一种基于视网膜主血管方向的视盘定位及提取方法

为分割出眼底图像中的视盘,构建基于眼底图像的计算机辅助诊断系统,提出了一种基于视网膜主血管方向的视盘定位及提取方法。首先,利用Otsu阈值分割眼底图像R通道获取视盘候选区域;然后利用彩色眼底图像的HSV空间的H通道提取视网膜主血管并确定主血管方向;在此基础上,通过在方向图内寻找出对加权匹配滤波器响应值最高的点确定视盘中心位置;最后,利用该位置信息从视盘候选区域中"挑选"出真正的视盘。利用该方法对100幅不同颜色、不同亮度的眼底图像进行视盘分割,得到准确率98%,平均每幅图像处理时间1.3 s。结果表明:该方法稳定可靠,能快速、有效分割出眼底图像中的视盘。     

A two-step procedure for automatic and accurate segmentation of volumetric CLSM biofilm images

This paper presents a robust two-step segmentation procedure for the study of biofilm structure. Without user intervention, the procedure segments volumetric biofilm images generated by a confocal laser scanning microscopy (CLSM). This automated procedure implements an anisotropic diffusion filter as a preprocessing step and a 3D extension of the Otsu method for thresholding. Applying the anisotropic diffusion filter to even low-contrast CLSM images significantly improves the segmentation obtained with the 3D Otsu method. A comparison of the results for several CLSM data sets demonstrated that the accuracy of this procedure, unlike that of the objective threshold selection algorithm (OTS), is not affected by biofilm coverage levels and thus fills an important gap in developing a robust and objective segmenting procedure. The effectiveness of the present segmentation procedure is shown for CLSM images containing different bacterial strains. The image saturation handling capability of this procedure relaxes the constraints on user-selected gain and intensity settings of a CLSM. Therefore, this two-step procedure provides an automatic and accurate segmentation of biofilms that is independent of biofilm coverage levels and, in turn, lays a solid foundation for achieving objective analysis of biofilm structural parameters.     

Effective pre-processing of long term noisy audio recordings: An aid to clinical monitoring

Nowadays, great attention is devoted to minimizing the discomfort caused by connection of patients to sensors for long-term monitoring of physiological parameters. Hence, the need for contact-less monitoring systems is increasingly recognized in clinical investigation. To this aim, audio signals recorded by ambient microphones are an appealing and increasing field of research: in the biomedical field, application of contact-less audio recording of long duration may concern obstructive apnoea syndrome, preterm newborns in Intensive Care Units, daily monitoring in occupational dysphonia, speech therapy, Parkinson and Alzheimer disease, monitoring of psychiatric and autistic subjects, etc. However, a significant amount of ambient noise is inevitably included in the records.Especially in the case of recordings that take a long time, manual extraction of clinically useful information from a whole record is a time-consuming operator-dependent task, the length of a whole recording (even several hours) being prohibitive both for perceptual analysis made by listening to it and for visual inspection of signal patterns. Moreover, objective measures of signal characteristics may serve clinicians as a common ground for diagnosis. Hence, automatic methods are needed to speed up and objectify the analysis task.The present work describes a new, automatic, fast and reliable method for extracting “voiced candidates” from audio recordings of long duration for both clinical and home applications.To demonstrate its effectiveness, the method is compared to existing software tools commonly used in biomedical applications using synthetic signals.