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Background: Dermatophyte, a keratinophilic fungus which can invade keratinized tissue cause dermatophytosis. It is one of the major superficial fungal infections. According to World Health Organization (WHO), 20-25% world population is affected by dermatophytes. The aim of this study was to identify different species of dermatophytes causing dermatophytosis at a tertiary care hospital in Bangladesh. Methods: Total 246 skin, nail and hair samples were collected from Dermatology and Venereology department of Dhaka Medical College Hospital (DMCH), Dhaka, Bangladesh. Dermatophytes were identified by microscopy, culture, biochemical test (urease test and hair perforation test) and polymerase chain reaction (PCR). Fungal elements were observed under microscope. Sabouraud Dextrose Agar (SDA) and Dermatophyte Test Medium (DTM) were used to culture the specimen. Species identification was confirmed by biochemical test and PCR. Results: Among 246 clinically diagnosed cases of dermatophytosis, 91 (36.98%) cases were observed among the age group of 21-30 years. Female were more affected than male (male-female ratio was1:1.34). Eighty-five (34.55%) cases were positive by microscopy and 76 (30.89%) cases were positive in culture. Trichophyton mentagrophyte was the common etiological dermatophyte species which was 59.21% followed by Trichophyton rubrum (40.79%). All culture isolates were further evaluated by biochemical test and PCR. Most of the tinea corporis was caused by Trichophyton mentagrophyte which was 64.81%. Conclusion: Dermatophytosis is a common infection in young adults specially females. Trichophyton mentagrophyte is more frequent type of dermatophyte. Majority of dermatophytes could be identified by both direct microscopy and culture methods.

Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i.e., quantitative ultrasound and elastography) features. The proposed transform domain bi-modal reduced feature set with different conventional classifiers will classify 201 breast tumors into benign-malignant as well as BI-RADS⩽3, 4, and 5 categories. For the latter case, an inadmissible error probability is defined for the subset selection using a wrapper/filter. The classifiers use train truth from histopathology/cytology for binary (i.e., benign-malignant) separation of tumors and then bi-modal BI-RADS scores from the radiologists for separating malignant tumors into BI-RADS category 4 and 5. A comparative performance analysis of several widely used conventional classifiers is also presented to assess their efficacy for the proposed transform domain reduced feature set for classification of breast tumors. The results show that our transform domain bimodal reduced feature set achieves improvement of 5.35%, 3.45%, and 3.98%, respectively, in sensitivity, specificity, and accuracy as compared to that of the original domain optimal feature set for benign-malignant classification of breast tumors. In quantitative classification of breast tumors into BI-RADS categories⩽3, 4, and 5, the proposed transform domain reduced feature set attains improvement of 3.49%, 9.07%, and 3.06%, respectively, in likelihood of malignancy and 4.48% in inadmissible error probability compared to that of the original domain optimal subset. In summary, the construction of a transform domain reduced feature set by extracting complementary information from a large set of available bi-modal features and use of qualitative bi-modal BI-RADS can contribute to improved quantitative classification of breast tumors and thereby help reduce the number of unnecessary biopsies, securing a nearly minimum chance of a life-endangering diagnosis.

Breast cancer is the leading cancer killer among women all over the world. Around 8% women suffer from this disease during their lifetime. Early detection and diagnosis through a noninvasive imaging modality can reduce the mortality rate from breast cancer. Because of its versatility to women's age and breast density, the state-of-the-art ultrasound (US) technology is one of the most frequently used imaging modalities for the detection of breast lesions in recent times. Moreover, the combined use of conventional US B-mode imaging and ultrasound elastography promises improvement in the diagnostic accuracy owing to the availability of complementary information simultaneously from a single clinical examination. Therefore, a brief introduction of this bimodal imaging - based diagnosis of breast cancer is the focus of this article.

Quantitative ultrasound (QUS) based parameters like the effective scatterer diameter (ESD) and mean scatterer spacing (MSS) are gaining attention recently as non-invasive biomarkers for soft tissue characterization. In this work, we propose a multiple QUS parameter based technique that employs ESD and MSS, for binary classification of breast lesions. In order to produce improved ESD estimates, we propose a modified frequency domain technique for ESD estimation of breast tissues from the diffuse component of backscattered radio-frequency (RF) data. Ensemble empirical mode decomposition (EEMD) is performed to separate the diffuse component from the coherent component by decomposing the RF data into their intrinsic mode functions (IMFs). A non-parametric Kolmogorov-Smirnov (K-S) test is employed for automatic IMF selection along with a multi-step system effect minimization process. The ESD is estimated using a nearest neighborhood average regression line fitting algorithm. Furthermore, we use an ameliorated EEMD domain autoregressive (AR) spectral estimation technique for MSS estimation. On using the ESD for binary classification of 159 lesions, we obtain high sensitivity, specificity, accuracy values of 91.07%, 96.12%, and 94.34%, respectively, with an area under the receiver operating characteristics (ROC) curve of 0.94. On combining ESD with MSS we obtain even more improved sensitivity, specificity, and accuracy values of 96.43%, 95.15%, and 95.60%, respectively, with an area under the ROC of 0.96. Such a high classification performance highlights the potential of these QUS parameters to be used as non-invasive biomarkers for breast cancer detection.

Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics (e.g., 94.64%–98.21% sensitivity, 97.24%–100.00% specificity and 96.52%–99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies.

We consider non-coherent detection of M-ary FSK modulated signals transmitted over a slow, Rayleigh fading channel in a wireless relay network. The network consists of a single source-destination pair and a number of relays (L), which employ cooperative diversity. Performances of a counting rule and square law combiner are studied. We derive closed form expressions for probabilities of error for equal relay channel average SNR. For unequal relay channel SNRs, we resort to Monte Carlo simulations to estimate the error probabilities. We examine different combinations of M and L for a range of average SNR values. Although the square law combiner outperforms the counting rule for equal and small average SNRs, the loss in performance is not high. Simplicity of counting rule may be advantageous in these cases.