A Novel Method for Breast Cancer Prognosis Using Wavelet Packet Based Neural Network

An automatic microcalcification detection system based on a hybrid neural network classifier

In this paper, we present a novel approach to detect microcalcifications (MCCs) in the mammograms. Microcalcification has approximately a size of several pixels in diameter on early cancer mammograms and has low contrast. So it is difficult to detection the microcalcifications. The essential idea of propose approach is to apply morphological and wavelet transform to locate the micro calcifications in digital mammograms. The presented method overcome higher true positive rate (TPR) but higher false positive (FP) problem by using single wavelet transform or morphology. The experiment results show the presented method has higher TPR and reducing FP obviously. Using the mammogram data form the Mammographic Image Analysis Society (MIAS) database the TPR is 80.2% at the cost of average 2.5 false microcalcifications per region of interest (ROI)

Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks

Mammography is one of the most efficient imaging techniques for the early detection and diagnosis of breast cancer in woman. The appearance of microcalcifications (MCs) in X-ray mammograms can be regarded as important early diagnostic feature of breast cancer. However, the detection of MCs in X-ray mammograms only depend on radiologists by visual inspection is a difficult task. In this paper, a novel algorithm for the automated detection of MCs in digital mammograms is described. The aim of approach is to locate suspicious MCs by analyzing the distribution of brightness over the digital mammograms. The algorithm for the detection of MCs is performed in two steps. First, the digital X-ray mammogram intensity surface is well-fitted by the least squares support vector machines (LS-SVM), and then the maximum extremum points are detected on the fitted intensity surface by convolving the image with the second order directional derivative operators deduced from the mapped LS-SVM with mixture of kernels. The proposed approach is developed and evaluated by using MIAS database. The experimental results demonstrate the possible MCs are detected accurately and efficiently.

Mammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by extracting the microcalcification features from the Contourlet coefficients of the image and the outcomes are used as an input to the SVM for classification. The system classifies the mammogram images as normal or abnormal and the abnormal severity as benign or malignant. The evaluation of the system is carried on using Mammography Image Analysis Society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate.

Microcalcification clusters are often an important indicator for the detection of malignancy in mammograms. In many cases, microcalcifications are the only indication of a malignancy. However, the detection of microcalcifications can be a difficult process. They are small and can be embedded in dense tissue. This paper presents a method for automatically detecting microcalcifications. We utilize a high-boost filter to suppress background clutter enabling segmentation even in very dense breast tissue. We then use a threshholding and region growing technique to extract candidate microcalcifications. Likely microcalcifications are then identified by a linear classifier. We apply this method to images selected from the LLNL/UCSF Digital Mammogram Library, and produce a receiver operating characteristic (ROC) curves to detail the trade-off between probability of detection and false alarms. Finally, we exam the ability to properly select a threshold to achieve a desired probability of detection based upon a training set. This is a US government work. There are no restrictions on its use.

In this paper, we describe an algorithm for enhancement and detection of microcalcifications in digital mammograms. The microcalcifications are first enhanced by a undecimated wavelet transform and segmented by a classification based method. After spatial features are extracted from the segmented images, a neural network and a Bayes classifier are used to identify clustered microcalcifications. We have tested this algorithm on 40 regions-of-interest images extracted from the database provided by the University Hospital of Nijmegen.

In power distribution fault data, the percentage of faults with different causes could be very different and varies from region to region. This data imbalance issue seriously affects the performance evaluation of fault diagnosis algorithms. Due to the limitations of conventional accuracy (ACC) and geometric mean (G-mean) measures, this paper discusses the application of Receiver Operating Characteristic (ROC) curves in evaluating distribution fault diagnosis performance. After introducing how to obtain ROC curves, Artificial Neural Networks (ANN), Logistic Regression (LR), Support Vector Machines (SVM), Artificial Immune Recognition Systems (AIRS), and K-Nearest Neighbor (KNN) algorithm are compared using ROC curves and Area Under the Curve (AUC) on real-world fault datasets from Progress Energy Carolinas. Experimental results show that AIRS performs best most of the time and ANN is potentially a good algorithm with a proper decision threshold.

This paper presents a computer-aided diagnosis (CAD) system for automatic detection of clustered microcalcifications (MCs) in digitized mammograms. The proposed system consists of two main steps. First, potential MC pixels in the mammograms are segmented out by using four mixed features consisting of two wavelet features and two gray level statistical features and then the potential MC pixels are labeled into potential individual MC objects by their spatial connectivity. Second, MCs are detected by extracting a set of 17 features from the potential individual MC objects. The classifier which is used in the first step is a multilayer feedforward neural network classifier but for the second step we have used Adaboost with SVM-based component classifier. Component classifiers which we used in our combining method are SVM classifiers with RBF kernel. The method was applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of MCs. A free-response operating characteristics (FROC) curve is used to evaluate the performance of CAD system. Results show that the proposed system gives quite satisfactory performance. In particular, 89.55% mean true positive detection rate is achieved at the cost of 0.921 false positive per image.

This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

Pectoral Muscle (PM), a significant region in Medio-Lateral Oblique (MLO) view of mammogram may adversely affect anomaly detection due to its resemblance to abnormal tissues. The removal of PM region can be considered as a prerequisite step for early breast cancer detection using mammographic images. The principal component of PM boundary component is extracted using the orientation and eccentricity property of Canny edge detected components of coarse mammographic image obtained after a multiscale decomposition technique using Laplacian Pyramid (LP). The principal component of PM boundary is extended to top and left boundaries using nearest neighbor approach. The algorithm was tested on images from the Mammographic Image Analysis Society (MIAS) database as well as mammograms obtained from a representative set of Indian populace provided by Lakeshore Hospital Kochi, India. On comparison with the PM boundary assessed by radiologists, the proposed method yielded an average false positive rate of 0.28%, average false negative rate of 3.67% and low Hausdorff distance for 83 images in mammographic database. Based on the performance analysis of the proposed algorithm, it is observed that 97% of images have an average error less than 3 mm which is promising.

Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features

A prevalent predictor of breast cancer is high density of the breast tissue. The characterization of density patterns of the breast is of clinical significance because, as has been stated frequently, the possibility of developing breast cancer is increased if the breast tissue is of high density. Radiologists predict breast tissue density by visually examining the mammogram, and the accuracy of this diagnosis is solely dependent on the experience of the radiologist. Moreover, a proper differentiation between the atypical cases of density patterns (with highly overlapping density patterns on the mammogram) is a formidable task, even for an experienced radiologist. Therefore, in the current work, different experiments were performed with a view to designing efficient computer aided diagnostic (CAD) models with which to characterize the density of breast tissue. To implement the proposed algorithms, mammographic images were taken from the mini-MIAS (Mammographic Image Analysis Society) dataset. From each mammographic image, a region of interest (ROI) (200 × 200 pixels in size) was cropped from the central part of the breast tissue. From the extracted ROI, texture information was computed in the form of mathematical descriptors using a 2D Gabor wavelet transform (GWT). The performance of GWT features for the characterization of the density patterns of the breast tissue was evaluated using different classifiers. For both 2-class and 3-class classification, neural network (NN)-based CAD models yielded better results compared with other classifier-based CAD models. The classification accuracy achieved for 2-class classification was 93.7% and for 3-class classification the classification accuracy was 87.5%.

Computer aided detection system for micro calcifications in digital mammograms

Clustered microcalcifications (MCCs) on mammograms are important hints of breast cancer. Nevertheless, it is a complex and difficult task for radiologists to detect the clustered MCCs from the tissue background of mammograms only by naked eyes. This paper presents a method for computer-aided detection of MCCs in digital mammograms. The detection algorithm mainly consists of two different methods. The first one, based on the difference-image technique, recognizes high-frequency signals and very high-frequency noise. The second one is able to extract high-frequency signal by exploiting a wavelet based noise suppression and neural network (NN) classification. In the false-positive reduction step, false signals are separated from MCCs by means of an AND operation on signals from two methods. The algorithm is tested with a series of clinical mammograms. A sensitivity ofmore than 90% is obtained at a relatively low false-positive (FP) detection of 2.18 per image. The results are compared with thejudgement ofradiological experts, and they are very encouraging.