of clustered nuclei as an optimization problem. A hypothesis concerning the nuclei, which involves a priori knowledge with respect to the shape of nuclei, is tested to solve the optimization problem. We first apply the distance transform to the clustered nuclei. A marker extraction scheme based on the H -minima transform is introduced to obtain the optimal segmentation result from the distance map. In order to estimate the optimal h-value, a size-invariant segmentation distortion evaluation function is defined based on the fitting residuals between the segmented region boundaries and fitted models. Ellipsoidal modeling of contours is introduced to adjust nuclei contours for more effective analysis. Experiments on a variety of real microscopic cell images show that the proposed method yields more accurate segmentation results than the state-of-the-art watershed-based methods.

Spatiotemporal Saliency Detection and Its Applications in Static and Dynamic Scenes

This paper presents a novel method for detecting salient regions in both images and videos based on a discriminant center-surround hypothesis that the salient region stands out from its surroundings. To this end, our spatiotemporal approach combines the spatial saliency by computing distances between ordinal signatures of edge and color orientations obtained from the center and the surrounding regions and the temporal saliency by simply computing the sum of absolute difference between temporal gradients of the center and the surrounding regions. Our proposed method is computationally efficient, reliable, and simple to implement and thus it can be easily extended to various applications such as image retargeting and moving object extraction. The proposed method has been extensively tested and the results show that the proposed scheme is effective in detecting saliency compared to various state-of-the-art methods.

Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification

In a fully automatic cell extraction process, one of the main issues to overcome is the problem related to extracting overlapped nuclei since such nuclei will often affect the quantitative analysis of cell images. In this paper, we present an unsupervised Bayesian classification scheme for separating overlapped nuclei. The proposed approach first involves applying the distance transform to overlapped nuclei. The topographic surface generated by distance transform is viewed as a mixture of Gaussians in the proposed algorithm. In order to learn the distribution of the topographic surface, the parametric expectation-maximization (EM) algorithm is employed. Cluster validation is performed to determine how many nuclei are overlapped. Our segmentation approach incorporates a priori knowledge about the regular shape of clumped nuclei to yield more accurate segmentation results. Experimental results show that the proposed method yields superior segmentation performance, compared to those produced by conventional schemes.

A Unified Spectral-Domain Approach for Saliency Detection and Its Application to Automatic Object Segmentation

In this paper, a visual attention model is incorporated for efficient saliency detection, and the salient regions are employed as object seeds for our automatic object segmentation system. In contrast with existing interactive segmentation approaches that require considerable user interaction, the proposed method does not require it, i.e., the segmentation task is fulfilled in a fully automatic manner. First, we introduce a novel unified spectral-domain approach for saliency detection. Our visual attention model originates from a well-known property of the human visual system that the human visual perception is highly adaptive and sensitive to structural information in images rather than nonstructural information. Then, based on the saliency map, we propose an iterative self-adaptive segmentation framework for more accurate object segmentation. Extensive tests on a variety of cluttered natural images show that the proposed algorithm is an efficient indicator for characterizing the human perception and it can provide satisfying segmentation performance.

Automatic segmentation of salient objects using iterative reversible graph cut

There have been several interactive approaches to extracting objects from still images, since it is significantly difficult to automatically segment objects in complex background. In this paper, we present a novel automatic scheme for extracting salient objects from natural images. To this end, segmentation of salient objects is formulated as a global energy minimization problem in an iterative self-adaptive framework. By employing a saliency detection technique, object and background seeds are inferred automatically. The problem in this step is that the automatically generated seeds may not be reliably positioned. An iterative reversible graph cut method is introduced to overcome the problem inherent in the saliency-based seed extraction method. In the iterative self-adaptive framework, bidirectional state transitions are iteratively involved to reduce the mis-classified pixels. Experimental results show that the proposed segmentation method yields more accurate segmentation results than previous segmentation approaches.

Real-time estimation of 3D scene geometry from a single image

Significant advances have recently been made in the development of computational methods for predicting 3D scene structure from a single monocular image. However, their computational complexity severely limits the adoption of such technologies to various computer vision and pattern recognition applications. In this paper, we address the problem of inferring 3D scene geometry from a single monocular image of man-made environments. Our goal is to estimate the 3D structure of a scene in real-time with a level of accuracy useful in certain real applications. Towards this end, we decompose the three-dimensional world space into a set of geometrically inspired primitive subspaces. One important advantage of our approach is that the complex estimation problem can be systematically broken down into a sequence of subproblems, which are easier to solve and more reliable even with the presence of occlusion or clutter, without loss of generality. The proposed algorithm also serves as the technical foundation for effective representation of the 3D scene geometry based on a simple description of the textural patterns present in the image and their spatial arrangement. Extensive experiments have been conducted on a large scale challenging dataset of real-world images. Our results demonstrate that the proposed method remarkably outperforms the recent state-of-the-art algorithms with respect to speed and accuracy.

Saliency detection: A self-ordinal resemblance approach

In saliency detection, regions attracting visual attention need to be highlighted while effectively suppressing non-salient regions for the semantic scene understanding. However, most previous methods tend to fail in suppressing highly textured backgrounds and also high contrast edges belonging to the non-salient regions. To address this problem, we propose a method for detecting salient regions based on a self-ordinal resemblance measure (SORM). Our saliency map is defined by using the center-surround computations based on the ordinal signatures obtained from local regions centered at each pixel. It can be regarded as an energy map and thus extended to image retargeting. Our approach is fully automatic and nonparametric. To justify robustness of our approach, the proposed method is compared with the state of the art methods on various images.1

Determining the Existence of Objects in an Image and Its Application to Image Thumbnailing

In recent years, computer vision applications dealing with foreground objects are becoming more important with an increasing demand of advanced intelligent systems. Most of these applications assume that an image contains one or more objects, which often produce undesired results when noticeable objects do not appear in the image. In this letter, we address the problem of ascertaining the existence of objects in an image. In the first step, the input image is partitioned into nonoverlapping local patches, then the patches are categorized into three classes, namely natural, man-made, and object to estimate object candidates. Then a Bayesian methodology is employed to produce more reliable results by eliminating false positives. To boost the object patch detection performance, we exploit the difference between coarse and fine segmentation results. To demonstrate the effectiveness of the proposed method, extensive experiments have been conducted on several benchmark image databases. Furthermore, we have shown the usefulness of our approach by applying it to a real application (i.e., image thumbnailing).

Detecting shadows from a single image.

We present a novel (to our best knowledge) optical recognition technique for detecting shadows from a single image. Most prior approaches definitely depend on explicit physical computational models, but physics-based approaches have the critical problem that they may fail severely even with slight perturbations. Unlike traditional approaches, our method does not rely on any explicit physical models. This breakthrough originates from a discovery of a new modeling mechanism, derived from a biological vision principle, the so-called retinex theory, which is well suited for single-image shadow detection. Experimental results demonstrate that the proposed method outperforms the previous optical recognition techniques and gives robust results even in real-world complex scenes.

Impact of the accuracy of automatic segmentation of cell nuclei clusters on classification of thyroid follicular lesions

Automatic segmentation of cell nuclei clusters is a key building block in systems for quantitative analysis of microscopy cell images. For that reason, it has received a great attention over the last decade, and diverse automatic approaches to segment clustered nuclei with varying levels of performance under different test conditions have been proposed in literature. To the best of our knowledge, however, so far there is no comparative study on the methods. This study is a first attempt to fill this research gap. More precisely, the purpose of this study is to present an objective performance comparison of existing state‐of‐the‐art segmentation methods. Particularly, the impact of their accuracy on classification of thyroid follicular lesions is also investigated “quantitatively” under the same experimental condition, to evaluate the applicability of the methods. Thirteen different segmentation approaches are compared in terms of not only errors in nuclei segmentation and delineation, but also their impact on the performance of system to classify thyroid follicular lesions using different metrics (e.g., diagnostic accuracy, sensitivity, specificity, etc.). Extensive experiments have been conducted on a total of 204 digitized thyroid biopsy specimens. Our study demonstrates that significant diagnostic errors can be avoided using more advanced segmentation approaches. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate automatic segmentation technique adopted for building automated systems for specifically classifying follicular thyroid lesions.