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IEEE 11th International Colloquium on Signal Processing & its Applications (CSPA)

作者： Qayyum, Abdul Malik, Aamir Saeed Saad, Mohamad Naufal Mohamad Ahmad, Rana Fayyaz Abdullah, Ab Rashid Taun Univ Teknol PETRONAS Tronoh CISIR Perak Malaysia Univ Tenaga Nas Inst Energy Policy & Res IEPRe Kajang Selangor Malaysia

ISBN: (纸本)9781479982493

Monitoring vegetation encroachment for high voltage transmission lines is a challenging job for electrical distribution companies. The encroachment interrupts electrical supply to industrial sectors and residential areas. It also endures a great cost to the authorities for maintenance and damage compensation. This brings a necessity for companies to monitor the vegetation near power lines. To solve these issues, existing monitoring methods are time consuming and expensive. In this paper, we proposed a new method to detect the vegetation using satellite stereo imagery. The method is based on graph-cut algorithm which applied on satellite stereo images. We incorporated the ordering constrain with graph-cut on stereo images to detect occlusion on stereo matching algorithm. This will increase the accuracy of the disparity map which is required task for identification of power poles near vegetation based on satellite stereo images. In conclusion, based on the results, Our proposed graph-cut algorithm produces more accurate disparity maps than the existing matching algorithm used for disparity calculation.

关键词： Pleiades satellite stereo Images graph-cut algorithm Power poles detection Stereo matching LIDAR

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Multiple texture mapping of alveolar bone area for implant treatment in prosthetic dentistry

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COMPUTERS IN BIOLOGY AND MEDICINE 2015年 56卷 89-96页

作者： Kwon, Koojoo Kang, Dong-Su Shin, Byeong-Seok Inha Univ Dept Comp & Informat Engn Inchon South Korea

Treatment using implants is frequently employed in prosthetic dentistry. In this method, determining the bone density of the upper and lower jaws is important. Generally, a dentist can recognize the condition of the alveolar bone to be manipulated using a cone-beam computed tomography (CBCT) image. However, communicating the data to the patient is a challenge because it is difficult for the nonprofessional person to interpret the image, which contains a distribution of pixels with similar density. We present an intuitive texture mapping method of the alveolar bone area for application in implant treatment. Our method aims to help patients better understand the treatment process by using a textured image that includes several different texture patterns that reflect the density of the alveolar bone area. We segment the area in accordance with the density of corresponding parts in the alveolar bone and the gingiva. By simplifying the boundary of each segmented region, the distribution of pixels with similar density on the alveolar bone area can be easily recognized. Next, the texture patterns for several segmented regions are mapped onto the alveolar bone area using the graph-cut algorithm, which is used for smooth texture mapping at the boundary of the segmented region. The result is an applied texture on the alveolar bone area that corresponds to the bone structure. Our method is helpful for facilitating communication and understanding of treatment using dental implants. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Bone density Dental implants graph-cut algorithm Prosthetic dentistry Texture mapping

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graph-cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior

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JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING 2015年第5期35卷 594-607页

作者： Huang, Junbin Griffith, James F. Wang, Defeng Shi, Lin Chinese Univ Hong Kong Dept Imaging & Intervent Radiol Hong Kong 999077 Hong Kong Peoples R China Chinese Univ Hong Kong Res Ctr Med Image Comp Hong Kong 999077 Hong Kong Peoples R China Chinese Univ Hong Kong Dept Med & Therapeut Hong Kong 999077 Hong Kong Peoples R China Chinese Univ Hong Kong Chow Yuk Ho Technol Ctr Innovat Med Hong Kong 999077 Hong Kong Peoples R China

Femur segmentation from computed tomography (CT) images is a fundamental problem in femur-related computer-assisted diagnosis and surgical planning/navigation. In this study, an automatic approach for the segmentation of proximal femur from CT images that incorporates the statistical shape prior into the graph-cut framework (SP-GC) is proposed. The proposed segmentation framework includes two major processes, namely training and segmentation. In the training stage, a training set of three-dimensional CT images from a group of normal subjects were segmented semi-automatically. The shape prior was generated by active shape modeling that included mean shape and shape variance information. In the segmentation stage, two shape terms originated from the training stage were included in the GC energy function. The minimization of the energy function was achieved using a max-flow/min-cut algorithm. The performance of the proposed segmentation method was evaluated by testing on 60 CT datasets from bilateral femurs of 30 normal subjects. Qualitative and quantitative analyses of the segmentation results of the proposed method were performed and the results were compared with two widely used methods, namely the active shape model (ASM) and traditional GC, with results from manual delineation used as the ground truth. The mean dice similarity coefficient of the proposed SP-GC was 0.9600, which is higher than those of ASM and GC (0.8769 and 0.9358, respectively). The mean normalized error rate of the SP-GC results was 10 and 6 % lower than those of ASM and GC, respectively. In terms of the average surface distance measurement, the value for SP-GC was 0.885 mm, compared with 2.148 and 1.154 mm for ASM and GC, respectively. In comparison with ASM, SP-GC has superior performance given a small training set (e.g., n = 12). With increasing number of training samples, the segmentation accuracy of ASM saturated, but that of SP-GC slightly increased.

关键词： graph-cut algorithm Active shape model Shape prior Computed tomography Proximal femur

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Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research

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MEDICAL IMAGE ANALYSIS 2011年第4期15卷 438-448页

作者： Ababneh, Sufyan Y. Prescott, Jeff W. Gurcan, Metin N. Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA

In this paper, a new, fully automated, content-based system is proposed for knee bone segmentation from magnetic resonance images (MRI). The purpose of the bone segmentation is to support the discovery and characterization of imaging biomarkers for the incidence and progression of osteoarthritis, a debilitating joint disease, which affects a large portion of the aging population. The segmentation algorithm includes a novel content-based, two-pass disjoint block discovery mechanism, which is designed to support automation, segmentation initialization, and post-processing. The block discovery is achieved by classifying the image content to bone and background blocks according to their similarity to the categories in the training data collected from typical bone structures. The classified blocks are then used to design an efficient graph-cut based segmentation algorithm. This algorithm requires constructing a graph using image pixel data followed by applying a maximum-flow algorithm which generates a minimum graph-cut that corresponds to an initial image segmentation. Content-based refinements and morphological operations are then applied to obtain the final segmentation. The proposed segmentation technique does not require any user interaction and can distinguish between bone and highly similar adjacent structures, such as fat tissues with high accuracy. The performance of the proposed system is evaluated by testing it on 376 MR images from the Osteoarthritis Initiative (OAI) database. This database included a selection of single images containing the femur and tibia from 200 subjects with varying levels of osteoarthritis severity. Additionally, a full three-dimensional segmentation of the bones from ten subjects with 14 slices each, and synthetic images with background having intensity and spatial characteristics similar to those of bone are used to assess the robustness and consistency of the developed algorithm. The results show an automatic bone detection rate of

关键词： Segmentation graph-cut algorithm Osteoarthritis Tibia Femur

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graph-based approach to scene text localisation and tracking in videos

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ELECTRONICS LETTERS 2016年第13期52卷 1110-1111页

作者： Kim, Y. G. Koo, H. I. Ajou Univ Dept Elect & Comp Engn Suwon 443749 South Korea

A text localisation and tracking method is presented that finds text-regions in videos and assigns unique IDs to their trajectories. For the goal, a graph-based framework that can work with existing text detection methods is developed. To be precise, graphs are built where vertices are image-level text detection results and edges represent the correspondence scores of the vertices. From these graphs, text-region trajectories by using the graph-cut algorithm are extracted. This approach allows considering false positives and misses, as well as their patch-based tracking results at the same time, and text trajectories are reliably extracted. Finally, the results are refined by interpolating misses and filtering out false positives. The proposed method is submitted to the International Conference on Document Analysis and Recognition 2015 robust reading competition (video text localisation) and the method showed the best performance in terms of CLEAR MOT metrics and was ranked third place according to VACE metrics among the seven participating methods.

关键词： feature extraction graph theory image filtering interpolation natural scenes object tracking text analysis text detection video signal processing CLEAR MOT metrics International Conference on Document Analysis and Recognition 2015 VACE metrics correspondence scores false positive filtering graph vertices graph-based framework graph-cut algorithm image-level text detection interpolation patch-based tracking scene text localisation scene text tracking text-region trajectories unique ID assignment video text localisation

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Measuring height of high-voltage transmission poles using unmanned aerial vehicle (UAV) imagery

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IMAGING SCIENCE JOURNAL 2017年第3期65卷 137-150页

作者： Qayyum, Abdul Malik, Aamir Saeed Saad, Naufal M. bin Abdullah, Mohd Faris Iqbal, Mahboob Rasheed, Waqas Bin Ab Abdullah, Ab Rashid Jaafar, Mohd Yaakob Hj CISIR Univ Teknol PETRONAS Dept Elect & Elect Engn Bandar Seri Iskandar 32610 Perak Malaysia Jalan IKRAM UNITEN Kajang Malaysia Wisma SESB Kota Kinabalu Malaysia

Aerial imagery is important in remote sensing applications. Unmanned aerial vehicle (UAV) has a wide range of applications in remote sensing and presents a substantial cost-effective solution when monitoring objects on the earth's surface. Moreover, object detection and classification are important aspects of global information system, especially for remote sensing applications and power line monitoring, which are essential for the proper distribution of electricity to consumers. Manual inspection consumes much time and involves risk, especially in remote areas that host dangerous wildlife;hence, UAV-based approaches are more feasible for such monitoring. The authors propose an UAV approach that utilises a digital surface model and incorporates a stereo matching algorithm based on UAV stereo images. The proposed algorithm was based on a graph-cut (GC) algorithm that measured the disparity map. Results were compared with well-known algorithms;including, for example, global and local stereo matching algorithms. The proposed solution introduces and integrates ordering constraints along with a submodular energy minimisation function to/with the GC algorithm to enhance performance. The authors measured sensitivity and recall for all parameters against ground truth data for differently cropped images of 16 power poles. Results showed that the proposed model performed more accurately compared to extant methods.

关键词： Depth map measurement UAV disparity map graph-cut algorithm orthorectification power transmission poles

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3D α-expansion and graph cut algorithms for automatic liver segmentation from CT images

3D α-expansion and graph cut algorithms for automatic liver...

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11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks

作者： Casiraghi, Elena Lombardi, Gabriele Pratissoli, Stella Rizzi, Simone Univ Milan Dept Comp Sci Via Comel 39 I-20135 Milan Italy

ISBN: (纸本)9783540748175

Abdominal CT images have been widely studied in the recent years as they are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, that is still an open problem. In this paper we describe an automatic method to segment the liver from abdominal CT data, by combining an a-expansion and a graph cut algorithm. When evaluated on the data of 40 patients, by comparing the automatically detected liver volumes to the liver boundaries manually traced by three experts, the method achieves a symmetric volume difference of 94%.

关键词： computed tomography liver segmentation energy minimization alpha-expansion graph-cut algorithm

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Accurate Airway Segmentation Based on Intensity Structure Analysis and graph-cut

Accurate Airway Segmentation Based on Intensity Structure An...

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Conference on Medical Imaging - Image Processing

作者： Meng, Qier Kitasaka, Takayuki Nimura, Yukitaka Oda, Masahiro Mori, Kensaku Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan Aichi Inst Technol Fac Informat Sci 1247 YachigusaYakusa Cho Toyota Aichi 4700392 Japan Nagoya Univ Strategy Off Informat & Commun Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781510600195

This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the hng parenchyma areas.

关键词： Hessian matrix analysis multiscale cavity enhancement filter graph-cut algorithm

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Auto-segmentation based on graphcut and template

Auto-segmentation based on graphcut and template

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5th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Zhang, Hong Zuo, Wei Mu, Ying Beihang Univ Image Proc Ctr Beijing 100083 Peoples R China

ISBN: (纸本)9780819469502

In this paper, the main idea is to use the prior knowledge to guide the segmentation. Firstly the continuity among adjacent frames is used to create a motion template according to the Displaced Frame Difference's (DFD) higher character([1]). And then the color template is established by using the k-means clustering. Based upon the information derived from the previous two templates, the segmentation image is defined as foreground, background and boundary regions. Then, the segmentation problem is formulated as an energy minimization problem. The hard edge of foreground is then obtained by implementing graph-cut algorithm. Experimental results demonstrate the effectiveness of proposed algorithm.

关键词： automatic video segmentation graph-cut algorithm video object template

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graph-cut Based Regional Risk Estimation for Traffic Scene 17

Graph-Cut Based Regional Risk Estimation for Traffic Scene

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IEEE 17th International Conference on Intelligent Transportation Systems (ITSC)

作者： Karaduman, Ozgur Eren, Haluk Kurum, Hasan Celenk, Mehmet Firat Univ Sch Elect & Elect Engn TR-23169 Elazig Turkey Firat Univ Sch Avion & Intelligent Vehicle Sci TR-23169 Elazig Turkey Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

ISBN: (纸本)9781479960781

In this study, we investigate the regional risk estimation of drivers for street environment involving different players such as pedestrians, other vehicles, traffic signs, traffic lights, and crosswalks. Various researches focusing on objects regarding traffic have been realized by means of traditional risk estimation. In turn, conventional methods have not presented a realistic solution for drivers at risky regions and moments;whereas, our approach considers emerging risks for a driver due to dynamic actions of street players. A chessboard is devised for representing the street players, each of which carries different potential risks. Every square of the chessboard refers to a partition, which can host one or multiple players. Further, a partition can have different risks for a driver. The proposed model is realized using a graph-cut algorithm for energy minimization. Each partition is considered as a vertex of the graph, which can transfer risks caused by street players. Vertexes are formed via behavior as those of memory cell structures. The memory cells have risk transfer capabilities allowing a driver to determine momentarily risks on urban traffic. Consequently, this captures the regional risk for driver in light of the detected street players as demonstrated through the paper.

关键词： computer games graph theory pedestrians risk management road traffic road vehicles traffic engineering computing behavior chessboard crosswalks driver risks dynamic actions energy minimization graph vertex graph-cut algorithm graph-cut based regional risk estimation memory cell structures risk transfer capabilities risky moments risky regions street environment street players traffic lights traffic scene traffic signs urban traffic vehicles Estimation Labeling Minimization Mobile communication Roads Vehicle dynamics Vehicles Pedestrians vehicle Crosswalks Risk estimation traffic signs Drivers Traffic lights traffic in cities road vehicles Computer Games Vehicle dynamics mobile communication Road traffic immunocytes graph theory

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