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Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map

JOURNAL OF NEURO-ONCOLOGY 2014年第1期120卷 187-198页

作者： Hoefnagels, Friso W. A. Hamer, Philip De Witt Sanz-Arigita, Ernesto Idema, Sander Kuijer, Joost P. A. Pouwels, Petra J. W. Barkhof, Frederik Vandertop, W. Peter Vrije Univ Amsterdam Med Ctr Dept Neurosurg Neurosurg Ctr Amsterdam NL-1007 MB Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Radiol Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Phys & Med Technol Amsterdam Netherlands

Conflicting results on differentiating edema and glioma by diffusion tensor imaging (DTI) are possibly attributable to dissimilar spatial distribution of the lesions. Combining DTI-parameters and enhanced registration might improve prediction. Regions of edema surrounding 22 metastases were compared to tumor-infiltrated regions from WHO grade 2 (12), 3 (10) and 4 (18) gliomas. DTI data was co-registered using Tract Based Spatial Statistics (TBSS), to measure Fractional Anisotropy (FA) and Mean Diffusivity (MD) for white matter only, and relative changes compared to matching reference regions (dFA and dMD). A two-factor principal component analysis (PCA) on metastasis and grade 2 glioma was performed to explore a possible differentiating combined factor. Edema demonstrated equal MD and higher FA compared to grade 2 and 3 glioma (P < 0.001), but did not differ from glioblastoma. Differences were non-significant when corrected for spatial distribution, since reference regions differed strongly (P < 0.001). The second component of the PCA (PCA-C2) did differentiate edema and low-grade tumor (sensitivity 91.7 %, specificity 86.4 %). PCA-C2 scores were plotted voxel-wise as a probability-map, discerning distinct areas of presumed edema or tumor infiltration. Correction of spatial dependency appears essential when differentiating glioma from edema. A tumor-infiltration probability-map is presented, based on supplementary information of multiple DTI parameters and spatial normalization.

关键词： Glioma infiltration Edema MR diffusion tensor imaging probability map Spatial normalization

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Creation of a global land cover and a probability map through a new map integration method

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第partb期28卷 70-77页

作者： Kinoshita, Tsuguki Iwao, Koki Yamagata, Yoshiki Ibaraki Univ Ami Ibaraki 3000393 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058568 Japan Natl Inst Environm Studies Tsukuba Ibaraki 3058506 Japan

Global land cover maps are widely used for assessment and in research of various kinds, and in recent years have also come to be used for socio-economic forecasting. However, existing maps are not very accurate, and differences between maps also contribute to their unreliability. Improving the accuracy of global land cover maps would benefit a number of research fields. In this paper, we propose a methodology for using ground truth data to integrate existing global land cover maps. We checked the accuracy of a map created using this methodology and found that the accuracy of the new map is 74.6%, which is 3% higher than for existing maps. We then created a 0.5-min latitude by 0.5-min longitude probability map. This map indicates the probability of agreement between the category class of the new map and truth data. Using the map, we found that the probabilities of cropland and grassland are relatively low compared with other land cover types. This appears to be because the definitions of cropland differ between maps, so the accuracy may be improved by including pasture and idle plot categories. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Global land cover map map integration Ground truth data probability map

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Automated coronary artery calcium segmentation in cardiac CT using combined probability map and feature analysis - A preliminary report

Automated coronary artery calcium segmentation in cardiac CT...

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Conference on Medical Imaging - Image Processing

作者： Rasul, Raisa Naghavi, Morteza Budoff, Matthew J. Reeves, Anthony P. Cornell Univ Sch Biomed Engn Ithaca NY 14853 USA Amer Heart Technol Palo Alto CA USA Harbor UCLA Med Ctr Lundquist Inst Torrance CA 90509 USA Cornell Univ Sch Elect & Comp Engn Ithaca NY USA

ISBN: (纸本)9781510660335;9781510660342

Coronary artery calcium (CAC) scoring has been adopted in US national guidelines as a diagnostic tool for risk assessment of asymptomatic individuals at risk of atherosclerotic cardiovascular disease which is the leading cause of death worldwide. To enable fast and accurate CAC scoring, we present a fully automated CAC scoring system for non-contrast ECG-gated cardiac CT scans. First, an anatomical model was developed for segmenting important structures in the heart region, including ascending and descending aortas as well as the myocardium, to limit the CAC search region. A novel trained probability map was developed and used for localizing and labeling the candidate calcified plaques based on coronary artery groups, including left main and left anterior descending artery (LMD), left circumflex artery (LCX), and right coronary artery (RCA). Finally, location, size and intensity features of each CAC candidate were used to remove false CAC candidates. This method was trained on 88 scans and tested on 559 scans from the Multi-Ethnic Study of Atherosclerosis (MESA) dataset. The combination of these methods yielded a Pearson correlation of 0.98, an average Dice score of 0.85, and a risk-category accuracy of 0.87. The artery specific detection precision, based on the probability map labelling, was 0.876 for LMD, 0.830 for LCX and 0.997 for RCA. These results indicate the potential of an automated CAC scoring system for easier and improved screening in cardiac CT scans, and a novel method for CAC labelling.

关键词： coronary artery calcium scoring cardiac CT probability map feature extraction random forest artery labelling

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Centroid tracking using a probability map for target segmentation

Centroid tracking using a probability map for target segment...

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Conference on Acquisition, Tracking, and Pointing XVI

作者： Albus, JE Lewins, LJ Schacht, JR Raytheon Elect Syst El Segundo CA 90245 USA

ISBN: (纸本)0819444642

The central problem of image based centroid tracking is that of target segmentation, that is, determining which pixels belong to the target and which belong to the background. Once the target pixels are identified, the centroid (the center of gravity) of these pixels can be used as the estimated target position. The underlying assumption made in centroid tracking is that the target image contains intensity values that are unlikely to occur in the background. Based on this assumption, the centroid tracker uses three concentric gates to determine which pixels are target pixels. The areas bounded by these gates form three disjoint regions;the inner region, the track region, and the outer region. An inner histogram is collected over inner region that should contain mostly target pixels. An outer histogram is collected over the outer region that should contain only background pixels. These histograms are then used to generate a probability map that indicates the probability that a pixel with a given intensity is part of the target. This probability map is then used to segment the target and find its centroid. This paper describes the methods used to generate the probability map and its use in the centroid tracking algorithm. The performance of this algorithm is compared to that of the previously used dual-threshold segmentation algorithm.

关键词： centroid tracking target segmentation probability map

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Wound Area Segmentation Using 4-D probability map and Superpixel Region Growing

Wound Area Segmentation Using 4-D Probability Map and Superp...

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IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA)

作者： Biswas, Topu Fauzi, Mohammad Faizal Ahmad Abas, Fazly Salleh Logeswaran, Rajasvaran Nair, Harikrishna K. R. Multimedia Univ Fac Engn Cyberjaya Malaysia Multimedia Univ Fac Engn & Technol Cyberjaya Malaysia Asia Pacific Univ Technol & Innovat Sch Comp Kuala Lumpur Malaysia Hosp Kuala Lumpur Dept Internal Med Wound Care Unit Kuala Lumpur Malaysia

ISBN: (纸本)9781728133775

Chronic wounds poses a significant health burden. Gradually it's becoming a major threat for world health and economy. In developed countries, 1-2% of all population is affected by chronic wounds in their lifetime and the duration for chronic wound healing varies from several months to several years. The change in wound area over time is a highly predictive clue for wound treatment. The current clinical techniques for wound area measurement is based on visual inspection, which is potentially inaccurate and represents a tedious clinical workload for the clinician. Hence, a computerized wound measurement system is more quantitative and cost-effective that enables more accurate and effective wound management. In this paper, the proposed wound area segmentation method based on 4-D probability map and superpixel region growing shows promising performance in terms of accuracy, stability and computational complexity. The proposed method has 79.2% accuracy, an improvement over previous method.

关键词： wound segmentation superpixel region growing probability map

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Vision-Based Road Region Detection Using probability map of Color Features of Road 17

Vision-Based Road Region Detection Using Probability Map of ...

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17th International Conference on Control, Automation and Systems (ICCAS)

作者： Lim, Jae-Hyun Chung, Jun-Ho Kang, Tae-Koo Lim, Myo-Taeg Korea Univ Sch Elect Engn Seoul South Korea Sangmyung Univ Sch Human Intelligence & Robot Engn Cheonan South Korea

ISBN: (纸本)9788993215144

This paper proposes a road region detection algorithm using a vision sensor. The proposed algorithm uses color features of road to detect the road region. We first segment the image into patches to reduce the computational complexity and the noise. Superpixel method is applied to the segmentation instead of square patches for the precise segmentation. After the segmentation, similarities of patches are calculated by undirected graph-based shortest path algorithm. The distance between neighboring patches is defined as the Euclidian distance of CIE-Lab color and Illumination-invariant color. By using the similarity of patches, isolated region of the image and similarity of patches with bottom of the images are obtained. To ensure robust performance even when the non-road region is located at the bottom of the image, a probability map is constructed by combining isolated region of the image and similarity of patches with bottom of the image. Experimental results show the robustness of the proposed algorithm in various conditions.

关键词： Autonomous driving Road region detection Superpixel Similarity of patches probability map

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CP-SLAM: Real-Time Visual SLAM Based on Continuous probability map 2

CP-SLAM: Real-Time Visual SLAM Based on Continuous Probabili...

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2nd Asia-Pacific Computer Technologies Conference (APCT)

作者： Li, Bonian Meng, Xianwei Beijing City Univ Dept Informat Studies Beijing Peoples R China Shanghai Jiao Tong Univ Coll Engn Shanghai Peoples R China

ISBN: (纸本)9798350336115

Robustness is an important criterion for evaluating semantic simultaneous localization and mapping (semantic SLAM). Limited to the computational cost and the complexity of the network, semantic information often has high uncertainty, this paper proposes a new method based on a continuous probability map, which integrates traditional feature points, semantic information, and historical data association. To be specific, an additional tread was added to the SLAM system. This thread utilizes an object detection network to generate high-level semantic information. On top of this semantic information, object detection was transformed into a novel probability map based on the Gaussian distribution assumption. This probability map also makes the optimizer focus on semantic-related objects while maintaining the background features. Further, we designed a continuous mechanism to update the probability map continuously. The mechanism was then applied to nonlinear optimization. The idea of probability map can be regarded as an individual module embedded into any other SLAM algorithm. The algorithm was tested on the TUM dataset. The results of the experiment show that, on average, our method improves localization accuracy by around 20% in indoor environments. Compared with many efficient SLAM systems including Dyna SLAM, the experimental results show that CP-SLAM achieves the effect of improving accuracy in the expected scenario.

关键词： SLAM semantics probability map localization object detection

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probability map-based grape detection and counting

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Yang, Canchen Geng, Tianyu Peng, Jian Song, Zichen Sichuan Univ Coll Comp Sci 24 Sect 1South First Ring Rd Chengdu 610065 Peoples R China Sichuan Univ Coll Elect & Informat Engn 24 Sect 1South First Ring Rd Chengdu 610065 Peoples R China

Grape-cluster detection, grape-berry counting, and berry-per-grape-cluster counting are the three major tasks in digital viticulture. Although the detection of clusters and berries was treated as separate tasks in previous studies, due to the clustered nature of the grape phenotype and the close hierarchical spatial distribution relationship between grape clusters and berries, those three tasks should be considered at the same time in our view;further, we define these tasks as subtasks of the grape detection and counting task. The major challenge of combining the three subtasks of grape detection and counting together is how to obtain the detection results of those subtasks from the output of a single neural network. To address this issue, we propose a probability map -based Grape Detection and Counting framework that first detects two intermediate probability maps through a neural network and utilizes three postprocessing stages to finish the three grape detection and counting subtasks sequentially. After being trained and validated on the trainval set of WGISD, combining 100 extra images collected in Chengdu by our team, the proposed framework is tested on the test set of WGISD to verify its performance. The proposed framework achieves a localization performance of average precision (AP)(iou0.5) 0.851 and a counting performance of MAE 1.845, RMSE 2.142 for grape clusters, a counting performance of MAE 23.414, RMSE 31.391 for grape berries, and a counting performance of MRD 0.142, 1-FVU 0.865 for berries per grape cluster on the test set of WGISD. Our source code will be publicly available at https://***/volcanoYcc/probability-map-based-Grape-Detection-and-Counting.

关键词： Digital viticulture Grape phenotyping Grape detection and counting Deep learning probability map

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Cooperative Searching Strategy for Multiple Unmanned Aerial Vehicles Based on Modified probability map 1

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Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

作者： Huang, Qiwang Yao, Jian Li, Qun Zhu, Yifan Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China

ISBN: (数字)9789811026669

ISBN: (纸本)9789811026669;9789811026652

Cooperative target-searching of multiple Unmanned Aerial Vehicles (UAVs) in uncertainty environment is an important research area in multi-UAVs cooperative control. The objective of multi-UAVs searching is to obtain the information of the searched area, decrease the uncertainty of this area and find the hidden targets as fast as possible. This paper introduces a new framework for UAV search operations and proposes a new approach to solve multi-UAVs cooperative searching problem. Aimed at the characteristics of the multi-UAVs cooperative searching problem, the modified probability map based cooperative searching strategy was discussed in detail. Based on the existing algorithms, the cooperative strategy was divided into three key parts, which are probability map initialization, probability map updating and the rules of UAV transfer. The search effectiveness of the Multi-UAVs system in the condition of Multi-target was analyzed based on the method of ABMS (Agent Based Modeling and Simulation). Simulation results demonstrated the effectiveness of the algorithm.

关键词： Unmanned aerial vehicle Searching Cooperative control probability map

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Segmentation and Automated Measurement of Chronic Wound Images: probability map Approach

Segmentation and Automated Measurement of Chronic Wound Imag...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Fauzi, Mohammad Faizal Ahmad Khansa, Ibrahim Catignani, Karen Gordillo, Gayle Sen, Chandan K. Gurcan, Metin N. Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Wexner Med Ctr Department Plast Surg Columbus OH USA Ohio State Univ Wexner Med Ctr Dept Internal Med Columbus OH USA Ohio State Univ Wexner Med Ctr Dept Surg Columbus OH USA Multimedia Univ Fac Engn Cyberjaya Selangor Malaysia Ohio State Univ Wexner Med Ctr OSU Comprehens Wound Ctr Columbus OH USA

ISBN: (纸本)9780819498281

A chronic wound, as defined by the Wound Healing Society, is a wound that has not healed in a timely fashion. An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than 25 billion US dollars and countless hours spent annually for all aspects of chronic wound care. There is need to develop software tools to analyze wound images that characterize wound tissue composition, measure their size, and monitor changes over time. This process, when done manually, is time-consuming and subject to intra-and inter-reader variability. In this paper, we propose a method that can characterize chronic wounds containing granulation, slough and eschar tissues. First, we generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the region growing segmentation process. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively found in wound tissues, while the white probability map is designed to detect the white label card for measurement calibration purpose. The innovative aspects of this work include: 1) Definition of a wound characteristics specific probability map for segmentation, 2) Computationally efficient regions growing on 4D map;3) Auto-calibration of measurements with the content of the image. The method was applied on 30 wound images provided by the Ohio State University Wexner Medical Center, with the ground truth independently generated by the consensus of two clinicians. While the inter-reader agreement between the readers is 85.5%, the computer achieves an accuracy of 80%.

关键词： Wound segmentation wound measurement wound analysis chronic wound probability map

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