Pregnancy requires constant monitoring by health care providers to avoid conditions that may threaten the lives of the fetus and the mother at birth. For labor management, the diagnosis of fetal presentation is essential to guarantee delivery viability. A direct indicator of fetal presentation is the fetal head location, which can be placed close to the canal birth (cephalic, head-first) or far from the canal birth (breech, feet first). Unlike urban areas, the population in rural zones experience difficulties in accessing healthcare monitoring. Although telemedicine has helped bring medical technology closer to these regions, the diagnosis still requires medical specialists. This study presents an automatic three-stage detection framework for assessing the fetal head position (and hence the fetal presentation). The first stage involves applying morphological filtering, intensity-based thresholding, and shapebased filtering for a preliminary head detection and segmentation. The second stage comprises segmentation enhancement using a combinatory approach. The third stage uses the detection results to depict the spatial location likelihood of the head, which indicates the head location and fetal presentation. Fifteen volunteers in the third trimester of pregnancy were evaluated, and the fetal presentation was diagnosed. These results were compared with the diagnosis of a radiologist (used as a gold standard). The proposed method presented a 100% accuracy in determining the fetal presentation, albeit a limited number of cases evaluated.
Breast cancer is a public health problem with ~ 1.7 million new cases per year worldwide and with several limitations in the state-of-art screening techniques. Ultrasound elastography involves a set of techniques intended to facilitate the noninvasive diagnosis of cancer. Among these, Vibro-elastography is an ultrasound-based technique that employs external mechanical excitation to infer the elastic properties of soft tissue. In this paper, we evaluate the Vibro-elastography performance in the differentiation of benign and malignant breast lesions. For this study, a group of 18 women with clinically confirmed tumors or suspected malignant breast lesions were invited to participate. For each volunteer, an elastogram was obtained, and the mean elasticity of the lesion and the adjacent healthy tissue were calculated. After the acquisition, the volunteers underwent core-needle biopsy. The histopathological results allowed to validate the Vibro-elastography diagnosis, which ranged from benign to malignant lesions. Results indicate that the mean elasticity value of the benign lesions, malignant lesions and healthy breast tissue were 39.4 ± 12 KPa, 55.4 ± 7.02 KPa and 23.91 ± 4.57 kPa, respectively. The classification between benign and malignant breast cancer was performed using Support Vector Machine based on the measured lesion stiffness. A ROC curve permitted to quantify the accuracy of the differentiation and to define a suitable cutoff value of stiffness, obtaining an AUC of 0.90 and a cutoff value of 44.75 KPa. The results obtained suggest that Vibro-elastography allows differentiating between benign and malignant lesions. Furthermore, the elasticity values obtained for benign, malignant and healthy tissue are consistent with previous reports.
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