3-D Lung Segmentation by Incremental Constrained Nonnegative Matrix Factorization.

Accurate lung segmentation from large-size 3-D chest-computed tomography images is crucial for computer-assisted cancer diagnostics. To efficiently segment a 3-D lung, we extract voxel-wise features of spatial image contexts by unsupervised learning with a proposed incremental constrained nonnegative matrix factorization (ICNMF). The method applies smoothness constraints to learn the features, which are more robust to lung tissue inhomogeneities, and thus, help to better segment internal lung pathologies than the known state-of-the-art techniques. Compared to the latter, the ICNMF depends less on the domain expert knowledge and is more easily tuned due to only a few control parameters. Also, the proposed slice-wise incremental learning with due regard for interslice signal dependencies decreases the computational complexity of the NMF-based segmentation and is scalable to very large 3-D lung images. The method is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (seven datasets), in vivo datasets for 17 subjects, and 55 datasets from the Lobe and Lung Analysis 2011 (LOLA11) study. For the in vivo data, the accuracy of our segmentation w.r.t. the ground truth is 0.96 by the Dice similarity coefficient, 9.0 mm by the modified Hausdorff distance, and 0.87% by the absolute lung volume difference, which is significantly better than for the NMF-based segmentation. In spite of not being designed for lungs with severe pathologies and of no agreement between radiologists on the ground truth in such cases, the ICNMF with its total accuracy of 0.965 was ranked fifth among all others in the LOLA11. After excluding the nine too pathological cases from the LOLA11 dataset, the ICNMF accuracy increased to 0.986.

Shape analysis of the human brain: a brief survey.

The survey outlines and compares popular computational techniques for quantitative description of shapes of major structural parts of the human brain, including medial axis and skeletal analysis, geodesic distances, Procrustes analysis, deformable models, spherical harmonics, and deformation morphometry, as well as other less widely used techniques. Their advantages, drawbacks, and emerging trends, as well as results of applications, in particular, for computer-aided diagnostics, are discussed.

Toward early diagnosis of lung cancer.

Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. In this paper, we focus on generating new probabilistic models for the estimated growth rate of the detected lung nodules from Low Dose Computed Tomography (LDCT). We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. Visual appearance of these chest images is described using a Markov-Gibbs random field (MGRF) model with multiple pairwise interaction. An affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special Gibbs energy function. To handle local deformations, we displace each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.

Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for CAD system design.

Automatic diagnosis of lung nodules for early detection of lung cancer is the goal of a number of screening studies worldwide. With the improvements in resolution and scanning time of low dose chest CT scanners, nodule detection and identification is continuously improving. In this paper we describe the latest improvements introduced by our group in automatic detection of lung nodules. We introduce a new template for nodule detection using level sets which describes various physical nodules irrespective of shape, size and distribution of gray levels. The template parameters are estimated automatically from the segmented data (after the first two steps of our CAD system for automatic nodule detection) - no a priori learning of the parameters density function is needed. We show quantitatively that this template modeling approach drastically reduces the number of false positives in the nodule detection (the third step of our CAD system for automatic nodule detection), thus improving the overall accuracy of CAD systems. We compare the performance of this approach with other approaches in the literature and with respect to human experts. The impact of the new template model includes: 1) flexibility with respect to nodule topology - thus various nodules can be detected simultaneously by the same technique; 2) automatic parameter estimation of the nodule models using the gray level information of the segmented data; and 3) the ability to provide exhaustive search for all the possible nodules in the scan without excessive processing time - this provides an enhanced accuracy of the CAD system without increase in the overall diagnosis time.