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Well-defined boundaries are necessary to allow precise delineation of morphological structures from magnetic resonance images. Existing state-of-the-art sharpening techniques like unsharp masking (UM) produce discontinuity artefacts and have multiple operational parameters. Tuning multiple parameters together is cumbersome. A computationally efficient and noise robust nonlinear sharpening scheme which is free from discontinuity and saturation artefacts, with very less number of arbitrary parameters, is proposed in this paper. As an inverse mathematical problem, the sharpened image is computed from the amplified first derivative. To constrain the noise amplification, the local value of the amplification factor is considered as a nonlinear function of local average of absolute directional gradients. The proposed sharpening scheme is compared with two of its best possible alternatives, contrast limited adaptive histogram equalization (CLAHE) and UM in terms of sharpness of the output image, thinness of salient edges, feature preservation, saturation and edge quality degradation due to noise, using perceptual sharpness index (PSI), second-order derivative-based measure of enhancement (SDME), edge model-based blur metric (EMBM), structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR), saturation evaluation index (SEI) and sharpness of ridges (SOR). The proposed nonlinear sharpening scheme exhibited higher PSI, SDME, PSNR and SSIM and lower EMBM, SOR and computational time, compared to CLAHE and UM. The proposed scheme is found to be superior to the existing state-of-the-art techniques like CLAHE and UM, in terms of sharpness as well as thinness of salient edges in the sharpened image, robustness to noise, discontinuity artefacts, saturation and computational time. Because of minimum number of operational parameters, it is user friendly too. [ABSTRACT FROM AUTHOR]

Understanding the biomechanical properties of the diabetic foot may detect the foot at risk of ulceration. Increased foot sole hardness and the peripheral neuropathy are suggested to be important risk factors for foot ulceration, in subject with diabetes. Therefore this study was to investigate the feasibility of measuring and variation of foot sole hardness in patients with diabetes. Assessment was performed on 30 subjects aged from forty to seventy years, classified into three groups: 10 subjects with diabetic neuropathy (Group A), 10 subjects without diabetic neuropathy (Group B) and 10 non-diabetes subjects without neuropathy (Group C). Subjects underwent an assessment of loss of protective sensation (LOPS) by means of a 10 gm Semmes Weinstein monofilament and measurement of foot sole hardness by means of a durometer (ASTM-D 2240) or shore meter in eight foot sole areas. Imperceptions of the 5.07 / 10 gm Semmes-Weinstein monofilament at four or more of the eight foot sole areas was considered the threshold for neuropathy. Data was analyzed using ANOVA to detect significant difference between the groups and also Dunnett's pair wise multiple comparison t-tests was used to compare Groups A and B against the control mean (Group C). Our result shows statistical significant ( P < 0.05) differences between Groups A and C in all foot areas, except area 6. Also significant differences were found in areas 5, 7 and 8 while comparing Groups B and C in both feet. We conclude that difference and variation in foot sole hardness were found to be significantly different in subjects with and without diabetic neuropathy using shore meter. Hence shore meter was more sensitive and provided a feasible means of measuring foot sole hardness in subjects with diabetic neuropathy. Foot sole hardness in diabetic feet can be considered as potential determinant of the foot sole ulcerations. [ABSTRACT FROM AUTHOR]

Diabetic ulcers in neuropathic feet are difficult to manage. In earlier studies, appropriate footwear helps healing but recurrent ulceration, recalcitrant and difficult ulcers need surgical intervention. Keeping surgically reconstructed ulcers healed is a great challenge in diabetes mellitus patients when the primary cause of ulceration, reduced sensibility of skin persists. We have developed a system of keeping these surgically treated ulcers under surveillance so that early danger signs are detected. Easy color-coded printouts of pressures in the foot help us to explain to the patients the severity of the foot at risk. It also motivates the patients to attend follow-up and comply with using the appropriate footwear thus keeping healed ulcer healed. Indirect evidence, looking at the footprints on the undersurface of the appropriate footwear demonstrates its effectiveness and patient compliance. [ABSTRACT FROM AUTHOR]

Diabetic neuropathy is one of the most important factors for foot ulceration in diabetes mellitus (DM) patients. Among different sensibility measures of neuropathy, two-point discrimination (TPD) has been suggested as a reliable method; however, the correlation of TPD with other well-known measures is not known. We measured the loss of protective sensation using Semmes-Weinstein Monofilaments (SWMF), hardness of the foot sole using shore meter (sh), power ratio (PR) using pedopowergraph and TPD using esthesiometer in foot areas of both left and right legs in 14 DM subjects. We have found no correlation either between TPD and shore values (sh) or between TPD and PR. The SWMF (10 g) is found not to provide any additional value in measuring loss of sensation in comparison to TPD. The TPD appears to be measuring different property of the foot compared to other measures. The mechanism of this independence is not well understood and more investigation is required to understand the mechanism. [ABSTRACT FROM AUTHOR]

This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated. Various parametric and nonparametric tests are conducted to evaluate the relevance of extracted features. Classifiers such as support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree and discriminant analysis (DA) are used to categorize normal and COPD breath sounds. Classification based on spirometry parameters as well as respiratory sound parameters are assessed. Maximum classification accuracy of 83.6% is achieved by the SVM classifier while using the most relevant lung sound parameters i.e. median frequency and linear predictive coefficients. Further, SVM classifier and LR classifier achieved classification accuracy of 100% when relevant lung sound parameters, i.e. median frequency and linear predictive coefficient are combined with the spirometry parameters, i.e. forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). It is concluded that combining lung sound based features with spirometry data can improve the accuracy of COPD diagnosis and hence the clinician's performance in routine clinical practice. The proposed approach is of great significance in a clinical scenario wherein it can be used to assist clinicians for automated COPD diagnosis. A complete handheld medical system can be developed in the future incorporating lung sounds for COPD diagnosis using machine learning techniques. [ABSTRACT FROM AUTHOR]