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
Background: Although mammography is a powerful screening tool in detection of early breast cancer, it is imperfect, particularly for women with dense breast, which have a higher risk to develop cancer and decrease the sensitivity of mammogram, Automated breast ultrasound is a recently introduced ultrasonography technique, developed with the purpose to standardize breast ultrasonography and overcome some limitations of handheld ultrasound, this study aims to evaluate the diagnostic efficacy of Automated breast ultrasound and compare it with handheld ultrasound in the detection and characterization of breast lesions in women with dense breasts. Objectives: To evaluate the diagnostic efficacy of Automated breast ultrasound and compare
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Background: Epstein Barr Virus (EBV) infection has been implicated in pathogenesis of several types of carcinomas such as nasopharyngeal carcinoma, gastric cancer and bladder cancer and has recently been associated with breast cancer.
Objective: To evaluate the relations between Epstein Barr virus-encoded small RNA (EBER) and breast cancer.
Methods: Twenty two cases of breast cancer were retrieved from the Al-Kadhimiya Teaching Hospital in Baghdad. Clinical data were analyzed from the medical records and formalin fixed, paraffin embedded tumor tissue were examined by Chromogeneic in situ hybridization (ISH) technique for the detection of EBER.
Results: The expression of EBER in tissues patients with breast cancer in the present
Segmentation is the process of partition digital images into different parts depending on texture, color, or intensity, and can be used in different fields in order to segment and isolate the area to be partitioned. In this work images of the Moon obtained through observations in Astronomy and space dep. College of science university of Baghdad by ( Toward space telescopes and widespread used of a CCD camera) . Different segmentation methods were used to segment lunar craters. Different celestial objects cause craters when they crash into the surface of the Moon like asteroids and meteorites. Thousands of craters appears on the Moon's surface with ranges in size from meter to many kilometers, it provide insights into the age and geology
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Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err
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Background:Gynecomastia and breast cancer are the two most common diseases of the male breast. Most other diseases found in male breast arise from the skin and subcutaneous tissues. Male breast cancer is relatively rare, in contrast to gynecomastia, which is relatively common.
Objectives: The aim of this report is to determine the prevalence of the different breast lesions among males; based upon the findings of fine needle aspiration cytology .
Patients and method:This study was carried out within the Main Referral Training Center for Early Detection of Breast Tumors, Medical City Teaching Hospital, Baghdad (from the
beginning of January 2003 till the end of November 2007). One hundred twenty four males 
Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o
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Background: Breast cancer is a highly heterogeneous disease globally. Trace elements such as copper and zinc have a role in many biochemical reactions as micro source, their metabolism is profoundly altered in neoplastic diseases especially breast cancer which is ranked as the first of female cancers
Objective: The aim of the present study is to study the impact of body mass index and some trace elements in Iraqi women with breast cancer.
Patients and methods: The group of the study consisted of 25 breast cancer patients; their age range was (25–65) years recruited from the Al-Kadhimia Teaching Hospital and 25 apparently healthy women age matched, over a period of 6 months from January 2015 until June 2015. After the diagnosis wa
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Background: image processing of medical images is major method to increase reliability of cancer diagnosis.
Methods: The proposed system proceeded into two stages: First, enhancement stage which was performed using of median filter to reduce the noise and artifacts that present in a CT image of a human lung with a cancer, Second: implementation of k-means clustering algorithm.
Results: the result image of k-means algorithm compared with the image resulted from implementation of fuzzy c-means (FCM) algorithm.
Conclusion: We found that the time required for k-means algorithm implementation is less than that of FCM algorithm.MATLAB package (version 7.3) was used in writing the programming code of our w