Avinash Kak | PubMed Health

PubMed2005

A deformable model for automatic CT liver extraction.

RATIONALE AND OBJECTIVES: This study was performed to design an automatic liver region extraction system to facilitate clinical liver size estimation and further serve as a prestage for liver reconstruction and volume estimation. MATERIALS AND METHODS: We present a modification of the well-known snakes algorithm for extracting liver regions in noisy CT images. Our modification addresses the issues of selection of the control points on an estimate of the contour and the determination of the weighting coefficients. The weighting coefficients are determined dynamically on the basis of the distance between the control points and the local curvature of the contour. RESULTS: The proposed method was used in extracting liver regions from 98 cross-sectional abdominal images. The overall performance was estimated by comparisons with original liver regions. CONCLUSION: The deformable model method enables an efficient and effective automatic liver region extraction in noisy environments. This approach eliminates human-in-the loop, which is the common practice for the majority of current methods.

Algorithms↗

PubMed2004

An interactive framework for acquiring vision models of 3-D objects from 2-D images.

This paper presents a human-computer interaction (HCI) framework for building vision models of three-dimensional (3-D) objects from their two-dimensional (2-D) images. Our framework is based on two guiding principles of HCI: 1) provide the human with as much visual assistance as possible to help the human make a correct input; and 2) verify each input provided by the human for its consistency with the inputs previously provided. For example, when stereo correspondence information is elicited from a human, his/her job is facilitated by superimposing epipolar lines on the images. Although that reduces the possibility of error in the human marked correspondences, such errors are not entirely eliminated because there can be multiple candidate points close together for complex objects. For another example, when pose-to-pose correspondence is sought from a human, his/her job is made easier by allowing the human to rotate the partial model constructed in the previous pose in relation to the partial model for the current pose. While this facility reduces the incidence of human-supplied pose-to-pose correspondence errors, such errors cannot be eliminated entirely because of confusion created when multiple candidate features exist close together. Each input provided by the human is therefore checked against the previous inputs by invoking situation-specific constraints. Different types of constraints (and different human-computer interaction protocols) are needed for the extraction of polygonal features and for the extraction of curved features. We will show results on both polygonal objects and object containing curved features.

Algorithms↗