Title :
Three-dimensional reconstruction of microcalcification clusters from two mammographic views
Author :
Yam, Margaret ; Brady, Michael ; Highnam, Ralph ; Behrenbruch, Christian ; English, Ruth ; Kita, Yasuyo
Author_Institution :
Robotics Res., Med. Vision Lab., Oxford, UK
fDate :
6/1/2001 12:00:00 AM
Abstract :
Classification of benign/malignant microcalcification clusters is a major diagnostic challenge for radiologists. Clinical studies have revealed that the shape of the cluster, and the spatial distribution of individual microcalcifications within it, are important indicators of its malignancy. However, mammographic images of clustered microcalcifications confound their three-dimensional (3-D) distribution with image projection and breast compression. This paper presents a novel model-based method for reconstructing microcalcification clusters in 3-D from two mammographic views (cranio-caudal and medio-lateral oblique-"shoulder to the opposite hip" or lateral-medio). The authors develop a 3-D breast representation and a parameterised breast compression model which constraints geometrically the possible 3-D positions of a calcification in a two-dimensional image. Corresponding calcifications in the two views are matched using an estimate of the calcification volume. Both the geometric constraint and the matching criterion are utilized in the final reconstruction step to build the 3-D reconstructed clusters. Validation experiments are described using 30 clusters to verify the individual steps of the model, and results consistent with known ground truth are obtained. Some of the approximations in the model and future work are discussed in the concluding section.
Keywords :
cancer; image matching; image reconstruction; mammography; medical image processing; modelling; benign microcalcifications; breast cancer; cranio-caudal oblique; geometric constraint; malignant microcalcifications; mammographic views; medical diagnostic imaging; medio-lateral oblique; microcalcification clusters; parameterised breast compression model; three-dimensional reconstruction; Biomedical engineering; Biomedical imaging; Breast cancer; Image coding; Image reconstruction; Medical diagnostic imaging; Medical robotics; Robot kinematics; Robot vision systems; Shape; Algorithms; Anatomy, Cross-Sectional; Artificial Intelligence; Carcinoma, Squamous Cell; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Microscopy, Video; Microtomy; Neoplasm Invasiveness; Neoplasm Staging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Uterine Cervical Neoplasms;
Journal_Title :
Medical Imaging, IEEE Transactions on