Challenges and Solutions in Automated Tongue Diagnosis Techniques: A Review.

Tongue diagnosis is used in various traditional medicine cultures as a non-invasive method for assessing an individual's health. Tongue image analysis has the potential for assessing the metabolism and functionality of the internal organs, making it a quick method of diagnosis. As automated systems give quantitative and objective results thereby effective in facilitating diagnosis, a review was conducted to evaluate literature on current methods of tongue diagnosis. Different methods of tongue diagnosis in the literature were identified and compared. Information on automated tongue diagnosis system, such as image acquisition, color correction, segmentation, feature extraction and classification, particularly in traditional medicine were reviewed. The aim of the review was to identify effective image processing techniques to be compatible with automated system for tongue diagnosis using some easily available to all imaging device rather than a dedicated state of art acquisition systems, which may not be easily accessible to general public. All methods identified were either being researched or developed and no specific system was identified that is currently available for routine use in clinics or home monitoring for patients. The healthcare sector could benefit from access to validated and automated tongue diagnosis systems. The feasibility of a mobile enabled platform to intelligently make use of this traditional method of diagnosis should be explored. In order to provide cheap and quick preliminary diagnosis for clinical practice automation of this noninvasive traditional technique can prove to be a boon for health care sector.

Segmentation of left ventricle in short-axis echocardiographic sequences by weighted radial edge filtering and adaptive recovery of dropout regions.

In this paper, we present a weighted radial edge filtering algorithm with adaptive recovery of dropout regions for the semi-automatic delineation of endocardial contours in short-axis echocardiographic image sequences. The proposed algorithm requires minimal user intervention at the end diastolic frame of the image sequence for specifying the candidate points of the contour. The region of interest is identified by fitting an ellipse in the region defined by the specified points. Subsequently, the ellipse centre is used for originating the radial lines for filtering. A weighted radial edge filter is employed for the detection of edge points. The outliers are corrected by global as well as local statistics. Dropout regions are recovered by incorporating the important temporal information from the previous frame by means of recursive least squares adaptive filter. This ensures fairly accurate segmentation of the cardiac structures for further determination of the functional cardiac parameters. The proposed algorithm was applied to 10 data-sets over a full cardiac cycle and the results were validated by comparing computer-generated boundaries to those manually outlined by two experts using Hausdorff distance (HD) measure, radial mean square error (rmse) and contour similarity index. The rmse was 1.83 mm with a HD of 5.12 ± 1.21 mm. We have also compared our results with two existing approaches, level set and optical flow. The results indicate an improvement when compared with ground truth due to incorporation of temporal clues. The weighted radial edge filtering algorithm in conjunction with adaptive dropout recovery offers semi-automatic segmentation of heart chambers in 2D echocardiography sequences for accurate assessment of global left ventricular function to guide therapy and staging of the cardiovascular diseases.