Visible light photocatalysts from low-grade iron ore: the environmentally benign production of magnetite/carbon (Fe3O4/C) nanocomposites.

Magnetite (Fe3O4) nanoparticles coated with dextrose and gluconic acid possessing both super-paramagnetism and excellent optical properties have been productively synthesized through a straightforward, efficient and cost-efficient hydrothermal reduction route using Fe3+ as sole metal precursor acquired from accumulated iron ore tailings-a mining waste that usually represents a major environmental threat. Fe3O4/C nanocomposites were fully elucidated by FEGSEM and TEM, revealing a combination of platelets (<1 µm) capped by particles (<10 nm) and magnetite which was verified by XPS, which demonstrated also oxygen deficiency. A dextrose/gluconic acid coating was elucidated by Fourier transform-infrared (FT-IR) spectroscopy and thermogravimetric analysis (TGA). The Fe3O4/C nanocomposites were found to be superparamagnetic at room temperature. Meanwhile, their optical properties were investigated by UV-visible diffuse reflectance spectroscopy (UV-vis DRS) and photoluminescence (PL) spectroscopy; an Eg of 1.86 eV was determined, and emissions at 612 and 650 nm (ex. 250 nm) were consistent with the XPS identification of oxygen vacancies. The efficacy of the as-synthesized magnetically recoverable magnetite/carbon (Fe3O4/C) nanocomposites has been exhibited in the photocatalytic degradation of the toxic textile (industrial) dye bodactive red BNC-BS.

HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion.

In prognostic evaluation of breast cancer, immunohistochemical (IHC) marker human epidermal growth factor receptor 2 (HER2) is used for prognostic evaluation. Accurate assessment of HER2-stained tissue sample is essential in therapeutic decision making for the patients. In regular clinical settings, expert pathologists assess the HER2-stained tissue slide under microscope for manual scoring based on prior experience. Manual scoring is time consuming, tedious, and often prone to inter-observer variation among group of pathologists. With the recent advancement in the area of computer vision and deep learning, medical image analysis has got significant attention. A number of deep learning architectures have been proposed for classification of different image groups. These networks are also used for transfer learning to classify other image classes. In the presented study, a number of transfer learning architectures are used for HER2 scoring. Five pre-trained architectures viz. VGG16, VGG19, ResNet50, MobileNetV2, and NASNetMobile with decimating the fully connected layers to get 3-class classification have been used for the comparative assessment of the networks as well as further scoring of stained tissue sample image based on statistical voting using mode operator. HER2 Challenge dataset from Warwick University is used in this study. A total of 2130 image patches were extracted to generate the training dataset from 300 training images corresponding to 30 training cases. The output model is then tested on 800 new test image patches from 100 test images acquired from 10 test cases (different from training cases) to report the outcome results. The transfer learning models have shown significant accuracy with VGG19 showing the best accuracy for the test images. The accuracy is found to be 93%, which increases to 98% on the image-based scoring using statistical voting mechanism. The output shows a capable quantification pipeline in automated HER2 score generation.

AutoIHC-Analyzer: computer-assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers.

Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2-stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often results in interobserver variability. Further, the burden of increasing number of patients is a challenge for the pathologists. To address these challenges, there is an urgent need with a rapid HER2 cell membrane extraction method. The proposed study aims at developing an automated IHC scoring system, termed as AutoIHC-Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. A series of image processing approaches have been used to automatically extract the stained cells and membrane region, followed by automatic assessment of complete and broken membrane. Finally, a set of features are used to automatically classify the tissue under observation for the quantitative scoring as 0/1+, 2+ and 3+. In a set of surgically extracted cases of HER2-stained tissues, obtained from collaborative hospital for the testing and validation of the proposed approach AutoIHC-Analyzer and publicly available open source ImmunoMembrane software are compared for 90 set of randomly acquired images with the scores by expert pathologist where significant correlation is observed [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001)] respectively. The output shows promising quantification in automated scoring. LAY DESCRIPTION: In cancer prognosis amongst the patient of breast cancer, human epidermal growth factor receptor 2 (HER2) is used as Immunohistochemical (IHC) biomarker. The correct assessment of HER2 leads to the therapeutic decision making. In regular practice, the stained tissue sample is observed under a bright microscope and the expert pathologists score the sample as negative (0/1+), equivocal (2+) and positive (3+) case. The scoring is based on the standard guidelines relating the complete and broken cell membrane as well as intensity of staining in the membrane boundary. Such evaluation is time consuming, tedious and quite often results in interobserver variability. To assist in rapid HER2 cell membrane assessment, the proposed study aims at developing an automated IHC scoring system, termed as AutoIHC-Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. The input image is preprocessed using modified white patch and CMYK and RGB colour space were used in extracting the haematoxylin (negatively stained cells) and diaminobenzidine (DAB) stain observed in the tumour cell membrane. Segmentation and postprocessing are applied to create the masks for each of the stain channels. The membrane mask is then quantified as complete or broken using skeletonisation and morphological operations. Six set of features were assessed for the classification from a set of 180 training images. These features are: complete to broken membrane ratio, amount of stain using area of Blue and Saturation channels to the image size, DAB to haematoxylin ratio from segmented masks and average R, G and B from five largest blobs in segmented DAB-masked image. These features are then used in training the SVM classifier with Gaussian kernel using 5-fold cross-validation. The accuracy in the training sample is found to be 88.3%. The model is then used for 90 set of unknown test sample images and the final labelling of stained cells and HER2 scores (as 0/1+, 2+ and 3+) are compared with the ground truth, that is expert pathologists' score from the collaborative hospital. The test sample images were also fed to ImmunoMembrane software for a comparative assessment. The results from the proposed AutoIHC-Analyzer and ImmunoMembrane software were compared with the expert pathologists' score where significant agreement using Pearson's correlation coefficient [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001) respectively] is observed. The results from AutoIHC-Analyzer show promising quantitative assessment of HER2 scoring.

A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms.

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

Mammogram segmentation using multi-atlas deformable registration.

Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. While traditional segmentation algorithms underperform due to variations in image quality and shape of the breast region, newer methods from machine learning cannot be readily applied as they need a large training dataset with segmented images. In this paper, we propose to overcome these limitations by combining clustering with deformable image registration. Using clustering, we first identify a set of atlas images that best capture the variation in mammograms. This is done using a clustering algorithm where the number of clusters is determined using model selection on a low-dimensional projection of the images. Then, we use these atlas images to transfer the segmentation to similar images using deformable image registration algorithm. Our technique also overcomes the limitation of very few landmarks for registration in breast images. We evaluated our method on the mini-MIAS and DDSM datasets against three existing state-of-the-art algorithms using two performance metrics, Jaccard Index and Hausdorff Distance. We demonstrate that the proposed approach is indeed capable of identifying different types of mammograms in the dataset and segmenting them accurately.

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval.

Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured.

Predicting Escherichia coli's chemotactic drift under exponential gradient.

Bacterial species are known to show chemotaxis, i.e., the directed motions in the presence of certain chemicals, whereas the motion is random in the absence of those chemicals. The bacteria modulate their run time to induce chemotactic drift towards the attractant chemicals and away from the repellent chemicals. However, the existing theoretical knowledge does not exhibit a proper match with experimental validation, and hence there is a need for developing alternate models and validating experimentally. In this paper a more robust theoretical model is proposed to investigate chemotactic drift of peritrichous Escherichia coli under an exponential nutrient gradient. An exponential gradient is used to understand the steady state behavior of drift because of the logarithmic functionality of the chemosensory receptors. Our theoretical estimations are validated through the experimentation and simulation results. Thus, the developed model successfully delineates the run time, run trajectory, and drift velocity as measured from the experiments.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images.

PURPOSE: Boundary roughness of a pulmonary nodule is an important indication of its malignancy. The irregularity of the shape of a nodule is represented in terms of a few diagnostic characteristics such as spiculation, lobulation, and sphericity. Quantitative characterization of these diagnostic characteristics is essential for designing a content-based image retrieval system and computer-aided system for diagnosis of lung cancer. METHODS: This paper presents differential geometry-based techniques for computation of spiculation, lobulation, and sphericity using the binary mask of the segmented nodule. These shape features are computed in 3D considering complete nodule. RESULTS: The performance of the proposed and competing methods is evaluated in terms of the precision, mean similarity, and normalized discounted cumulative gain on 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The proposed methods are comparable to or better than gold standard technique. The reproducibility of proposed feature extraction techniques is evaluated using RIDER coffee break data set. The mean and standard deviation of the percent change of spiculation, lobulation, and sphericity are [Formula: see text], [Formula: see text], and [Formula: see text] %, respectively. CONCLUSION: The prior works of computation of spiculation, lobulation, and sphericity require a set of four ground truths from radiologists and, hence, can not be used in practice. The proposed methods do not require ground truth information of nodules from radiologists, and hence, it can be used in real-life computer-aided diagnosis system for lung cancer.

A Segmentation Framework of Pulmonary Nodules in Lung CT Images.

Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

Signal parameter estimation of complex exponentials using fourth order statistics: additive Gaussian noise environment.

A novel approach based on fourth order statistics is presented for estimating the parameters of the complex exponential signal model in additive colored Gaussian noise whose autocorrelation function is not known. Monte Carlo simulations demonstrate that the proposed method performs better than an existing method which also utilizes fourth order statistics under the similar noise condition. To deal with the non-stationarity of the modeled signal, various concepts are introduced while extending the estimation technique based on linear prediction to the higher order statistics domain. It is illustrated that the accuracy of parameter estimation in this case improves due to better handling of signal non-stationarity. While forming the fourth order moment/ cumulant of a signal, the choice of the lag-parameters is crucial. It has been demonstrated that the symmetric fourth order moment/ cumulant as defined in this paper will have many desirable properties.

A heuristic approach to automated nipple detection in digital mammograms.

In this paper, a heuristic approach to automated nipple detection in digital mammograms is presented. A multithresholding algorithm is first applied to segment the mammogram and separate the breast region from the background region. Next, the problem is considered separately for craniocaudal (CC) and mediolateral-oblique (MLO) views. In the simplified algorithm, a search is performed on the segmented image along a band around the centroid and in a direction perpendicular to the pectoral muscle edge in the MLO view image. The direction defaults to the horizontal (perpendicular to the thoracic wall) in case of CC view images. The farthest pixel from the base found in this direction can be approximated as the nipple point. Further, an improved version of the simplified algorithm is proposed which can be considered as a subclass of the Branch and Bound algorithms. The mean Euclidean distance between the ground truth and calculated nipple position for 500 mammograms from the Digital Database for Screening Mammography (DDSM) database was found to be 11.03 mm and the average total time taken by the algorithm was 0.79 s. Results of the proposed algorithm demonstrate that even simple heuristics can achieve the desired result in nipple detection thus reducing the time and computational complexity.

Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms.

PURPOSE: We propose a method for the detection of architectural distortion in prior mammograms of interval-cancer cases based on the expected orientation of breast tissue patterns in mammograms. METHODS: The expected orientation of the breast tissue at each pixel was derived by using automatically detected landmarks including the breast boundary, the nipple, and the pectoral muscle (in mediolateral-oblique views). We hypothesize that the presence of architectural distortion changes the normal expected orientation of breast tissue patterns in a mammographic image. The angular deviation of the oriented structures in a given mammogram as compared to the expected orientation was analyzed to detect potential sites of architectural distortion using a measure of divergence of oriented patterns. Each potential site of architectural distortion was then characterized using measures of spicularity and angular dispersion specifically designed to represent spiculating patterns. The novel features for the characterization of spiculating patterns include an index of divergence of spicules computed from the intensity image and Gabor magnitude response using the Gabor angle response; radially weighted difference and angle-weighted difference (AWD) measures of the intensity, Gabor magnitude, and Gabor angle response; and AWD in the entropy of spicules computed from the intensity, Gabor magnitude, and Gabor angle response. RESULTS: Using the newly proposed features with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases, through feature selection and pattern classification with an artificial neural network, an area under the receiver operating characteristic curve of 0.75 was obtained. Free-response receiver operating characteristic analysis indicated a sensitivity of 0.80 at 5.3 false positives (FPs) per patient. Combining the features proposed in the present paper with others described in our previous works led to significant improvement with a sensitivity of 0.80 at 3.7 FPs per patient. CONCLUSION: The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, but the FP rate needs to be reduced.

Automatic detection of pectoral muscle using average gradient and shape based feature.

In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its' simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches.