A Concise Method for Locating the Nipple Position on Mammogram Images

In this paper, Metaheuristic Algorithms such as Genetic Algorithm (GA) and Parallel Ant Colony Optimization (ACO) are implemented to extract the suspicious region based on the asymmetry approach. In bilateral subtraction, the asymmetries between corresponding left and right breast images are considered for extracting the suspicious region from the background tissue. The breast border and the nipple position are used as reference points for alignment of mammograms. The Genetic Operators such as reproduction crossover and mutations are used to detect breast border and using a novel method called Parallel Ant Colony Optimization algorithm identifies the nipple position. The mammogram images are aligned with the help of border points and nipple position and the suspicious regions are extracted by subtracting the left and the right breast image.

Purpose:Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most effective imaging modalities for early detection. However mammogram images often come with low contrast, high background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram images to improve results with a computer aided diagnosis system. The focus is on three preprocessing methods: a breast border segmentation method; a contrast enhancement method; and a pectoral muscle removal method.Methods:The proposed breast border extraction method employs a threshold based segmentation technique along with a combination of morphological operations. The contrast enhancement method presented here is divided into two phages. In phase I, a bi-level histogram modification technique is applied to enhance the image globally and in phase II a non-linear filter based on local mean and local standard deviation for each pixel is applied to the histogram modified image. The pectoral muscle removal method discussed here is implemented by applying a region growing algorithm.Results:The proposed techniques are tested with the Mini MIAS dataset. The breast border extraction method is applied to 322 images and achieved 98.7% segmentation accuracy. The contrast enhancement method is evaluated based on quantitative measures like measure of enhancement, absolute mean brightness error, combined enhancement measure and discrete entropy. The proposed contrast enhancement method when applied to 14 images with different types of masses, the quantitative measures showed an optimum level of contrast enhancement compared to other enhancement methods with preservation of local detail. Removal of the pectoral muscle from MLO mammogram images reduced the search region while identifying abnormalities like masses and calcification.Conclusions:The preprocessing steps proposed here show promising results in terms of both qualitative and quantitative analysis.

Women Breast Cancer has high incidence rate in worldwide. Computer aided diagnosis helps the radiologist to diagnose and treat the breast cancer at early stage. Recent studies states that Adaptive Neural Fuzzy Inference System (ANFIS) classifier achieves notable performance than the other classifiers. The major forte of ANFIS is that it has the robust learning mechanism with fuzzy data. However, the connections between the layers are not pruned for their significance. An Ant Colony Optimization (ACO) based learning is proposed in this paper to improve ANFIS classifier with a novel pruning strategy. The proposed algorithm is inspired from the social life of a special species called ‘Weaver Ants’. The proposed classifier is evaluated with the mammogram images from MIAS database, the quantified results show that this weaver ant based learning strategy improves the ANFIS classifier’s performance.

Breast biopsies based on the results of mammography and ultrasound have been diagnosed as benign at a rate of approximately 40 to 60 percent. Negative biopsy results have negative impacts on many aspects such as unnecessary operations, fear, pain, and cost. Therefore, there is a need for a more reliable technique to reduce the number of unnecessary biopsies in the diagnosis of breast cancer. So, computer-aided diagnostic methods are very important for doctors to make more accurate decisions and to avoid unnecessary biopsies. For this purpose, we apply deep learning using Convolutional Neural Networks (CNN) to classify abnormalities as benign or malignant in mammogram images by using two different databases namely, mini-MIAS and BCDR. While mini-MIAS database has valuable information like location of the center of abnormality and radius of the circle that surrounds the abnormality, BCDR database does not have. When we use both dataset as they are, we observe accuracy, precision, recall, and f-score values between around 60% and 72%. In order to improve our results, we take the benefit of preprocessing methods containing cropping, augmentation, and balancing image data. In an effort to crop image data sourced from BCDR, we create a mask to find region of interest. After applying our preprocessing methods over the BCDR dataset, we observe that classification accuracy improves from 65% to around 85%. When we compare the classification accuracy, precision, recall and f-score obtained from the MIAS database with those obtained from the BCDR database we found that after applying preprocessing methods to BCDR dataset, the classification performance become very close to each other for the two datasets.

Microcalcification detection is a very important issue in early diagnosis of breast cancer. Generally physicians use mammogram images for this task; however, sometimes analyzing these images become a hard task because of problems in images such as high brightness values, dense tissues, noise, and insufficient contrast level. In this paper, we present a novel technique for the task of microcalcification detection. This technique consists of three steps. The first step is focused on removing pectoral muscle and unnecessary parts from the mammogram images by using cellular neural networks (CNNs), which makes this a novel process. In the second step, we present a novel image enhancement technique focused on enhancing lesion intensities called the automated lesion intensity enhancer (ALIE). In the third step, we use a special CNN structure, named multistable CNNs. After applying the combination of these methods on the MIAS database, we achieve 82.0% accuracy, 90.9% sensitivity, and 52.2% specificity values.

To establish a practical CAD (Computer-Aided Diagnosis) system to facilitate the diagnosis of microcalcifications, we propose a filter-based technique to detect microcalcifications. Via examination of an existing optimal filter-based technique, it is found that its performance on highlighting the energy of mammograms is seriously affected by artefacts and the background of breast. As a result, four methods in pre and post-processing are described in this paper to improve the optimal filtering, leading to an adaptive selection of thresholds for input mammograms. These methods have been tested by using 30 mammograms (with 25 microcalcifications) from the MIAS database and 23 mammograms from DDSM database. Comparing with the original optimal filter-based technique, our technique reduces the false detections (FD), eliminates the influence of the background in mammograms and is able to adaptively select the threshold for the detection of microcalcifications.

Registration and difference analysis of corresponding mammogram images

Localization of surface dent deformation and inter-laminated damage in CFRP laminates under low-velocity impact behavior based on multi-channel one-dimensional convolutional gated recurrent unit

This paper presents a method for automated mammographic risk classification based on breast density estimation in mammograms. The overall profile of breast tissue density is represented using a topographic map, which is a hierarchical representation, obtained from the upper level sets of an image. A shape tree is constructed to describe the topological and geometrical structure of the shapes (i.e. connected components) within the topographic map. Two properties, saliency and independency, are defined to detect shapes of interest (i.e. dense regions) based on the shape tree. A density map is further generated focusing on dense regions, which provides a quantitative description of breast density. Finally, mammographic risk classification is performed based on the breast density measures derived from the density map. The validity of this method is evaluated using the full MIAS database and a large dataset taken from the DDSM database. A high agreement with expert radiologists is indicated according to the BIRADS density classification. The obtained classification accuracies are 76.01% and 81.22%, respectively.

Nowadays, X-ray mammography is the most common method in early detection and diagnosis of breast cancer. In order to shorten the duration of mammogram analysis, and also to contribute radiologists review and analysis, Computer Aided Systems have been developed. One of the first and most important steps of these systems is the segmentation of breast region. In this study, for the breast region segmentation a new derivative-based threshold detection algorithm has been developed. In distinguishing the breast area from the background to determine the most appropriate threshold value, mammogram histograms in MIAS database are examined and classified, phantom mammogram image is prepared and the global threshold detection algorithm is validated.

Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severity of abnormality (benign or malign) using combined transforms domain features. In this paper two such domains are explored, Discrete Cosine Transform - Discrete Wavelet Transform (DCT-DWT) and Discrete Cosine Transform - Stationary Wavelet Transform (DCT-SWT). The method is tested on 221 mammogram images from the MIAS database. The combined transform domain features proves to be a promising tool for precise classification with SVM classifier. The DCT-DWT domain yields 96.26% accuracy for discrimination between normal-malign samples comparing to DCT-SWT which gives 93.14%. The novelty of the proposed method is demonstrated by comparing with nearest neighbor classification technique.

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.

Microcalcification cluster detection using multiscale products based Hessian matrix via the Tsallis thresholding scheme

A scattering layer is introduced at a horizontal water–bottom interface to account for effects of attenuation due to bottom volume scattering. Using a wide-angle parabolic equation, a formula for the local mean intensity level is found for a shallow-ocean model consisting of three isospeed fluid layers: water, scattering layer, and bottom. The sensitivity of the theoretical mean to environmental parameters is studied, and the standard deviation of intensity is examined also. Comparisons are made between intensity statistics of the three-layer model and of other isospeed models. Also, comparisons are made between three-layer model statistics and data from a recent New Jersey Shelf experiment. Results from the inclusion of a scattering layer for the prediction of relative intensity statistics are evaluated over a wide range of acoustic frequencies. It is found that, with parameter values representative of the experimental site, the three-layer model can successfully match the local mean and standard deviation of the intensity data.