Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two stages performed using the minimum distance (MD) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLCM for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from Al-Ilwiya Hospital in Baghdad, Iraq. The proposed technique shows better results
Two homopolymeric and three copolymeric additives for base oil were synthesized using octyl acrylate (OA) and tert-butyl acrylamide (TBA) monomers. The two additives named P1 and P2 are the homopolymers of TBA and OA, respectively, whereas copolymeric additives named Co1, Co2, and Co3 were synthesized by varying the ratios of TBA:OA as 1:3, 3:1 and 1:1, respectively. The prepared polymers were characterized by Fourier Transform Infrared (FTIR). Based on the solubility of synthesized polymers in base oil and reactivity ratios of TBA/OA copolymer (0.222, 0.434) calculated by Fineman-Ross method, P2, Co1, Co2 and Co3 were selected to evaluate their performance as pour point depressant (PPD), viscosity improver (VII), and anticorrosion addit
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Background: Oral squamous cell carcinoma (OSCC) remains a lethal and deforming disease, with a significant mortality and a rising incidence in younger and female patients. It is thus imperative to identify potential risk factors for OSCC and oral PMDs and to design an accurate data collection tool to try to identify patients at high risk of OSCC development. 14 factors consistently found to be associated with the pathogenesis of OSCC and oral PMDs. Eight of themwere identified as high risk (including tobacco, alcohol, betel quid, marijuana, genetic factors, age, diet and immunodeficiency) and 6 low risk (such as oral health, socioeconomic status, HPV, candida infection, alcoholic mouth wash and diabetes) were stratified according to severit
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Combination of natural poly-phenolic compounds with chemotherapeutic agents is recently being a novel strategy in cancer therapy researches owing to their potential antioxidant and anti-inflammatory properties that modulate several intracellular signaling pathways.
Resveratrol and Baicalein are well known poly-phenolic compounds that belong to stilbene and flavone subclasses, respectively.
This study aims to investigate the possible enhancement effect of resveratrol and Baicalein when combined with doxorubicin using a different combination ratio and applied on two cancer cell lines: HCT116 (colorectal cancer cells) and HepG2 (hepatocellular cancer cells). It also investigates the possibility of such natural compounds to p
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Heart sound is an electric signal affected by some factors during the signal's recording process, which adds unwanted information to the signal. Recently, many studies have been interested in noise removal and signal recovery problems. The first step in signal processing is noise removal; many filters are used and proposed for treating this problem. Here, the Hankel matrix is implemented from a given signal and tries to clean the signal by overcoming unwanted information from the Hankel matrix. The first step is detecting unwanted information by defining a binary operator. This operator is defined under some threshold. The unwanted information replaces by zero, and the wanted information keeping in the estimated matrix. The resulting matrix
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