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A 3D α-hull approach to juxta-pleural nodule detection in chest Computed Tomography images

The aim of this work is therefore to contribute to the development of a CAD system for the automatic detection of juxta-pleural nodules in CT scans.
The basic steps of the CAD system presented in this work are:
- segmentation of the lung parenchyma, after which juxta-pleural nodules appear as concavities of the lung surface;
- concavity closing in the binary segmented mask with the alpha-hull concavity patching method in order to include in the segmented volume those concavities that may be related to juxta-pleural nodules. By applying the difference operation between the closed and the original mask, a list of nodule candidates is obtained;
- feature (candidate characteristic parameters) extraction and calculation for each nodule candidate;
- nodule candidate classification by using Artificial Neural Networks.
In this work, in order to overcome the limitations imposed by the 2D analysis previously performed by our research group, a 3D approach
is performed. The method proposed consists of stacking 2D single slice binary masks whose concavities have been closed by the multiscale alphahull algorithm in order to obtain a 3D reconstruction of the closed lung. By applying a difference operation with the segmented original volume, we detect a list of concavities (either natural or due to nodules). The study of the performance of the nodule hunting based on alpha-hull for a fixed range of α values is also presented.
This study leads to the assessment of the optimum parameter optimizing detection sensitivity, which identifies all juxta-pleural nodules in the CT data set. In order to reduce the number of false positives (regions reported as healthy by radiologists and incorrectly identified as pathological by the CAD system) a research on the most effective characteristics of the nodules is carried out. Finally, the classification step is performed, preceded by 3D features extraction, analysis and filtering.
The classifier developed in this work, is an Artificial Neural Network. Training and test of the network are performed. By varying the number of neurons in the hidden layer, the structure with the best performance is determined. The results obtained are then compared to other CAD systems performance.

Mostra/Nascondi contenuto.
Introduction Lung cancer is one of the main causes of death among both men and women [1] [2], with about 28% and 19% of all cancer-related deaths in the United States [3] and in European Union [4], respectively. The survival rate is estimated to be between 10% and 15% after 5 years from diagnosis, with an increase up to 50% if the cancer is detected in its early stage [5]. In this scenario, early diagnosis plays an important role in increasing the survival rate of people afiected by lung cancer. ComputedTomography(CT)isconsideredthebestimagingmodalityforthe detection of lung nodules, particularly after the introduction of the multi- detector-row and helical CT technologies [6]. Therefore, screening programs based on low-dose CT are regarded as a promising approach for detecting early-stage lung cancers and reducing the number of lung cancer deaths. A recent work [7] indeed demonstrates the real efiectiveness of screening programs for lung cancer carried out with low-dose CT. The work shows a reduction of 5-year mortality of more than 20% for subjects in the screening program with low-dose CT. The development of a Computer-Assisted Detection (CAD) system to automatically identify lung nodules can enhance diagnosis accuracy in the usual clinical practice providing valuable assistance to the radiologist by giving a second opinion on a diagnosis. Along these lines, the MAGIC-5 1

Tesi di Dottorato

Dipartimento: Fisica

Autore: Marco Peccarisi Contatta »

Composta da 175 pagine.

 

Questa tesi ha raggiunto 64 click dal 12/06/2013.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.