Microcalcification and Macrocalcification Detection in Mammograms Based on GLCM and ODCM Texture Features Using SVM Classifier

Breast cancer is a common cancer in women and the second leading cause of cancer deaths worldwide. Photographing the changes in internal breast structure due to formation of masses and microcalcification for detection of Breast Cancer is known as Mammogram, which are low dose x-ray images. These images play a very significant role in early detection of breast cancer. Usually in pattern recognition texture analysis is used for classification based on content of image or in image segmentation based on variation of intensities of gray scale levels or colours. Similarly texture analysis can also be used to identify masses and microcalcification in mammograms. However Grey Level Co-occurrence Matrices (GLCM) technique introduced by Haralick was initially used in study of remote sensing images. Radiologists f i n d i t d i f f i c u l t to identify the mass in a mammogram, since the masses are surrounded by pectoral muscle and blood vessels. In breast cancer screening, radiologists usually miss approximately 10% - 30% of tumors because of the ambiguous margins of tumors resulting from long-time diagnosis. Computer-aided detection system is developed to aid radiologists in detecting ma mammographic masses which indicate the presence of breast cancer. In this paper the input image is pre-processed initially that includes noise removal, pectoral muscle removal, thresholding, contrast enhancement and suspicious mass is detected and the features are extracted based on the mass detected. A feature extraction method based on grey level co- occurrence matrix and optical density features called GLCM -OD features is used to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, a support vector machine is used to classify abnormal regions by selecting the individual performance of each feature. The results prove that the proposed system achieves an excellent detection performance using SVM classifier

Software for Modeling Ultrasound Breast Cancer Imaging

Computer-based models are increasingly used in biomedical imaging research to clarify links between anatomical structure, imaging physics, and the information content of medical images. A few three-dimensional breast tissue software models have been developed for mammography simulations to optimize current mammography systems or to test novel systems. It would be beneficial in the development of ultrasound breast imaging to have a similar computational model for simulation. A three-dimensional breast anatomy model with the lobular ducts, periductal and intralobular loose fibrous tissue, interlobular dense fibrous tissue, fat, and skin has been implemented. The parenchymal density of the model can be varied from about 20 to 75% to represent a range of clinically relevant densities. The anatomical model was used as a foundation for a three-dimensional breast tumour model. The tumour model was designed to mimic the ultrasound appearance of features used in tumour classification. Simulated two-dimensional ultrasound images were synthesized from the models using a first-order k-space propagation simulator. Similar to clinical ultrasound images, the simulated images of normal breast tissue exhibited non-Rayleigh speckle in regions of interest consisting of primarily fatty, primarily fibroglandular, and mixed tissue types. The simulated images of tumours reproduced several shape and margin features used in breast tumour diagnosis. The ultrasound wavefront distortion produced in simulations using the anatomical model was evaluated and a second method of modeling wavefront distortion was also proposed in which 10 to 12 irregularly shaped, strongly scattering inclusions were iii superimposed on multiple parallel time-shift screens to create the screen-inclusion model. Simulations of planar pulsed wave propagation through the two proposed models, a conventional parallel time-shift screen model, and digitized breast tissue specimens were compared. The anatomical model and screen-inclusion model were able to produce arrival-time fluctuation and energy-level fluctuation characteristics comparable to the digitized tissue specimens that the parallel-screen model was unable to reproduce. This software is expected to be valuable for imaging simulations that require accurate and detailed representation of the ultrasound characteristics of breast tumours

Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy

Image analysis in medical imaging: recent advances in selected examples

Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic