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Automatic classification of cancer lesions for gastroenterology imaging scenarios poses novel challenges to computer assisted decision systems, owing to their distinct visual characteristics such as reduced color spaces or natural organic textures. In this paper, we explore the prospects of using Gabor filters in a texton framework for the classification of images from two distinct imaging modalities...
The ability to accurately interpret large image scenes is often dependent on the ability to extract relevant contextual, domain-specific information from different parts of the scene. Traditionally, techniques such as multi-scale (i.e. multi-resolution) frameworks and hierarchical classifiers have been used to analyze large images. In this paper we present a novel framework that classifies entire...
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum the Posteriori (MAP)...
Breast cancer is the second leading cause of cancer deaths in women in the U.S. Two main problems appear to affect the decision of detecting and diagnosing breast cancer: the accuracy of the CAD systems used, and the radiologists' performance in reading mammograms. The main challenge in designing any CAD system is to maintain a high sensitivity level in detecting the abnormalities as the density of...
Prostate cancer is considered to be one of the main causes of cancer related death for men in the United States. Automated methods for prostate cancer localization based on multispectral magnetic resonance imaging (MRI) haver recently emerged as a non invasive technique for this purpose as an alternative to transrectal ultrasound. However, the automated methods developed to this date require a manual...
This paper presents a domain-specific automated image analysis framework for the detection of pre-cancerous and cancerous lesions of the uterine cervix. Our proposed framework departs from previous methods in that we include domain-specific diagnostic features in a probabilistic manner using conditional random fields. Likewise, we provide a novel window-based performance assessment scheme for 2D image...
In this paper, we designed a Computer-Aided-Diagnosis (CAD) system for lesion detection in breast MR images. The CAD process begins with analysis of MR images to detect the existence of lesion. If lesion exists, it is then coloured based on its type; benign, suspicious or malignant. Our CAD system enables better visualization of the lesions and improves accuracy as well as speed for breast cancer...
Breast cancer is the most common cancer in many countries all over the world. Early detection of cancer, in either diagnosis or screening programs, decreases the mortality rates. Computer Aided Detection (CAD) is software that aids radiologists in detecting abnormalities in medical images. In this article we present our approach in detecting abnormalities in mammograms using digital mammography. Each...
This paper proposes a method for breast cancer diagnosis in digital mammogram. The article focuses on using texture analysis based on curvelet transform for the classification of tissues. The most discriminative texture features of regions of interest are extracted. Then, a nearest neighbor classifier based on Euclidian distance is constructed. The obtained results calculated using 5-fold cross validation...
The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, one novel scheme based on multi-view information fusion is proposed, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates. Five contour and shape features of the masses...
Ovarian Carcinoma (OvCa) is the most lethal type of gynecological cancer. The studies show that about 90% patients could be saved if they are treated in the early stage. In this study, a novel biomarker selection approach is proposed which combines singular value decomposition (SVD) and Monte Carlo strategy to early OvCa detection. Other than supervised classification methods or differential expression...
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...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
The most reliable way to diagnose breast cancer in the current practice of medicine is through pathological examination of a biopsy which has a certain level of subjectivity. To reduce this subjectivity and have a mathematical model for diagnosing breast cancer tissues, a fully automatic method based on microscopic biopsy image is presented. The novel technique is based on a four-step procedure: the...
This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem; minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and...
Computerized diagnostic tools have received significant attention over the past few decades, in order to assist medical practitioners in diagnosis of disease based on a variety of test results. It provides a fast and accurate method for diagnosis, particularly in cases where medical practitioners need to deal with difficult diagnosis problems. In this paper, we present an examination of two popular...
HER-2/neu, a protein often giving higher aggressiveness in breast cancers, has been shown that if the gene is expanded for some reason, the Her-2 protein produced by the cells will be over-expressed to enhance the cancer cells reproduced ability, the prognosis will be also relatively less, too. The HER-2 immunohistochemical stained provides a simple and reliable method for pathologist in clinical...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques and allows computer to learn from past examples and detect patterns from large data sets, which is particularly well-suited to assist medical practitioners in diagnosis of disease based on a variety of test results. Therefore, in this research, we deemed further...
This paper proposes a microcalcification shape feature to aid in classifying regions of interest that are difficult to diagnosis. The proposed feature extraction method is based on a wavelet approximation of a microcalcification's contour distance sequence, which is the Euclidean distance of each contour point to the centroid. A novel metric is proposed to quantify the roughness of a microcalcification...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
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