Medical Image Fusion using Combined Multi-Resolution and Multi-Scaling Transform

Rana, Dipixa H.; Degadwala, Sheshang

doi:10.5120/18757-0024

Cited by 8 publications

(1 citation statement)

References 6 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, wavelet transform is usually adopted [5]. However, it can only preserve efficient spectral information, but cannot express sufficient curves and edges of images [6]. To solve this problem, multi‐scale decomposition transforms have been developed like curvelet transform (CVT), contourlet transform (CRT), non‐subsampled contourlet transform (NSCT), ripplet transform (RT), shearlet transform (ST) etc.…”

Section: Introductionmentioning

confidence: 99%

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

2018

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non‐subsampled shearlet transform (NSST) and simplified pulse‐coupled neural network model (S‐PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y‐component of PET image are decomposed into low‐frequency (LF) and high‐frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S‐PCCN which is motivated by an adaptive‐linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.

show abstract

Section: Introductionmentioning

confidence: 99%

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

2018

View full text Add to dashboard Cite

show abstract

Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis

Shrivastava¹,

Nayak

Dilip³

et al. 2023

Materials Today: Proceedings

View full text Add to dashboard Cite

Integration of Cassandra and Spark in Computer Aided Drug Design

Nitha¹

2021

IJSRCSEIT

View full text Add to dashboard Cite

The primary purpose of this paper is to provide feasibility study of Cassandra and spark in Computer Aided Drug Design (CADD). The Apache Cassandra database is a big data management tool which can be used to store huge amount of data in different file formats. A huge database can be designed with details of all known molecules or compounds that are existing on earth. The information regarding the compounds such as selectivity, solubility, synthetic viability, affinity, adverse reactions, metabolism and environmental toxicity along with the 3 D structure of molecule can be stored in this big database. A data analytics tool “spark” can be efficiently used in mining and managing huge data stored in the database. Integrating big data in CADD helps in identifying the candidate drugs within minutes, not years. It may take eight to fifteen years to develop a new drug traditionally. Spark is written in Scala Programming Language which runs on Java Virtual Machine (JVM) and it supports Scala, Java and Python Programming languages .Cassandra can provide connectors to different programming languages, hence it’s very easy to integrate any other molecular modeling tool with Spark. A python based molecular modeling tool called Pymol can be easily implemented with Spark. CADD helps in identifying new drugs by computational means thus eliminating unnecessary cost incurred in chemical testing of drugs.

show abstract

Medical Image Fusion using Combined Multi-Resolution and Multi-Scaling Transform

Cited by 8 publications

References 6 publications

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis

Integration of Cassandra and Spark in Computer Aided Drug Design

Contact Info

Product

Resources

About