ABSTRACT
Abstract Introduction Statistical data reveal that approximately 140 million radiological exams are performed annually in Brazil. These exams are designed to detect and to analyze fractures, caused by different types of trauma; as well as, to diagnose pathologies such as pulmonary diseases. For better visualization of those lesions or abnormalities, methods of image segmentation can be implemented. Such methods lead to the separation of the region of interest, which allows extracting the characteristics and anomalies of the desired tissue. However, the methods developed by researchers in this area still have restrictions. Consequently, we present an automatic pulmonary segmentation approach that overcomes these constraints. Methods This method is composed of a combination of Discrete Wavelet Packet Frame (DWPF), morphological operations and Gradient Vector Flow (GVF). The methodology is divided into four steps: Pre-processing - the original image is enhanced by discrete wavelet; Processing - where occurs a combination of the Otsu threshold with a series of morphological operations in order to identify the pulmonary object; Post-processing - an innovative form of using GVF improves the binary information of pulmonary tissue, and; Evaluation - the segmented images were evaluated for accuracy of detection the pulmonary region and border. Results The evaluation was carried out by segmenting 247 digital X-ray challenging images of the thorax human. The results show high for values of Overlap (97,63% ± 3.34%), and Average Contour Distance (0.69mm ± 0.95mm). Conclusion The results allow verifying that the proposed technique is robust and more accurate than other methods of lung segmentation, besides being a fully automatic method of lung segmentation.
ABSTRACT
Por ser capaz de mostrar aspectos morfológicos e patológicos de ateroscleroses, o Ultrassom Intravascular (IVUS) se tornou uma das modalidades de imagens médicas mais confiáveis e empregadas em intervenções cardíacas. As características de sua imagem aumentam as chances de um bom diagnóstico, resultando em terapias mais precisas. O estudo de segmentação da fronteira média-adventícia, dentre muitas aplicações, é importante para o aprendizado das propriedades mecânicas e determinação de algumas medidas específicas (raio, diâmetro, etc.) em vasos e placas. Neste trabalho, uma associação de técnicas de processamento de imagens está sendo proposta para atingir alta acurácia na segmentação da borda média-adventícia. Para tanto, foi feita uma combinação das seguintes técnicas: Redução do Speckle por Difusão Anisotrópica (SRAD), Wavelet, Otsu e Morfologia Matemática. Primeiramente, é usado SRAD para atenuar os ruídos speckle. Posteriormente, é executada Transformada Wavelet para extração das características dos vasos e placas. Uma versão binarizada dessas características é criada na qual o limiar ótimo é definido por Otsu. Finalmente, é usada Morfologia Matemática para obtenção do formato da adventícia. O método proposto é avaliado ao segmentar 100 imagens de alta complexidade, obtendo uma média de Verdadeiro Positivo (TP(%)) = 92,83 ± 4,91, Falso Positivo (FP(%)) = 3,43 ± 3,47, Falso Negativo (FN(%)) = 7,17 ± 4,91, Máximo Falso Positivo (MaxFP(mm)) = 0,27 ± 0,22, Máximo Falso Negativo (MaxFN(mm)) = 0,31 ± 0,2. A eficácia do nosso método é demonstrada, comparando este resultado com outro trabalho recente na literatura.
By being able to show morphological and pathological aspects of atherosclerosis, the Intravascular Ultrasound (IVUS) be¬came one of the most reliable and employed medical imaging modality in cardiac interventions. Its image characteristics in¬crease the chances of a good diagnostic, resulting in a precise therapy. The study of media-adventitia borders segmentation in IVUS, among many applications, is important for learning about the mechanical properties and determining some specific measurements (radius, diameter, etc.) in vases and plaques. An approach is proposed to achieve high accuracy in media-adventitia borders segmentation, by making a combination of different image processing operations: Speckle Reducing Anisotropic Diffusion (SRAD), Wavelet, Otsu and Mathematical Morphology. Firstly, SRAD is applied to attenuate the speckle noise. Next, the vessel and plaque features are extracted by performing Wavelet Transform. Optimal thresholding is car¬ried out by Otsu method to create a binarized version of these features. Then, Mathematical Morphology operations are used to obtain an adventitia shape. The proposed approach is evaluated by segmenting 100 challenging images, obtaining an average of True Positive (TP(%)) = 92.83 ± 4.91, False Positive (FP(%)) = 3.43 ± 3.47, False Negative (FN(%)) = 7.17 ± 4.91, Max False Positive (MaxFP(mm)) = 0.27 ± 0.22, Max False Negative (MaxFN(mm)) = 0.31 ± 0.2. The effectiveness of our approach is demonstrated by comparing this result with another recent work in the literature.