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16 results for “Mammogram”
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Research ArticleOpen accessEvery word in the titleMass Classification in Digital Mammograms based on Discrete Shearlet TransformJ. Amjath Ali, J. JanetThe most significant health problem in the world is breast cancer and early detection is the key to predict it. Mammography is the most reliable method to diagnose breast cancer at the earliest. The classification of the two most findings in the digital mammograms, micro calcifications and mass are valuable for early detection. Since, the appearance of the masses are … …Journal of Computer Science, Vol. 9, Issue 6 · 1 Jun 2013 · 6,549 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector MachineJ. S. Leena Jasmine, S. Baskaran, A. GovardhanMammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by … …American Journal of Engineering and Applied Sciences, Vol. 6, Issue 1 · 28 Feb 2013 · 7,856 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleMammogram Analysis Based on Pixel Intensity Mean FeaturesR. Nithya, B. SanthiProblem statement: In the recent years, Computer Aided Diagnosis (CAD) can be very useful for detection of breast cancer. Mammography can be used as an efficient tool for breast cancer diagnosis. A computer based diagnosis and classification system can reduce unnecessary biopsy. Approach: This study investigates a new approach to the classification of mammogram images based on pixel intensity mean … …Journal of Computer Science, Vol. 8, Issue 3 · 7 Jan 2012 · 7,666 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEfficient Technique to Detect the Region of Interests in Mammogram ImagesMoussa H. Abdallah, Ayman A. AbuBaker, Rami S. Qahwaji, Mohammed H. SalehProblem Statement: Breast cancer is the second leading cause of cancer deaths in women today after lung cancer and is the most common cancer among women. The development of efficient technique to early detect the region of microcalcifications mammogram images is a must. Approach: The method proposed in this paper is to enhance the Computer Aided Diagnosis (CAD) performance. This … …Journal of Computer Science, Vol. 4, Issue 8 · 31 Aug 2008 · 6,629 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEfficient Pre-processing of USF and MIAS Mammogram ImagesAyman A. AbuBaker, R. S. Qahwaji, Musbah J. Aqel et al.High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. In this paper, a pre-processing technique for reducing the size and enhancing the quality of USF and MIAS mammogram images is introduced. … …Journal of Computer Science, Vol. 3, Issue 2 · 28 Feb 2007 · 7,167 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleGAUSSIAN MIXTURE MODEL BASED CLASSIFICATION OF MICROCALCIFICATION IN MAMMOGRAMS USING DYADIC WAVELET TRANSFORMSuman Mishra, Hariharan RanganathanBreast cancer is a serious health related issue for women in the world. Cancer detected at premature stages has a higher probability of being cured, whereas at advanced stages chances of survival are bleak. Screening programs aid in detecting potential breast cancer at early stages of the disease. Among the various screening programs, mammography is the proven standard for screening … …Journal of Computer Science, Vol. 9, Issue 10 · 5 Sep 2013 · 6,867 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleClassification of Breast Tumor in Mammogram Images Using Unsupervised Feature LearningAidarus M. Ibrahim, Baharum Baharudin, Abas Md Said, P. N. HashimahIn this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with well-known feature … …American Journal of Applied Sciences, Vol. 13, Issue 5 · 13 May 2016 · 7,497 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleWavelet Analysis and Orthogonal Moments based Classification of Microcalcification in Digital MammogramsN. V.S. Sree Rathna Lakshmi, C. ManoharanProblem statement: An important early sign of breast cancer is the clusters of micro calcifications in digital mammograms. To assist radiologists in the diagnosis of mammographic clusters a novel hybrid algorithm has been developed. Approach: A method, for detecting micro calcification in mammograms based on combined feature set with Ant Colony Optimization (ACO) was proposed. The diagonal matrix ‘S’ obtained … …Journal of Computer Science, Vol. 7, Issue 10 · 6 Aug 2011 · 6,558 accessesView PDFCite
Research ArticleOpen accessIn the keywordsNew Computer Aided Detection Method for the Effective Detection of Breast CancerBalasubramaniam Senthilkumar, Govindaswamy UmamaheswariAnalyzing mammogram images is the most challenging task for radiologists in detecting breast cancer. Computer Aided Detection (CAD) plays major role in detecting such disease. Preprocessing, segmentation and detection are the processes involved in CAD. In this study, we have designed a CAD by improving the processes for the effective detection for breast cancer. Selective median filter has been used … …OnLine Journal of Biological Sciences, Vol. 12, Issue 4 · 13 Mar 2013 · 7,174 accessesView PDFCite
Research ArticleOpen accessIn the keywordsNovel Preprocessing Technique in the Computer Aided Detection of Breast CancerSenthilkumar Balasubramaniam, Umamaheswari GovindaswamyAnalyzing medical images is the most challenging task in medical image processing. Computer Aided Detection (CAD) tool is the aid for the radiologists in analyzing such images for the effective detection and diagnosis of the disease. Such a CAD tool consists of Preprocessing, Segmentation and detection processes. In this study we have improved the preprocessing by using the Selective Median … …Journal of Computer Science, Vol. 8, Issue 12 · 8 Nov 2012 · 6,650 accessesView PDFCite