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This work describes the synthesis and characterization of a new series of semifluorinated sulfonated polytriazole copolymers (PTATSH‐XX) with varying degree of sulfonation and higher IEC. The copolymers have been prepared by click reaction using a combination of diazide monomers; a newly synthesized diazide monomer namely 4,4‐bis[3‐trifluoromethyl‐4(4‐azidophenoxy)phenyl]benzene (TAZ) and a commercially available diazide monomer namely 4,4′‐diazido‐2,2′‐stilbenedisulfonic acid disodium salt (DADSDB) with a bis‐alkyne monomer namely 4,4′‐(propane‐2,2‐diyl)bis((prop‐2‐ynyloxy)benzene) (BPEBPA). The copolymers are well characterized by FTIR and NMR spectroscopy and have exhibited good solubility and film forming abilities along with good thermal and chemical stability, low water uptake, good dimensional stability and high mechanical properties. The TEM micrographs of the copolymer membranes show good phase separated morphology with cluster size in the range 10–60 nm. The proton conductivity values of the copolymer membranes are found in the range of 15–90 mS cm−1 at 80°C and 16–97 mS cm−1 at 90°C. POLYM. ENG. SCI., 57:312–323, 2017. © 2016 Society of Plastics Engineers

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The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).

Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.

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A series of new copolymers HPPQSH-XX PS were synthesized from preformed sulfonated ionomers HPPQSH-XX in order to get high proton conductivity along with other excellent properties.