This paper describes a preliminary study on feature selection from the gray level co-occurrence matrix (GLCM) among the 14 features proposed by R.M. Haralick (1979) with the aim to apply them to ultrasound image classification and Quality Assessment. In particular4 main-classes of images with different patterns (Lines, Chess, alternates Row and Circles)have been implemented and different levels ofspeckle noisehave been added to simulate ultrasound images with different textures.With the aim to characterize therelationship betweenHaralickfeatures and the pattern type, size, contrastand noise, someCorrelation Matrices have been implemented. Preliminary results are explained and discussed.
Schinaia, L., Scorza, A., Orsini, F., Sciuto, S.A. (2017). Feature classification in ultrasound textures for image quality assessment: A preliminary study on the characterization and selection of haralick parameters by means of correlation matrices. In 22nd IMEKO TC4 International Symposium and 20th International Workshop on ADC Modelling and Testing 2017: Supporting World Development Through Electrical and Electronic Measurements (pp.170-174). IMEKO-International Measurement Federation Secretariat.
Feature classification in ultrasound textures for image quality assessment: A preliminary study on the characterization and selection of haralick parameters by means of correlation matrices
Schinaia, L.Software
;Scorza, A.
Writing – Review & Editing
;Orsini, F.Membro del Collaboration Group
;Sciuto, S. A.Supervision
2017-01-01
Abstract
This paper describes a preliminary study on feature selection from the gray level co-occurrence matrix (GLCM) among the 14 features proposed by R.M. Haralick (1979) with the aim to apply them to ultrasound image classification and Quality Assessment. In particular4 main-classes of images with different patterns (Lines, Chess, alternates Row and Circles)have been implemented and different levels ofspeckle noisehave been added to simulate ultrasound images with different textures.With the aim to characterize therelationship betweenHaralickfeatures and the pattern type, size, contrastand noise, someCorrelation Matrices have been implemented. Preliminary results are explained and discussed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
