Thermal and phase behaviour studies of hydrogen-bonded compounds (SA:nOBA) using POM, DSC and image-processing techniques

Rambabu, M.; Prasad, K. R. S.; Rao, M. Venu Gopala; Madhav, B. T. P.; Pisipati, V. G. K. M.

doi:10.1080/1358314x.2015.1039195

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…The LBP operator was originally developed to define texture (Ojala et al , 1996). The operator assigns a mark to every pixel by thresholding in an image with the centre pixel value, and by taking the result as a binary quantity (Rambabu et al , 2015). The image of Lung CT can be seen as a micro-pattern structure that can be well represented by the LBP operator: Partition the examined window into cells. Collate each pixel with its neighbours in a cell. Assign “1”if the centre pixel value is greater than that of the neighbour pixel otherwise assign “0”.…”

Section: Methodsmentioning

confidence: 99%

IoT based lung cancer detection using machine learning and cuckoo search optimization

Venkatesh

Bojja

2021

IJPCC

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Purpose Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers. Design/methodology/approach IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious. Findings The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer. Practical implications The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis. Originality/value The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.

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Section: Methodsmentioning

confidence: 99%

IoT based lung cancer detection using machine learning and cuckoo search optimization

Venkatesh

Bojja

2021

IJPCC

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“…Contrast enhancement is one of the requirements to visualise and understand the microscopic image content [2][3][4][5][6][7][8][9][10][11][12]. LCD crystal microscopic imaging has been around for some time now.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement of nano-dispersed N-(p-n-decyloxybenzylidene)-p-n-hexyloxy aniline using combined unsharp masking

Madhav

Pardhasaradhi

Kishore

et al. 2016

Liquid Crystals Today

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The main objective of the image enhancement is to process an image with suitable technique to produce better visibility for a specific application. To identify key features like transition temperatures, clear phase identification in the liquid crystalline images, we require some novel image processing techniques. Characterisation and mesomorphic behaviour in pure and 1% ZnO nanodispersed liquid crystalline N-(p-n-decyloxybenzylidene)-p-n-hexyloxy anilines, 10O.O6 compounds are carried out using a polarising microscope and images are preserved for enhancement. Both the compounds exhibits NACIG (nematic, smectic-A, smectic-C, smectic-I, smectic-G) phases and the transition temperatures of the 1% ZnO nano-dispersed 10O.O6 are reduced compared with pure 10O.O6. Further, in this paper, a novel image enhancement technique of combined unsharp masking is proposed on pure and 1% ZnO nano-dispersed 10O.O6 liquid crystalline compounds for better visibility of phases at transition temperatures. The proposed method is used to identify the uniform regions and to detect the defects which may not be clearly observed from the textures that are recorded by polarising microscope.

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“…Over the temperature range, the images collected from the POM will be considered to find the statistical parameter mean. Image processing with Matlab will provide the transition temperatures of the compound without taking the help of DSC [48][49][50] . The time and cost will be reduced to find the transition temperatures of the images through image processing technique.…”

Section: Textural Analysis With Statistical Parametersmentioning

confidence: 99%

Schiff Base Liquid Crystalline Compounds With Dispersed Citrate Capped Gold Nanoparticles - Optical and Textural Analysis

2017

Rasayan J Chem

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In this paper, 20 µl citrate capped Gold (Au) nanoparticles are dispersed in Schiff base Liquid Crystalline compounds 6O.Om (m = 7 and 9) and characterizations are carried-out to observe the changes occurred with the dispersion of nanoparticles in Liquid Crystalline compounds. The Polarizing Microscopy (POM) technique is used to measure the phase transition temperatures. Differential Scanning Calorimetry (DSC) is used to determine the transition temperatures and enthalpy values. Further characterization is carried-out by Scanning Electron Microscopy (SEM). The nematic transition temperature is decreased by 1 o C with the dispersion of citrate capped Au nanoparticles into the Liquid Crystalline compounds. Image processing technique of textural analysis is also carried-out to identify the statistical parameters of the images and their transition temperatures.

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Thermal and phase behaviour studies of hydrogen-bonded compounds (SA:nOBA) using POM, DSC and image-processing techniques

Cited by 10 publications

References 19 publications

IoT based lung cancer detection using machine learning and cuckoo search optimization

IoT based lung cancer detection using machine learning and cuckoo search optimization

Image enhancement of nano-dispersed N-(p-n-decyloxybenzylidene)-p-n-hexyloxy aniline using combined unsharp masking

Schiff Base Liquid Crystalline Compounds With Dispersed Citrate Capped Gold Nanoparticles - Optical and Textural Analysis

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