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Segmentation of medical images combining local, regional, global, and hierarchical distances into a bottom-up region merging scheme

Thomas M. Lehmann, Daniel Beier, Christian Thies, Thomas Seidl, J. Michael Fitzpatrick, Joseph M. Reinhardt
2005 Medical Imaging 2005: Image Processing  
Local, regional, global, and hierarchical components are combined task-specifically guiding the iterative region merging process.  ...  Region merging based on a local model fails to detect most bones, while a correct localization and delineation is obtained with the combined model.  ...  Le 1108/4 and Le 1108/6.  ... 
doi:10.1117/12.595468 dblp:conf/miip/LehmannBT005 fatcat:4sdwhppa7bgt7g3pjil3djfm4a

Hierarchical Segmentation Satisfying Constraints

L. D. Griffin, A. C. F. Colchester, S. A. Roll, C. S. Studholme
1994 Procedings of the British Machine Vision Conference 1994  
A new hierarchical segmentation algorithm is described. Its computational complexity and memory requirements are detailed, showing it to be practicably applicable to images of useful size.  ...  A simple modification of the algorithm adapts it to produce hierarchical segmentations that satisfy a constraint set.  ...  Hierarchical Segmentation A hierarchical segmentation of an image is a tree structure by inclusion of connected image regions.  ... 
doi:10.5244/c.8.13 dblp:conf/bmvc/GriffinCRS94 fatcat:a5qpxzu55ngj3o5nsizyzwwshu

Low level image partitioning guided by the gradient watershed hierarchy

I.E. Pratikakis, H. Sahli, J. Cornelis
1999 Signal Processing  
For both algorithms, a stopping criterion which de"nes and extracts automatically each hierarchical level is proposed.  ...  The proposed bottom-up procedure provides an essential aid to the precise classi"cation of anatomical objects when used together with a user-interface environment. 1999 Elsevier Science B.V.  ...  Introduction A meaningful image segmentation groups the pixels into disjoint regions that consist of uniform components.  ... 
doi:10.1016/s0165-1684(98)00232-1 fatcat:cpcufivfzzc7jawwzp7yjver7e

KL divergence based agglomerative clustering for automated Vitiligo grading

Mithun Das Gupta, Srinidhi Srinivasa, J. Madhukara, Meryl Antony
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper we present a symmetric KL divergence based agglomerative clustering framework to segment multiple levels of depigmentation in Vitiligo images.  ...  We introduce albedo and reflectance fields as features for the distance computations. We compare against other established methods to bring out possible pros and cons of the proposed method.  ...  Extension to Vitiligo image segmentation We develop a Vitiligo image segmentation routine based on the bottom up hierarchical agglomerative clustering algorithm developed in the previous section.  ... 
doi:10.1109/cvpr.2015.7298886 dblp:conf/cvpr/GuptaSMA15 fatcat:os3gvi6efvg3xgljdclfkzmnwa

Adaptive strategy for superpixel-based region-growing image segmentation

Mahaman Sani Chaibou, Pierre-Henri Conze, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub
2017 Journal of Electronic Imaging (JEI)  
From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions.  ...  Secondly, we propose a global merging strategy to efficiently guide the region merging process.  ...  This hierarchical tree is constructed in a bottom-up fashion. Two similar neighbor regions are grouped into one parent region node.  ... 
doi:10.1117/1.jei.26.6.061605 fatcat:4vmtybmlc5duxgf2xqgts44qfu

Figure-Ground Segmentation Techniques

2015 International Journal of Science and Research (IJSR)  
Image segmentation is the process of dividing an image into multiple segments, such that the representation of an image changes, which makes analysis of image easier.  ...  This paper deals with survey of various figureground segmentation techniques and efficient approaches for object recognition.  ...  Regional contrast (RC) evaluates global contrast differences and spatial coherence. Two-Stage Scheme(TSS) is used for bottom-up saliency detection.  ... 
doi:10.21275/v4i11.nov151567 fatcat:n343yvs7mfdhdjvtmeigh2x6wu

Learning to Segment Neurons with Non-local Quality Measures [chapter]

Thorben Kroeger, Shawn Mikula, Winfried Denk, Ullrich Koethe, Fred A. Hamprecht
2013 Lecture Notes in Computer Science  
Segmentation schemes such as hierarchical region merging or correllation clustering rely on edge weights between adjacent (super-)voxels.  ...  This ignores that a few local mistakes (tiny boundary gaps) can cause catastrophic global segmentation errors.  ...  We would like to thank Harald Hess and C. Shan Xu at Janelia Farm Howard Hughes Medical Institute for providing the drosophila dataset.  ... 
doi:10.1007/978-3-642-40763-5_52 fatcat:batec4i7j5h5netavqko2jlz2a

A Randomized Ensemble Approach to Industrial CT Segmentation

Hyojin Kim, Jayaraman J. Thiagarajan, Peer-Timo Bremer
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
Tuning the models and parameters of common segmentation approaches is challenging especially in the presence of noise and artifacts.  ...  We demonstrate the effectiveness of our approach using a set of noise and artifact rich CT images from baggage security and show that it significantly outperforms existing solutions in this area.  ...  Instead, we introduce a new ensemble based segmentation framework that uses a simple bottom-up hierarchical segmentation with a randomized merge order to create multiple hypotheses, similar to the approach  ... 
doi:10.1109/iccv.2015.199 dblp:conf/iccv/KimTB15 fatcat:zltkelzusvh25nzb7dz6cwfvee

Scene analysis with structural prototypes for content-based image retrieval in medicine

Benedikt Fischer, Michael Sauren, Mark O. Güld, Thomas M. Deserno, Joseph M. Reinhardt, Josien P. W. Pluim
2008 Medical Imaging 2008: Image Processing  
The content of medical images can often be described as a composition of relevant objects with distinct relationships.  ...  As new image content is represented by hierarchical attributed region adjacency graphs (HARAGs) which are obtained by region-growing, the task of object or scene identification corresponds to the problem  ...  We also acknowledge the contributions of Ilja Bezrukov to the graph matching framework.  ... 
doi:10.1117/12.770541 dblp:conf/miip/FischerSGD08 fatcat:2querfke2bguzh6v2hho6nvo5i

Anomaly detection and target prioritization in planetary imagery via the automated global feature analyzer (AGFA): Progress towards a driver for autonomous C4ISR missions

Wolfgang Fink, Alexander J. W. Brooks, Mark A. Tarbell, M. Saif Islam, Achyut K. Dutta, Thomas George
2018 Micro- and Nanotechnology Sensors, Systems, and Applications X  
Imaged operational areas are locally processed via a cascade of image segmentation, visual and geometric feature extraction, agglomerative clustering, and principal components analysis.  ...  Anomalous regions may be considered immediate targets for follow-up in-situ investigation by local robotic agents, which can be directed via autonomous telecommanding, e.g., as part of a Tier-Scalable  ...  Image Segmentation An image is partitioned into superpixels using the SLIC algorithm, which consists of localized clustering in a 5D space composed of X-and Y-coordinates and three LAB color space values  ... 
doi:10.1117/12.2303795 fatcat:4mgglpdf55akvcxklcjd5txp4i

Review of Graph, Medical and Color Image base Segmentation Techniques

Patel Janakkumar Baldevbhai
2012 IOSR Journal of Electrical and Electronics Engineering  
Rather than focusing on local features and their consistencies in the image data, their approach aims at extracting the global impression of an image.  ...  In order to capture the colour-texture content, Rosenfeld et al. [16] calculated the absolute difference distributions of pixels in multi-band images, while Hild et al. [17] proposed a bottom-up segmentation  ...  Next, the extraction of the colour and local edge pattern histograms are performed. P.Nammalwar et al. [43, 44] presents a similar strategy for a split and merge segmentation scheme.  ... 
doi:10.9790/1676-0110119 fatcat:qavtttve6bbq5bha7whhlgdco4

Image segmentation based on the integration of colour–texture descriptors—A review

Dana E. Ilea, Paul F. Whelan
2011 Pattern Recognition  
We conclude with a discussion that samples our views on the field of colour-texture image segmentation and this is complemented with an examination of the potential future directions of research.  ...  Over the past three decades a substantial number of techniques in the field of colour-texture segmentation have been reported and it is the aim of this article to thoroughly evaluate and categorise the  ...  [23] proposed a bottom-up segmentation framework where the colour and texture feature vectors were separately extracted and then combined for knowledge indexing.  ... 
doi:10.1016/j.patcog.2011.03.005 fatcat:6zdfsvzq7vgafgemz3x2aslomu

Scale-dependent hierarchical unsupervised segmentation of textured images

Antonio Bandera, Cristina Urdiales, Fabián Arrebola, Francisco Sandoval
2001 Pattern Recognition Letters  
The method achieves low error rates and it does not require any knowledge about the number of textures in the image. Ó  ...  The main novelty of the approach is that spatial proximity has a progressively decreasing importance on lower resolution levels when hierarchically segmenting the data structure.  ...  Unsupervised clustering Once the image has been split into regions of roughly uniform texture, we apply a hierarchical clustering method to merge similar regions until a fusion criterion fails.  ... 
doi:10.1016/s0167-8655(00)00103-3 fatcat:nnp6lo5ldvavrewk7ouy3mmbq4

Classifying image analysis techniques from their output

C. Guada, D. Gómez, J.T. Rodríguez, J. Yáñez, J. Montero
2016 International Journal of Computational Intelligence Systems  
In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.  ...  Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed  ...  Acknowledgments This research has been partially supported by the Government of Spain, grant TIN2015-66471-P, and by the Government of the Community of Madrid, grant S2013/ICE-2845 (CASI-CAM-CM).  ... 
doi:10.1080/18756891.2016.1180819 fatcat:lhyv2aqc2fejxpitati6s7hswa

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

G. Scarpa, R. Gaetano, M. Haindl, J. Zerubia
2009 IEEE Transactions on Image Processing  
In this work, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images.  ...  ) which takes into account both region scale and inter-region interactions.  ...  The authors are also grateful to the authors of the SWA algorithm [19] for providing their segmentation tool. Finally, a special thank goes to Prof. G.  ... 
doi:10.1109/tip.2009.2020534 pmid:19447707 fatcat:kpcwy7wswbgebimdxalaemk7du
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