Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-screening breast cancer, and primary health care can introduce this method to the community. This study aimed to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass widely covered in the study work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system

Mammographic Mass Detection with Statistical Region Merging

An automatic method for detection of mammographic masses is presented which utilizes statistical region merging for segmentation (SRM) and linear discriminant analysis (LDA) for classification. The performance of the scheme was evaluated on 36 images selected from the local database of mammograms and on 48 images taken from the Digital Database for Screening Mammography (DDSM). The Az value (area under the ROC curve) for classifying each region was 0.90 for the local dataset and 0.96 for the images from DDSM. Results indicate that SRM segmentation can form part of an robust and efficient basis for analysis of mammograms

Spatial priors for tomographic reconstructions from limited data

Tomografie is het reconstrueren van het inwendige van een object a.d.h.v externe metingen, b.v. beelden verkregen met X-stralen of microgolven. Deze thesis bekijkt de specifieke aspecten van microgolftomografie en magnetische resonantie beeldvorming (Magnetic Resonance Imaging – MRI); beide technieken zijn onschadelijk voor de mens. Terwijl het gebruik van MRI wijdverspreid is voor veel klinische toepassingen, is microgolftomografie nog niet in klinisch gebruik ondanks zijn potentiële voordelen. Door de lage kost en draagbaarheid van de toestellen is het een waardevolle aanvulling aan het assortiment

A new contour detection approach in mammogram using rational wavelet filtering and MRF smoothing

Los Alamitos, C