Métodos de contornos ativos Crisp adaptativo 2D e 3D aplicados na segmentação dos pulmões em imagens de tomografia computadorizada do tórax

Abstract

Computer systems have been playing a very important role in many areas of medicine, particularly, on medical diagnosis through image processing. Therefore, studies on the field of Computer Vision are made to develop techniques and systems to perform automatic detection of several diseases. Among the existing tests that enable the diagnosis and the application of computational system together, there is the Computed Tomography (CT), which allows the visualization of internal organs, such as the lung and its structures. Image analysis techniques applied to CT scans are able to extract important information to segment and recognize details on regions of interest on these images. This work focuses its e↵orts on the stage of lungs segmentation through CT images, using techniques based on Active Contour Method (ACM), also known as snake. This method consists in tracing an initial curve, around or inside the object of interest, wich deform itself according to forces that act over the same, shifting to the object edge. This process is performed by successive iterations of minimization of a given energy, associated to the curve. In this context, this work proposes a new aproach for lung segmentation of chest CT images, which is called Adaptative Crisp Active Contour Method. This ACM is an improvement the previous developed Crisp ACM. The purpose of this new ACM is to increase accuracy, decrease analysis time and reduce segmentation subjectivity in the manual analysis of specialized doctors. This method is proposed to isolated images segmentation or the complete exam, being first in 2D, then expanding to 3D. The 2D Adaptative Crisp ACM is compared to ACMs THRMulti, THRMod, GVF, VFC, Crisp and also with the system SISDEP, being this evaluation performed by using a set of 36 manually segmented images by one pulmonologist. The 3D Adaptative Crisp ACM is applied on lung segmentation in CT exams and compared with the 3D Region Growing method, which segmentation results were evaluated by two pulmonologists. The obtained results shows that the proposed methods are superior to the other methods on lung segmentation in chest CT images, both as in one image by 2D Adaptative Crisp ACM as in full exam by the 3D Adaptative Crisp ACM. Thus, it is possible to conclude that these method can integrate systems to aid medical diagnosis in the field of pulmonology.