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Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader systems for a correct diagnosis and treatment process. In this paper, a deep learning based feature extraction method by convolutional neural network (CNN) is proposed for automated mitosis...
Microcalcifications are the earliest sign of breast carcinoma. Their typical size is about 1 mm, which is why it is difficult to detect for an expert. Therefore, a tool that eases their visualization becomes relevant. Segmentation gives the candidate areas that could contain microcalcifications. A preprocessing step can improve segmentation performance but the algorithm becomes database dependent...
Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups:...
This paper introduces an automatic classification of mammogram images by categorizing malignant or normal after segmenting the suspected region. Fuzzy and fuzzy soft set approaches have been used successfully to deal with diverse uncertainties, imprecision and vagueness in data. We have advocated a method of fuzzy soft set using fuzzy soft aggregation operator for solving the problem. The proposed...
Ability to clearly delineate the nuclei of microscopic cancer cells is crucial to the accuracy and efficiency of image-based approaches to cancer diagnosis and treatment. Oftentimes, however, such cells contain overlapped (or touched) nuclei. The study proposed in this work presents a hybrid trichotomic technique that combines the Gram-Schmidt method (GSM), handling of relevant geometric features...
Breast cancer is a highly heterogeneous disease and very common among women worldwide. Inter-observer and intra-observer errors occur frequently in analyzing the lesion portion of medical images, giving high variability in results interpretations. Computer Aided Diagnosis system (CAD) plays a vital role to overcome this variability. Segmentation is the second critical stage in CAD system to extract...
The proposed contribution uses median fuzzy c-means approach for detection of masses and macrocalcificaiton in mammogram images. Median clustering is a powerful methodology for prototype based clustering of similarity/dissimilarity data. In the MFCM instead of calculating the mean for each cluster to determine its centroid, it calculates the median. This has the consequence of reducing error on the...
This paper presents a method for segment and detects the boundary of different breast tissue regions in mammograms by using dynamic K-means clustering algorithm and Seed Based Region Growing (SBRG) techniques. Firstly, the K-means clustering is applied for dynamically and automatically generated the seeds points and determines the thresholds' values for each region. Secondly, the region growing algorithm...
In this paper we present a novel fully automated scheme for detection of abnormal masses by anatomical segmentation of Breast Region and classification of regions of Interest (ROI). The system consists of three main processing steps, we perform essential pre-processing to remove noise, suppress artifacts and labels, enhance the breast region, extract breast region by the process of segmentation and...
Mass detection in mammogram is one of effective technology for breast cancer diagnosis. A novel method of mass segmentation in mammogram is proposed in this paper. First, a mathematical model (MM) of the mass is presented to detect the location of mass. Second, based on the time series features generated by Pulse Coupled Neural Network (PCNN), the pixels are classified by Fuzzy C-Means clustering...
Breast cancer is one of the main causes of death among women worldwide. Mammography is an effective imaging modality for early diagnosis of breast cancer. Understanding the nature of data in breast images is very important for developing a model that fits well the data. Gaussian distribution is widely used for modeling the data in breast images but due to the asymmetric nature of the distribution...
Automatic segmentation of stained breast tissue images helps pathologists to discover the cancer disease earlier. Separation of touching cells presents many difficulties to the traditional segmentation algorithms. In this paper, we propose a new automatic method to segment clustered cancer cells. In the first step, we detect cell regions using a modified geometric active contour based on Chan-Vese...
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...
In this work we explore the application of graph cuts techniques to the problem of finding the boundary of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if graph cuts algorithm could separate different densities for the different breast patterns. The graph cut is applied with multi-selection of seeds label to provide the hard constraint, whereas the...
Tissue classification in mammography can help the diagnosis of breast cancer by separating healthy tissue from lesions. We present herein the use of three texture descriptors for breast tissue segmentation purposes: the Sum Histogram, the Gray Level Co-Occurrence Matrix (GLCM) and the Local Binary Pattern (LBP). A modification of the LBP is also proposed for a better distinction of the tissues. In...
The expression of the HER-2/neu (HER2) gene, a member of the epidermal growth factor receptor family, has been shown to be a valuable prognostic indicator for breast cancer. However, interobserver variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective evaluation of HER2...
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