Design, Calibration, and Testing of an Automated Near-Infrared Liquid-Crystal Polarimetric Imaging System for Discrimination of Lung Cancer Cells

Shrestha, Suman; Petermann, Jeff; Farrahi, Tannaz; Deshpande, Aditi; Giakos, G.C.

doi:10.1109/tim.2015.2415013

Cited by 19 publications

(4 citation statements)

References 27 publications

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“…Lung nodes are defects of the spherical and circular opaque lung tissue with a diameter of 30 mm. Clinical picture processing is widely and widely used to build expert systems for the diagnosis of many diseases, including the diagnosis of gout, the detection of parasites, and the diagnosis and rehabilitation of lung cancer [4] [5]. Significant studies are being conducted on the early detection of lung cancer using CAT methods.…”

Section: Introductionmentioning

confidence: 99%

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Gugulothu

Balaji

2022

Preprint

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Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. The use of hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans.Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and the complexity of the surrounding environment.Here, we proposed alung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce achaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate anImproved Fish Bee (IFB) algorithm for feature extraction and selection process. Third, we develop hybrid classifier i.e. hybrid differential evolution based neural network (HDE-NN) for tumor prediction and classification.Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

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Section: Introductionmentioning

confidence: 99%

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Gugulothu

Balaji

2022

Preprint

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“…During the last decade, Giakos et al [27][28][29][30] have extensively worked on a number of approaches for the differentiation of healthy and cancerous cells, introducing label-free NIR-IR polarimetric diffuse reflectance-based cancer detection methodologies in conjunction with a wavelet and fractal analysis. Visualizing the interaction of IR with healthy and malignant (cancerous) lung cells through polarimetry under diffuse reflectance geometry, in connection with polarimetric exploratory data analysis (pEDA) [29] enables the development of robust and competent diagnostic tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Deshpande

Cambria

Barnes

et al. 2021

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This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation.

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“…The N channel usually covers the spectrum in the range of 800 − 1000nm. RGBN images have been widely used in many fields such as remote sensing [4] , medical imaging [5], and computer vision [6].…”

Section: Introductionmentioning

confidence: 99%

Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal

Mohajerani¹,

Drew²,

Saeedi³

2020

Preprint

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Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation. Although generating illumination-invariant images has been studied in the literature before, it has not been investigated on real 4-channel (4D) data. In this study, we examine the quality of illumination-invariant images generated from red, green, blue, and near-infrared (RGBN) data. Our experiments show that the near-infrared channel substantively contributes toward removing illumination. As shown in our numerical and visual results, the illumination-invariant image obtained by RGBN data is superior compared to that obtained by RGB alone.

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Design, Calibration, and Testing of an Automated Near-Infrared Liquid-Crystal Polarimetric Imaging System for Discrimination of Lung Cancer Cells

Cited by 19 publications

References 27 publications

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal

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