BackgroundBody image is a subjective concept encompassing a person’s views and emotions about their body. Head and neck cancer (HNC) diagnosis and treatment affects several psychosocial concepts including body image. Large numbers of HNC patients are diagnosed each year in India but there are no suitable measures in regional languages to assess their body image. This study assessed the psychometric properties of the Body Image Scale (BIS), a measure suitable for clinical and research use in HNC populations, translated into Tamil, (...) Telugu and Hindi and compared body image distress between language groups.MethodsTranslated versions of BIS were completed by HNC patients recruited from three cancer centers across India one time only. Psychometric evaluation was conducted including factor analysis using principal component analysis and internal consistency reliability using Cronbach’s alpha. Patients completed the EORTC Quality of Life Questionnaire (QLQ) C-30 and EORTC QLQ HN-35 measures to enable exploration of convergent and discriminant validity. ANOVA was used to calculate difference in mean values for body image.ResultsOur sample included 621 HNC patients (Tamil = 205, Telugu = 216, Hindi = 200). Factor analysis revealed a one-factor solution and Cronbach’s alpha coefficients ranged between 0.891 and 0.969 indicating good reliability. Hypothesized correlations between similar and different constructs were as expected, supporting construct validity. On the BIS, we found a statistically significant difference (F= 11.0954,P< 0.05) between means of Tamil, Telugu, and Hindi groups, with higher body image scores in Telugu (M= 12.86;SD= 7.65) and Hindi groups (M= 12.52;SD= 7.36) indicating more symptoms/body image distress, when compared to Tamil population (M= 9.28;SD= 10.04).ConclusionThe reliability and validity of the three translated Indian versions of the BIS were maintained, providing a method for assessing body image of HNC population worldwide speaking Tamil, Telugu, and Hindi across the illness trajectory. (shrink)

In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges (...) while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques. (shrink)