Browsing by Author "Kulshrestha, Niharika"

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    PublicationArticle
    A complex diffusion based modified fuzzy C- means approach for segmentation of ultrasound image in presence of speckle noise for breast cancer detection
    (International Information and Engineering Technology Association, 2020) Srivastava, Subodh; Kumar, Guddu; Mishra, Ritesh K.; Kulshrestha, Niharika
    This paper proposes a single framework for segmentation of abnormalities for breast cancer detection from Ultrasound images in presence of Rayleigh noise i.e. noise removal and segmentation are embedded in single step. It accomplishes dual purpose in a single framework simultaneously for the preprocessing and segmentation. The proposed framework comprises of two terms, first term, is used for segmentation which is a modified fuzzy c-means segmentation (MFCM) approach while second term is an adaptive complex diffusion based non linear filter (ACDPDE) that performs as regularization function for removal of Rayleigh noise, enhancement, and edge preservation of ultrasound Image. The various existing segmentation methods viz. K-Means, Texture based, Fuzzy C-Means (FCM), total variation based FCM (TVFCM), Adaptive fourth order PDE based FCM (AFPDEFCM), and the proposed method are evaluated for 50 sample ultrasound images of breast cancer. The region of interest (ROI) segmented image of ultrasound breast tissue is compared with ground truth images. From the acquired results and its analysis, it is observed that the proposed method is more robust and provides better segmentation result for ultrasound images in terms of various performance measures such as Global Constancy error (GCE), Tanimoto coefficient, Variation of Information (VOI), Probability Random Index (PRI), Jaccard coefficient, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR), dice index, False Negative Rate (FNR), and Area under curve (AUC). The proposed approach is capable of handling segmentation problem of blocky artifacts while achieving good tradeoff between Rayleigh noise removal and edge preservation. The proposed method may be useful for finding additional 33%cases of breast cancer which is missed or not detected by mammography. © 2020 Lavoisier. All rights reserved.
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    PublicationArticle
    Effect of ZnO nanoparticles in 70:30 PVA:LiI polymer electrolyte films
    (American Scientific Publishers, 2016) Kulshrestha, Niharika; Chatterjee, Bhaskar; Gupta, Prem Narain
    ZnO nanoparticles are prepared by the polymer precursor method using zinc oxide, polyethylene glycol and citric acid. Solution cast technique is used to prepare polymer electrolyte films having 70:30 weight ratio of the polyvinyl alcohol and lithium iodide salt incorporating 0, 1 and 2 wt% of ZnO nanoparticles. X-ray diffraction (XRD) pattern indicates that crystallinity of the polymer electrolyte films increases with the dispersal of ZnO nanoparticles in the polymer matrix. Vibrational changes in the nanocomposite polymer electrolyte films are shown by Fourier Transform Infra-Red (FTIR) Spectroscopy. The highest ionic conductivity is found to be ∼3_5×10−5 S/cm at room temperature for 70:30 PVA:LiI polymer electrolyte films. Ionic conductivity is found to be decreased with increasing amount of ZnO nanoparticles. Dielectric properties of the polymer electrolyte films are observed with varying ZnO nanoparticles in the polymer electrolyte films. Enhancement in the relaxation time and reduction in the dielectric loss are monitored with increase in the crystallinity of the polymer electrolyte films. © 2016 American Scientific Publishers All rights reserved.
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    PublicationArticle
    Nano composite solid polymer electrolytes based on biodegradable polymers starch and poly vinyl alcohol
    (Elsevier B.V., 2016) Chatterjee, B.; Kulshrestha, Niharika; Gupta, P.N.
    An attempt has been made to prepare nano composite solid polymer electrolytes by adding nano fumed silica in different weight ratios in a polymer matrix composed of biodegradable polymers Starch and Poly Vinyl Alcohol (PVA) to get high ionic conductivity with good mechanical strength. Fourier Transform InfraRed spectroscopy (FTIR) was used to confirm the complexation and interaction of the nano filler with the polymer matrix. From the FTIR investigations it could be established that the formation of C-O-Si bonds by nano fumed SiO2 with Starch and PVA lead to the crucial structural modification that finally increase the conductivity of the sample. Dielectric and frequency dependent measurements substantiate the sharp increase in number of charge carriers as the major factor contributing to the enhancement of the conductivity for 3 wt% nano fumed silica. Electric modulus studies confirmed the ionic nature and indicate absence of electrode polarization in the system. Maximum ionic conductivity ∼10-3 S/cm could be realized for 3 wt% silica at ambient condition. © 2016 Elsevier Ltd. All rights reserved.