A Compact Linear Programming Relaxation for Binary Sub-modular MRF

We propose a novel compact linear programming (LP) relaxation for binary sub-modular MRF in the context of object segmentation. Our model is obtained by linearizing an $l_1^+$-norm derived from the quadratic programming (QP) form of the MRF energy. The resultant LP model contains significantly fewer variables and constraints compared to the conventional LP relaxation of the MRF energy. In addition, unlike QP which can produce ambiguous labels, our model can be viewed as a quasi-total-variation minimization problem, and it can therefore preserve the discontinuities in the labels. We further establish a relaxation bound between our LP model and the conventional LP model. In the experiments, we demonstrate our method for the task of interactive object segmentation. Our LP model outperforms QP when converting the continuous labels to binary labels using different threshold values on the entire Oxford interactive segmentation dataset. The computational complexity of our LP is of the same order as that of the QP, and it is significantly lower than the conventional LP relaxation

Medical image analysis using deep learning: Recent advances, applications, challenges and future directions

Deep learning is now causing a paradigm change in medical image analysis. This technology has lately gained the attention of the medical imaging community, prompting the organization of a dedicated conference on "Deep Learning based Medical Imaging". This survey of the literature looks at recent developments in the field and provides an insightful analysis of the key issues facing the field. This review report organizes the evaluated literature into sub-categories based on the underlying pattern recognition tasks and taxonomy based on the anatomy of humans. Without assuming a prior knowledge of deep learning, this literature review makes a significant contribution to educating non-experts in the medical community about the basic concepts of deep learning. In this literature review, the advancements of deep learning (DL) in medical image analysis are investigated from a distinct computer vision and machine learning viewpoint. This allows us to identify the "lack of correctly labeled huge scale data sets" as the primary problem in this study direction. The study also focuses on the sister research domains such as pattern recognition, computer vision, and machine learning. Also mentioned were approaches to finding plausible future scenarios in which the medical imaging community may fully utilize deep learning

The pathology and pathogenesis of fatal hepatic amoebiasis-a study based on 79 autopsy cases

The present study is based on a retrospective analysis of 79 autopsy cases of hepatic amoebiasis. An attempt has been made to reconstruct the sequence of events starting from intestinal infection to invasion and transport of amoebae along the radicles of the portal veins, the formation of early Zahn's infarct and the proliferation of amoebae in such foci leading to the formation of small abscesses The coalescence of small abscesses gives rise to the apparently large abscesses. Apart from direct contiguity, more distant extension leading to a satellite abscess is due to involvement of the hepatic and/or portal venous radicles. It seems that obstruction of the hepatic vein contributes substantially towards the enlargement of the liver and its exaggerated nutmeg appearance. Signs and symptoms of hepatic vein obstruction sometimes overshadow the abscess pathology. Thrombosis or pressure of a neighbouring abscess over the portal vein obstruction sometimes overshadow the abscess pathology. Thrombosis or pressure of a neighbouring abscess over the portal vein and bile-duct lead to development of portal hypertension and jaundice. Both cell-mediated and humoral immunity are depressed in fatal cases of hepatic amoebiasis

Cervid Distribution Browse and Snow Cover in Alberta

Abstract. In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster’s theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.

Curvature Regularity for Multi-Label Problems- Standard and Customized Linear Programming

Abstract. We follow recent work by Schoenemann et al. [25] for expressing curvature regularity as a linear program. While the original formulation focused on binary segmentation, we address several multi-label problems, including segmentation, denoising and inpainting, all cast as a single linear program. Our multi-label segmentation introduces a “curvature Potts model ” and combines a well-known Potts model relaxation [14] with the above work. For inpainting, we improve on [25] by grouping intensities into bins. Finally, we address the problem of denoising with absolute differences in the data term. Furthermore, we explore alternative solving strategies, including higher order Markov Random Fields, min-sum diffusion and a combination of augmented Lagrangians and an accelerated first order scheme to solve the linear programs.