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In order to improve the performance of mass segmentation on mammograms, an intelligent algorithm is proposed in this paper. It establishes two mass models to characterize the various masses, and the ones in the denser tissue are represented with Model I, while the ones in the fatty tissue are represented with Model II. Then, it uses iterative thresholding to extract the suspicious area, as well as the rough regions of those masses matching Model II, and applies a DWT-based technique to locate those masses matching Model I, which are hidden in the high gray-level intensity and contrast area. A region growing process restricted by Canny edge detection is subsequently used to segment the rough regions of those masses matching Model I, and finally snakes are carried out to find all the mass regions roughly extracted above. Thirty patient cases with 60 mammograms and 107 masses were used for evaluation, and the experimental result has demonstrated the algorithm's better performance over the conventional methods.

Breast cancer is one of the major causes of death among women. If a cancer can be detected early, the options of treatment and the chances of total recovery will increase. From a woman's point of view, the procedure practiced (compression of breasts to record an image) to obtain a digital mammogram (DM) is exactly the same that is used to obtain a screen film mammogram (SFM). The quality of DM is undoubtedly better than SFM.

However, obtaining DM is costlier and very few institutions can afford DM machines. According to the National Cancer Institute 92% of breast imaging centers in India do not have digital mammography machines and they depend on the conventional SFM. Hence in this context, one should answer "Can SFM be enhanced up to a level of DM?" In this paper, we discuss our experimental analysis in this regard. We applied elementary image enhancement techniques to obtain enhanced SFM. We performed the quality analysis of DM and enhanced SFM using standard metrics like PSNR and RMSE on more than 350 mammograms. We also used mean opinion score (MOS) analysis to evaluate enhanced SFMs. The results showed that the clarity of processed SFM is as good as DM.

Furthermore, we analyzed the extent of radiation exposed during SFM and DM. We presented our literally findings and clinical observations.

For establishing risk factor of breast cancer requires highly specific breast density measure that can result in a more focused breast cancer prevention, diagnosis and treatment. This paper proposes a new CAD system for density estimation using progressive elimination method. The lower intensity pixels are eliminated in multiple phases by targeting specific intensity bands in each phase, using established statistical techniques. Local Standard Deviation (LSD) values are used to identify significant transitions and MLSD values to isolate the most significant transitions or edges. The results are compared to ACR BI RAD system of classification to establish the risk factor. Accuracy estimation on the proposed segmentation method signifies satisfactory qualitative results. The proposed algorithm implemented on all 322 mammograms of MIAS shows 73.91% agreement. The obtained Kappa (κ) value for the proposed method is 0.673.

In this paper, the issue of classifying mammogram abnormalities using images from an mammogram image analysis society (MIAS) database is discussed. We compare a feature extractor based on Legendre moments (LMs) with six other feature extractors. To determine the best feature extractor, the performance of each was compared in terms of classification accuracy rate and extraction time using a $k$-nearest neighbors ($k$-NN) classifier. This study shows that feature extraction using LMs performed best with an accuracy rate over 84% and requiring relatively little time for feature extraction, on average only 1s.

Cancer is a life-threatening disease which reduces the lifespan of humans. If the disease is treated early, the lifespan can be extended. This paper provides a useful method for detecting the abnormalities in the mammograms. The proposed method uses four phases such as pre-processing, segmentation, feature extraction and classification. In the pre-processing phase, median filter is utilized to enhance the quality of an image. The pre-processed image is then segmented by fuzzy C means (FCM). Three different features such as Gaussian–Hermite moments (GHM), Jacobi moments and pseudo Zernike moments (PZM) are extracted from the segmented image. Finally, extreme learning machine (ELM) classifier identifies the normal, malignant and benign kinds of cancer. This method is compared with four different classifiers. The proposed method is tested on mammographic image analysis society (MIAS) dataset and the performance is evaluated against several analogous approaches in terms of accuracy, sensitivity and specificity. The proposed approach substantially provides the best result.

Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., Jaccard & Dice co-efficient, relative error, segmentation accuracy, error and Fowlkes–Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62%, 4.38% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results.

The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provide a second opinion for the early detection of breast cancer and therefore, aids to reduce the mortality rates. In this work, we try to develop a new (CAD) system to classify mammograms into benign or malignant. The proposed system consists of three main steps. The preprocessing stage consists of noise filtering, elimination of unwanted objects and suppressing the pectoral muscle. The Seeded Region Growing (SRG) segmentation technique is applied in a triangular region that contains the pectoral muscle to localize it and extract the region of interest (ROI). The features extraction step is performed by applying the discrete wavelet transform (DWT) to each obtained ROI, and the most discriminating coefficients are selected using the discrimination power analysis (DPA) method. Finally, the classification is carried out by the support vector machine (SVM), artificial neural networks (ANN), random forest (RF) and Naive Bayes (NB) classifiers. The evaluation of the proposed system on the mini-MIAS database shows its effectiveness compared to other recently published CAD systems, and a classification accuracy of about 99.41% with the SVM classifier was obtained.

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBC_SD), target-to-background contrast enhancement measurement based on entropy (TBC_E), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

In the paper, we proposed a pyramid-based mass detection method based on texture analysis and neural classifier for digital mammograms. The proposed mass detection method is composed of four parts: pyramid decomposition, region of interest (ROI) selection, feature extraction and neural classifier. Based on pyramid decomposition, a coarse-to-fine approach was utilized to achieve mass detection for reducing computational complexity in the proposed scheme. For decreasing computational complexity, ROI selection where a thresholding algorithm and polynomial function fitting were to find the breast area is also exploited to remove nonbreast regions in the proposed scheme. In the texture analysis, the intensity and texture information extracted from spatial and wavelet domains are utilized to analyze each pixel within the ROI. After feature extraction, these extracted texture features are combined with a supervised neural network to detect masses in the ROI. To evaluate the performance of the proposed scheme, the mammograms of 19 patients captured in Taiwan are used for testing. The experimental result shows that ROI selection can localize breast regions well for further analysis. In addition, the average recall rate of our proposed scheme is more than 86%. Therefore, these experimental results demonstrate that the proposed pyramid-based scheme can achieve mass detection.

Breast cancer is the most frequent cancer type that is diagnosed in women. The exact causes of such cancer are still unknown. Early and precise detection of breast cancer using mammogram images or biopsy to provide the required medications can increase the healing percentage. There are much current research efforts to developed a computer aided diagnosis (CAD) system based on mammogram images for detecting and classification of breast masses. In this research, a CAD system is developed for automated segmentation and two-stages classification of breast masses. The first stage includes the classification of the masses into seven classes (normal, calcification, circumscribed, spiculated, ill-defined, architectural distortion, asymmetry), which is done using probabilistic neural network (PNN). The second classification stage is to define the severity of abnormality into two classes (Benign and Malignant) which were done using support vector machine (SVM). The results of applying the proposed method on two mammogram image show that the accuracy of detection and segmentation of the breast mass was 99.8% for mammographic image analysis society database (MIAS-DB) with 322 images and 97.5% for breast cancer digital repository (BCDR), BCDR-F03 and BCDR-DN01 with 936 images, while for the first classification stage has accuracy of 97.08%, sensitivity of 98.30% and specificity of 89.8%, and the second classification stage has an accuracy of 99.18%, sensitivity of 98.42% and specificity of 94.90%.

Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGG16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.

Nowadays, breast cancer is the most founded cancer among women. Moreover, 2.3 million new cases of breast cancer have been detected among women since 2020 as reported by World Health Organization (WHO). Many research studies based on breast cancer mainly aim at ultrasound, mammography, and Magnetic Resonance Images (MRI). However, there exist certain limitations such as lack of access for the detection of disease in rural and remote cities and insufficient knowledge about the availability of computer-aided systems. In order to overcome the problem, it is necessary to develop an effective detection model for breast cancer. For the experimentation, the input images like mammogram images and tomosynthesis images are garnered from the benchmark online resources. The input image further undergoes the pre-processing stage, which is made by using “Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram equalization (HE)”. Then, the pre-processed images are further given as input to the segmentation, in which deeplabv3 is deployed. Consequently, with the assistance of segmented images, the relevant features like “texture features, color features, shape features, deep features, statistical features and morphological features” are extracted. Then, these obtained features are used in weighted feature selection, where the optimal feature selection is performed, and weights are optimized by the Enhanced Adaptive Prey Location-Based Pelican Optimization Algorithm (APL-POA). Finally, the weighted accurate features are given as input to the Ensemble Deep Learning (EDC) model. The ensemble model is structured by “Deep Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Temporal Convolution Networks (DTCN) and Gated Recurrent Unit (GRU)”, in which the hyperparameters of every classifier are smoothened optimally by the enhanced APL-POA algorithm. Through the experimental analysis, the proposed work tends to provide an improved classification rate and rapid detection of disease that aids in better diagnosis of the patients.

Breast cancer is one of the most common forms of cancer found in Australian women, and it has a high mortality rate. With proper diagnosis and treatment methods, the high mortality rate could be reduced. The telltale sign in the mammograms which signals the development of tumour is microcalcifications, and this is where the attention should be focused on. The focus of this paper is on developing a fuzzy detection method of microcalcifications in a mammogram. A review of the previous work in the area of automatic detection of microcalcifications is conducted. The issue of uncertainty in medical imaging is discussed and appropriateness of fuzzy set theory for such applications is justified. The algorithm developed in this work to identify microcalcifications in a mammogram is described. The performance of this algorithm is finally demonstrated and some conclusions are drawn.