Segmenting mammographic microcalcifications using a semi-automatic procedure based on Otsu's method and morphological filters

INTRODUCTION: Breast cancer has the second highest world's incidence rate, according to the Brazilian National Cancer Institute (INCa). Clinical examination and mammography are the best methods for early diagnosis. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems are developed to improve mammographic diagnosis. Basically, CADx systems have three components: (i) segmentation, (ii) parameters extraction and selection, (iii) lesion classification. The first step for a CADx system is segmentation. METHODS: A microcalcification segmentation method is proposed, based on morphological operators, Otsu's Method and radiologists' knowledge. Pre-processing with top-hat operators improves contrast and reduces background noise. The Otsu's method automatically selects the best grey-level threshold to segment microcalcifications, obtaining binary images. Following, inferior reconstruction and morphological dilatation operators are applied to reconstruct lost structure details and fill small flaws in the segmented microcalcifications. Finally, the Canny edge detection is applied to identify microcalcifications contour candidates for each region-of-interest (ROI). Two experienced radiologists intervene in this semi-automatic method, firstly, selecting the ROI and, then, analyzing the segmentation result. The method was assessed in 1000 ROIs from 158 digital images (300 dpi, 8 bits). RESULTS: Considering the radiologists opinion, the rates of ROIs adequately segmented to establish a diagnosis hypothesis were 97.8% for one radiologist and 97.3% for the other. Using the Area Overlap Measure (AOM) and the 2136 microcalcifications delineated by an experienced radiologist as gold standards, the method achieved an average AOM of 0.64±0.14, being 0.56±0.09 for small microcalcifications and 0.66±0.13 for the large ones. Moreover, AOM was 0.64±0.13 for the benign and 0.64±0.14 for the malignant lesions with no statistical differences between them. CONCLUSION: Based on these findings, the proposed method could be used to develop a CADx system that could help early breast cancer detection.

An approach to automatically segment the media-adventitia borders in IVUS / Uma associação de técnicas para segmentação automática da fronteira da média-adventícia em IVUS

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.

Drift correction in fMRI signal based on mathematical morphology / 中国医学影像技术

Objective To establish a new approach based on mathematical morphology that can effectively reduce the drifts in functional magnetic resonance imaging (fMRI) signals. Methods Based on investigation of the characteristic of drifted fMRI signals, a mathematical morphology method for baseline drift correction was presented. Results With both simulated data and real fMRI data, the results of the experiment showed that the mathematical morphology method can effectively correct the baseline drifts. Conclusion Both linear and nonlinear drifts can be removed with the proposed method without any statistical model assumption.

A Novel Method for Extraction of Heart Sounds Envelope Based on Interpolation of Local Peak and Valley Points / 航天医学与医学工程

Objective To extract envelope of heart sounds exactly,for the purpose of the further analysis of its characteristics.Methods The way that envelope extraction of heart sounds based on key-points was given.The points of local peak and valley were calculated firstly,and then heart sound envelope was gotten by the interpolation of these points.Results Compared with the envelope extracted by Hilbert-transform and mathematical morphology,respectively,the outline of heart sounds was extracted more accurately,and its time-domain characters were acquired by this method.Conclusion The envelope of heart sound is extracted correctly by this method,which is useful for the further analysis.

Method for QRS complex self-assortment based on mathematical morphology / 医疗卫生装备

This paper presents a new method for QRS complex self-assortment based on mathematical morphology, which measures the comparability of QRS complex with weighted Hausdorff distance. Beginning with the traditional Hausdorff distance, this paper introduces the weighted Hausdorff distance of planar object and the detailed algorithm of self-assortment of QRS complex. The evaluation results of this algorithm by MIT-BIHECG database show that this method has high self-assortment capability.

Segmentation of 3D MRI Image of Children Brain / 医疗卫生装备

Objective To study a semi-automatic segmentation framework for children's brain MRI image ( weighted), which is capable of identifying the different structures of children's MRI images by using the histogram analysis and morphological operations. Methods The framework consisted of four-step segmentation procedures. First, the non-brain structures removal was addressed to obtain the mask of encephalon, then the brain stem and cerebellum were separated respectively from the encephalon mask, and finally, two hemispheres were separated. Results The method behaved well in the segmentation of MRI images of the brain stem, cerebel and two brain hemispheres. Conclusion The method gave the good segmentation results in the children MRI image aging from 5 to 15 years old.