Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis (original) (raw)
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Detection of microcalcifications in mammograms using error of prediction and statistical measures
Journal of Electronic Imaging, 2009
Breast cancer is one of the diseases causing the largest number of deaths among women. Its early detection has been proved to be the most effective way to combat it. This work is focused on developing an integral tool able to detect microcalcifications in mammographies, since the presence of these particles is a clear symptom of an incipient cancer. The proposed approach combines two techniques successfully used in other areas separately, such as linear pixel prediction and support-vector machines, in order to obtain almost perfect prediction accuracy. Moreover, a filter has been designed with the aim of decrease the processing time. The result verges on 96% of hits, improving previous works by 6%, on average.
2008
Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. In this paper an approach is proposed to develop a Computer-Aided Diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs. The proposed method has been implemented in three stages: (a) the region of interest (ROI) selection of 32×32 pixels size which identifies clusters of microcalcifications, (b) the feature extraction stage is based on the wavelet decomposition of locally processed image (region of interest) to compute the important features of each cluster and (c) the classification stage, which classify between normal and microcalcifications' patterns and then classify between benign and malignant microcalcifications. In classification stage, four methods were used, the voting K-Nearest Neighbor classifier (K-NN), Support Vector Machine (SVM) classifier, Neural Network (NN) classifier, and Fuzzy classifier. The proposed method was evaluated using the Mammographic Image Analysis Society (MIAS) mammographic databases. The proposed system was shown to have the large potential for microcalcifications detection in digital mammograms.
Microcalcification Detection in Mamography Images Using 2D Wavelet Coefficients Histogram
Breast Cancer is one of the most common illnesses in recent years. Diagnosing cancer at early stages can have a considerable effect on the therapy, so that many several attempts have been made for diagnosing this illness at its first stage recently. Mammography imaging is the most commonly used technique to detect breast cancer before appearing the clinical symptoms. Extracting features which facilitate cancer symptoms detection without significant decrease in sensitivity, minimizes false positives and is of great importance. Microcalcification is an important indicator of cancer. In this research a new method for detecting microcalcifications in mammography is presented. Due to the ability of wavelet transform in image decomposition and detaching details, it can be used to expose this symptom in mammograms. In this work, a two dimensional wavelet transform is performed for feature extraction; and these features are used to diagnose cancer symptoms in mammography images. After the feature extraction step, classification is done using Support Vector Machine (SVM). In the performed evaluation, Regions of Interest (ROIs) with different dimensions have been used as input data and the results show that the proposed feature extraction method can have a significant impact in improving the performance of detection systems.
Hybrid wavelet features for the classification of microcalcification clusters in digital mammograms
University, 2019
This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations were carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: OPTIMAM, DDSM, and MIAS. The best classification accuracy (95.00 ± 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01.
A Microcalcification Detection System for Digital Mammography using the Contourlet Transform
Proceedings of the …, 2004
Mammograms depict most of the significant changes in breast disease. In this paper, a computer aided diagnosis (CAD) system is presented. First, the mammographic image is enhanced using an orientation space analysis based on a contourlet transform. Then a multirresolution analysis based on the dyadic wavelet transform and wavelet transform modulus maxima is computed, and the resulting image is classified by a support vector machine (SVM). Performance results are presented.
Lecture Notes in Computer Science, 2003
This paper presents and tests a methodology that sinergically combines a select of successful advances in each step to automatically classify microcalcifications (MCs) in digitized mammography. The method combines selection of regions of interest (ROI), enhancement by histogram adaptive techniques, processing by multiscale wavelet and gray level statistical techniques, generation, clustering and labelling of suboptimal feature vectors (SFVs), and a Neural feature selector and detector to finally classify the MCs. The experimental results with the method promise interesting advances in the problem of automatic detection and classification of MCs 1 .
Informatics for Health and Social Care, 2001
Evaluation of an automated wavelet-based system dedicated to the detection of clustered microcalcifications in digital mammograms Abstract. Mammographic screening programs are delivering reductions in breast cancer mortality. However, breast cancer screening will be cost effective and will provide a real profit only when reach both high sensitivity and specificity levels. To date, because of human or technical factors, a significant number of breast cancers are still missed or misinterpreted on the mammograms. Computer methodologies, developed to assist radiologists, could represent further amelioration by increasing diagnostic accuracy in the screening programs. We have tested a computerized scheme to detect clustered microcalcifications in digital mammograms, employing 360 mammograms that were randomly selected from the mammographic screening program, currently undergoing at the Galicia Community (Spain). After the digitization process, the breast border was initially determined. A wavelet-based algorithm was employed to detect the clusters of microcalcifications. The performance of the automated system over the test set was evaluated employing Free-Response Receiver Operating Characteristic (FROC) methodology. The sensitivity achieved was 74% at a false positive detection rate of 1.83. The corresponding area under the Alternative FROC (AFROC) was A 1 =0.667±0.09.
Mammographic Microcalcifications Detection using Discrete Wavelet Transform
International Journal of Computer Applications, 2013
Breast cancer can be diagnosed with an early training course by detecting the presence of microcalcifications in screening mammograms. The multiresolution analysis using discrete wavelet transform presents characteristics which can be exploited to develop tools for detection of microcalcifications. The objective of this work is to study the best type of wavelet and the optimal level of decomposition for a better detection.
Support vector machine learning for detection of microcalcifications in mammograms
2002
Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance.
Computer aided diagnosis system for classification of microcalcifications in digital mammograms
… , 2009. NRSC 2009. …, 2009
Breast cancer is the main cause of death for women between the ages of 35 to 55. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. Microcalcifications are among the earliest signs of a breast carcinoma. Actually, as radiologists point out, microcalcifications can be the only mammographic sign of non-palpable breast disease which are often overseen in the mammogram. In this paper a method is proposed to develop a Computer-Aided Diagnostic system for classification of microcalcifications in digital mammograms, it splits into three-step process. The first step is Region of Interest extraction of 32 x 32 pixels size. The second step is the features extraction, where we used a set of 234 features from Region of Interest by employing wavelet decomposition, 1st order statistics from wavelet coefficients algorithms; also, we extracted 1st order statistics, median contrast and local binary partition features. The third step is the classification process where differentiation between normal and abnormal is performed using a Minimum Distance Classifier and K-Nearest Neighbor Classifiers employing the leave-one-out training-testing methodology. The results show acceptable sensitivity and specificity for the proposed system.