Guo-Qing Wei

Knowledge-based automatic detection of multitype lung nodules from multidetector CT studies

Multi-slice computed tomography (CT) provides a promising technology for lung cancer detection and treatment. To optimize automatic detections of a more complete spectrum of lung nodules on CT requires multiple specialized algorithms in a coherently integrated detection system. We have developed a knowledge-based system for automatic lung nodule detection and analysis, which coherently integrates several robust novel detection algorithms to detect different types of nodules, including those attached to the chest wall, nodules adjacent to or fed by vessels, and solitary nodules, simultaneously. The system architecture can be easily extended in the future to include a still greater range of nodule types, most importantly so-called ground-glass opacities (GGOs). In addition, automatic local adaptive histogram analysis, dynamic cross-correlation analysis, and the automatic volume projection analysis by using by data dimension reduction method, are used in nodule detection. The proposed system has been applied to 10 patients screened with low-dose multi-slice CT. Preliminary clinical tests show that (1) the false positive rate averages about 3.2 per study; and (2) by using the system radiologists are able to detect nearly twice the number of nodules as compared with working alone.

Automatic Detection of Nodules Attached to Vessels in Lung CT by Volume Projection Analysis

Automatic detection of abnormalities or lesions that are attached to other anatomies in medical image analysis is always a very challenge problem, especially when the lesions are small. In this paper a novel method for the automatic detection of lung cancers or nodules attached to vessels in high-resolution multi-slice CT images is presented. We propose to use volume projection analysis to mimic physicians practices in making diagnosis. The volume projection analysis is performed on 1-dimensional curves obtained from the 3-dimensional volume. A multiscale detection framework is proposed to detect nodules of various sizes. A set of features for characterizing nodules is defined. Results of experimental evaluation of the method are presented.

A Dual Dynamic Programming Approach to the Detection of Spine Boundaries

Spine boundary is one of the key landmarks for quantifying deformities of pathological spines. In this paper, we propose a new approach to the detection of spine boundaries. We integrate two dynamic programming procedures into a single one to enable constraints between the left and right parts of the boundary to be enforced. This dual dynamic programming approach detects two boundary curves at the same time. Moreover we propose to use angular limits in a piecewise linear model as a new smoothness constraint. This leads to a computationally efficient way of introducing high order priors. Experimental results show a very robust performance of the method.

Identification of missed pulmonary nodules on low-dose CT lung cancer screening studies using an automatic detection system

Multi-slice CT (MSCT) scanners allow nodules as small as 3mm to be identified during screening. However the associated large data sets make it challenging for radiologists to identify all small nodules in a reasonable amount of time. Computer-aided detection may play a critical role in identifying missed nodules. 13 MSCT screening studies, initially interpreted as non-actionable by a radiologist, were selected from participants in a lung cancer screening study. The study protocol defines actionable studies as those containing at least 1 solid non-calcified nodule larger than 3mm, for which follow-up studies are recommended to exclude interval growth. An automatic detection algorithm was applied to the 13 studies to determine whether it might detect missed nodules, and whether any of these were of sufficient size to be considered actionable. There were a total of 138 automatically detected candidate nodules, an average of 10.6 per patient. 83 candidates were characterized as true positives, yielding a positive predictive value of 60.1%. 10 automatically detected candidates were judged to be actionable nodules greater than 3mm in diameter. 6 of 13 (46%) patients had at least one actionable finding detected by the computer that had been overlooked in the initial exam.

Method for intensity correction in CR mosaic by combined nonlinear and linear transformations

In this paper, we present a method to correct for intensity artifacts in mosaic composition of Computed Radiography (CR) images. The white band artifacts not only distort diagnostic information, but also cause visual disturbances in the examination by physicians. We propose a hybrid method to enhance the image intensity and to correct the brightness differences. A nonlinear transformation method is presented for enhancement, whereas a linear regression method is utilized to compensate for the intensity differences between the white band and normal exposure regions. A knowledge-based method is proposed which can autonomously decide whether the nonlinear enhancement step needs to be bypassed, since in some cases over-enhancement may result from the correction algorithm. Experimental results with different images are presented to show the effectiveness of the proposed method.