This paper proposes a novel system for the automatic detection of important anatomical structures such as the Optic Disc (OD), Blood Vessels and Macula in digital fundus retinal images. The novelty is in extraction of blood vessels and localization of macula. OD localization is done using Principle

In the context of medicine cutting is one of the most important operation. Many surgical tasks start with an incision, allowing the surgeon to access the region of interest, using either conventional or minimal invasive surgery techniques. The work presented herein should be seen in the context of surgical training and preoperative planning using computer assisted medical simulation systems. The main result of this work is the proposed cutting algorithm based on predefined templates. To integrate this approach in the existing surgical training simulator and to handle additional tasks like soft tissue deformation, needed by the medical environment, a generic simulation framework is introduced. This framework is able to utilize given multiprocessor- and network-environments to achieve the desired realtime interactivity.

In this thesis, we develop a computational framework for image-based statistical analysis of anatomical shape in different populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients vs. normal controls, studying

. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating

from MR brain scans can be a daunting task due to large inhomogeneities in image intensities across an image, and possible lack of precisely defined shape boundaries for certain anatomical structures. One approach that has been quite popular in the recent past for these situations is the atlasbased

This paper examines an approach for integrating 3D structural reasoning, using computer models of the human anatomy, with diagnostic reasoning based on Bayesian networks in order to probabilistically predict injuries to anatomic structures from gunshot wounds. An interactive 3D graphical system has

The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., “high

in living nematodes. Many of the cell lineages are involved in sexual maturation. At hatching, the hermaphrodite and male are almost identical morphologically; by the adult stage, gross anatomical differences are obvious. Some of these sexual differences arise from blast cells whose division patterns

Abstract. In computer assisted surgery 3D models are now routinely used to plan and navigate a surgery. These models enhance the surgeon’s capability to decrease the invasiveness of surgical procedures and increase their accuracy and safety. Models obtained from specifically acquired CT scans have the disadvantage that they induce high radiation dose to the patient. In this paper we propose a novel method to construct a patient-specific model that provides an appropriate intra-operative 3D visualization without the need for a pre or intra-operative imaging. The 3D model is reconstructed by fitting a statistical deformable model to minimal sparse 3D data consisting of digitized landmarks and surface points that are obtained intra-operatively. The statistical model is constructed using Principal Component Analysis from training objects. Our morphing method then computes a Mahalanobis distance weighted least square fit of the model by solving a linear equation system. The refined morphing scheme has better convergence behaviour because of the additional parameter that relaxes the Mahalanobis distance term as additional points are incorporated. We present leave-one-out experiments with model generated from proximal femors and hippocampi. 1

The Problem: In this work, we develop a computational framework for image-based statistical analysis of anatomical shape in different populations. Fig. 1 shows exampleofthe hippocampus-amygdala complex from the comparative study of the structure in schizophrenia. Figure 1: Example input data from