Adaptive λenhancement: Type i versus type ii fuzzy implementation

Hamid R. Tizhoosh

doi:10.1109/CIIP.2009.4937872

Adaptive λenhancement: Type i versus type ii fuzzy implementation

Hamid R. Tizhoosh

Artificial Intelligence and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

λ-enhancement, introduced by Tizhoosh et al., is a contrast adjustment technique that uses involutive fuzzy complements to find the best gray-level transformation in order to increase the image contrast. Applied on medical images, λ-enhancement can provide good results with respect to visually perceived improvement of object-background discrimination. In this work, we provide two extensions of λ-enhancement. First we extend it to employ interval-valued fuzzy sets (special case of type II fuzzy sets), and second, we provide an adaptive version of both regular (type I) and interval-value (type II) fuzzy λ-enhancement. Using breast ultrasound images, we demonstrate the enhancement effect and compare them with the well-established CLAHE method (contrast-limited adaptive histogram equalization).

Original language	English (US)
Title of host publication	2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Processing
Pages	1-7
Number of pages	7
DOIs	https://doi.org/10.1109/CIIP.2009.4937872
State	Published - 2009
Event	2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Nashville, TN, United States Duration: Mar 30 2009 → Apr 2 2009

Publication series

Name	2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Proceedings

Conference

Conference	2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009
Country/Territory	United States
City	Nashville, TN
Period	3/30/09 → 4/2/09

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Vision and Pattern Recognition

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10.1109/CIIP.2009.4937872

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Adaptive λenhancement: Type i versus type ii fuzzy implementation. / Tizhoosh, Hamid R.
2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Processing. 2009. p. 1-7 4937872 (2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tizhoosh, HR 2009, Adaptive λenhancement: Type i versus type ii fuzzy implementation. in 2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Processing., 4937872, 2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009 - Proceedings, pp. 1-7, 2009 IEEE Symposium on Computational Intelligence in Image Processing, CIIP 2009, Nashville, TN, United States, 3/30/09. https://doi.org/10.1109/CIIP.2009.4937872

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abstract = "λ-enhancement, introduced by Tizhoosh et al., is a contrast adjustment technique that uses involutive fuzzy complements to find the best gray-level transformation in order to increase the image contrast. Applied on medical images, λ-enhancement can provide good results with respect to visually perceived improvement of object-background discrimination. In this work, we provide two extensions of λ-enhancement. First we extend it to employ interval-valued fuzzy sets (special case of type II fuzzy sets), and second, we provide an adaptive version of both regular (type I) and interval-value (type II) fuzzy λ-enhancement. Using breast ultrasound images, we demonstrate the enhancement effect and compare them with the well-established CLAHE method (contrast-limited adaptive histogram equalization).",

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AB - λ-enhancement, introduced by Tizhoosh et al., is a contrast adjustment technique that uses involutive fuzzy complements to find the best gray-level transformation in order to increase the image contrast. Applied on medical images, λ-enhancement can provide good results with respect to visually perceived improvement of object-background discrimination. In this work, we provide two extensions of λ-enhancement. First we extend it to employ interval-valued fuzzy sets (special case of type II fuzzy sets), and second, we provide an adaptive version of both regular (type I) and interval-value (type II) fuzzy λ-enhancement. Using breast ultrasound images, we demonstrate the enhancement effect and compare them with the well-established CLAHE method (contrast-limited adaptive histogram equalization).

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Adaptive λenhancement: Type i versus type ii fuzzy implementation

Abstract

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