Y. Su. Simulation model for resolution and contrast analysis of microscopic images based on optical coherence contrast method. In BMTMedPhys 2017, vol. 62(s1) , pp. S87, 2017
Optical methods of imaging biological tissues are non-contact and low-risk techniques, which are of great advantage over conventional medical imaging modalities such as X-ray computed tomography (CT) and ultrasound imaging. On the microscopic level, scattering property of biological tissues is associated with tissue inhomogeneity. By detecting the backscattered light signal, structures of different reflectance can be distinguished. Coherent-gating gets rid of unwanted light resulting in further improvements of signal-to-noise ratio (SNR) and image contrast. Recent years optical imaging methods based on coherent-gating, optical coherence tomography (OCT) in particular, have been applied to medical diagnosis for its ability to localize pathological changes in tissues. Even cellular-resolution images have been demonstrated, where enlarged nuclei of neoplastic cells are visualized. However, a realistic modeling of microscopic image resolution and contrast using such methods are rarely reported. In this work, simulation of en face images from low-coherence interferometry (LCI) and spectral domain OCT (SD-OCT) is performed by our self-developed Matlab tool-kit. The image resolution and contrast are then examined with different system configurations, i.e. objectives with different numerical apertures (NAs), low-coherent light source of various central wavelength, bandwidth and optical power. Unlike the standard applications in ophthalmology, the tissue model in our tool-kit is designed to be less transparent than the eye. Thus the microscopic image contrast of such methods on turbid tissues is studied. This tool-kit can be further used to evaluate the performance of LCI or OCT systems for en face imaging, as well as to improve system design.
Y. Shang, G. Su, and O. Dössel. Hierarchical 3D shape model for segmentation of 4D MR cardiac images. In Lecture Notes in Computer Science. Medical Imaging and Augmented Reality, vol. 4091, pp. 333-340, 2006
A novel method for the segmentation of 4D MR cardiac images is introduced in this paper. The method improves the traditional active shape model method by adopting an 3D spatially hierarchical expression of the shape model, which is used as an internal regulation force during the segmentation process. Generation of the landmarks for constructing the shape model is based on the active surface method itself utilizing the long range image force, gradient vector flow (GVF). For constructing hierarchical statistical shape models, initial landmarking is done on a manually segmented training set with different spatial resolutions. Principal component analysis is then used to derive the hierarchical expression of the shape model. Experimental results for 4D MR cardiac image segmentation are presented.