Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems that motivate our approach originate from microscopy imaging commonly used in materials science, medicine, and biology. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe

Image segmentation using region merging combined with a multi-class spectral method

In this paper we propose an image segmentation algorithm that combines region merging with spectral-based techniques. An initial partitioning of the image into primitive regions is produced by applying a region merging approach which produces a chunk graph that takes in attention the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process that produces the final segmentation. The latter process uses a multi-class partition that minimizes the normalized cut value for the region graph. We have efficiently applied the proposed approach with good visual and objective segmentation quality results

Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology

Image segmentation refers to the division of a digital image into distinct segments or groups of pixels/voxels. However, most of the existing deep learning approaches lack the utilization of prior knowledge, such as shape information, which could improve segmentation accuracy. In addition, conventional image segmentation frequently falls short in preserving intricate spatial details, motivating the innovation of strategies for multi-scaled feature integration. Furthermore, traditional image segmentation methods primarily concentrate on pixel-level or region-level analysis. However, given the inherent morphological similarities among various image objects, the significance of topology information surpasses that of pixel-level data in the realm of medical image semantic segmentation, and the incorporation of topology information for image segmentation is important. The first aim of this dissertation is to incorporate shape priors into medical image segmentation. A shape-prior-V-Net (SP-V-Net) is proposed, which contains a shape transformation module to refine the segmentation results according to the shape prior. SP-V-Net has been applied to lung segmentation and proximal femur segmentation. The second aim aims to improve image segmentation by leveraging hierarchical features. Two approaches are proposed: the feature pyramid U-Net++ (FP-U-Net++), which dynamically aggregates the feature pyramid in the decoder of U-Net ++, and the multi-input multi-scale U-Net (MIMS U-Net), which integrates the features in the encoder of the U-Net. The third aim explores topology-based image semantic segmentation using graph neural networks. Three graph-matching networks have been developed, including association graph-based, edge attention graph matching, and hyper-association graph matching networks. The proposed graph-matching networks convert the graph-matching problems into a vertex classification problem using an association graph, where the positive vertex indicates the nodes from two individual graphs are matched. These models were applied to coronary artery semantic labeling on invasive coronary angiograms. Moreover, this study presents a pioneering approach for topology-based image semantic labeling using graph matching. The successful completion of these aims contributes technically accurate and clinically applicable algorithms and techniques for medical image segmentation. The outcomes of this dissertation provide valuable tools for the medical imaging and computer vision communities, advancing the field and improving patient care through accurate and efficient medical image segmentation

Fast Graph-Based Object Segmentation for RGB-D Images

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods

Region-based spatial and temporal image segmentation

This work discusses region-based representations for image and video sequence segmentation. It presents effective image segmentation techniques and demonstrates how these techniques may be integrated into algorithms that solve some of the motion segmentation problems. The region-based representation offers a way to perform a first level of abstraction and to reduce the number of elements to process with respect to the classical pixel-based representation. Motion segmentation is a fundamental technique for the analysis and the understanding of image sequences of real scenes. Motion segmentation 'describes' the sequence as sets of pixels moving coherently across one sequence with associated motions. This description is essential to the identification of the objects in the scene and to a more efficient manipulation of video sequences. This thesis presents a hybrid framework based on the combination of spatial and motion information for the segmentation of moving objects in image sequences accordingly with their motion. We formulate the problem as graph labelling over a region moving graph where nodes correspond coherently to moving atomic regions. This is a flexible high-level representation which individualizes moving independent objects. Starting from an over-segmentation of the image, the objects are formed by merging neighbouring regions together based on their mutual spatial and temporal similarity, taking spatial and motion information into account with the emphasis being on the second. Final segmentation is obtained by a spectral-based graph cuts approach. The initial phase for the moving object segmentation aims to reduce image noise without destroying the topological structure of the objects by anisotropic bilateral filtering. An initial spatial partition into a set of homogeneous regions is obtained by the watershed transform. Motion vector of each region is estimated by a variational approach. Next a region moving graph is constructed by a combination of normalized similarity between regions where mean intensity of the regions, gradient magnitude between regions, and motion information of the regions are considered. The motion similarity measure among regions is based on human perceptual characteristics. Finally, a spectral-based graph cut approach clusters and labels each moving region. The motion segmentation approach is based on a static image segmentation method proposed by the author of this dissertation. The main idea is to use atomic regions to guide a segmentation using the intensity and the gradient information through a similarity graph-based approach. This method produces simpler segmentations, less over-segmented and compares favourably with the state-of-the-art methods. To evaluate the segmentation results a new evaluation metric is proposed, which takes into attention the way humans perceive visual information. By incorporating spatial and motion information simultaneously in a region-based framework, we can visually obtain meaningful segmentation results. Experimental results of the proposed technique performance are given for different image sequences with or without camera motion and for still images. In the last case a comparison with the state-of-the-art approaches is made

SLFS: Semi-supervised light-field foreground-background segmentation

Efficient segmentation is a fundamental problem in computer vision and image processing. Achieving accurate segmentation for 4D light field images is a challenging task due to the huge amount of data involved and the intrinsic redundancy in this type of images. While automatic image segmentation is usually challenging, and because regions of interest are different for different users or tasks, this paper proposes an improved semi-supervised segmentation approach for 4D light field images based on an efficient graph structure and user's scribbles. The recent view-consistent 4D light field superpixels algorithm proposed by Khan et al. is used as an automatic pre-processing step to ensure spatio-angular consistency and to represent the image graph efficiently. Then, segmentation is achieved via graph-cut optimization. Experimental results for synthetic and real light field images indicate that the proposed approach can extract objects consistently across views, and thus it can be used in applications such as augmented reality applications or object-based coding with few user interactions.info:eu-repo/semantics/acceptedVersio