A hybrid ASM approach for sparse volumetric data segmentation

Three-Dimensional (3D) Active Shape Modeling (ASM) is a straightforward extension of 2D ASM. 3D ASM is robust when true volumetric data is considered. However, when the information in one dimension is sparse, pure 3D ASM tends to be less robust. We present a hybrid 2D + 3D methodology which can deal with sparse 3D data. 2D and 3D ASMs are combined to obtain a “global optimal” segmentation of the 3D object embedded in the data set, rather than the “locally optimal” segmentation on separate slices. Experimental results indicate that the developed approach shows equivalent precision on separate slices but higher consistency for whole volumes when compared to 2D ASM, while the results for whole volumes are improved when compared to the pure 3D ASM approach. The text was submitted by the authors in English. Stuart Michael Williams, born in 1967, graduated with BAHons in 1989, BMBCh in 1992 from Oxford University, UK; MRCP (1995), FRCR(1999); Stuart Michael Williams is currently the Consultant Radiologist of Norfolk and Norwich University Hospital, Norwich, UK. His research areas include oncological radiology with an interest in image analysis and medical education. Stuart Michael Williams has 24 publications (monographs and articles). He is a member of the Royal College of Radiologists; member of the European Congress of Radiology; and a member of the European Society of Magnetic Resonance in Medicine and Biology. Yanong Zhu, born in 1975, graduated with B. Sci. in 1997 and M. Sci. in 2002 from Northwest University, China and PhD in 2006 from the University of East Anglia, Norwich, UK. His research areas include computer vision, medical image understanding, and analysis. Yanong Zhu has eight publications (monographs and articles). Reyer Zwiggelaar, born in 1963, graduated with B. Sci. from State University Groningen, the Netherlands in 1989. He was awarded his PhD in 1993 by University College London, UK. Reyer Zwiggelaar is currently the Senior Lecturer at the University of Wales Aberystwyth, UK. Dr. Zwiggelaar has more than 80 publications (monographs and articles). His research areas include medical image understanding, especially concentrating on mammographic data, pattern recognition, statistical methods, and feature detection techniques.