Tumor Prediction in Mammogram using Neural Network

Detecting micro calcifications - early breast cancer indicators 2013; is visually tough while recognizing malignant tumors is a highly complicated issue. Digital mammography ensures early breast cancer detection through digital mammograms locating suspicious areas with benign/- malignant micro calcifications. Early detection is vital in treatment and survival of breast cancer as there are no sure ways to prevent it. This paper presents a method of tumor prediction based on extracting features from mammogram using Gabor filter with Discrete cosine transform and classify the features using Neural Network

Fuzzy technique for microcalcifications clustering in digital mammograms

Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available. Results The comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%). Conclusions Thanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected

A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound

Computer Aided Detection of Microcalcifications Utilizing Texture Analysis

A comparative study of texture measures for the classification of breast tissue is presented. The texture features investigated include Angular Second Moments, Power Spectrum Analysis and a novel feature, Laws Energy Ratios. The texture study was accomplished as part of the development of a Model Based Vision (MBV) system for the automatic detection of microcalcifications. An overview of the Microcalcification Detection System is presented, which applies image differencing techniques, feature selection methods, and neural networks for locating microcalcification clusters in mammograms. The Power Spectrum Analysis feature set had the best overall performance with an 83% Probability of Detection and an average False ROl Rate of 2.17 ROIs per image over 53 mammograms. A combination of Laws Energy Ratio and Power Spectrum Analysis features selected using Ruck Saliency metrics achieved an increased Probability of Detection of 85% with an average 4 false ROIs per image

Computer-aided diagnosis in mammography : correlation of regions in multiple standard mammographic views of the same breast.

Thesis (Ph.D.)-University of KwaZulu-Natal, 2006.Abstract available in PDF file

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