Automated seeded lesion segmentation on digital mammograms

Matthew A. Kupinski, Maryellen L. Giger

Research output: Contribution to journalArticle

229 Scopus citations

Abstract

Segmenting lesions is a vital step in many computerized mass-detection schemes for digital (or digitized) mammograms. We have developed two novel lesion segmentation techniques-one based on a single feature called the radial gradient index (RGI) and one based on simple probabilistic models to segment mass lesions, or other similar nodular structures, from surrounding background. In both methods a series of image partitions is created using gray-level information as well as prior knowledge of the shape of typical mass lesions. With the former method the partition that maximizes the RGI is selected. In the latter method, probability distributions for gray-levels inside and outside the partitions are estimated, and subsequently used to determine the probability that the image occurred for each given partition. The partition that maximizes this probability is selected as the final lesion partition (contour). We tested these methods against a conventional region growing algorithm using a database of biopsy-proven, malignant lesions and found that the new lesion segmentation algorithms more closely match radiologists' outlines of these lesions. At an overlap threshold of 0.30, gray level region growing correctly delineates 62% of the lesions in our database while the RGI and probabilistic segmentation algorithms correctly segment 92% and 96% of the lesions, respectively.

Original languageEnglish (US)
Pages (from-to)510-517
Number of pages8
JournalIEEE Transactions on Medical Imaging
Volume17
Issue number4
DOIs
StatePublished - Jan 1 1998
Externally publishedYes

Keywords

  • Computer-aided diagnosis
  • Digital mammography
  • Lesion segmentation
  • Mass detection

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Automated seeded lesion segmentation on digital mammograms'. Together they form a unique fingerprint.

  • Cite this