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Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期60卷 101945-101945页

作者： Ramadan, Hiba Lachqar, Chaymae Tairi, Hamid Univ Sidi Mohamed Ben Abdellah Fac Sci Dhar El Mahraz Dept Informat BP 1796 Atlas Fez 30000 Fez Morocco Univ Sidi Mohamed Ben Abdellah Fac Med & Pharm Fes 30000 Morocco

The development of computer aided diagnosis (CAD) systems for the task of tumor detection and segmentation in breast ultrasound (BUS) images presents one of the most active research fields. In this paper, a saliency-guided approach for fast and automatic tumor segmentation in BUS images is proposed. We explore the ability of the concept of saliency to emulate radiologist expertise for tumor lesion detection in BUS images. For that, we compute BUS image saliency to generate effective tumor seeds and background seeds. Then, a graph-based interactive image segmentation method is applied automatically using the generated seeds to extract tumor region. For more accurate segmentation, we propose a novel saliency score measure to apply a post-processing step for result refinement. Our results have been compared with other approaches in challenged datasets and demonstrated the effectiveness of our saliency-guided approach for tumor detection and segmentation using different evaluation metrics. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Computer aided diagnosis Breast ultrasound Tumor detection Tumor segmentation Saliency detection graph-based segmentation Saliency score Active contours

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Stereo matching using census cost over cross window and segmentation-based disparity refinement

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JOURNAL OF ELECTRONIC IMAGING 2018年第2期27卷 023014-023014页

作者： Li, Qingwu Ni, Jinyan Ma, Yunpeng Xu, Jinxin Hohai Univ Coll Internet Things Changzhou Peoples R China Hohai Univ Key Lab Sensor Networks & Environm Sensing Changzhou Peoples R China

Stereo matching is a vital requirement for many applications, such as three-dimensional (3-D) reconstruction, robot navigation, object detection, and industrial measurement. To improve the practicability of stereo matching, a method using census cost over cross window and segmentation-based disparity refinement is proposed. First, a cross window is obtained using distance difference and intensity similarity in binocular images. Census cost over the cross window and color cost are combined as the matching cost, which is aggregated by the guided filter. Then, winner-takes-all strategy is used to calculate the initial disparities. Second, a graph-based segmentation method is combined with color and edge information to achieve moderate under-segmentation. The segmented regions are classified into reliable regions and unreliable regions by consistency checking. Finally, the two regions are optimized by plane fitting and propagation, respectively, to match the ambiguous pixels. The experimental results are on Middlebury Stereo Datasets, which show that the proposed method has good performance in occluded and discontinuous regions, and it obtains smoother disparity maps with a lower average matching error rate compared with other algorithms. (C) 2018 SPIE and IS&T

关键词： stereo matching census cost guided filter graph-based segmentation plane fitting

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graph-based Layout Analysis for PDF Documents

Graph-based Layout Analysis for PDF Documents

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Conference on Imaging and Printing in a Web 2.0 World IV

作者： Xu, Canhui Tang, Zhi Tao, Xin Li, Yun Shi, Cao Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China

ISBN: (纸本)9780819494375

To increase the flexibility and enrich the reading experience of e-book on small portable screens, a graph based method is proposed to perform layout analysis on Portable Document Format (PDF) documents. Digital born document has its inherent advantages like representing texts and fractional images in explicit form, which can be straightforwardly exploited. To integrate traditional image-based document analysis and the inherent meta-data provided by PDF parser, the page primitives including text, image and path elements are processed to produce text and non text layer for respective analysis. graph-based method is developed in superpixel representation level, and page text elements corresponding to vertices are used to construct an undirected graph. Euclidean distance between adjacent vertices is applied in a top-down manner to cut the graph tree formed by Kruskal's algorithm. And edge orientation is then used in a bottom-up manner to extract text lines from each sub tree. On the other hand, non-textual objects are segmented by connected component analysis. For each segmented text and non-text composite, a 13-dimensional feature vector is extracted for labelling purpose. The experimental results on selected pages from PDF books are presented.

关键词： graph-based segmentation PDF document analysis image based document analysis

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graph-based Hierarchical Video Cosegmentation 19th

Graph-Based Hierarchical Video Cosegmentation

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19th International Conference on Image Analysis and Processing (ICIAP)

作者： Rodrigues, Franciele Leal, Pedro Kenmochi, Yukiko Cousty, Jean Najman, Laurent Guimaraes, Silvio Patrocinio, Zenilton, Jr. Pontificia Univ Catolica Minas Gerais ICEI DCC VIPLAB Belo Horizonte MG Brazil Univ Paris Est LIGM ESIEE Paris CNRS Champs Sur Marne France

ISBN: (纸本)9783319685601;9783319685595

The goal of video cosegmentation is to jointly extract the common foreground regions and/or objects from a set of videos. In this paper, we present an approach for video cosegmentation that uses graph-based hierarchical clustering as its basic component. Actually, in this work, video cosegmentation problem is transformed into a graph-based clustering problem in which a cluster represents a set of similar supervoxels belonging to the analyzed videos. Our graph-based Hierarchical Video Cosegmentation method (or HVC) is divided in two main parts: (i) supervoxel generation and (ii) supervoxel correlation. The former explores only intra-video similarities, while the latter seeks to determine relationships between supervoxels belonging to the same video or to distinct videos. Experimental results provide comparison between HVC and other methods from the literature on two well known datasets, showing that HVC is a competitive one. HVC outperforms on average all the compared methods for one dataset;and it was the second best for the other one. Actually, HVC is able to produce good quality results without being too computational expensive, taking less than 50% of the time spent by any other approach.

关键词： graph-based segmentation Video cosegmentation Hierarchical clustering

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AUTOMATED ANALYSIS OF CARTILAGE MORPHOLOGY

AUTOMATED ANALYSIS OF CARTILAGE MORPHOLOGY

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IEEE 10th International Symposium on Biomedical Imaging - From Nano to Macro (ISBI)

作者： Kashyap, Satyananda Yin, Yin Sonka, Milan Univ Iowa Dept Elect & Comp Engn Iowa City IA 52246 USA iCAD Inc Nashua NH 03062 USA

ISBN: (纸本)9781467364553

Magnetic Resonance Imaging (MRI) allows early detection of and assessment of progression in Osteoarthritis (OA). It is known that the rate of cartilage loss varies in individual cartilage plates. No known completely automated algorithms exist to reliably segments MRI image data and accurately delineate cartilage plates. We report an automated method for cartilage segmentation and definition of cartilage plates. We present a modified Layered Optimal graph Image segmentation of Multiple Objects and Surfaces (LOGISMOS) with a robust steerable-feature-based design and demonstrate improvements of segmentation accuracy. Our new method was tested on 60 data sets provided by the MICCAI segmentation challenge 2010. The mean and standard deviation of surface positioning errors are reported for individual cartilage plate regions.

关键词： Osteoarthritis Knee segmentation Steerable Features Cartilage Plates LOGISMOS graph-based segmentation

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graph Matching based Side Information Generation for Distributed Multi-view Video Coding

Graph Matching Based Side Information Generation for Distrib...

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IEEE International Conference on Communications (ICC 2009)

作者： Lv, Hui Xiong, Hongkai Song, Li He, Zhihai Chen, Tsuhan Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781424434343

In this paper, we adopt constrained relaxation for distributed multi-view video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate inter-view correlated side information without knowing the camera parameters. Moreover, graph-based representations of multi-view images are incorporated to form more distinctive feature constraints. The sparse data as a good hypothesis space aim for a best matching optimization of inter-view side information with compact syndromes, from inferred relaxed coset. The plausible filling-in from a priori feature constraints between neighboring views could reinforce a promising compensation to inter-view side information generation for joint multi-view decoding. In order to find distinctive feature matching with a more stable approximation, PCA-SIFT and TPS (thin plate spline) are adopted to reduce the dimension of SIFT descriptors and construct a more accurate inter-view motion model. The experimental results validate the high estimation precision and the rate-distortion improvements.

关键词： Multi-view video coding distributed video coding PCA-SIFT thin plate spline graph-based segmentation

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RETINAL VESSEL WIDTH MEASUREMENTS based ON A graph-THEORETIC METHOD

RETINAL VESSEL WIDTH MEASUREMENTS BASED ON A GRAPH-THEORETIC...

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8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Xu, X. Niemeijer, M. Song, Q. Garvin, M. K. Reinhardt, J. M. Abramoff, M. D. Univ Iowa Dept Biomed Engn Iowa City IA 52242 USA Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Dept Ophthalmol & Visual Sci Iowa City IA 52242 USA Univ Iowa Ctr Excellence Prevent Visual Loss & Blindness Dept Vet Affairs Iowa City IA 52242 USA

ISBN: (纸本)9781424441280

A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, three-dimension surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. Two datasets from the REVIEW database were used to assess the performance of proposed method. This algorithm is robust and is able to produce accurate measurements.

关键词： retinal photography graph-based segmentation vessel width measurement

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graph-based surface extraction of the liver using locally adaptive priors for multimodal interventional image registration

Graph-based surface extraction of the liver using locally ad...

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Conference on Medical Imaging - Image-Guided Procedures, Robotic Interventions and Modeling

作者： Kadoury, Samuel Wood, Bradford J. Venkatesan, Aradhana M. Ardon, Roberto Jago, James Kruecker, Jochen Philips Res N Amer 345 Scarborough Rd Briarcliff Manor NY 10510 USA Natl Inst Hlth Dept Diagnost Radiol Bethesda MD 20892 USA Philips Medisys Res Lab Paris France Philips Healthcare Bothell WA 98021 USA

ISBN: (纸本)9780819489654

The 3D fusion of tracked ultrasound with a diagnostic CT image has multiple benefits in a variety of interventional applications for oncology. Still, manual registration is a considerable drawback to the clinical workflow and hinders the widespread clinical adoption of this technique. In this paper, we propose a method to allow for an image-based automated registration, aligning multimodal images of the liver. We adopt a model-based approach that rigidly matches segmented liver shapes from ultrasound (U/S) and diagnostic CT imaging. Towards this end, a novel method which combines a dynamic region-growing method with a graph-based segmentation framework is introduced to address the challenging problem of liver segmentation from U/S. The method is able to extract liver boundary from U/S images after a partial surface is generated near the principal vector from an electromagnetically tracked U/S liver sweep. The liver boundary is subsequently expanded by modeling the problem as a graph-cut minimization scheme, where cost functions used to detect optimal surface topology are determined from adaptive priors of neighboring surface points. This allows including boundaries affected by shadow areas by compensating for varying levels of contrast. The segmentation of the liver surface is performed in 3D space for increased accuracy and robustness. The method was evaluated in a study involving 8 patients undergoing biopsy or radiofrequency ablation of the liver, yielding promising surface segmentation results based on ground-truth comparison. The proposed extended segmentation technique improved the fiducial landmark registration error compared to a point-based registration (7.2mm vs. 10.2mm on average, respectively), while achieving tumor target registration errors that are statistically equivalent (p > 0.05) to state-of-the-art methods.

关键词： CT ultrasound surface-based registration graph-based segmentation image-guided liver interventions

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Automatic Object segmentation Using Perceptual Grouping of Regions with Contextual Constraints

Automatic Object Segmentation Using Perceptual Grouping of R...

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International Conference on Image Processing Theory, Tools and Applications

作者： Mohsen Zand Shyamala Doraisamy Alfian Abdul Halin Mas Rina Mustaffa Department of Multimedia Faculty of Computer Science and Information Technology Universiti Putra Malaysia

ISBN: (纸本)9781479986385

Image segmentation is still considered a very challenging subject despite years of research effort poured into the field. The problem is exacerbated when there is need for specific object detection. Since objects can be visually non-homogeneous, techniques that attempt to segment images into visually uniform regions using only the bottom-up cues, tend to fail. We propose a novel two-step model that incorporates both bottom-up information and top-down object constraints. Firstly, a set of uniform regions are generated using an extension of contour detection, seeded region growing, and graph-based methods. The second step applies co-occurrence constraints on the image regions in order to perceptually group regions into objects. This unsupervised segmentation process models each object using higher-level knowledge in the form of visual co-occurrences of its constituent parts. Experiments on the horse and ImageCLEF databases show that the proposed technique performs comparably well with existing state-of-the-art techniques.

关键词： Automatic image segmentation graph-based segmentation Object segmentation Contextual relationships Object segmentation image segmentation region of an image edge-based segmentation region segmentation texture-based segmentation contour detection Object detection bottom-up

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An 3D MRI Unsupervised graph-based Skull Stripping Algorithm 27

An 3D MRI Unsupervised Graph-based Skull Stripping Algorithm

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27th International Conference on Knowledge based and Intelligent Information and Engineering Sytems, KES 2023

作者： Popa, Maria Department of Computer Science Babes-Bolyai University Mihail Kogalniceanu 1 Cluj-Napoca400084 Romania

Brain tumours and strokes are increasingly common, even from a young age. For this reason preliminary screenings, such as MRI scans, are crucial steps in detecting the abnormalities and finding a treatment. Given the increasing number of people developing different brain problems, there is a need in developing automated segmentation systems. Regardless the various methods presented in the literature, the lack of ground truth images determines the researchers to move towards unsupervised techniques. This paper presents an modified graph-based unsupervised brain segmentation method that uses a minimal spanning tree to segment brain from non-brain tissue. The original method uses human intervention for node selection. The aim is to eliminate human intervention and reduce the computational time required for brain segmentation. The presented method was compared with the original one in two ways: by resizing and scaling the images, and using images without any pre-processing step. Experimental results were obtained on the NFBS dataset and in both experiments the proposed approach delivers better results, both visually and numerically. Performance measures have improved, leading to better overall segmentation. The new method increased the precision and dice coefficient by 20%, resulting in a more accurate segmentation. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： brain segmentation graph-based segmentation graph-cut image segmentation

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