Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Roof plane segmentation by combining multiple images and point clouds

A new method for roof plane detection using multiple aerial images and a point cloud is presented. It takes advantage of the fact that segmentation results for different views look different even if the same parameters are used for the original segmentation algorithm. The point cloud can be generated by image matching or by airborne laserscanning. Plane detection starts by a segmentation that is applied to each of the images. The point cloud is used to determine which image segments correspond to planes. The best plane according to a criterion is selected and matched with segments in the other images. Matching of segments requires a DSM generated from the point cloud, and it takes into account the occlusions in each image. This procedure is repeated until no more planes can be found. After that, planar segments are extracted based on region growing in the point cloud in areas of severe under-segmentation, and the multiple-image segmentation procedure is repeated. Finally, neighbouring regions found to be co-planar are merged. First results are presented for test site with up to nine-fold overlap. Our tests show that the method can deliver a good separation of roof planes under difficult circumstances, though the level of detail that can be achieved is limited by the resolution of the point cloud

Segmentation and 3D reconstruction approaches for the design of laparoscopic augmented reality environments

A trend in abdominal surgery is the transition from minimally invasive surgery to surgeries where augmented reality is used. Endoscopic video images are proposed to be employed for extracting useful information to help surgeons performing the operating techniques. This work introduces an illumination model into the design of automatic segmentation algorithms and 3D reconstruction methods. Results obtained from the implementation of our methods to real images are supposed to be an initial step useful for designing new methodologies that will help surgeons operating MIS techniques

Watershed segmentation with boundary curvature ratio based merging criterion

This paper proposes to incorporate boundary curvature ratio, region homogeneity and boundary smoothness into a single new merging criterion to improve the oversegmentation of marker-controlled watershed segmentation algorithm. The result is a more refined segmentation result with smooth boundaries and regular shapes. To pursue a final segmentation result with higher inter-variance and lower intra-variance, an optimal number of segments could be self-determined by a proposed formula. Experimental results are presented to demonstrate the merits of this method.postprintThe 9th IASTED International Conference on Signal and Image Processing (SIP 2007), Honolulu, HI., 20-22 August 2007. In Proceedings of SIP, 2007, p. 7-1

Analysis of lesion border segmentation using watershed algorithm

Automatic lesion segmentation is an important part of computer-based skin cancer detection. A watershed algorithm was introduced and tested on benign and melanoma images. The average of three dermatologists\u27 manually drawn borders was compared as the benchmark. Hair removing, black border removing and vignette removing methods were introduced in preprocessing steps. A new lesion ratio estimate was added to the merging method, which was determined by the outer bounding box ratio. In postprocessing, small blob removing and border smoothing using a peninsula removing method as well as a second order B-Spline smoothing method were included. A novel threshold was developed for removing large light areas near the lesion boundary. A supervised neural network was applied to cluster results and improve the accuracy, classifying images into three clusters: proper estimate, over-estimate and under-estimate. Comparing to the manually drawn average border, an overall of 11.12% error was achieved. Future work will involve reducing peninsula-shaped noise and looking for other reliable features for the classifier --Abstract, page iii

Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

International audienceStochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a Gaussian model of image regions. In this framework, the basic ingredient is the distance between pairs of regions, hence a distance between normal distributions. Hence several alternatives of statistical distances for normal distributions are compared, namely Bhattacharyya distance, Hellinger metric distance and Wasserstein metric distance