Correspondence in texture features between two mammographic views.
It is well established that radiologists are better able to interpret mammograms when two mammographic views are available. Consequently, two mammographic projections are standard: mediolateral oblique (MLO) and craniocaudal (CC). Computer-aided diagnosis algorithms have been investigated for assisting in the detection and diagnosis of breast lesions in digitized/digital mammograms. A few previous studies suggest that computer-aided systems may also benefit from combining evidence from the two views. Intuitively, we expect that there would only be value in merging data from two views if they provide complementary information. A measure of the similarity of information is the correlation coefficient between corresponding features from the MLO and CC views. The purpose of this study was to investigate the correspondence in Haralick's texture features between the MLO and CC mammographic views of breast lesions. Features were ranked on the basis of correlation values and the two-view correlation of features for subgroups of data including masses versus calcification and benign versus malignant lesions were compared. All experiments were performed on a subset of mammography cases from the Digital Database for Screening Mammography (DDSM). It was observed that the texture features from the MLO and CC views were less strongly correlated for calcification lesions than for mass lesions. Similarly, texture features from the two views were less strongly correlated for benign lesions than for malignant lesions. These differences were statistically significant. The results suggest that the inclusion of texture features from multiple mammographic views in a CADx algorithm may impact the accuracy of diagnosis of calcification lesions and benign lesions.
Gupta
Shalini
S
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712-0240, USA.
Markey
Mia K
MK
D000465:Algorithms; D001940:Breast / Q000473:pathology; D001941:Breast Diseases / Q000175:diagnosis; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D019992:Databases as Topic; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008327:Mammography / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011871:Radiology / Q000379:methods*; D011873:Radiology Information Systems; D012984:Software