Title :
A hybrid system for detecting masses in mammographic images
Author :
Székely, Nóra ; Tóth, Norbert ; Pataki, Béla
Author_Institution :
Dept. of Meas. & Inf. Syst., Budapest Univ. of Technol. & Econ.
fDate :
6/1/2006 12:00:00 AM
Abstract :
This paper discusses the hybrid system for detecting masses in mammographic images. The proposed approach analyzes mammograms in three major steps. First, a global segmentation method is applied to find regions of interest. This step uses texture features, decision trees, and a multiresolution Markov random field model. The second stage works on the output of the previous algorithm. Here, a combination of three different local segmentation methods is used, and then, some relevant features are extracted. Some of them refer to the shape of the object; others are texture parameters. The final decision is made using a linear combination of these features
Keywords :
Markov processes; decision trees; feature extraction; image segmentation; image texture; mammography; medical image processing; biomedical imaging; computer-aided diagnostics; decision trees; features extraction; global segmentation method; hybrid system; local segmentation methods; mammographic images; mammography; mass detection; multiresolution Markov random field model; texture analysis; Cancer; Decision trees; Feature extraction; Image analysis; Image segmentation; Image texture analysis; Lesions; Mammography; Markov random fields; Shape; Biomedical imaging; computer-aided diagnostics; mammography; shape description; texture analysis;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2006.870104