Image Fusion on the Basis of Fuzzy Transforms

Perfilieva, Irina; Daňková, Martina

doi:10.1142/9789812799470_0077

Cited by 29 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The original motivation for the F-transform (an abbreviated name for the fuzzy transform) came from fuzzy modeling Perfilieva [9][10]. The intention was to show that, similarly to conventional transforms (Fourier and wavelet). ]…”

Section: F-transformmentioning

confidence: 99%

See 1 more Smart Citation

Rigid Point Feature Registration for MRI Brain Images based on Fuzzy Transform

Al-Azzawi¹

2013

IJCA

View full text Add to dashboard Cite

Radiological images are gradually more being used in healthcare and medical research. There is, accordingly, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. In this paper we formulate feature registration problems by using F-transform (Fuzzy-Transform) to extract the salient edges and extracted control points (CP) of medical Images. The MRI brain Images were first decomposed using F-transform, then Edge and CP were extracted from F-transform error function, and some proposed rules. After edge and CP extraction, mutual information (MI) was adopted for the registration of feature points, and translation parameters are calculated by using particle swarm optimization (PSO). We implement experiments to evaluate the performance of the FTransform and MI similarity measures for 2-D/3-D rigid point feature registration. The experimental results showed that the proposed method produces totally accurate performance for rigid point feature registration for MRI brain images.

show abstract

Section: F-transformmentioning

confidence: 99%

“…Canny edge detection is commonly used to generate features for image registration to handle the accurate feature extraction [7][8]. The F-transform [9][10] is an efficient intelligent method to handle uncertain information. It represents those natural phenomena which we observe in our real lives.…”

Section: Introductionmentioning

confidence: 99%

Rigid Point Feature Registration for MRI Brain Images based on Fuzzy Transform

Al-Azzawi¹

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…The purpose was to show that, similarly to traditional transforms (Fourier and wavelet), the F-transform performs a transformation of an original universe of functions into a universe of their "skeleton models" (vectors of F-transform components) in which further computation is easier (e.g., an application to the initial-value problem with a fuzzy initial condition Perfilieva, De Meyer, De Baets & Plšková (2008)). In this respect, the F-transform can be as useful in many applications as traditional transforms (see applications to image compression Perfilieva, Pavliska, Vajgl & De Baets (2008) and time-series procession Perfilieva, Novák, Pavliska, Dvořák &Štěpnička (2008)). Moreover, sometimes the F-transform can be more efficient than its counterparts.…”

Section: Introductionmentioning

confidence: 99%

F-Transform Based Image Fusion

Perfilieva¹,

Daňková²,

Hoďáková³

et al. 2011

Image Fusion

Self Cite

View full text Add to dashboard Cite

“…The author [16] shows that on the basis of pre-serving monotonicity an improved algorithm of image compression and reconstruction can be proposed. The algorithm is based on partitioning the range of the function fI which corresponds to an image I.A new image compression algorithm that combines a fuzzy technique and wavelet transform [17] is presented.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Neural Networks based EZW Image Compression System

Jilani¹,

Sattar²

2010

IJCA

View full text Add to dashboard Cite

The transport of images across communication paths is an expensive process. The limitation in allocated bandwidth leads to slower communication. To exchange the rate of transmission in the limited bandwidth the Image data must be compressed before transmission. JPEG2000 image compression system follows huffman coding for image compression. Embedded zero tree wavelet (EZW) coding exploits the multi-resolution properties of the wavelet transform when compared to existing wavelet transforms. Artificial Neural Network has been applied to many problems in image processing and has demonstrated their superiority over classical methods when dealing with noisy or incomplete data for image compression applications. A fuzzy optimization design based on neural networks is presented as a new method of image processing. The combination system adopts a new fuzzy neuron network (FNN) which can appropriately adjust input and output values, and increase robustness, stability and working speed of the network by achieving high compression ratio.

show abstract

Image Fusion on the Basis of Fuzzy Transforms

Cited by 29 publications

References 2 publications

Rigid Point Feature Registration for MRI Brain Images based on Fuzzy Transform

Rigid Point Feature Registration for MRI Brain Images based on Fuzzy Transform

F-Transform Based Image Fusion

A Fuzzy Neural Networks based EZW Image Compression System

Contact Info

Product

Resources

About