Seeded region growing

doi:10.1109/34.295913

Home
/
Papers
/
Seeded region growing

Journal Article•DOI•

Seeded region growing

Richard Adams¹, Leanne Bischof¹•Institutions (1)

Commonwealth Scientific and Industrial Research Organisation¹

01 Jun 1994-IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE Computer Society)-Vol. 16, Iss: 6, pp 641-647

TL;DR: This correspondence presents a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters, and suggests two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.

read less

Abstract: We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation

[...]

Song-Chun Zhu¹, Alan L. Yuille²•Institutions (2)

Harvard University¹, Smith-Kettlewell Institute²

01 Sep 1996-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A novel statistical and variational approach to image segmentation based on a new algorithm, named region competition, derived by minimizing a generalized Bayes/minimum description length (MDL) criterion using the variational principle is presented.

...read moreread less

Abstract: We present a novel statistical and variational approach to image segmentation based on a new algorithm, named region competition. This algorithm is derived by minimizing a generalized Bayes/minimum description length (MDL) criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and combines aspects of snakes/balloons and region growing. The classic snakes/balloons and region growing algorithms can be directly derived from our approach. We provide theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions, and the relationship to edge detection using filters. It is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images, color images and texture images. The novel color model allows us to eliminate intensity gradients and shadows, thereby obtaining segmentation based on the albedos of objects. It also helps detect highlight regions.

...read moreread less

2,181 citations

Journal Article•

STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS

[...]

Sandrine Dudoit, Y ee Hwa Yang, Matthew J. Callow, Terence P. Speed

01 Jan 2002-Statistica Sinica

TL;DR: Differentially expressed genes are identified based on adjusted p-values for a multiple testing procedure which strongly controls the family-wise Type I error rate and takes into account the dependence structure between the gene expression levels.

...read moreread less

Abstract: DNA microarrays are a new and promising biotechnology which allows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. Although it is not the main focus of the paper, new methods for the important pre-processing steps of image analysis and normalization are proposed. Given suitably normalized data, the biological question of differential expression is restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and responses or covariates of interest. Differentially expressed genes are identified based on adjusted p-values for a multiple testing procedure which strongly controls the family-wise Type I error rate and takes into account the dependence structure between the gene expression levels. No specific parametric form is assumed for the distribution of the test statistics and a permutation procedure is used to estimate adjusted p-values. Several data displays are suggested for the visual identification of differentially expressed genes and of important features of these genes. The above methods are applied to microarray data from a study of gene expression in the livers of mice with very low HDL cholesterol levels. The genes identified using data from multiple slides are compared to those identified by recently published single-slide methods.

...read moreread less

1,514 citations

Cites methods from "Seeded region growing"

...The segmentation component is based on the seeded region growing algorithm of Adams and Bischof (1994) and places no restriction on the size or shape of the spots. The background adjustment method relies on a non-linear filter known as morphological opening to generate an image of the estimated background intensity for the entire slide. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing (Buckley (2000), Ihaka and Gentleman (1996)). A detailed discussion of the proposed image analysis methods and a comparison to popular alternatives can be found in Yang, Buckley, Dudoit and Speed (2001a). Thus, starting with two images for each slide, the image processing steps outlined above produce two main quantities for each spot on the array: the red and green fluorescence intensities, R and G, which are measures of transcript abundance for the red and green labeled mRNA samples, respectively....
[...]
...The segmentation component is based on the seeded region growing algorithm of Adams and Bischof (1994) and places no restriction on the size or shape of the spots. The background adjustment method relies on a non-linear filter known as morphological opening to generate an image of the estimated background intensity for the entire slide. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing (Buckley (2000), Ihaka and Gentleman (1996))....
[...]
...The segmentation component is based on the seeded region growing algorithm of Adams and Bischof (1994) and places no restriction on the size or shape of the spots....
[...]
...The segmentation component is based on the seeded region growing algorithm of Adams and Bischof (1994) and places no restriction on the size or shape of the spots. The background adjustment method relies on a non-linear filter known as morphological opening to generate an image of the estimated background intensity for the entire slide. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing (Buckley (2000), Ihaka and Gentleman (1996)). A detailed discussion of the proposed image analysis methods and a comparison to popular alternatives can be found in Yang, Buckley, Dudoit and Speed (2001a). Thus, starting with two images for each slide, the image processing steps outlined above produce two main quantities for each spot on the array: the red and green fluorescence intensities, R and G, which are measures of transcript abundance for the red and green labeled mRNA samples, respectively. 2.2. Single-slide data displays Single-slide expression data are typically displayed by plotting the log intensity log2 R in the red channel vs. the log intensity log2 G in the green channel (Newton et al. (2001), Sapir and Churchill (2000), Schena (2000))....
[...]
...The segmentation component is based on the seeded region growing algorithm of Adams and Bischof (1994) and places no restriction on the size or shape of the spots. The background adjustment method relies on a non-linear filter known as morphological opening to generate an image of the estimated background intensity for the entire slide. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing (Buckley (2000), Ihaka and Gentleman (1996)). A detailed discussion of the proposed image analysis methods and a comparison to popular alternatives can be found in Yang, Buckley, Dudoit and Speed (2001a). Thus, starting with two images for each slide, the image processing steps outlined above produce two main quantities for each spot on the array: the red and green fluorescence intensities, R and G, which are measures of transcript abundance for the red and green labeled mRNA samples, respectively. 2.2. Single-slide data displays Single-slide expression data are typically displayed by plotting the log intensity log2 R in the red channel vs. the log intensity log2 G in the green channel (Newton et al. (2001), Sapir and Churchill (2000), Schena (2000)). (It is preferable to work with logged intensities rather than absolute intensities for a number of reasons, including the facts that: (i) the variation of logged intensities and ratios of intensities is less dependent on absolute magnitude; (ii) normalization is usually additive for logged intensities; (iii) taking logs evens out highly skewed distributions; and (iv) taking logs gives a more realistic sense of variation. Logarithms base 2 are used instead of natural or decimal logarithms as intensities are typically integers between 0 and 216 − 1.) We find that such log2 R vs. log2 G plots give an unrealistic sense of concordance between the red and green intensities and can mask interesting features of the data. We thus prefer to plot the intensity log-ratio M = log2 R/G vs. the mean log intensity A = log2 √ RG (a similar display was used in Roberts et al. (2000))....
[...]

Proceedings Article•DOI•

Ilastik: Interactive learning and segmentation toolkit

[...]

Christoph Sommer¹, Christoph N. Straehle¹, Ullrich Köthe¹, Fred A. Hamprecht¹•Institutions (1)

Heidelberg University¹

09 Jun 2011

TL;DR: Ilastik as mentioned in this paper is an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way, based on labels provided by the user through a convenient mouse interface.

...read moreread less

Abstract: Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.

...read moreread less

1,158 citations

Cites background from "Seeded region growing"

...The majority of interactive segmentation approaches are based on user seeds [2, 3, 4, 5, 6, 7]....
[...]

Journal Article•DOI•

Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

[...]

Nikos Paragios¹, Rachid Deriche²•Institutions (2)

Princeton University¹, French Institute for Research in Computer Science and Automation²

11 Feb 2002-International Journal of Computer Vision

TL;DR: A novel variational framework to deal with frame partition problems in Computer Vision that exploits boundary and region-based segmentation modules under a curve-based optimization objective function is presented.

...read moreread less

Abstract: This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.

...read moreread less

867 citations

Cites background or methods from "Seeded region growing"

...– The region-based segmentation techniques are more suitable for segmenting the textured images and can be roughly classified into two categories: The regiongrowing techniques has been widely used (Chen and Pavlidis, 1979; Raafat and Wong, 1988; Reed et al., 1990; Adams and Bischof, 1994; Leonardis et al., 1995)....
[...]
...…segmentation techniques are more suitable for segmenting the textured images and can be roughly classified into two categories: The regiongrowing techniques has been widely used (Chen and Pavlidis, 1979; Raafat and Wong, 1988; Reed et al., 1990; Adams and Bischof, 1994; Leonardis et al., 1995)....
[...]

Journal Article•DOI•

Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery

[...]

Qian Yu, Peng Gong, Nicholas Clinton, Greg S. Biging, Maggi Kelly, Dave Schirokauer - Show less +2 more

01 Jul 2006-Photogrammetric Engineering and Remote Sensing

TL;DR: In this paper, the authors evaluated the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data.

...read moreread less

Abstract: In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a non-parametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixel-based maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The objectbased classification approach overcame the problem of saltand-pepper effects found in classification results from traditional pixel-based approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computer-assisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present ground-based inventory of National Park lands.

...read moreread less

767 citations

Cites background from "Seeded region growing"

...Region growing requires consideration of seed selection, growing criteria, and processing order (Beaulieu and Goldberg, 1989; Gambotto, 1993; Adams and Bischof, 1994; Lemoigne and Tilton, 1995; Mehnert and Jackway, 1997)....
[...]

1
2
3
4
…
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200

Collapse

References

PDF

Open Access

More filters

Book•

Image Analysis and Mathematical Morphology

[...]

Jean Serra

11 Feb 1984

TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.

...read moreread less

Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.

...read moreread less

9,566 citations

Book•

Computer vision

[...]

Dana H. Ballard, Chris Brown

01 Jan 1982

5,834 citations

Journal Article•DOI•

Watersheds in digital spaces: an efficient algorithm based on immersion simulations

[...]

Luc Vincent¹, Pierre Soille¹•Institutions (1)

Mines ParisTech¹

01 Jun 1991-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced, based on an immersion process analogy, which is reported to be faster than any other watershed algorithm.

...read moreread less

Abstract: A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided. >

...read moreread less

4,983 citations

Journal Article•

Computer vision

[...]

Azriel Rosenfeld¹•Institutions (1)

University of Maryland, College Park¹

01 Aug 1988-Advances in Computers

TL;DR: How the field of computer (and robot) vision has evolved, particularly over the past 20 years, is described, and its central methodological paradigms are introduced.

...read moreread less

3,112 citations

Journal Article•DOI•

A survey of thresholding techniques

[...]

Prasanna K. Sahoo¹, S. Soltani¹, Andrew K. C. Wong¹, Y. C. Chen¹•Institutions (1)

University of Waterloo¹

01 Feb 1988-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing

TL;DR: This paper presents a survey of thresholding techniques and attempts to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures.

...read moreread less

Abstract: In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.

...read moreread less

2,771 citations