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

Magnetite (FeO) 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 Fe as sole metal precursor acquired from accumulated iron ore tailings-a mining waste that usually represents a major environmental threat. FeO/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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.