Unsupervised image segmentation with neural networks

The segmentation of colour images (RGB), distinguishing clusters of image points, representing for example background, leaves and flowers, is performed in a multi-dimensional environment. Considering a two dimensional environment, clusters can be divided by lines. In a three dimensional environment by planes and in an n-dimensional environment by n-1 dimensional structures. Starting with a complete data set the first neural network, represents an n-1 dimensional structure to divide the data set into two subsets. Each subset is once more divided by an additional neural network: recursive partitioning. This results in a tree structure with a neural network in each branching point. Partitioning stops as soon as a partitioning criterium cannot be fulfilled. After the unsupervised training the neural system can be used for the segmentation of images

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Three dimensional transparent structure segmentation and multiple 3D motion estimation from monocular perspective image sequences

A three dimensional scene can be segmented using different cues, such as boundaries, texture, motion, discontinuities of the optical flow, stereo, models for structure, etc. We investigate segmentation based upon one of these cues, namely three dimensional motion. If the scene contain transparent objects, the two dimensional (local) cues are inconsistent, since neighboring points with similar optical flow can correspond to different objects. We present a method for performing three dimensional motion-based segmentation of (possibly) transparent scenes together with recursive estimation of the motion of each independent rigid object from monocular perspective images. Our algorithm is based on a recently proposed method for rigid motion reconstruction and a validation test which allows us to initialize the scheme and detect outliers during the motion estimation procedure. The scheme is tested on challenging real and synthetic image sequences. Segmentation is performed for the Ullmann's experiment of two transparent cylinders rotating about the same axis in opposite directions

Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes

In this paper, we introduce a novel parametric method for segmentation of three-dimensional images. We consider a piecewise constant version of the Mumford-Shah and the Chan-Vese functionals and perform a region-based segmentation of 3D image data. An evolution law is derived from energy minimization problems which push the surfaces to the boundaries of 3D objects in the image. We propose a parametric scheme which describes the evolution of parametric surfaces. An efficient finite element scheme is proposed for a numerical approximation of the evolution equations. Since standard parametric methods cannot handle topology changes automatically, an efficient method is presented to detect, identify and perform changes in the topology of the surfaces. One main focus of this paper are the algorithmic details to handle topology changes like splitting and merging of surfaces and change of the genus of a surface. Different artificial images are studied to demonstrate the ability to detect the different types of topology changes. Finally, the parametric method is applied to segmentation of medical 3D images

An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images

Abstract: This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.Ministerio de Economía y competitividad; TIN2015-63646-C5-1-RMinisterio de Economía y competitividad; RTI2018-101114-B-I00Xunta de Galicia: ED431C 2017/1

Denoising and Segmentation of MCT Slice Images of Leather Fiber

Content: The braiding structure of leather fibers has not been understood clearly and it is very useful and interesting to study it. Microscopic X-ray tomography (MCT) technology can produce cross-sectional images of the leather without destroying its structure. The three-dimensional structure of leather fibers can be reconstructed by using MCT slice images, so as to show the braiding structure and regularity of leather fibers. The denoising and segmentation of MCT slice images of leather fibers is the basic procedure for three-dimensional reconstruction. In order to study the braiding structure of leather fibers in the round, the image of resinembedded leather fibers MCT slices and in situ leather fibers MCT slices were analyzed and processed. It is showed that the resin-embedded leather fiber MCT slices were quite different from that of in situ leather fiber MCT slices. In-situ leather fiber MCT slice image could be denoised relatively easily. But denoising of resin-embedded leather fiber MCT slice image is a challenge because of its strong noise. In addition, some fiber bundles adhere to each other in the slice image, which are difficult to be segmented. There are many methods of image denoising and segmentation, but there is no general method to process all types of images. In this paper, a series of computer-aided denoising and segmentation algorithms are designed for in-situ MCT slice images of leather fibers and resin-embedded MCT slice images. The fiber bundles in wide field MCT images are distributed densely, adherent to each other. Many fiber bundles are separated in one image and tightly bound in another. This brings great difficulties to image segmentation. To solve this problem, the following segmentation methods are used: Grayscale-threshold segmentation method, The region-growing segmentation method, Three-dimensional image segmentation method. The denoising and segmentation algorithm proposed in this paper has remarkable effect in processing a series of original MCT slice images and resin-embedded leather fibers MCT slice images. A series of threedimensional images based on this work demonstrate the fine spatial braiding structure of leather fiber, which would help us to understand the braiding structure of leather fibers better. Take-Away: presentation ppt, Figure

Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means

This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images

Three-Dimensional Segmentation of Retinal Vessels in Optical Coherence Tomography with the Help of Scanning Laser Ophthalmoscopy

Purpose: To evaluate a new approach based on luminance changes by combining the scanning laser ophthalmoscopy and optical coherence tomography imaging techniques to acheive 3D segmentation of the retinal vessels and improve the retinal vasculature imaging.Methods: A multifaceted process for the 3D segmentation of retinal blood vessels in optical coherence tomography slices of fundus ophthalmic images with the help of scanning laser ophthalmoscopy images was devised. The proposed algorithm has two distinct clauses, which include 2D segmentation of the retinal blood vessels and three-dimensional segmentation of these vessels based on the calculation of the 2D features of the blood vessels.Results: Our method for three-dimensional segmentation of retinal vessels in optical coherence tomography images with the help of scanning laser ophthalmoscopy imaging achieved a better reconstruction of retinal vessels in optical coherence tomography image.Conclussion: Our method was able to improve the imaging of retinal vesels. Further studies in the field of retinal imaging are recommended to achieve better imaging of retinal vesels. Keywords: Tomography; Optical coherence; ophthalmoscopy; Retinal vessels; Imaging

3D cell nuclei segmentation based on gradient flow tracking

<p>Abstract</p> <p>Background</p> <p>Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p> <p>Results</p> <p>Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p> <p>Conclusion</p> <p>The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p