Higher Order Energies for Image Segmentation

A novel energy minimization method for general higher-order binary energy functions is proposed in this paper. We first relax a discrete higher-order function to a continuous one, and use the Taylor expansion to obtain an approximate lower-order function, which is optimized by the quadratic pseudo-boolean optimization (QPBO) or other discrete optimizers. The minimum solution of this lower-order function is then used as a new local point, where we expand the original higher-order energy function again. Our algorithm does not restrict to any specific form of the higher-order binary function or bring in extra auxiliary variables. For concreteness, we show an application of segmentation with the appearance entropy, which is efficiently solved by our method. Experimental results demonstrate that our method outperforms state-of-the-art methods

Sub-Markov random walk for image segmentation

A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation

Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation

Blood vessel segmentation plays a fundamental role in many computer-aided diagnosis (CAD) systems, such as coronary artery stenosis quantification, cerebral aneurysm quantification, and retinal vascular tree analysis. Fine blood vessel segmentation can help build a more accurate computer-aided diagnosis system and help physicians gain a better understanding of vascular structures. The purpose of this article is to develop a blood vessel segmentation method that can improve segmentation accuracy in tiny blood vessels. In this work, we propose a tensor-based graph-cut method for blood vessel segmentation. With our method, each voxel can be modeled by a second-order tensor, allowing the capture of the intensity information and the geometric information for building a more accurate model for blood vessel segmentation. We compared our proposed method’s accuracy to several state-of-the-art blood vessel segmentation algorithms and performed experiments on both simulated and clinical CT datasets. Both experiments showed that our method achieved better state-of-the-art results than the competing techniques. The mean centerline overlap ratio of our proposed method is 84% on clinical CT data. Our proposed blood vessel segmentation method outperformed other state-of-the-art methods by 10% on clinical CT data. Tiny blood vessels in clinical CT data with a 1-mm radius can be extracted using the proposed technique. The experiments on a clinical dataset showed that the proposed method significantly improved the segmentation accuracy in tiny blood vessels

Computer-aided Detection of Breast Cancer in Digital Tomosynthesis Imaging Using Deep and Multiple Instance Learning

Breast cancer is the most common cancer among women in the world. Nevertheless, early detection of breast cancer improves the chance of successful treatment. Digital breast tomosynthesis (DBT) as a new tomographic technique was developed to minimize the limitations of conventional digital mammography screening. A DBT is a quasi-three-dimensional image that is reconstructed from a small number of two-dimensional (2D) low-dose X-ray images. The 2D X-ray images are acquired over a limited angular around the breast. Our research aims to introduce computer-aided detection (CAD) frameworks to detect early signs of breast cancer in DBTs. In this thesis, we propose three CAD frameworks for detection of breast cancer in DBTs. The first CAD framework is based on hand-crafted feature extraction. Concerning early signs of breast cancer: mass, micro-calcifications, and bilateral asymmetry between left and right breast, the system includes three separate channels to detect each sign. Next two CAD frameworks automatically learn complex patterns of 2D slices using the deep convolutional neural network and the deep cardinality-restricted Boltzmann machines. Finally, the CAD frameworks employ a multiple-instance learning approach with randomized trees algorithm to classify DBT images based on extracted information from 2D slices. The frameworks operate on 2D slices which are generated from DBT volumes. These frameworks are developed and evaluated using 5,040 2D image slices obtained from 87 DBT volumes. We demonstrate the validation and usefulness of the proposed CAD frameworks within empirical experiments for detecting breast cancer in DBTs

Randomly-connected Non-Local Conditional Random Fields

Structural data modeling is an important field of research. Structural data are the combination of latent variables being related to each other. The incorporation of these relations in modeling and taking advantage of those to have a robust estimation is an open field of research. There are several approaches that involve these relations such as Markov chain models or random field frameworks. Random fields specify the relations among random variables in the context of probability distributions. Markov random fields are generative models used to represent the prior distribution among random variables. On the other hand, conditional random fields (CRFs) are known as discriminative models computing the posterior probability of random variables given observations directly. CRFs are one of the most powerful frameworks in image modeling. However practical CRFs typically have edges only between nearby nodes. Utilizing more interactions and expressive relations among nodes make these methods impractical for large-scale applications, due to the high computational complexity. Nevertheless, studies have demonstrated that obtaining long-range interactions in the modeling improves the modeling accuracy and addresses the short-boundary bias problem to some extent. Recent work has shown that fully connected CRFs can be tractable by defining specific potential functions. Although the proposed frameworks present algorithms to efficiently manage the fully connected interactions/relatively dense random fields, there exists the unanswered question that fully connected interactions are usually useful in modeling. To the best of our knowledge, no research has been conducted to answer this question and the focus of research was to introduce a tractable approach to utilize all connectivity interactions. This research aims to analyze this question and attempts to provide an answer. It demonstrates that how long-range of connections might be useful. Motivated by the answer of this question, a novel framework to tackle the computational complexity of a fully connected random fields without requiring specific potential functions is proposed. Inspired by random graph theory and sampling methods, this thesis introduces a new clique structure called stochastic cliques. The stochastic cliques specify the range of effective connections dynamically which converts a conditional random field (CRF) to a randomly-connected CRF. The randomly-connected CRF (RCRF) is a marriage between random graphs and random fields, benefiting from the advantages of fully connected graphs while maintaining computational tractability. To address the limitations of RCRF, the proposed stochastic clique structure is utilized in a deep structural approach (deep structure randomly-connected conditional random field (DRCRF)) where various range of connectivities are obtained in a hierarchical framework to maintain the computational complexity while utilizing long-range interactions. In this thesis the concept of randomly-connected non-local conditional random fields is explored to address the smoothness issues of local random fields. To demonstrate the effectiveness of the proposed approaches, they are compared with state-of-the-art methods on interactive image segmentation problem. A comprehensive analysis is done via different datasets with noiseless and noisy situations. The results shows that the proposed method can compete with state-of-the-art algorithms on the interactive image segmentation problem

A Principled Deep Random Field Model for Image Segmentation

We discuss a model for image segmentation that is able to overcome the short-boundary bias observed in standard pairwise random field based approaches. To wit, we show that a random field with multi-layered hidden units can encode boundary preserving higher order potentials such as the ones used in the cooperative cuts model of [12] while still allowing for fast and exact MAP inference. Exact inference allows our model to outperform previous image segmentation methods, and to see the true effect of coupling graph edges. Finally, our model can be easily extended to handle segmentation instances with multiple labels, for which it yields promising results. 1