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GHT based automatic kidney image segmentation using modified AAM and GBDT

  • R. Amala RoseEmail author
  • A. Annadhason
Original Paper
  • 1 Downloads
Part of the following topical collections:
  1. Internet Of Medical Things In E-Health

Abstract

These days age development to be more prominent in biometric angle. Particularly CAD machine is essentially renowned for ordering and division. In this theory Kidney issue and the division systems are examined. The kidney inconvenience recognizable proof and the finding in logical region are well focused on kidney’s external layer. Accordingly, the inward inconvenience isn’t yet considered in each case. This is mulled over and another time is produced in this examination for division of Kidney using GBDT (Gradient Boosting Decision Tree) thought. The exploration managed with a novel proficient componentThis research was handled with novel efficient mechanism named as GBDT. A systematic technique termed GBDT was utilized to enhance the predictive model. In the process of renal cortex phase localization, a technique which integrates Generalized Hough Transform (GHT) with Active Appearance Models (AAM) was enforced for kidney localization to appraise the renal cortex thickness. The AAM method always matches a new data to the appearance model by minimizing the intensity of root mean square (RMS) between the new data as well as appearance model instance. And finally, from the result of the localization phase, the proposed method GBDT was employed to segregate the kidney into various components. Then an accumulator matrix indicating the possible position of an object was constructed pursuant to the R-table, where the training set data is normally used in this form of table. The results were evaluated to reveal the higher achievement of the proposed system.

Keywords

Kidney classification CAD Biometric Segmentation AAM GBDT Kidney renal cortex Renal column Renal medulla and renal pelvis 

Notes

Compliance with ethical standards

Conflict of interest

None of the author received fund from any agencies or committee or organization.

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Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceSt. John’s College of Arts And ScienceKanyakumariIndia
  2. 2.MS UniversityTirunelveliIndia
  3. 3.Department of Computer ScienceGovernment Arts & Science CollegeRamanathapuramIndia

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