Auxílio computadorizado à identificação do câncer de pulmão baseado em mapas de conexidade Fuzzy 3-D

Abstract

Lung cancer is the most common and deadly of all kinds of cancers. Nowadays, the best chances of cure rely on early diagnosis. During diagnosis, the specialist must perform segmentation of a suspect nodule and analyze its shape to classify the nodule as benign or malignant. Nodule segmentation is commonly performed manually, and because of that, it is considered tedious, time consuming and can be compromised from observer variations. It may still be subject to human fail, and aspects like eyestrain, emotional factors, and even the specialist experience can. Hence the use of a semiautomatic process of segmentation provides the specialist a tool capable of aiding the early detection, which improves the patient’s chance of survival. Current segmentation techniques based on fuzzy connectivity maps provide a satisfatory level of 84% of acceptance by specialists. Therefore, the purpose of this work is to combine an optimized version of an algorithm based on fuzzy connectivity maps with other segmentation algorithms obtain a robust method for lung nodules segmentation. The method combines three algorithms to obtain the nodule segmentation; a pre-processsing stage based on local adaptive segmentation, which removes vascular attachments and undesired structures, and forms an optimum mask containing the nodule and obtains an optimum seed for the method; a 3-D fuzzy connectivity map, formed by the evaluation of relevance through all image voxels according to the optimum seed; a contrast-based sphericity oriented region growing algorithm, which establishes an automated halting criteria for the fuzzy connectivity map. As a result, the method presents an optimal solution and several alternative solutions for the specialist. The method was evaluated using a benchmark of lung nodules, composed of images manually segmented by specialists, applied in several studies of lung cancer. The test used 140 nodules, divided in two groups according to its malignancy levels. The method showed capable of segmenting different kinds of nodules with a satisfactory precision between others works in literature that used the same dataset. The work has also found reference values of segmentation algorithms constants and evaluated the impact of the optimized fuzzy connectivity maps to the processing cost.