Extraction of Landmarks and Features from Virtual Colon Models Krishna Chaitanya Gurijala, Arie Kaufman, Wei Zeng Xianfeng Gu Computer Science Department, Stony Brook University

Need for Feature Extraction? Virtual Colonoscopy - CT scans are taken in supine and prone positions Colon has tube-like structure, flexible with lots of twists and folds Change of position results in drastic change in the topology of the colon Landmarks and features help in segmentation and understanding the surface of the colon and to confirm the polyp location.

Feature Extraction - Applications Registration of supine-prone colon surfaces Virtual colon navigation Virtual colon dissection Polyp matching Segment wise comparison Bookmarking the polyp location Knowing the location of the user inside the colon

Features Taeniae coli and flexures are anatomical landmarks that do not change despite the change in the position of the person We propose methods for the detection of these landmarks and internal feature points on the colon surface Landmarks and features help to toggle between corresponding positions in supine and prone

Taeniae coli – three bands of longitudinal muscle on the surface of the colon Taeniae coli detection based on haustral fold detection Haustral folds detected using heat diffusion, curvature filter and connected components Taeniae Coli Extraction

Using the haustral folds, taeniae coli are obtained by applying fuzzy C-means clustering algorithm iteratively

Detection of Flexures Four flexures, two major and two minor :  A-T (Hepatic)  T-D (Splenic)  D-S  S-R

Detection of Flexures Projection of colon centerline onto a 2D coordinate system in positive z-x and y-z planes Bends in the centerline are identified by iterative evaluation of slopes in the two planes Only major bends are retained and sorted based on the z-coordinate

Detection of Flexures Splenic flexure identified as the bend with highest z-coordinate Hepatic flexure identified as the bend with second highest z-coordinate Two other flexures between the descending colon and sigmoid and between the sigmoid and rectum are identified similarly

Detection of Internal Features The colon surface is opened up along the taenia coli and cut along the flexures to obtain five flat anatomical segments For each of the flat segments, color encoded mean curvature images are generated

Segmentation Folds – only tangible regions of interest Use the graph cut algorithm to separate the folds (blue color encoded) from the rest of the surface (red and green color encoded) Significant folds are retained by thresholding

Feature Points Detection Detected folds approximated as ellipses The axial points of the folds are extracted as the feature point set

Feature Matching Feature matching formulated as energy minimization problem – solved using dual decomposition technique Only the border feature points are considered for correspondence to overcome the possibility of any wrongly matched feature points

Conclusion Anatomical landmarks namely taeniae coli and flexures are located automatically and in robust manner – these are used for colon flattening and partitioning Feature points are automatically extracted from the flattened colon surface – correspondences are obtained between supine and prone These landmarks and feature points have various applications for the VC system

Thanks! Questions?