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Variational PET/CT Tumor Co-Segmentation Integrated With PET Restoration
Variational PET/CT Tumor Co-Segmentation Integrated With PET Restoration
Variational PET/CT Tumor Co-Segmentation Integrated With PET Restoration

Variational PET/CT Tumor Co-Segmentation Integrated With PET Restoration

₹5,000.00
Product Code: Matlab- Machine Learning
Availability: In Stock
Viewed 7173 times

Product Description

                                        Domain: Machine Learning                                                                Tool: MATLAB R2018a


The main aim of this project is used to segment the tumor by using deep learning techniques.

Abstract:

Multi-modality imaging technologies have been routinely used in the clinical practice nowadays. Information fusion of multi-modality medical images can reduce randomness and redundancy, and has been proved to be useful for medical diagnosis, analysis, treatment and outcome assessment. A PET restoration process is further integrated into the co-segmentation process to handle the uncertainty introduced by the blurred tumor edges in the PET image. The new information fusion strategy can automatically decide which modality should be more trustful for localizing the tumor boundary, in accord to the medical knowledge the images conveyed. In this proposed system, two input images are given namely CT images and MRI images. The quaternion wavelet transform (QWT) is one of the effective multi scale image fusion method. Active Contour segmentation is designed in the proposed area. Here the threshold required for segmenting adjusts itself according to the segmented area and position. The trained data are then used to reconstruct the fused image to reduce the noise. The deep neural networks are used to train the input medical images for detecting the tumor whether it is benign or malignant.

 

Proposed System:

In this proposed system, two input images are given namely CT images and MRI images. The quaternion wavelet transform (QWT) is one of the effective multi scale image fusion method. Active Contour segmentation is designed in the proposed area. Here the threshold required for segmenting adjusts itself according to the segmented area and position. The trained data are then used to reconstruct the fused image to reduce the noise. The deep neural networks are used to train the input medical images for detecting the tumor whether it is benign or malignant.



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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.