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- 实验方案
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Hybrid technique for the detection of suspicious lesions in digital mammograms
摘要： This paper presents an efficient system for the detection of suspicious lesions in mammograms. The proposed detection system consists of three steps. In the first step, an efficient pre-processing technique is developed using Top-Hat morphological filter and NL means filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM), gradient magnitude (GM), and intensity contrast (IC). Finally, computed threshold is used to extract the suspicious lesions in mammograms. The Free Response Operating Characteristics (FROC) curve is used to assess the performance of the proposed system. Proposed system achieved the sensitivity of 93.8% at the rate of 0.51 false positives per image.
关键词： breast cancer,segmentation,computer-aided diagnosis,fuzzy C-means,mammograms
更新于2025-09-23 15:22:29
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Investigation on ROI size and location to classify mammograms
摘要： Breast cancer is the major cause of death among women and early detection can lead to a longer survival. Computer Aided Diagnosis (CAD) system helps radiologists in the accurate detection of breast cancer. In medical images a Region of Interest (ROI) is a portion of image which carries the important information related to the diagnosis and it forms the basis for applying shape and texture techniques for cancer detection. Several ROI sizes and locations have been proposed for computer aided diagnosis systems. In the present work various ROI sizes have been used to determine the appropriate ROI size to classify fatty and dense mammograms. Two types of mammograms i.e. fatty and dense are used from the MIAS database. Various texture features have been determined from each ROI size for the analysis of texture characteristics. Fisher discriminant ratio is used to select the most relevant features for classification. Finally linear SVM is used for the purpose of classification. Highest classification accuracy of 96.1% was achieved for ROI size 200×200 pixels.
关键词： classification,breast cancer,digital mammograms,breast tissue,ROI,feature selection
更新于2025-09-23 15:22:29
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RAMS: Remote and automatic mammogram screening
摘要： About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings. We propose a total screening solution comprised of three main components: a web service for uploading images and reviewing results, a machine learning algorithm for accepting or rejecting images as valid mammograms, and an artificial neural network for locating potential malignancies. Once an image is uploaded to our web service, an image acceptor determines whether or not the image is a mammogram. The image acceptor is primarily a one-class SVM built on features derived with a variational autoencoder. If an image is accepted as a mammogram, the malignancy identifier, a ResNet-101 Faster R-CNN, will locate tumors within the mammogram. On test data, the image acceptor had only 2 misclassifications out of 410 mammograms and 2 misclassifications out of 1,640 non-mammograms while the malignancy identifier achieved 0.951 AUROC when tested on BI-RADS 1, 5, and 6 images from the INbreast dataset.
关键词： Faster R-CNN,SVM,Deep Learning,DDSM,Convolutional,TensorFlow,INbreast,Mammograms,Telemedicine,Artificial Neural Network
更新于2025-09-19 17:15:36