A survey on image segmentation

doi:10.1016/0031-3203(81)90028-5

Pattern Recognition

Volume 13, Issue 1, 1981, Pages 3-16

https://doi.org/10.1016/0031-3203(81)90028-5 Get rights and content

Abstract

For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques. In the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection.

References (97)

S.W. Zucker
Region growing: childhood and adolescence
Computer Graphics and Image Processing
(1976)
W.A. Yasnoff et al.
Error measures for scene segmentation
Pattern Recognition
(1977)
J.S. Weszka
A survey of threshold selection techniques
Computer Graphics and Image Processing
(1978)
S. Watanabe
An automated apparatus for cancer prescreening: CYBEST
Computer Graphics and Image Processing
(1974)
S. Tsuji et al.
A structural analyzer for a class of textures
Computer Graphics and Image Processing
(1973)
L.S. Davis
A survey of edge detection techniques
Computer Graphics and Image Processing
(1975)
E.M. Riseman et al.
Computational techniques in the visual segmentation of static scenes
Computer Graphics and Image Processing
(1977)
R. Kirsch
Computer determination of the constituent structure of biological images
Computer biomed. Res.
(1971)
T. Kasvand
Iterative edge detection
Computer Graphics and Image Processing
(1975)
A. Martelli
Edge detection using heuristic search methods
Computer Graphics and Image Processing
(1972)

S.D. Shapiro

Transformations for the computer detection of curves in noisy pictures

Computer Graphics and Image Processing

(1975)

C. Brice et al.

Scene analysis using regions

Artificial Intell.

(1970)

J.A. Feldman et al.

Decision theory and artificial intelligence, I. A semantics based region analyzer

Artificial Intell.

(1974)

T. Pavlidis

Segmentation of pictures and maps through functional approximation

Computer Graphics and Image Processing

(1972)

A. Rosenfeld et al.

Digital Picture Processing

(1976)

A. Rosenfeld

Picture processing by computer

Computing Surveys

(1969)

A. Rosenfeld

Progress in picture processing: 1969–1971

Computings Surveys

(1973)

A. Rosenfeld

Picture processing: 1972

Computer Graphics and Image Processing

(1972)

A. Rosenfeld

Picture processing: 1973

Computer Graphics and Image Processing

(1974)

A. Rosenfeld

Picture processing: 1974

Computer Graphics and Image Processing

(1975)

A. Rosenfeld

Picture processing: 1975

Computer Graphics and Image Processing

(1976)

A. Rosenfeld

Picture processing: 1976

Computer Graphics and Image Processing

(1977)

A. Rosenfeld

Picture processing: 1977

Computer Graphics and Image Processing

(1978)

K.P. Kruger et al.

Computer diagnosis of pneumoconiosis

IEEE Trans. Systems, Man Cybernet. SMC-4

(1974)

J.R. Green

Parallel processing in a pattern recognition based image processing systems: The Abbott ADC-500 differential counter

P.H. Bartels et al.

High resolution prescreening systems for cervical cancer

K.R. Castleman et al.

An automated system for chromosome analysis

S.L. Horowitz et al.

Picture segmentation by a directed split-and-merge procedure

T. Pavlidis

Structural Pattern Recognition

(1977)

W. Doyle

Operations useful for similarity-invariant pattern recognition

J. Ass. comput. Mach.

(1962)

J.M.S. Prewitt et al.

The analysis of cell images

Trans. N.Y. Acad. Sci.

(1966)

J.S. Weszka et al.

A threshold selection technique

IEEE Trans. Comput. C-23

(1974)

J.S. Weszka et al.

Threshold Selection Techniques 5

A. Rosenfeld et al.

Iterative histogram modification

J.S. Weszka et al.

Threshold selection techniques 2

R.K. Aggarwal et al.

A multi-spectral approach for scene analysis of cervical cytology smears

J. Histochem. Cytochem.

(1977)

L.S. Davis et al.

Region extraction by averaging and thresholding

IEEE Trans. Systems, Man Cybernet.

(1975)

S.W. Zucker et al.

Picture segmentation by texture discrimination

IEEE Trans. Comput.

(1975)

R.B. Ohlander

Analysis of natural scenes

F. Tomita et al.

Detection of homogeneous regions by structural analysis

J. Keng et al.

A syntax-directed method for land-use classification of LANDSAT images

I.T. Young et al.

Localization of cellular structures

IEEE Trans. biomed. Engng BME-22

(1975)

M. Ingram et al.

Automatic analysis of blood cells

Sci. Am.

(1970)

J.F. Brenner

Scene segmentation techniques for the analysis of routine bone marrow smears from acute lymphoblastic leukemia patients

J. Histochem. Cytochem.

(1977)

A. Rosenfeld et al.

Picture recognition and scene analysis

Computer

(1976)

J. Keng

Syntactic algorithms for image segmentation and a special computer architecture for image processing

A.G. Wacker

A cluster approach to finding spatial boundaries in multispectral imagery

P.H. Swain et al.

On the applications of nonparametric techniques to crop classification problems

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^☆: This work was supported by the National Science Foundation Grant ENG 78-16970.

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A survey on image segmentation☆

Abstract

Computer Graphics and Image Processing

Pattern Recognition

Computer Graphics and Image Processing

Computer Graphics and Image Processing

Computer Graphics and Image Processing

Computer Graphics and Image Processing

Computer Graphics and Image Processing

Computer biomed. Res.

Computer Graphics and Image Processing

Computer Graphics and Image Processing

Computer Graphics and Image Processing

Artificial Intell.

Artificial Intell.

Computer Graphics and Image Processing

Digital Picture Processing

Picture processing by computer

Computing Surveys

Progress in picture processing: 1969–1971

Computings Surveys

Picture processing: 1972

Computer Graphics and Image Processing

Picture processing: 1973

Computer Graphics and Image Processing

Picture processing: 1974

Computer Graphics and Image Processing

Picture processing: 1975

Computer Graphics and Image Processing

Picture processing: 1976

Computer Graphics and Image Processing

Picture processing: 1977

Computer Graphics and Image Processing

Computer diagnosis of pneumoconiosis

IEEE Trans. Systems, Man Cybernet. SMC-4

Parallel processing in a pattern recognition based image processing systems: The Abbott ADC-500 differential counter

High resolution prescreening systems for cervical cancer

An automated system for chromosome analysis

Picture segmentation by a directed split-and-merge procedure

Structural Pattern Recognition

Operations useful for similarity-invariant pattern recognition

J. Ass. comput. Mach.

The analysis of cell images

Trans. N.Y. Acad. Sci.

A threshold selection technique

IEEE Trans. Comput. C-23

Threshold Selection Techniques 5

Iterative histogram modification

Threshold selection techniques 2

A multi-spectral approach for scene analysis of cervical cytology smears

J. Histochem. Cytochem.

Region extraction by averaging and thresholding

IEEE Trans. Systems, Man Cybernet.

Picture segmentation by texture discrimination

IEEE Trans. Comput.

Analysis of natural scenes

Detection of homogeneous regions by structural analysis

A syntax-directed method for land-use classification of LANDSAT images

Localization of cellular structures

IEEE Trans. biomed. Engng BME-22

Automatic analysis of blood cells

Sci. Am.

Scene segmentation techniques for the analysis of routine bone marrow smears from acute lymphoblastic leukemia patients

J. Histochem. Cytochem.

Picture recognition and scene analysis

Computer

Syntactic algorithms for image segmentation and a special computer architecture for image processing

A cluster approach to finding spatial boundaries in multispectral imagery

On the applications of nonparametric techniques to crop classification problems