A New Hybrid Algorithm Based on Watershed Method, Confidence Connected Thresholding and Region Merging as Preprocessing for Statistical Classification of General Medical Images

Research output: Chapter in Book/Report/Conference proceedingsConference contribution

Abstract

Segmentation of morphology in medical image data is a highly context specific and differs from various imaging modalities, necessitating the use of sophisticated mathematical models and algorithms to achieve good results. In this work an algorithm is presented for presegmentation of general medical input data, based on a watershed-segmentation strategy utilizing both, original intensities and derived gradient magnitudes for region growing. The number of resulting pre-classified regions is iteratively reduced to a user-defined threshold using merge metrics, accounting for the similarity of intensity profiles of two neighboring regions to merge, as well as the height of the gradient barriers to overcome and geometric aspects like sphericity and size of the border area with respect to the total region size. Based on such a context-independent pre-segmentation, the resulting manageable number of regions can be further merged and classified, utilizing texture features and a priori statistical models. Results are presented from brainweb database.
Original languageEnglish
Title of host publicationProceedings of the 24th European Modeling and Simulation Symposium EMSS 2012
Pages59-67
Number of pages9
Publication statusPublished - 2012
EventThe 24th European Modeling & Simulation Symposium (EMSS 2012) - Vienna, Austria
Duration: 19 Sep 201221 Sep 2012
http://www.msc-les.org/conf/EMSS2012/

Conference

ConferenceThe 24th European Modeling & Simulation Symposium (EMSS 2012)
CountryAustria
CityVienna
Period19.09.201221.09.2012
Internet address

Keywords

  • Statistical image classification
  • Texture features
  • Watershed-segmentation

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