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Segmenting Brain Tumors Using Pseudo–Conditional Random Fields [chapter]

Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R. G. Brown, Russell Greiner
2008 Lecture Notes in Computer Science  
Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer.  ...  This segmentation task requires classifying each voxel as either tumor or nontumor, based on a description of that voxel.  ...  Our thanks to Dale Schuurmans for helpful discussions on problem formulation and to BTAP members for help in data processing.  ... 
doi:10.1007/978-3-540-85988-8_43 fatcat:kldyqdjsbjac7eeket6o2zp2ie

Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis [article]

Bingyu Xin, Yifan Hu, Yefeng Zheng, Hongen Liao
2020 arXiv   pre-print
We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.  ...  Most existing methods only focus on synthesizing missing images between two modalities, which limits their robustness and efficiency when multiple modalities are missing.  ...  (b) generator G learns to generate realistic target modality to fake D and segmentor S forces G to focus on cross modality mapping in tumor area.  ... 
arXiv:2005.00925v1 fatcat:i3yywzec5fbh7err3e3mmzvyou

Recognizing Deviations from Normalcy for Brain Tumor Segmentation [chapter]

David T. Gering, W. Eric L. Grimson, R. Kikinis
2002 Lecture Notes in Computer Science  
A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue.  ...  The technique is an extension of EM segmentation that considers information on five layers: voxel intensities, neighborhood coherence, intra-structure properties, inter-structure relationships, and user  ...  In contrast to existing methods for tumor segmentation, the hypothesis underlying our work is that we can segment brain tumors by focusing not on what typically represents tumor, but on what typically  ... 
doi:10.1007/3-540-45786-0_48 fatcat:lhmmcw7qzjfj7efsa5vo5dz75e

Analysis of Fast Adaptive Bilateral Filter and Morphological Segmentation on MRI Images

Deepanjali Joshi, Umesh Joshi
2019 International Journal of Advances in Computer Science and Technology  
The main focus of this paper is to review and analyze the effect of segmentation over different tumor images for different parts of the body.  ...  Segmentation of an image is one of the most conjoint scientific matter, essential technology and critical constraint for image investigation and dispensation.  ...  Instead of considering the whole data presented in an image all at once, it is better to focus on a certain region-based semantic object in image segmentation.  ... 
doi:10.30534/ijacst/2019/01842019 fatcat:qkt35yn5jjgq5odd4r6i3poqtq

A Review on Segmentation Techniques in Medical Images

Aayushi Priya, Rajeev Tiwari
2017 INTERNATIONAL JOURNAL ONLINE OF SCIENCE  
It is one of the most difficult and challenging tasks in image processing which determines the quality of the final result of the image analysis.  ...  Image segmentation is an essential but critical component in low level vision image analysis, pattern recognition, and in robotic systems.  ...  One of such is brain image segmentation which is quite complicated and challenging but its accurate segmentation is very important for detecting tumors, edema, and necrotic tissues.  ... 
doi:10.24113/ijoscience.v3i2.190 fatcat:m7jqegd37fctrmkepufjsf45hu

Optimal Statistical Structure Validation of Brain Tumors Using Refractive Index

Sushmit Ghosh, Soham Kundu, Sushovan Chowdhury, Aurpan Majumder
2015 Procedia Computer Science  
In this paper, we present this segmentation problem for the purpose of determining the exact location of brain tumour using refractive index study on the structural analysis of both tumorous and normal  ...  Till date automated brain tumour segmentation happens to be a difficult task due to the variance and complexity of tumour growth.  ...  The brain image with tumor turns asymmetric because tumor usually occurs in one cerebral hemisphere and doesn't occur symmetrically in the other.  ... 
doi:10.1016/j.procs.2015.07.412 fatcat:jjcysen5z5dqhkstm3oaspjtiu

A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection Segmentation [article]

Ngan Le, Kashu Yamazaki, Dat Truong, Kha Gia Quach, Marios Savvides
2020 arXiv   pre-print
The first objective is performed by our proposed contextual brain tumor detection network, which plays a role of an attention gate and focuses on the region around brain tumor only while ignoring the far  ...  In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segmentation.  ...  In our detection network, we focus on the brain tumor region and its near neighbors instead of far neighbors which are less correlated to the brain tumor or brain.  ... 
arXiv:2012.02073v1 fatcat:kscpmwxqwzdufacktt47xc4cda

Brain Tumors: How Can Images and Segmentation Techniques Help? [chapter]

Alejandro Veloz, Antonio Orellana, Juan Vielma, Rodrigo Salas, Steren Chabert
2011 Diagnostic Techniques and Surgical Management of Brain Tumors  
Techniques and Surgical Management of Brain Tumors www.intechopen.com Brain Tumors: How Can Images and Segmentation Techniques Help?  ...  Regarding brain tumor image processing, what is usually expected is to detect the localization and extension of the tumor, in other words to segment the tumor in the image. www.intechopen.com Diagnostic  ...  ., 2004) proposed a segmentation method based on evidence theory for brain tumors on MRI.  ... 
doi:10.5772/22466 fatcat:utzlbgyzxrb3dgcjee5bskoi2e

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Théo Estienne, Marvin Lerousseau, Maria Vakalopoulou, Emilie Alvarez Andres, Enzo Battistella, Alexandre Carré, Siddhartha Chandra, Stergios Christodoulidis, Mihir Sahasrabudhe, Roger Sun, Charlotte Robert, Hugues Talbot (+2 others)
2020 Frontiers in Computational Neuroscience  
In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly.  ...  In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation.  ...  Azoulay, and Gustave Roussy Cancer Campus DTNSI team for providing the infrastructure resources used in this work as well as Amazon Web Services for their partial support.  ... 
doi:10.3389/fncom.2020.00017 pmid:32265680 pmcid:PMC7100603 fatcat:nrvk7v5rdjhp3kbkh2liezxl3q

Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

Guohua Cheng, Hongli Ji
2020 IEEE Access  
The goal of brain tumor segmentation is to detect and localize tumor regions by comparing the tested brain tissue images to the normal brain tissue images [12] .  ...  tasks including lung segmentation [9] [10], brain tumor segmentation [11] , etc.  ... 
doi:10.1109/access.2020.3030235 fatcat:nycdixjkuvc7fg7yixi4cbsbfm

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Khurram Ejaz, Mohd Shafry Mohd Rahim, Muhammad Arif, Diana Izdrui, Daniela Maria Craciun, Oana Geman, M. Pallikonda Rajasekaran
2022 Contrast Media & Molecular Imaging  
Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid.  ...  The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction.  ...  If TR is applied to pixels in the neighborhood yield and the (x, y) value of output, then g is equal to the neighbor with an origin at (x, y). us, segmentation directly focuses on the characteristics of  ... 
doi:10.1155/2022/1541980 pmid:35919500 pmcid:PMC9293518 fatcat:5oed3yl2nnbghieoast7qf6hrq

Quantitative Multicomponent T2 Relaxation Showed Greater Sensitivity Than Flair Imaging to Detect Subtle Alterations at the Periphery of Lower Grade Gliomas

Pietro Bontempi, Umberto Rozzanigo, Dante Amelio, Daniele Scartoni, Maurizio Amichetti, Paolo Farace
2021 Frontiers in Oncology  
non-enhancing gliomas underwent T2 relaxation and FLAIR imaging before a radiation treatment by proton therapy (PT) and were examined at follow-up.  ...  images, with peri-tumoral areas of high T2 that typically extended outside the area of abnormal FLAIR hyper-intensity.  ...  However, the focus of the present study was on normal appearing brain regions on T2-weighted FLAIR imaging and, particularly, on the mismatch area identified by IEw T2 mapping.  ... 
doi:10.3389/fonc.2021.651137 pmid:33828992 pmcid:PMC8019971 fatcat:ehilw5znmzhsnhusm6db7xsewy

MACHINE INTELLIGENCE APPROACH FOR OPTIMIZATION OF CRANIAL TUMOR IMAGE
English

Tamsekar PB, Gomase VS
2009 International Journal of Machine Intelligence  
Image analysis technology has shown new advancements in the field of biomedical research and diagnosis, it allows studying and understanding tumor activities and interactions in malignancies or diseases  ...  Image optimization is a powerful tool has multiple applications both in clinical and cellular and molecular biology arenas.  ...  We focus on two important issues; the first issue is related to automatic gridding and spot segmentation for tumor images.  ... 
doi:10.9735/0975-2927.1.2.50-54 fatcat:q7uvqkyemjcxplgloihkwysngy

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies

Lior Weizman, Liat Ben Sira, Leo Joskowicz, Daniel L. Rubin, Kristen W. Yeom, Shlomi Constantini, Ben Shofty, Dafna Ben Bashat
2014 Medical Physics (Lancaster)  
Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve followup of brain tumors, with indolent growth and behavior.  ...  The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans.  ...  A variety of automatic methods for the segmentation and classification of brain tumors have been recently published. 3, [9] [10] [11] [12] The International Conference on Medical Image Computing and  ... 
doi:10.1118/1.4871040 pmid:24784396 pmcid:PMC4000396 fatcat:h2webe6yond45fxucouuca73vq

Predictive modeling in glioma grading from MR perfusion images using support vector machines

Kyrre E. Emblem, Frank G. Zoellner, Bjorn Tennoe, Baard Nedregaard, Terje Nome, Paulina Due-Tonnessen, John K. Hald, David Scheie, Atle Bjornerud
2008 Magnetic Resonance in Medicine  
Acknowledgements: We thank Bard Nedregaard, MD, and Bjorn Tennoe, MD, from the Clinic for Imaging and Intervention, Rikshospitalet University Hospital, Oslo, Norway for selecting white matter areas and  ...  Brain Tumor Segmentation using Knowledge-based Fuzzy Clustering.  ...  Hence, although not a focus of Paper II, the manual and automatic segmentation routine used in our study may not be an adequate measure of tumor volume for quantitative assessment of tumor growth and for  ... 
doi:10.1002/mrm.21736 pmid:18816815 fatcat:5ai63xhrhzb7pl5bgpa7ddlwua
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