Digital Mammography

Silicon microstrip detectors in the edge on geometry, i.e. oriented with the strips parallel to the incoming beam, allow a high absorption efficiency for X-rays in the 10–100 keV energy range. In medical imaging, this results in a significant reduction of the dose given to the patient with respect to conventional screen-film systems. This geometrical configuration was first proposed in the early 90s and since then it has been applied to several medical imaging projects. In this short review, an overview of some medical imaging applications using edge on detectors will be presented, focusing the attention on the main characteristics of the detection systems. The studies performed in Trieste in order to optimize such detectors for digital mammography with synchrotron radiation will be described in detail. In particular, novel imaging techniques (phase contrast, refraction and scatter imaging, computed tomography) implemented and tested at the Trieste synchrotron light source will be shortly discussed.

The last decade witnessed a growing interest in research on content-based image retrieval (CBIR) and related areas. Several systems for managing and retrieving images have been proposed, each one tailored to a specific application. Functionalities commonly available in CBIR systems include: storage and management of complex data, development of feature extractors to support similarity queries, development of index structures to speed up image retrieval, and design and implementation of an intuitive graphical user interface tailored to each application. To facilitate the development of new CBIR systems, we propose an image-handling extension to the relational database management system (RDBMS) PostgreSQL. This extension, called PostgreSQL-IE, is independent of the application and provides the advantage of being open source and portable. The proposed system extends the functionalities of the structured query language SQL with new functions that are able to create new feature extraction procedures, new feature vectors as combinations of previously defined features, and new access methods, as well as to compose similarity queries. PostgreSQL-IE makes available a new image data type, which permits the association of various images with a given unique image attribute. This resource makes it possible to combine visual features of different images in the same feature vector. To validate the concepts and resources available in the proposed extended RDBMS, we propose a CBIR system applied to the analysis of mammograms using PostgreSQL-IE.

A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this paper which employs features extracted by a new technique based on independent component analysis. Our approach is concentrated in finding a set of independent source regions that generate the observed mammograms. The coefficients of the linear transformation of the source regions are used as features that describe effectively any normal and abnormal region in digital mammograms as well as benign and malignant ROS in the latter case. Extensive experiments in the MIAS Database have shown a recognition accuracy of 88.23% in the detection of all kinds of abnormalities and 79.31% in the task of distinguishing between benign and malignant regions, outperforming in both cases standard textural features, widely used for cancer detection in mammograms.

Digital imaging systems for medical applications use amorphous silicon thin-film transistor (TFT) technology due to its ability to be manufactured over large areas. However, TFT technology is far inferior to crystalline silicon CMOS technology in terms of the speed, stability, noise susceptibility, and feature size. This work investigates the feasibility of integrating an imaging array fabricated in CMOS technology with an a-Se detector. The design of a CMOS passive pixel sensor (PPS) array is presented, in addition to how an 8×8 PPS array is integrated with the 75 micron thick stabilized amorphous selenium detector. A non-linear increase in the dark current of 200 pA, 500 pA and 2 nA is observed with 0.27, 0.67 and 1.33 V/micron electric field respectively, which shows a successful integration of selenium layer with the CMOS array. Results also show that the integrated Selenium-CMOS PPS array has good responsivity to optical light and X-rays, leaving the door open for further research on implementing CMOS imaging architectures going forward. Demonstrating that the PPS chips using CMOS technology can use a-Se as a detector is thus the first step in a promising path of research, which should yield substantial and exciting results for the field. Though area may still prove challenging, larger CMOS wafers can be manufactured and tiled to allow for a large enough size for certain diagnostic imaging applications and potentially even large area applications like digital mammography.

— we present a new approach to classify breast tissue density which is widely accepted to be an important risk indicator for the development of breast cancer. The computer aided diagnosis (CAD) framework developed, first segments the breast area then represents each image using the bag-of-features (BoF) approach. To represent the images, we first extract local binary patterns (LBP) features, which are then quantized to create a codebook. Second, we encode the features using the obtained codebook and a sparse coding algorithm to obtain a final image representation. Finally, we use support vector machines (SVM) classifier to carry out the classification task. In order to evaluate the efficiency of the proposed approach, we tested the framework using the digital database of screening mammograms (DDSM). The results showed that 91.25% of the samples were correctly classified. We also investigated the codebook size and selected the one that enhanced the results. Our proposal showed better performance compared to previous methods that classified the same dataset. Keywords— breast tissue density classification; computer-aided diagnostic systems; bag of features; local binary patterns; sparse coding; LLC;max-pooling .

Breast Cancer is the most common Cancer among women. Currently there are no methods to prevent breast cancer that is the reason why early detection represents a very important factor in cancer treatment. Mammography is currently the best available radiological technique for early detection of breast cancer. Mass detection using Computer Aided Design and Detection systems serve as a second decision tool to radiologists for discovering masses in the mammograms. This work presents a methodology for segmentation of masses in Mammogram Images. The proposed algorithm uses Median filtering, Optimal Global thresholding using Otsu's method and morphological processing in order to: (1) enhance quality of mammography image, (2) Segmentation of masses based on threshold resulting Region of Interest (ROI)(3) extracting segmented masses from image. The proposed techniques are implemented and tested under MATLAB environment.

This paper presents a fully automated scheme for breast den- sity estimation and asymmetry detection on mammographic images. Image preprocessing and segmentation techniques are first applied to the image, in order to extract the feature s for the breast density categorization. Also a new fractal- related feature is proposed for the classification. The clas - sification to 3 classes is

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early ...

Breast papilloma is a term that describes an intraductal papillary configuration of the mammary epithelium on macroscopic or microscopic examination. It includes solitary intraductal papillomas, multiple papillomas, papillomatosis, and juvenile papillomatosis (JP).Recent advances in mammary ductoscopy (MD) have raised new possibilities in the diagnosis and treatment of breast papillomas. This technique represents an important diagnostic adjunct in patients with pathological nipple discharge (PND) by allowing direct visualisation and biopsy of intraductal lesions and guiding duct excision surgery. Treatment of breast papillomas often entails surgical duct excision for symptomatic relief and histopathological examination. Recently, more conservative approach has been adapted. MD-assisted microdochectomy should be considered the procedure of choice for a papilloma-related single duct discharge. Furthermore, there is increasing evidence that MD has the potential to reduce the number of ...

Reducing the classification process time, for identifying male tissues in medical
images, has always been a challenge. In this paper, an efficient approach is
presented that improves the classification time of breast cancer mammogram
images. First, a preprocessing phase is applied for background correction and
noise removal using Wave Atom transform and a universal cascaded filter,
respectively. Then, the segmentation process is carried out through Particle
Swarm Optimization, Hidden Markov Random Field Model with its Expectation Maximization algorithm, and Kirsch's templates. Finally, features extraction and
classification are employed. The proposed method is used to efficiently reduce
the amount of data that represent the statistically based textural features of the
image. Experimental results show that the employment of the selected
preprocessing algorithms leads to a better classification accuracy than that of the
commonly known digital filters. Moreover, the proposed method improves the
classification time by using only six, out of twenty-two, features that mostly
contains the image information. Finally, classification procedures using Naive Bayes (NB) is applied depending
on the reduced features. The accuracy of the obtained results is compared with literature and good results are
achieved.

— Breast cancer is the most harmful of all types of cancers that leads to the death of women. The initial screening test for breast cancer is made using Mammography. Masses, Calcification and Architectural distortion are the three major signs notable as causes for cancer recognized using mammogram images. Soft Computing methods provide solutions to biologically inspired problem of medical domain like breast cancer. Neural Networks, Fuzzy Logic and Genetic Algorithms contribute novel algorithms to deal with breast cancer. Breast cancer can be diagnosed using soft computing methods. The effective diagnosis of breast cancer can be achieved by using feature reduction and classification methods. The feature reduction method applied is Principal Component Analysis (PCA) and the classification method includes Support Vector Machines (SVM). The objective of this study is to improve the breast cancer early detection with the application of soft-computing techniques.

Mammography x-rays uses to demonstrate different lesions among women by using ionizing radiation. This irradiation exposes the patient to slight high radiation amounts. This study conducted in amongst mammography patients in Khartoum, Sudan. The sample magnitude was 100 patients whose different types of diseases. All patients were underwent mammography. The factors were documented in this study were patient's characteristics and radiation exposure related factors. The mean MGD values calculated were 3.6 + 0.29 (p<0.05). Those results were suggestively more than the standard dose determined by international agencies (IAEA, NCRP and ACR).

— In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of de-scriptors, based on Shapelet decomposition, estimate the source images that generate the observed ROS in mammo-grams. The Shapelet decomposition coefficients can be used to efficiently detect ROS areas using a new classifier base on quaternionic representation. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have shown high recognition accuracy over 86% for all kinds of breast, with less computational cost.

To report our experience with targeted-ultrasound in assessing 142 cases with clustered microcalcifications of intermediate concern detected on digital mammography. All cases had histopathologically-proven microcalcifications within the biopsied or surgical specimens. There were 30%[43/142] breast cancers and 70%[99/142] benign lesions. Only 26%[37/142] of clustered microcalcifications were identified on targeted-ultrasound and other findings including negative study (n = 33), anechoic ducts or cysts