On fuzzy enhancement of megavoltage images in radiation therapy

Tizhoosh, Hamid R.; Krell, Gerald; Michaelis, Bernd

doi:10.1109/fuzzy.1997.619748

Cited by 33 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• Since we only require and compute the partial histogram instead of the complete histogram to determine the fuzzy partitioning of the input domain, the proposed method computationally compares favourably with that proposed in Tizhoosh et al, [23].…”

Section: Advantages Of the Proposed Methodsmentioning

confidence: 54%

“…Also, as mentioned in Tizhoosh et al, [23], their work was primarily intended to show the potential of fuzzy rule-based approach for image enhancement and was not further explored in a detailed manner. In this work, we intended to do this.…”

Section: Proposed Approachmentioning

confidence: 99%

“…This approach has turned out to be both powerful and universal for many tasks in image processing. In Tizhoosh et al, [23] a simple inference system is proposed to increase the contrast of medical image data. Based on the full histogram of the image I they partition the input domain X = [0, L − 1] into 3 fuzzy sets, viz., dark, gray and bright.…”

Section: Tizhoosh's Approachmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Inference System based Contrast Enhancement

Jayaram¹,

Narayana²,

Vetrivel³

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

View full text Add to dashboard Cite

In this work, we propose a fuzzy inference system based contrast enhancement of gray level images. We propose a new method of generating the fuzzy if-then rules specific to a given image based on the local information available to be used by a fuzzy inference system. To this end, we only generate a partial histogram and not a complete histogram thus saving on computational costs. We also give a compartive study of our approach and some classical and existing fuzzy techniques, and show that the enhanced images from the proposed algorithm are comparable.

show abstract

Section: Advantages Of the Proposed Methodsmentioning

confidence: 54%

Section: Proposed Approachmentioning

confidence: 99%

Section: Tizhoosh's Approachmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Inference System based Contrast Enhancement

Jayaram¹,

Narayana²,

Vetrivel³

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

View full text Add to dashboard Cite

show abstract

“…Contrast enhancement improves the overall visibility of the image gray levels by transforming the dark pixels to appear darker and the light pixels to appear lighter. The advantage of the fuzzy methods compared to conventional image enhancement algorithm is their stability under nearly uncertain conditions on the one hand and the simple adjustment to reach a desired effect in the resulting image on the other hand; The fuzzy rules based approach is a powerful and universal method for many tasks in image processing [15]. Hassanien et al [4] have done a comparative study of 5 fuzzy based image enhancement algorithms on digital mammogram images and showed that fuzzy histogram hyperbolization method and fuzzy rule based system (FRBS) are comparatively better.…”

Section: Proposed Fuzzy Hybrid Framework For Signature Verificationmentioning

confidence: 99%

A Fuzzy Hybrid Framework for Offline Signature Verification

Ganapathi¹,

Nadarajan²

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Signatures are widely used means of personal verification. This paper presents a fuzzy hybrid framework based person-dependent off-line signature verification using fuzzy inference rules in image contrast enhancement, fuzzy rough reduction for feature selection and Simplified fuzzy ARTMAP for verification. Three sets of experimental studies are conducted on CEDAR benchmark dataset and the results reported are comparable to other techniques in terms of classification accuracy and time.

show abstract

“…In [44] a simple inference system is proposed to increase the contrast of medical image data (see also [53 ,22 ,6,7)) . The algorithm can be given as follows:…”

Section: Rule-based Contrast Enhancementmentioning

confidence: 99%