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Object information based interactive segmentation for fatty tissue extraction

COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1462-1470页

作者： Zhou, Zhi-Guo Liu, Fang Jiao, Li-Cheng Li, Ling-Ling Wang, Xiao-Dong Gou, Shui-Ping Wang, Shuang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Lymph nodes are very important factors for diagnosing gastric cancer in clinical use, and are usually distributed within the fatty tissue around the stomach. When extracting fatty tissues whose structures and textures are complicated, automatic extraction is still a challenging task, while manual extraction is time-consuming. Consequently, semi-automatic extraction, which allows introducing interactive operations, appears to be more realistic. Currently, most interactive methods need to indicate the position and main features in both the object and background. However, it is easier for radiologists to only mark object information. Due to this issue, a new Object Information based interactive segmentation (OHS) method is proposed in this paper. Different from the most existing methods, OIIS just needs to input the object information, while the background information is not required. Experimental results and comparative studies show that OIIS is effective for fatty tissue extraction. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Fatty tissue Gastric cancer interactive image segmentation Object information Mean shift

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SuperCut: Superpixel Based Foreground Extraction With Loose Bounding Boxes in One Cutting

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IEEE SIGNAL PROCESSING LETTERS 2017年第12期24卷 1803-1807页

作者： Wu, Shuqiong Nakao, Megumi Matsuda, Tetsuya Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

interactive image segmentation that uses a bounding box containing the foreground has gained great popularity because of its convenience. However, its performance is often degraded when the bounding box is not tight enough or covers large background regions. To solve this problem, this letter proposes a novel segmentation algorithm called "SuperCut". This algorithm provides robust segmentation in one cut even with loose bounding boxes. In the proposed SuperCut, superpixels of the whole image are first computed, and then a background similarity map for the superpixels inside the bounding box is created based on Haar-Wavelet feature and pixel intensities. Finally, a max-flow cut is made based on the map. The background similarity map reduces the influence from possible background regions inside the bounding box, which leads to the efficient segmentation of SuperCut. To evaluate the effectiveness of the proposed SuperCut, we compared it with some recent published interactive image segmentation techniques. The experimental results show that the proposed SuperCut outperforms other compared methods especially when using loose bounding boxes.

关键词： Bounding box GrabCut Graph cut interactive image segmentation OneCut

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Combining strings and necklaces for interactive three-dimensional segmentation of spinal images using an integral deformable spine model

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2004年第10期51卷 1821-1829页

作者： Ghebreab, S Smeulders, AWM Univ Amsterdam Intelligent Sensory Informat Syst Grp Inst Informat Fac Sci NL-1098 SJ Amsterdam Netherlands

segmentation of the spine directly from three-dimensional (3-D) image data is desirable to accurately capture its morphological properties. We describe a method that allows true 3-D spinal image segmentation using a deformable integral spine model. The method learns the appearance of vertebrae from multiple continuous features recorded along vertebra boundaries in a given training set of images. Important summarizing statistics are encoded into a necklace model on which landmarks are differentiated on their free dimensions. The landmarks are used within a priority segmentation scheme to reduce the complexity of the segmentation problem. Necklace models are coupled by string models. The string models describe in detail the biological variability in the appearance of spinal curvatures from multiple continuous features recorded in the training set. In the segmentation phase, the necklace and string models are used to interactively detect vertebral structures in new image data via elastic deformation reminiscent of a marionette with strings allowing for movement between interrelated structures. Strings constrain the deformation of the spine model within feasible solutions. The driving application in this work is analysis of computed tomography scans of the human lumbar spine. An illustration of the segmentation process shows that the method is promising for segmentation of the spine and for assessment of its morphological properties.

关键词： energy-minimization methods functional data analysis interactive image segmentation landmark-based object segmentation statistical deformable models

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interactive object segmentation from multi-view images

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2013年第4期24卷 477-485页

作者： Thi Nhat Anh Nguyen Cai, Jianfei Zheng, Jianmin Li, Jianguo Danang Univ Technol Da Nang City Vietnam Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Despite the great progress on interactive image segmentation, image co-segmentation, 2D and 3D segmentation, there is still no workable solution to the problem: given a set of calibrated or un-calibrated multi-view images (say, more than 40 images), by interactively cutting 3 similar to 4 images, can the foreground object of the rest images be quickly cutout automatically and accurately? In this paper, we propose a non-trivial engineering solution to this problem. Our basic idea is to integrate 3D segmentation with 2D segmentation so as to combine their advantages. Our proposed system iteratively performs 2D and 3D segmentation, where the 3D segmentation results are used to initialize 2D segmentation and ensure the silhouette consistency among different views and the 2D segmentation results are used to provide more accurate cues for the 3D segmentation. The experimental results show that the proposed system is able to generate highly accurate segmentation results, even for some challenging real-world multi-view image sequences, with a small amount of user input. (c) 2013 Elsevier Inc. All rights reserved.

关键词： interactive image segmentation Multi-view image segmentation image co-segmentation image segmentation 3D graph-cut Active contour Object cutout Joint 3D and 2D segmentation

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Enhancing human-computer interaction in medical segmentation

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PROCEEDINGS OF THE IEEE 2003年第9期91卷 1430-1442页

作者： Harders, M Székely, G Swiss Fed Inst Technol Comp Vis Lab ETH Zentrum CH-8092 Zurich Switzerland

Medical image acquisition devices are becoming increasingly multidimensional, and predictions assume 5600 images per patient study within the next decade. Therefore, new paradigms for computerized medical image analysis and visualization are of fundamental importance to make possibly full use of the information buried in the enormous flood of image data. The aim of the presented project is the optimal cooperation between computer-based image,analysis algorithms and human operators using new closed-loop segmentation systems for improved information flow. This paper describes an enhanced, visuo-haptic interaction tool we have developed for medical segmentation. Evaluation studies with the system, which confirm the value of adding haptic feedback, are also presented.

关键词： haptic interaction human-computer interaction interactive image segmentation

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Lazy snapping

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ACM TRANSACTIONS ON GRAPHICS 2004年第3期23卷 303-308页

作者： Li, Y Sun, J Tang, CK Shum, IY Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China

In this paper, we present Lazy Snapping, an interactive image cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy. Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel image segmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.

关键词： user interface image cutout interactive image segmentation graph cut

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Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第5期37卷 2612-2626页

作者： Diot-Dejonghe, Tiphaine Leporq, Benjamin Bouhamama, Amine Ratiney, Helene Pilleul, Frank Beuf, Olivier Cervenansky, Frederic Univ Claude Bernard Lyon 1 INSA Lyon Inserm CNRSCREATIS UMR 5220U1294 F-69XXX Lyon France Ctr Leon Berard Dept Radiol 28 Prom Lea & Napoleon Bullukian F-69008 Lyon France

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians.

关键词： OHIF interactive image segmentation Girder Web-viewer Radiomics Deep learning Medical imaging

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LEARNING TO DETECT SALIENT REGION OF image UNDER WEAK SUPERVISION

LEARNING TO DETECT SALIENT REGION OF IMAGE UNDER WEAK SUPERV...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Cheng, Jian Fu, Yu Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781612843490

Salient region of an image usually contains the crucial information for image analysis and understanding. Most conventional approaches learn the saliency by utilizing the low-level features, which ignore the participation of human. In this pap er, we propose an effective and robust approach to detect the salient region of an image by combining the bottom-up and top-down cues. The proposed method not only consider the low-level attention features, but also take human into the loop for better understanding of human attention. Furthermore, we build an asymmetrical graph model to integrate these bottom up and top-down cues into an energy function of saliency. A compact but exact saliency region can be obtained by minimizing posterior energy function. The compact constraint and global minimization manner of the asymmetrical graph cuts guarantee the good performance of saliency extraction. Extensive experiments demonstrate the proposed method is promising.

关键词： Saliency detection interactive image segmentation graph cuts

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Random walks for interactive alpha-matting

Random walks for interactive alpha-matting

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5th IASTED International Conference on Visualization, Imaging, and image Processing

作者： Grady, L Schiwietz, T Aharon, S Westermann, R Siemens Corp Res Imaging & Visualizat Princeton NJ USA

ISBN: (纸本)0889865280

interactive, efficient, methods of foreground extraction and alpha-matting are of increasing practical importance for digital image editing. Although several new approaches to this problem have recently been developed, many challenges remain. We propose a new technique based on random walks that has the following advantages: First, by leveraging a recent technique from manifold learning theory, we effectively use RGB values to set boundaries for the random walker, even in fuzzy or low-contrast images. Second, the algorithm is straightforward to implement, requires specification of only a single free parameter (set the same for all images), and performs the segmentation and alpha-matting in a single step. Third, the user may locally fine tune the results by interactively manipulating the foreground[background maps. Finally, the algorithm has an inherit parallelism that leads to a particularly efficient implementation via the graphics processing unit (GPU). Our method. processes a 1024 x 1024 image at the interactive speed of 0.5 seconds and, most importantly, produces high-quality results. We show that our algorithm can generate good segmentation and matting results at an interactive rate with minimal user interaction.

关键词： interactive image segmentation alpha matting random walks general purpose GPU image editing object extraction

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A Kernel Method with Manifold Regularization for interactive segmentation 1

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36th National Conference of Theoretical Computer Science (NCTCS)

作者： Chen, Haohao Zhu, En Liu, Xinwang Zhang, Junnan Yin, Jianping Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Dongguan Univ Technol Guangzhou 523000 Guangdong Peoples R China

ISBN: (数字)9789811327124

ISBN: (纸本)9789811327124;9789811327117

interactive segmentation has been successfully applied to various applications such as image editing, computer vision, image identification. Most of existing methods require interaction for each single image segmentation, which costs too much labor interactions. To address this issue, we propose a kernel based semi-supervised learning framework with manifold regularization for interactive image segmentation in this paper. Specifically, by manifold regularization, our algorithm makes similar superpixel pair bearing the same label. Moreover, the learned classifier on one single image is directly used to similar images for segmentation. Extensive experimental results demonstrate the effectiveness of the proposed approach.

关键词： interactive image segmentation Semi-supervised learning Manifold regularization

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