Deformable models are common in image modeling and analysis. Random objects provide major challenges as shapes and appearances are hard to quantify; hence, formulation of deformable models are much harder to construct and validate. In this paper, we examine the effect of randomness on building the shape and appearance models for small-size lung nodules (≤ 1cm) which appear in computed tomography (CT) of the human chest. We devise an approach for annotation, which lends a standard mechanism for building traditional active appearance (AAM), active shape (ASM) and active tensor models (ATM). We illustrate the effectiveness of AAM for nodule detection.

In this paper, a novel method to solve the shape registration problem covering both global and local deformations is proposed. The vector distance function (VDF) is used to represent source and target shapes. The problem is formulated as an energy optimization process by matching the VDFs of the source and target shapes. The minimization process results in estimating the transformation parameters for the global and local deformation cases. Gradient descent optimization handles the computation of scaling, rotation, and translation matrices used to minimize the global differences between source and target shapes. Nonrigid deformations require a large number of parameters which make the use of the gradient descent minimization a very time-consuming process. We propose to compute the local deformation parameters using a closed-form solution as a linear system of equations derived from approximating an objective function. Extensive experimental validations and comparisons performed on generalized 2D shape data demonstrate the robustness and effectiveness of the method.

A new variational level set approach is proposed for lung nodule segmentation in lung CT scans. A general lung nodule shape model is proposed using implicit spaces as a signed distance function. The shape model is fused with the image intensity statistical information in a variational segmentation framework. The nodule shape model is mapped to the image domain by a global transformation that includes inhomogeneous scales, rotation, and translation parameters. A matching criteria between the shape model and the image implicit representations is employed to handle the alignment process. Transformation parameters evolve through gradient descent optimization to handle the shape alignment process and hence mark the boundaries of the nodule “head.” The embedding process takes into consideration the image intensity as well as prior shape information. A nonparametric density estimation approach is employed to handle the statistical intensity representation of the nodule and background regions. The proposed technique does not depend on nodule type or location. Exhaustive experimental and validation results are demonstrated on 742 nodules obtained from four different CT lung databases, illustrating the robustness of the approach.

Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper presents an approach for segmentation of lung nodules detected by a prior step. First, regions around the detected nodules are segmented; using automatic seed point placement levels sets. The outline of the nodule region is further improved using the curvature characteristics of the segmentation boundary. We illustrate the effectiveness of this method for automatic segmentation of the Juxta-pleural nodules.

This work presents a novel approach in automatic detection of the lung nodules and is compared with respect to parametric nodule models in terms of sensitivity and specificity. A Statistical method is used for generating data driven models of the nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using the Procrustes based AAM method to create descriptive lung nodules. Performance of the new nodule models on clinical datasets is significant over parametric nodule models in both sensitivity and specificity. The new nodule modeling approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer.

This paper deals with segmentation of the lung tissues from low dose CT (LDCT) scans of the chest. Goal is correct segmentation as well as maintaining the details of the lung region in the chest cavity. In particular, it is essential that the lung nodules inside the lungs as well as on the boundary regions be maintained for subsequent steps that aim at automatic detection and classification of nodules from LDCT scanning; a step for early diagnosis of lung cancer. An approach for segmentation based on combination of EM algorithm and morphological operations is presented. This algorithm is compared with respect two other approaches that are based on level sets and energy optimization by the Graph Cuts technique. Performance evaluation is conducted on a labeled data set from the Early Lung Cancer Action Program (ELCAP) database. The new segmentation approach provides comparable results to Level sets and Graph-Cuts, with the advantage of faster execution time, and minimal user interception.

The quality of the lung nodule models determines the success of lung nodule detection. This paper describes aspects of our data-driven approach for modeling lung nodules using the texture and shape properties of real nodules to form an average model template per nodule type. The ELCAP low dose CT (LDCT) scans database is used to create the required statistics for the models based on modern computer vision techniques. These models suit various machine learning approaches for nodule detection including Bayesian methods, SVM and Neural Networks, and computations may be enhanced through genetic algorithms and Adaboost. The eminence of the new nodule models are studied with respect to parametric models showing significant improvements in both sensitivity and specificity.

Parametric density estimation is widely used to solve many image processing problems. We examined the parametric estimation using linear combination of 1D Gaussians in many works. In this work, we extend our model to estimate density of the colors in color images. We approximate the marginal density of each class in the empirical probability density function by a 3D Gaussian distribution. Then, the deviation between the estimated and the empirical densities is modelled using a linear combination of 3D Gaussians with positive and negative components. We estimate the parameters of this model using our modified EM algorithm. The proposed framework demonstrates very promising experimental results of color images labelling and can be integrated with many other frameworks.

Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.