Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main objective of this paper is to enhance, detect and classify masses in digital mammogram. We develop a performance based case-based reasoning classification algorithm for mammographic findings to provide support for the clinical decision to perform biopsy of the breast. The developed classifier will be used for training and testing the images which is cancerous and noncancerous and improve the performance of the system.

The computer-aided detection (CAD) system plays a vital role in early cancer diagnosis and thus aids in reducing the mortality rate of breast cancer. In this study, a hierarchical template matching method is proposed to detect the breast mass in mammograms. First, three types of templates are designed and compared, and the sech template is selected to match the mass. Then, a hierarchical matching method, which makes full use of the high sensitivity of the local part-based template and the low false positive rate of the complete template, is used to detect the mass. At last, to eliminate some of flase positives, a set of rules are set based on various features. The performance of this proposed method is evaluated by 368 mammograms selected from the digital database of screening mammography. The sensitivity and the false positive rate are 96.2% and 5.2 per image, respectively. This preliminary study indicates that the proposed method is a promising strategy, and it has great potential in improving the accuracy of CAD systems in interpreting mammograms.

Mammography is the most effective procedure for an early detection of the breast abnormalities. Masses are a type of abnormality, which are very difficult to be visually detected on mammograms. In this paper an efficient method for detection of masses in mammograms is implemented. The proposed mass detector consists of two major steps. In the first step, several suspicious regions are extracted from the mammograms using an adaptive thresholding technique. In the second step, false positives originating by the previous stage are reduced by a machine learning approach. All modules of the mass detector were assessed on mini-MIAS database. In addition, the algorithm was tested on INBreast database for more validation. According to FROC analysis, our mass detection algorithm outperforms other competing methods. We should not just insist on sensitivity in the segmentation phase because if we forgot FP rate, and our goal was just higher sensitivity, then the learning algorithm would be biased more toward false positives and the sensitivity would decrease dramatically in the false positive reduction phase. Therefore, we should consider the mass detection problem as a cost sensitive problem because misclassification costs are not the same in this type of problems.

For the purpose of accurate and efficient mass detection in full-field digital mammograms, weproposeamethodfor automated mass detection that consists oftwostages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues). In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions. The main aim of this stage is to reduce the false-positive suspicious regions while keeping the true-positive suspicious regions. To further improve the performance of the FP reduction, the effectiveness of different transfer learning strategies is further explored and the best fine-tuning strategy in training InceptionV3 model is determined experimentally. The experimental results show that the proposed method can achieve an overall performance of 0.98 TPR @1.43 FPI on the INbreast database. In addition, the suggested segmentation method detects the mass location with 100% sensitivity and average of 5.4 false positives per image. Based on the obtained results,the introduced method was able to successfully detectand classify suspicious regions in digital mammograms and provide better TPR and FPI results in comparison with other state-of-the-art method.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

Detection of suspicious masses in mammograms play a vital role in early diagnosis of breast cancer, to reduce the death rate among women. The presence of masses and calcification’s in mammograms is distinguishing signs for breast cancer diagnosis. But in some cases, due to contrast variation, fuzzy boundaries and presence of noise in mammograms, segmentation and detection of masses is challenging assignment. This paper presents a new segmentation approach to detect masses in mammographic images. The proposed approach consist of artifacts elimination and pectoral region extraction, suspicious mass enhancement using dual morphological operation technique and finally, extraction of Regions of Interest (ROIs) from background using scaled Reyni entropy. The proposed system has been tested on two data-sets i.e. mini- Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM), over 50 and 90 mammograms respectively. Performance achieved with proposed system in terms of True Positive Fraction (TPF) yields 93.2% and 93.9% respectively, at the rate of 1.48 and 0.74 average False Positive per Image (FP/I), tested on both Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) views. The obtained experimental results demonstrates that proposed method gives improved results for mass detection and can be useful for radiologists in diagnosis of breast cancer at early stage.

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aidradiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodologyto detect masses from mammograms. The K-means clustering algorithm is used to split the mammogramsin regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-massregion. SVM is a machine-learning method, based on the principle of structural risk minimization, whichperforms well when applied to data outside the training set. We use a set of textural and shape measures todetect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

Computer-aided diagnosis: Detection of masses on digital mammograms

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.

Computer-aided diagnosis (CAD) system is helpful for lesion detection. In this study, we proposed a new mass detection method with analysis of bilateral mammograms. First of all, the mass candidates were detected in single view. To utilize the information in dual view, we match corresponding regions in mediolateral oblique (MLO) and craniocaudally (CC) views of the breast. In this paper, we introduced twin support vector machines (TWSVM) as classifier for mass detection, and proposed a new method for feature selection called multiple twin support vector machines (MTWSVM-RFE) to improve the accuracy of detection.

Abstract—Mass in mammogram can be an indicator of breast cancer. In this work we propose a new approach using Twin Support Vector Machine (TWSVM) for automated detection of mass in digital mammograms. This algorithm finds two hyperplanes to classify data points into different classes according to the relevance between a given point and either plane. It works much faster than original SVM classifier. The proposed scheme is evaluated by a data set of 100 clinical mammograms from DDSM. Experimental results demonstrate that the proposed TWSVM-based CAD system offers a very satisfactory performance for mass detection in digitizing mammograms. Compare with previous SVM-based classifier, it provides higher classification accuracy and computational speed.

A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms

This paper presents an efficient technique for the detection of spiculated massesin the digitized mammogram to assist the attending radiologist in making his decisions. The presented technique consists of two stages, enhancement of spiculation masses followed by the segmentation process. Fuzzy Histogram Hyperbolization (FHH) algorithm is first used to improve the quality of the digitized mammogram images. The Fuzzy C-Mean (FCM) algorithm is then applied to the preprocessed image to initialize the segmentation. Four measures of quantifying enhancement have been developed in this work. Each measure is based on the statistical information obtained from the labelled region of interest and a border area surrounding it. The methodology is based on the assumption that target and background areas are accurately specified. We have tested the algorithms on digitized mammograms obtained from the Digital Databases for Mammographic Image Analysis Society (MIAS).KeywordsGrey ScaleDigital MammographyEnhancement TechniqueBackground AreaFuzzy EntropyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms