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Automatic registration and segmentation of abdominal images and detection of pancreatic cancer

Abstract

dc:description.abstract

Localized and detailed analyses of 3D abdominal images obtained through different imaging modalities help greatly in determining the progression of a disease or for postoperative treatment / evaluation. However, such analyses become difficult and sometimes unfeasible due to the effects of patient motion and breathing. This is particularly evident during analysis of the pancreas for cancer, due to its proximity to other intra-abdominal organs. Within subject registration thus becomes imperative for pathological analysis of pancreatic cancer. An intensity-based, global image registration algorithm was developed in the present work, for registration of pancreatic abdominal images. The registration algorithm was automatic and could register three dimensional MR and CT images of the abdomen. Once registered, localization and analysis of the pancreas was facilitated by a semi-automatic k-means clustering based segmentation procedure. Such a registration and segmentation based method could be used as a valuable tool for pancreas cancer screening and analysis.

Degree

thesis:*
Name thesis:degree_name
Master of Science in Biomedical Engineering - (M.S.)
Discipline thesis:degree_discipline
Biomedical Engineering
Year
2007

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Maniprasad, Girish Kumar
Contributors dc:contributor
  • Bharat Biswal
  • Richard A. Foulds
  • Tara L. Alvarez

Subjects

dc:subject × 3

Identifiers

dc:identifier.*
Repository record dc:identifier
https://digitalcommons.njit.edu/theses/392
OAI identifier oai:identifier
oai:digitalcommons.njit.edu:theses-1391

Chain of custody

source
Harvested from
NJIT
Base URL
digitalcommons.njit.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Maniprasad, Girish Kumar. Automatic registration and segmentation of abdominal images and detection of pancreatic cancer. 2007. https://digitalcommons.njit.edu/theses/392