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This work utilizes xenograft murine breast cancer tumors to develop terahertz imaging methodology for freshly excised breast cancer tissue. Tumors are grown via cell injection into mice on a high-fat diet and excised for subsequent terahertz imaging. Following imaging of both freshly excised tumor bulk and interior cross-sections, the tumors are then subjected to histopathology processing for correlation...
In this article, we propose an automatic method for the detection and extraction of the tumor on mammogram images. Most methods of detection of a tumor require the extraction of a large number of texture features from multiple calculations. The study first examines a technique of preprocessing images to obtain the Otsu thresholding method to eliminate items that do not belong in. After performing...
Breast cancer care involves a number of clinical considerations, such as relevant patient characteristics, including age, hormone-receptor status and cancer stage, and choice among several interventions, like surgery, radiation therapy, and administered drugs. Discovering these relationships in real word care is a challenging problem due to the fragmentation of relevant data among multiple information...
An important problem in quantitative medical image analysis is a large number of features (often highly correlated) to instance ratio. To handle this, we developed a feature selector and an ensemble classifier based on a modified version of random subspace method. We propose using a fusion of feature selection concepts: ranking based, correlation based and random subspaces, to develop a concordance...
A novel Adaptive Combining via Correlation Exploration (ACE) algorithm of ultrawideband (UWB) imaging for breast cancer detection is proposed. ACE explores and exploits the correlation between backscattered signals and local coherence reference signals generated within each group of neighboring antennas. High-correlation signals are adaptively selected, summed, and weighted by the product of their...
Microarray technology has been recently used to analyze the behavior of thousands of genes simultaneously, and have an important role in diagnosis, detection and treatment methods. Reducing the size of the attributes (genes) with high potential for classification of microarray data analysis is thus an important goal. In this paper, we propose a new feature selection method based on maximum correlation...
Breast cancer is one of the most common malignancy in women. Recently, the development in medical imaging technology increases the diagnosis effectiveness in predicting breast tumor in the early stage. The trend in breast cancer diagnose is to predict what kind of breast cancer could be happened instead of detecting the disease. In this paper, a breast magnetic resonance imaging is applied to compute...
This work aims at selecting useful features in critical angles and distances by Gray Level Co-occurrence Matrix (GLCM). In this project, images were labeled based on physician opinion in two groups (malignant or benign). These labeled images were used in classification analysis. Images were opened and read in Matlab software. The tumors were cropped in rectangular shape manually; then graycomatrix...
Breast cancer is one of the most common cancers among woman of the developing countries in the world, and it has also become a major cause of death. Treatment of breast cancer is effective only if it is detected at an early stage. X-ray mammography is the most effective method for early detection but the mammography images are complex. Thus nowadays, image processing and image analysis techniques...
The aim of this study is to identify a gene expression signature which is characteristic of ER status in breast cancer patients. To our knowledge, this is the first microarray study in Greece involving clinical samples. We identified 97 genes that are characteristic for ER status and can well distinguish the ER+ from the ER- samples. We shrank our list to a 11-gene list correlating to the same patient...
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