Mammographic Image Analysis Society Research Articles

Automatic suspicions lesions segmentation based on variable-size windows in mammography images

Breast cancer is the second main cause of death in women of western countries, so early detection and prevention is crucial. Early detection increases the likelihood of treatment as well as patient resistance. Among breast cancer detection methods, mammography is the most effective diagnostic method. For radiologists, diagnosing a cancerous mass on mammographic images is prone to error, which shows there is a need for a method to reduce the errors. In this study, a new adaptive thresholding method is proposed based on variable-sized windows. This method estimates the location of the mass and then determines the exact location of the cancerous tissue to reduce false positives. To detect the mass automatically, firstly, the histograms diagram and its relative maximums have been used to calculate the initial threshold for estimating the mass location. Two windows that contain information around each pixel and their size varies according to the mean value of each image due to the preservation of useful information. Secondly, two windows are used for the final threshold in order to discover the location of the mass and its exact shape. The proposed approach has been applied to 170 images of the Mammographic Image Analysis Society MiniMammographic database. Evaluations have shown 96.7% sensitivity and 0.79 false-positive rates, which prove an improvement in comparison with other state-of-the-art methods.

Automatic Detection of Masses From Mammographic Images via Artificial Intelligence Techniques

A novel computer-aided tool for automated sensing of normal tissue and abnormal masses from mammographic X-ray images is described. The pre-processing technique was firstly adopted for noise elimination on mammographic images. The automatic initialization of active contour was then placed on the pre-processed image for segmentation followed by deep convolutional neural networks to extract the features. Principal component analysis was then applied to choose the most significant features as input to the support vector machine classifier. Lastly, k-fold cross-validation techniques were executed for results validation. The developed tool was tested on public available datasets, namely Mammographic Image Analysis Society, and Digital Database for Screening Mammogram, based on eight evaluation methods: accuracy, sensitivity, specificity, receiver operating characteristic curve, area under curve (AUC), F1-score, precision, and recall. The outcome demonstrated the proposed system as a competitive tool in assisting radiologists as it attains an average of 95.24%, 93.94%, 96.61%, 94.66, 93.00%, 94.34%, and 0.98 for accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC, respectively for testing on a combination of the aforementioned two datasets.

A novel improved crow‐search algorithm to classify the severity in digital mammograms

AbstractThe survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either benign (B) or malignant (M) using an improved crow‐search optimization algorithm (ImCSOA). In the literature, the CSOA is generally used for solving several feature selection and numerical optimization problems. The objective is to utilize this popular optimization algorithm for the problem of biomedical image classification. However, if this algorithm is applied directly to classification problems, then it will result in poor classification of data. Hence, the original CSO (OCSO) algorithm undergoes suitable enhancements using a novel controlled parameter tuning, control operator and chaotic‐maps‐based controlled randomness. Four distinct chaotic maps are used for controlling the randomness in the OCSO algorithm. The mammogram images are obtained from the Mammographic Image Analysis Society and Digital Database for Screening Mammography data sets for the evaluation. The classification is accomplished through discrete wavelet transform‐based statistical features that are extracted at two levels [level 4 (L4) and level 6 (L6)] of decomposition. For both data sets, the ImCSOA with L4 and L6 decomposed bior4.4 wavelet features provides the maximum accuracy of around 85% to 86%, which is approximately 62% to 88% better than the OCSO algorithm with L4 and L6 decomposed bior4.4 wavelet features.

Novel breast cancer classification framework based on deep learning

AbstractBreast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG-16, are utilised and re-trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k-fold cross-validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end-to-end fully convolutional neural networks without any prepossessing or post-processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s.

Deep learning algorithm for breast masses classification in mammograms

A mammogram is an image of a breast used to detect and diagnose breast cancer. This paper emphases a Computer-Aided Detection system based on convolutional neural network (CNN) that uses the concept of deep learning to classify the mammogram images into benign, malignant and normal. The proposed CNN model consists of eight convolutional, four max-pooling and two fully connected layers and achieved better results compared to the pre-trained nets, AlexNet and VGG16. The proposed model demonstrates the feasibility of using CNNs on medical image processing techniques for the classification of breast masses. The results are also compared with the state-of-the-art machine learning algorithm like kNN classifier. Experimentation is done with three datasets. Among them, two are publicly available, Mammographic Image Analysis Society (MIAS), digital database for screening mammography (DDSM) and an internally collected dataset. The proposed model achieved accuracies of 92.54, 96.47 and 95 and the Area under the ROC curve (AUC) score of 0.85, 0.96 and 0.94 for MIAS, DDSM and the internally collected dataset respectively. Furthermore, the images of the three datasets are merged to build one large set and used to fine tune the proposed CNN model and produced accuracy of 98.32 and AUC of 0.98.

McCulloch’s algorithm inspired cuckoo search optimizer based mammographic image segmentation

Multi-level thresholding for mammogram image segmentation leads to much better sub-sections of the intensity span, and hence very useful in breast cancer detection. In order to segment the mammogram image efficiently, in this paper, three popular nature inspired algorithms namely Harmony Search Algorithm (HSA), Electro-magnetism Optimization (EMO) and McCulloch’s Algorithm inspired Cuckoo Search Optimization algorithm (MACSO) are studied in detail; and are employed for desired cost function maximization for two well-known multi-level thresholding methods like Otsu and Kapur efficiently. The proposed approach is applied to all the 322 test images of database presented by Mammographic Image Analysis Society (MIAS), to detect pectoral muscle, breast and suspicious mass efficiently. Performance of EMO, MACSO and HSA were analysed using measures like best fitness, MSE, PSNR, SSIM and TIME. From the experimental results, it is concluded that MACSO with Otsu was found to be robust for segmentation of mammogram images accurately.

Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

AbstractBreast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high‐accuracy computing, computer‐aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.

Detection of Breast Lesion Using Improved GLCM Feature Based Extraction in Mammogram Images

A classic mammogram image processing method commonly illustrates of mammogram image pre-processing, segmentation, feature extraction and classification techniques. Feature extraction is a significant measure in Computer Assisted Diagnosis (CAD) of Breast cancer abnormalities. Feature Extraction is the method of shrinking the dimension of image data by finding an essential detail as of the segmented image. The visual substance of a segmented image portions can be confined using this procedure. Feature extraction is one of the Image retrieval methods for achieving higher accuracy. In this paper an Improved Gray Level Co-occurrence Matrix (GLCM) Feature based Extraction with shape and Tamura features are discussed. Tamura features are extracted from Optimized Kernel Fuzzy Clustering Algorithm (OKFCA) which is one of the segmentation methods. This paper proposed Improved GLCM Feature Based Extraction algorithm to obtain the trained features which will be used for further processing in future. Experimental results of 322 images were evaluated from Mammographic Image Analysis Society (MIAS) database and used to evaluate the feature extraction process.

Breast tumor detection and classification based on density

Breast cancer is the most widely disease in women and is considered one of the biggest causes of death in women. Early detection, classification, and diagnosis of this cancer are essential to reduce the death cases. The efficiency of the diagnosis of breast cancer depends on the accuracy of the segmentation of the tumor and its classification. Before the segmentation of mammogram images, it is necessary to extract the Region of Interest (ROI) because it can assume a significant job in the efficiency of tumor detection. In this study, the method of automatic extraction of ROI from the mammogram image is presented. After that, the preprocessing of both mammogram and MR images has been done which removes the noise and enhances the contrast of the images. On the processed images, segmentation of tumors from mammograms as well as Magnetic Resonance Imaging (MRI) breast images has been performed using automatic seed point extraction and threshold calculation in Seeded Region Growing (SRG). After the successful detection of breast tumor area, classification of the tumor as benign or malignant has been done. The test was performed on three publicly available data sets; RIDER breast MR Images, Mammographic Image Analysis Society (MIAS), and Digital Database for Screening Mammography (DDSM). The test showed that classification accuracy is better than previously accessible cutting edge techniques, which is 91.4%. The proposed method not only detects the shape and size of the tumor from both mammograms and MR Images but also efficiently classifies the tumor as benign or malignant.

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

Classification of breast cancer mammogram images using convolution neural network

SummaryMedical imaging systems have broadly used in the diagnosis and identification of breast cancer. It is essential to recognize breast tumor as soon as possible. Mammography is a widely utilized method for the identification of breast cancer. The identification of cancer is trailed by the segmentation of the cancer area in an image of the mammogram. Numerous researches have been made on the diagnosing and identification of breast cancer utilizing different classification and image processing methods. In this work, we proposed the Convolutional Neural Network (CNN) classifier for diagnosing breast cancer utilizing MIAS (Mammographic Image Analysis Society)‐dataset. CNN established as an efficient class of methods for image recognition problems. CNN is a deep learning system that extricates the feature of an image and utilizes those features for classification of the image. Because deep learning methods are utilized for high task objective Computer Vision, Medical Diagnosis, Image processing, and so on. Wiener filter is utilized to expel the noise and background of the image and the K‐means clustering technique was utilized for the segmentation. After segmentation, the features are extracted and classified utilizing CNN classifier. The performance of this proposed method was analyzed and compared with the conventional techniques based on accuracy, sensitivity, and specificity result parameters. From the comparison result, it is seen that the CNN classifier performed better compared with different techniques with 0.5‐4% additional accuracy and 3‐13% specificity.

Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns

We investigate various channel encoding techniques applied to breast density classification in mammograms; specifically, local binary, ternary, and quinary encoding approaches are considered. Subsequently, we propose a new encoding approach based on a seven-encoding technique, yielding a new local pattern operator called a local septenary pattern operator. Experimental results suggest that the proposed local pattern operator is robust and outperforms the other encoding techniques when evaluated on the Mammographic Image Analysis Society (MIAS) and InBreast datasets. The local septenary pattern operator achieved a maximum classification accuracy of 83.3% and 80.5% on the MIAS and InBreast datasets, respectively. The closest comparison achieved by the other local pattern operators is the local quinary operator, with maximum accuracies of 82.1% (MIAS) and 80.1% (InBreast), respectively.

Segmentation and Classification of Mammogram into Normal and Abnormal using Texture Features

Breast cancer is known to be a fatal disease since decades in women worldwide. Mammography is an effective tool used for the detection of breast cancer in the early stage. Computer aided tools helps medical field by ruling out the false identification of cancer cells in mammograms. Breast region extraction and classification of the extracted region into normal and abnormal is a crucial step in mammographic based diagnosis of breast cancer. Hence, in the proposed paper a method for segmentation of breast region and classification of breast region is presented. Breast region extraction is performed using Otsu’s thresholding method and intensity adjustments, enhancement is performed by Contrast Limited Adaptive Histogram Equalization (CLAHE). Gray Level Co-Occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) features are extracted to classify the breast region using K-Nearest Neighbors (KNN) classifier. The proposed algorithm is tested on Mammographic Image Analysis Society (MIAS) dataset, obtained minimum Root Mean Square Error (RMSE) and maximum Peak Signal-to-Noise Ratio (PSNR). For classification, 80.12% of accuracy is obtained with TPR and FPR of about 0.8317 and 0.3412 respectively.

Dynamic distance learning for joint assessment of visual and semantic similarities within the framework of medical image retrieval

The similarity measure is an essential part of medical image retrieval systems for assisting in radiological diagnosis. Attempts have been made to use distance metric learning approaches to improve the retrieval performance while decreasing the semantic gap. However, existing approaches did not resolve the problem of dependency between images (e.g. normal and abnormal images are compared with the same distance). This affects the semantic and the visual similarity. Thus, this work aims at learning a distance metric which preserves both visual resemblance and semantic similarity and modeling this distance in order to treat each query independently. The proposed method is described in three stages: (1) low-level image feature extraction, (2) offline distance metric modeling, and (3) online retrieval. The first stage exploits transform-domain texture descriptors based on local binary pattern histogram Fourier, shearlet, and curvelet transforms. The second stage is carried out using low-level features and machine learning. Given a query image, the online retrieval is based on the evaluation of the similarity between this image and each image within the dataset, while using a distance that is dynamically defined according to the query image. Realized experiments on the challenging Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets prove the effectiveness of the proposed method in determining dynamically the adequate distance and retrieving the most semantically similar images, while investigating single low-level features as well as fused ones.

Multi-stage System for Preprocessing Mammograms

Breast image analysis is important for the detection and diagnosis of various diseases, but more specifically, breast cancer. The most popular and widely used screening tool is mammography. It gives a better result than other screening tools for breast image analysis. Mammograms are difficult to analyze because of noise, poor quality, artifacts, and the inclusion of pectoral muscle. The technique proposed in this paper has several stages for preprocessing mammograms. First, artifacts and the background are removed by changing the original image into a binary image using Otsu’s thresholding. The medio-lateral view of mammography images has two orientations: left or right. Orientation detection in the next step is important for pectoral muscle removal, which is done using the line segment method. Contrast is enhanced using contrast limited adaptive histogram equalization, and noise is reduced using a median filter. The proposed technique was applied to a publicly available dataset from the Mammographic Image Analysis Society, from which 322 images were analyzed, and performance parameters calculated. After calculations for accuracy, sensitivity, and specificity, the accuracy achieved was 94.62%, sensitivity achieved was 90.21%, and specificity was 97.20%.

Enhanced Mammography image for Breast cancer detection using LC-CLAHE technique

Breast cancer is the greatest challenging health complexities that medical science is facing. Most cases can be prevented by early detection and diagnosis which are the best way to cure breast cancer to decrease the mortality rate. The aim of this research is to obtain a method for enhancing the mammography images by using the proposed method which is incorporating the Local Contrast with Contrast Limited Adaptive Histogram Equalization (LC-CLAHE) to improve the appearance and to increase the contrast of the image and then de-noised by 2D wiener filter techniques. To extract the region of interest (tumor), we used region growing technique for the segmentation process. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. Efficiency is measured by Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). It is observed that the proposed method with wiener filter gives higher (PSNR) and lower (RMSE), with a significant filter mask [3 3].

Qualitative assessment of image enhancement algorithms for mammograms based on minimum EDV

Breast cancer is one of the leading reason of death among women. Nevertheless, medications for this fatal disease are still away of ambitions. Patients (thought to have breast cancer) should go through several advanced medical diagnostic procedures like mammography, biopsy analysis, ultrasound imaging, etc. Mammography is one of the medical imaging techniques used for detecting breast cancer. However, its resulted images may not be clear enough or helpful for physician to diagnose each case correctly. This fact has pushed researchers towards developing effective ways to enhance images throughout using various enhancement algorithms. In this paper, a comparison amongst these applied algorithms was done to evaluate the optimum enhancement technique. A morphology enhancement, which is resulted from combining top-hat operation and bottom-hat operation, was used as a proposed enhancement algorithm. The proposed enhancement algorithm was compared to three other well-known enhancement algorithms, specifically histogram equalization, logarithmic transform, and gamma correction with different gamma values. Twenty-five mammographic images were taken from the mammography image analysis society (MIAS) database samples. The minimum entropy difference value (EDV) was used as metric to evaluate the best enhancement algorithm. Results has approved that the proposed enhancement algorithm gave the best-enhanced images in comparison to the aforementioned algorithms.

Inconsistent Performance of Deep Learning Models on Mammogram Classification.

Performance of recently developed deep learning models for image classification surpasses that of radiologists. However, there are questions about model performance consistency and generalization in unseen external data. The purpose of this study is to determine whether the high performance of deep learning on mammograms can be transferred to external data with a different data distribution. Six deep learning models (three published models with high performance and three models designed by us) were evaluated on four different mammogram data sets, including three public (Digital Database for Screening Mammography, INbreast, and Mammographic Image Analysis Society) and one private data set (UKy). The models were trained and validated on either Digital Database for Screening Mammography alone or a combined data set that included Digital Database for Screening Mammography. The models were then tested on the three external data sets. The area under the receiver operating characteristic curve (auROC) was used to evaluate model performance. The three published models reported validation auROC scores between 0.88 and 0.95 on the validation data set. Our models achieved between 0.71 (95% confidence interval [CI]: 0.70-0.72) and 0.79 (95% CI: 0.78-0.80) auROC on the same validation data set. However, the same evaluation criteria of all six models on the three external test data sets were significantly decreased, only between 0.44 (95% CI: 0.43-0.45) and 0.65 (95% CI: 0.64-0.66). Our results demonstrate performance inconsistency across the data sets and models, indicating that the high performance of deep learning models on one data set cannot be readily transferred to unseen external data sets, and these models need further assessment and validation before being applied in clinical practice.

Deep learning and optimization algorithms for automatic breast cancer detection

AbstractBreast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer‐aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state‐of‐the‐art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy.

Breast Tumor Detection in Digital Mammogram Based on Efficient Seed Region Growing Segmentation

ABSTRACT Breast cancer is increasing rapidly in women these days. It is diagnosed after the symptoms appear. The detection at an initial stage and the procedure of the treatment are the most significant strategies curing it effectively. For the determination of breast cancer, breast mammography is required. It can be one of the methods to recognize tumor in women's breasts because it lessens the risk of death by recognizing the tumor at an early level. This paper shows a combination of selection methods for step by step evaluation for Efficient Seeded Region Growing with enhanced performance to spot breast cancer. The suggested method has been divided into the following parts: First, preprocessing of breast image is performed to estimate the automatic extraction of Region of Interest. Second, an automatic threshold is calculated for the binarization process. Third, the numbers of seed points are determined automatically and their positions in the breast image are identified using the density of pixels value. Fourth, a method calculating the threshold value is proposed for the purpose of region creation in seed region growing. The given method is applied and tested on the two publicly available mammogram data sets; Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). It was observed that the proposed algorithm gives 94.6% and 95.3% True Positive Fraction (TPF) in case of MIAS and DDSM images, respectively. The parameters demonstrate that the given algorithm gives better results than the previously available methods.