Pulmonary nodules: Assessing the imaging biomarkers of malignancy in a "coffee-break".

INTRODUCTION: Although nodule volumetry is a recognized biomarker of malignancy in pulmonary nodules (PNs), caution is needed in its interpretation because of variables such as respiratory volume variation and inter-scan variability of up to 25%. CT Texture Analysis (CTTA) is a potential independent biomarker of malignancy but inter-scan variability and respiratory volume variation has not been assessed. METHODS: In this prospective cohort study, 40 patients (20 with an indeterminate PN and 20 with pulmonary metastases) underwent two LDCTs within a 60-min period (the "Coffee-break") with the aim of assessing the repeatability of CTTA and semi-automated volume measurements. Texture features were extracted from each automatic contoured region surrounding the PN. Patients were also randomized to two inspiratory control groups: normal breath hold, and controlled lung volume to study the influence of inspiratory control on these measurements. RESULTS: The mean difference in volume between the two scans was 6.3%,SD:29.9%. The textural features displayed 95% CI below ±17.8%, and were less variable than nodule volume (95%CI ±â€¯28.9%). All features had high repeatability, calculated by the concordance correlation coefficient, (0.84 ≤ CCC ≤ 0.99). All measurements were more repeatable for the controlled lung volume group than the normal breath-hold group. CONCLUSION: CTTA repeatability was comparable to automatic volumetric measurements, and appears to be improved using controlled volume breath holding.

A multiparametric and multiresolution segmentation algorithm of 3-D ultrasonic data.

An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a Maximum A posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data.

Enhancement of contrast regions in suboptimal ultrasound images with application to echocardiography.

In this paper we propose a novel feature-based contrast enhancement approach to enhance the quality of noisy ultrasound (US) images. Our approach uses a phase-based feature detection algorithm, followed by sparse surface interpolation and subsequent nonlinear postprocessing. We first exploited the intensity-invariant property of phase-based acoustic feature detection to select a set of relevant image features in the data. Then, an approximation to the low-frequency components of the sparse set of selected features was obtained using a fast surface interpolation algorithm. Finally, a nonlinear postprocessing step was applied. Results of applying the method to echocardiographic sequences (2-D + T) are presented. The results demonstrate that the method can successfully enhance the intensity of the interesting features in the image. Better balanced contrasted images are obtained, which is important and useful both for manual processing and assessment by a clinician, and for computer analysis of the sequence. An evaluation protocol is proposed in the case of echocardiographic data and quantitative results are presented. We show that the correction is consistent over time and does not introduce any temporal artefacts.

Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation.

OBJECTIVE: A specific algorithm is presented for the automatic extraction of breast tumors in ultrasonic imaging. METHOD: The algorithm involves two-dimensional adaptive K-means clustering of the gray scale and textural feature images. The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem. The MAP estimation is achieved using Besag's iterated conditional modes algorithm for the minimization of an energy function. This function has three components: the first constrains the region to be close to the data; the second imposes spatial continuity; and the third takes into consideration the texture of the various regions. A multiresolution implementation of the algorithm is performed using a waveless basis. RESULTS: Experiments were carried out on synthetic images and on in vivo breast ultrasound images. Various parameters involved in the algorithm are discussed to evaluate the robustness and accuracy of the segmentation method. CONCLUSION: Including textural features in the segmentation of ultrasonic data improves the robustness of the algorithm and makes the segmentation result less parameter dependent.