Non linear image analysis for fuzzy classification of breast cancer

E. Martinez Marroquin; C. Vos; E. Santamaria; X. Jove; J. C. Socoro

Non linear image analysis for fuzzy classification of breast cancer

E. Martinez Marroquin^*
, C. Vos
, E. Santamaria
, X. Jove
, J. C. Socoro

^*Corresponding author for this work

La Salle Digital Engineering School

Research output: Conference paper › Contribution › peer-review

5 Citations (Scopus)

Abstract

Non linear image processing proves to be a powerful tool for segmenting images preserving only the interesting regions; those are the cells' nucleous (CN) in this application. All CN are preserved and identified even if they are in a cluster, while the rest of the image is considered to be part of the background. The morphological segmentation makes it possible to calculate outstanding features that could not be obtained by simple observation. These are passed to a fuzzy classifier which decides the probability of the biopsy to belong to a high or low cancer level. Obtaining these measures by human observation is a hard, and nonprecise task. The extracted features will make it possible to reach a parametric classification that is more efficient than the subjective classification made by human observation.

Original language	English
Pages	943-946
Number of pages	4
Publication status	Published - 1996
Event	Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz Duration: 16 Sept 1996 → 19 Sept 1996

Conference

Conference	Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
City	Lausanne, Switz
Period	16/09/96 → 19/09/96

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

@conference{f0819a8e83044d2891511dbc03e3faec,

title = "Non linear image analysis for fuzzy classification of breast cancer",

abstract = "Non linear image processing proves to be a powerful tool for segmenting images preserving only the interesting regions; those are the cells' nucleous (CN) in this application. All CN are preserved and identified even if they are in a cluster, while the rest of the image is considered to be part of the background. The morphological segmentation makes it possible to calculate outstanding features that could not be obtained by simple observation. These are passed to a fuzzy classifier which decides the probability of the biopsy to belong to a high or low cancer level. Obtaining these measures by human observation is a hard, and nonprecise task. The extracted features will make it possible to reach a parametric classification that is more efficient than the subjective classification made by human observation.",

author = "Marroquin, \{E. Martinez\} and C. Vos and E. Santamaria and X. Jove and Socoro, \{J. C.\}",

year = "1996",

language = "English",

pages = "943--946",

note = "Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) ; Conference date: 16-09-1996 Through 19-09-1996",

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TY - CONF

T1 - Non linear image analysis for fuzzy classification of breast cancer

AU - Marroquin, E. Martinez

AU - Vos, C.

AU - Santamaria, E.

AU - Jove, X.

AU - Socoro, J. C.

PY - 1996

Y1 - 1996

N2 - Non linear image processing proves to be a powerful tool for segmenting images preserving only the interesting regions; those are the cells' nucleous (CN) in this application. All CN are preserved and identified even if they are in a cluster, while the rest of the image is considered to be part of the background. The morphological segmentation makes it possible to calculate outstanding features that could not be obtained by simple observation. These are passed to a fuzzy classifier which decides the probability of the biopsy to belong to a high or low cancer level. Obtaining these measures by human observation is a hard, and nonprecise task. The extracted features will make it possible to reach a parametric classification that is more efficient than the subjective classification made by human observation.

AB - Non linear image processing proves to be a powerful tool for segmenting images preserving only the interesting regions; those are the cells' nucleous (CN) in this application. All CN are preserved and identified even if they are in a cluster, while the rest of the image is considered to be part of the background. The morphological segmentation makes it possible to calculate outstanding features that could not be obtained by simple observation. These are passed to a fuzzy classifier which decides the probability of the biopsy to belong to a high or low cancer level. Obtaining these measures by human observation is a hard, and nonprecise task. The extracted features will make it possible to reach a parametric classification that is more efficient than the subjective classification made by human observation.

UR - https://www.scopus.com/pages/publications/0030418775

M3 - Contribution

AN - SCOPUS:0030418775

SP - 943

EP - 946

T2 - Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)

Y2 - 16 September 1996 through 19 September 1996

ER -

Non linear image analysis for fuzzy classification of breast cancer

Abstract

Conference

UN SDGs

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