Mammogram Image Analysis Society Research Articles

Mammogram Images Using Noise Removal of Filtering Techniques

Historically, image denoising methods were proposed for reducing salt and pepper noise in mammography images through enhanced filtering techniques. The Mammogram Image Analysis Society (MIAS) benchmark database for DICOM visuals is used to detect benign and malignant breast cancer. Thus, mean and median filtering techniques, as well as spatial filtering methods, are used to implement medical image analysis with the modalities of low and high radiation for medical images. Similar to these experimental and suggested techniques for removing salt and pepper noise, these statistical techniques assess MSE, SNR, and PSNR data for enhanced quality of signal in the image.

Fast Watershed Segmentation for Breast Cancer Detection

Within engineering and computer specializations, image processing is the most important study topic. It is one of today's fastest-growing technologies, with applications in a variety of biological sectors, including cancer sickness. According to the latest data from throughout the world, breast cancer is the most lethal of all cancer kinds. It is the most frequent cancer in women and the second leading cause of cancer mortality in women. In this study, we advocate using a watershed transformation to create a fast segmentation technique. This allows for the blending of updated information about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique. The method requires a mechanism to express the test picture in terms of the amount of change around every given pixel before it can begin the watershed modification. Each pixel in the greyscale representation of the original picture is subjected to the Sobel operator. According to the form, the tumors identified are round or semicircular and the light of the tumor dims as we travel away from its core. The complement for this previous data may be seen as a local minimum that necessitated the start of the watershed process. As a result, each tumor picture may be represented as a lake, with the center in the complement tumor picture being the least value. The identification of tumor percentage gets more reliable after using the approach. As a consequence, our computer-aided diagnostic method for mammographic breast cancer detection has improved significantly thanks to the novel methodology. The method was written in MATLAB and tested on a Windows computer. The strategy was put to the test using photos from MIAS (Mammogram Image Analysis Society, UK), which offers a consistent categorization system for mammographic examinations. In this study, we advocate using a watershed transformation to create a rapid segmentation technique. This allows for the blending of data about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique.

Breast Cancer Detection in Mammogram Images Using K-Means++ Clustering Based on Cuckoo Search Optimization.

Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high-quality features, which can result in very long processing times. Therefore, this paper proposes a new K-means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU's threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini-MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score.

Classification of mammograms based on features extraction techniques using support vector machine

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the region of interest (ROI). In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), Finally, support vector machine (SVM) has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the mammogram image analysis society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

Internet of medical things embedding deep learning with data augmentation for mammogram density classification.

Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.

Chaotic Duck Traveler Optimization (cDTO) Algorithm for Feature Selection in Breast Cancer Dataset Problem

Objective: 1 of every 3 individuals will be determined to have malignancy in the course of their life. Currently, there are more than 3.8 million ladies who have been determined to have breast malignancy in the United States. 2021 is practically around the bend, yet there's still an ideal opportunity to help ladies confronting breast malignancy in 2020. In this paper, chaotic based duck travel optimization (cDTO) meta-heuristic algorithm is introduced to classifying the input images from Mammogram Image Analysis Society (MIAS) database. Methods: Linear Discriminant Analysis is used to extract the mammogram image features. (cDTO-LDA) is an intrinsic algorithm to remove irrelevant features and select the optimal features by using wavelet families Haar (harr), db4 (daubechies), bior4.4 (Biorthogonal), Symlets (SYM8), “Discrete” FIR approximation of Meyer wavelet (dmey) features. Results: These selected features are evaluated by the quality measures such as accuracy, sensitivity, specificity, error rate that are clearly shows the high exactness of cDTO classifier is 98.5%. CSA-LDA classifier has the minimum exactness. Conclusion: Algorithm efficiency is proved by the promising results achieved by the proposed algorithm for selecting the best feature of breast cancer classification.

Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold.

Breast cancer is a leading cause of mortality affecting women across the world. Early detection and diagnosis can decrease the mortality rate due to this cancer. Machine learning-based models are gaining popularity for biomedical applications due to the ability of nonlinear mapping between input and output patterns using supervised training phase. The research work in the paper is focused on the optimal adaptive threshold for mammogram mass segmentation, and detection in order to assist radiologist in accurate diagnosis Legendre neural network with single layer is used to develop the model, and the training is performed through Block Based Normalized Sign-Sign Least Mean Square (BBNSSLMS) algorithm. Legendre neural network expands the input vector using standard Legendre polynomial, and the recursive update principle is followed for the weight vector in higher dimension. The optimal threshold isindirectly used for proper segmentation of mammogram mass. The proposed segmentation method involves training phase with 30 images and testing phase by 151 images obtained from standard Mammogram Image Analysis Society (MIAS) database. The proposed model achieved a sensitivity of 95% and accuracy of 96% with false positives per image calculated as 1.19. • Threshold selection is carried out using single-layer Legendre NN with reduced computational complexity. BNSSLMS algorithm process the data samples block wise instead of sample by sample basis. Optimal threshold is generated according to the varying image properties which helps in correct segmentation and detection. • Due to sparse nature of the adaptive model, more numbers of weight coefficients are tending to zero which also helps in faster convergence. Mammogram Mass Detection Steps.

Ensemble Flower-Pollination Algorithm for Automatic Breast Cancer Classification using Haar Features

Breast cancer is a deadly disease which reports a higher mortality rate, every year. This type of cancer is more common in women of developed countries while compared with the incident rate in developing countries. The objective of this work is to propose a methodology to increase earlier breast cancer detection. The methodology makes use of ensemble-based classifiers namely histogram-based Gradient Boosting Machine (hbGBM), Gradient Boosting with Light GBM (lGBM), and Gradient Boosting with CatBoost (GBCB) algorithms. The paper adopts the Mammogram Image Analysis Society (MIAS) database for testing the effectiveness of the algorithms. Haar wavelet transform is used for feature extraction and Flower-Pollination Algorithm (FPA) is employed for selecting the best features. Thus, a flower-pollination based haar wavelet feature with three different ensemble-based classifiers is proposed for the automatic mammogram classification. The performance results are then compared briefly by using some benchmark metrics such as specificity, sensitivity, precision, accuracy, and precision, F1 score, and Matthews Correlation Coefficient (MCC) analysis. The result reveals that the highest classification accuracy is obtained for ensemble GBCB based FPA algorithm i.e., 91% when comparing with other existing models.

WITHDRAWN: A new proposal for the segmentation of breast lesion in mammogram images using optimized kernel fuzzy clustering algorithm

Abstract Breast Cancer is one of the fatal diseases which is caused due to the un natural development of the tissues in the breasts which are abnormal. The level of sarcoma or the stage of the cancer is mostly relied upon the doctor’s analysis. In order to provide a technical contribution which supports the doctor to take decision, this paper is intended to develop a framework which can help in determining the stage in which the cancer is at present. The major issues in the prediction of breast cancer through mammograms are the diverged artifacts, similar breast tissues and lower contrast on the boundary between skin and air. To overcome these issues, Optimized Kernel Fuzzy Clustering Algorithm is developed (OKFCA) and to determine the cancer portions in mammogram images. The OKFCA algorithm has described to identify the segmented regions in Mammogram Image Analysis Society (MIAS) database. The proposed segmentation algorithm is carried out with pre-processed mammogram images, noise free image that was obtained by using Hybrid Denoising Filter (HDF) algorithm and the proposed OKFCA is a significant approach to find out the cancer segment of mammogram image. Data clustering facilitates to place data of similar types in one group and of dissimilar types in different group. The results from the experiments which were carried on the MIAS data confirms the efficiency of the proposed system in terms of accuracy when compared to that of the famous K-Means, OKFCA and Otsu methods.

Mammogram density classification using deep convolutional neural network

Artificial intelligence plays an important role in the classification of medical images for computerized diagnosis of the disease. The computer-aided medical imaging analysis system is developed for breast tissue density classification in mammogram images. Mammogram density is considered as significant predictive markers for breast cancer detection, treatment and management. Recently, deep learning techniques achieved impressive results in computer-assisted disease diagnosis. The deep learning technique such as the convolution neural network (CNN) is used for automated classification of mammogram density as fatty, dense and glandular. This study investigates how computer-aided medical imaging analysis system provides a reliable classification of mammogram density. The proposed methodology is evaluated using a mini-MIAS (Mammogram Image Analysis Society) database. We obtained an average accuracy of 98.5%. So, the proposed CAD system aids the clinicians in the classification of mammogram density.

Classification of Mammogram Abnormalities Using Legendre Moments

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 [Formula: see text]-nearest neighbors ([Formula: see text]-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 1[Formula: see text]s.

Classification of mammograms based on features extraction techniques using support vector machine

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a Hybrid Bounding Box and Region Growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM), Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison

AbstractIn this study, abnormalities in medical images are analysed using three classifiers, and the results are compared. Breast cancer remains a major public health problem among women worldwide. Recently, many algorithms have evolved for the investigation of breast cancer diagnosis through medical imaging. A computer‐aided microcalcification detection method is proposed to categorise the nature of breast cancer as either benign or malignant from input mammogram images. The standard mammogram image corpus, the Mammogram Image Analysis Society database is utilised, and feature extraction is performed using five different wavelet families at level 4 and level 6 decomposition. The work is accomplished through firefly algorithm (FA), extreme learning machine (ELM) and least‐square‐based non‐linear regression (LSNLR) classifiers. The performance of the classifiers is compared by benchmark metrics, such as total error rate, specificity, sensitivity, area under the receiver operating characteristic curve, precision, F1 score and the Matthews correlation coefficient. As validation of the classifier results, a kappa analysis is included to determine the agreement among classifiers. The LSNLR classifier attains a 3% to 7% improvement in average accuracy compared with the average classification accuracy of the FA (86.75%) and ELM (90.836%) classifiers.

A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms

Breast cancer is one of the most common cancer risks to women in the world. Amongst multiple breast imaging modalities, mammography has been widely used in breast cancer diagnosis and screening. Quantitative analyses including breast boundary segmentation and calcification localization are essential steps in a Computer Aided Diagnosis system based on mammography analysis. Due to uneven signal spatial distributions of pectoral muscle and glandular tissue, plus various artifacts in imaging, it is still challenging to automatically analyze mammogram images with high precision. In this paper, a fully automated pipeline of mammogram image processing is proposed, which estimates skin-air boundary using gradient weight map, detects pectoral-breast boundary by unsupervised pixel-wise labeling with no pre-labeled areas needed, and finally detects calcifications inside the breast region with a novel texture filter. Experimental results on Mammogram Image Analysis Society database show that the proposed method performs breast boundary segmentation and calcification detection with high accuracy of 97.08% and 96.15% respectively, and slightly higher accuracies are achieved on Full-Field Digital Mammography image datasets. Calculation of Jaccard and Dice indexes between segmented breast regions and the ground truths are also included as comprehensive similarity evaluations, which could provide valuable support for mammogram analysis in clinic.

Subclass based parallel learning neural network for classification of masses in mammograms

Computer aided detection assists radiologists by providing second opinion in the mammography detection, and reduce misdiagnosis. An expert system with novel subclass based learning multiple neural network classifier (SBLMNN) has been proposed to solve the mammogram mass classification problem. This work explores the significance of the modular learning in artificial neural networks, inspired from the visual cortex basis of human learning. It is a two stage learning process. In stage I, the proposed architecture processes parallel on the radiological characteristics of mass like shape, margin and texture features in separate modules similar to the visual cortex to identify the subclasses. The intermediate outputs of the independent modules are processed to classify the mass into benign or malignant in stage II. Modularization and deep learning considered in the proposed method improves the performance of the classifier and speed of learning. For the experimental analysis, images are obtained from the mammogram image analysis society. The experiments were implemented in MATLAB. For benign and malignant classification, the shows that SBLMNN accuracy is 92%, which is higher than monolithic MLP neural network architecture.

Computer-aided diagnosis system for mammogram density classification

Mammography is the widely used technique in breast cancer diagnosis. This paper presents a computer-aided diagnosis system to classify the mammograms into three different densities including fatty, glandular and dense. Mammographic density is an important factor of breast cancer risk. Higher breast densities increase the difficulty of detecting cancer in a mammogram. The accuracy of breast cancer detection depends on the breast tissue characteristics. Several texture features such as histogram, local binary pattern, gray-level co-occurrence matrix, gray-level difference matrix, gray-level run-length matrix, Gabor transform and discrete wavelet transform were extracted from the mammograms. In this work, correlation-based feature selection technique was used. The breast tissue classification based on texture features was evaluated by artificial neural network, linear discriminant, Support Vector Machine (SVM) and Naive Bayes classifier. The performance of the proposed method was examined using the Mammogram Image Analysis Society (MIAS) database. Experimental results demonstrate that the best performance was achieved by SVM yielding an accuracy of 96.11%.

Breast Cancer Detection using Local Binary Patterns

Breast cancer is a leading cause of cancer type for death among women in most of popular countries, breast cancer detection is important and challenging role in worldwide to save women’s life. Due to inexperience to detect cancer, the doctors and radio logistic can miss the abnormality, which leads to death. Mammography is the most used method for breast cancer detection used by the radiologists. In this experiment, the MIAS (Mammogram Image Analysis Society) database is used and the MIAS database consists of normal and abnormal type of 322 mammograms. The pre-processing is most important step to capture quality mammogram image for next study and processing in mammogram analysis. Texture analysis plays important role to identify normal and abnormal types. Texture feature extraction can be done by local binary patterns (LBP) operator and by using LBP we can consider only sign parameters, it may loss the some texture information. The local binary pattern is a rotation invariant approach for the texture analysis. In this experiment famous completed LBP (CLBP) method used for extracting texture features. Completed LBP considering the sign, magnitude and centre gray level values. By using the joint or hybrid distributions combine CLBP_Sign, CLBP_Magnitude and CLBP_Center gray level values.LBP is one type of Completed LBP for texture analysis, advantage of CLBP is rotation invariant. Finally extracted texture features can be trained and classified by using the SVM classifier for identifying the normal and abnormal cancer type.

A novel statistical approach for detection of suspicious regions in digital mammogram

In this paper, we propose a novel algorithm to detect the suspicious regions on digital mammograms that based on the Fisher information measure. The proposed algorithm is tested different types and categories of mammograms (fatty, fatty-glandular and dense glandular) within mini-MIAS database (Mammogram Image Analysis Society database (UK)). The proposed method is compared with a different segmentation based information theoretical methods to demonstrate their effectiveness. The experimental results on mammography images showed the effectiveness in the detection of suspicious regions. This study can be a part of developing a computer-aided decision (CAD) system for early detection of breast cancer.

Identification of abnormalities in mammograms images using methods in the spatial domain

This paper presents a fast and efficient automated method for segmentation abnormalities present in mammogram images. For this, it uses methods in the spatial domain such as high-boost filter, binarization by thresholding and mainly related components. The method was applied to images obtained from the database mini-MIAS data base (Mammogram Image Analysis Society database (UK)), for this, it has used images that have abnormalities regardless if they are benign or malignant. . Identification of abnormalities in mammograms images 6697

A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis

One of the issues when interpreting a mammogram is that the density of a pectoral muscle region is similar to the tumor cells. The appearance of pectoral muscle on medio-lateral oblique (MLO) views of mammograms will increase the false positives in computer aided detection (CAD) of breast cancer. For this reason, pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm is based on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphological processing to find a rough border of the pectoral muscle. The multiple regression analysis (MRA) is then employed on this rough border to obtain an accurate segmentation of the pectoral muscle. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the pectoral muscle extracted by the presented algorithm approximately follows that extracted by an expert radiologist.