Combination of SVM Knowledge for Microcalcification Detection in Digital Mammograms

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.

A model was developed to understand the effects of spatial resolution and Signal to Noise ratio on the detection and tracking performance of wide-field, diffraction-limited electro-optic and infrared motion imagery systems. False positive detection probability and false positive rate per frame were calculated as a function of target-to-background contrast and object size. Results showed that moving objects are fundamentally more difficult to detect than stationary objects because SNR for fixed objects increases and false positive probability detection rates diminish rapidly with successive frames whereas for moving objects the false detection rate remains constant or increases with successive frames. The model specifies that the desired performance of a detection system, measured by the false positive detection rate, can be achieved by image system designs with different combinations of SNR and spatial resolution, usually requiring several pixels resolving the object; this capability to tradeoff resolution and SNR enables system design trades and cost optimization. For operational use, detection thresholds required to achieve a particular false detection rate can be calculated. Interestingly, for moderate size images the model converges to the Johnson Criteria. Johnson found that an imaging system with an SNR >3.5 has a probability of detection >50% when the resolution on the object is 4 pixels or more. Under these conditions our model finds the false positive rate is less than one per hundred image frames, and the ratio of the probability of object detection to false positive detection is much greater than one. The model was programmed into Matlab to generate simulated images frames for visualization.

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.

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.

Linear structures are a major source of false positives (FPs) in computer-aided detection of clustered microcalcifications (MCs) in mammograms. In this work, we investigate whether it is feasible to improve the performance in MC detection by directly exploiting the FPs associated with linear structures. We analyze the cause of FPs by linear structures and their characteristics with an SVM detector, and design a linear structure detection procedure together with a dual-thresholding scheme to separate the linear structures from other tissue background in a mammogram. The proposed procedure was demonstrated on a set of 200 mammograms containing clustered MCs. The results show that it could effectively reduce the FPs in the SVM detector by as much as 30% with the true detection rate at 85%.

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

In computer-aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference-of-Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free-response receiver operating characteristic (FROC) analysis on a set of 141 screen-film mammogram (SFM) images (66 cases) and a set of 188 full-field digital mammogram (FFDM) images (95 cases). The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true-positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (such as MC-like noise patterns and linear structures) in MC detection. The framework is general and can be applicable for further improving the detection accuracy of existing MC detectors.

Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

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)

GravityNet for end-to-end small lesion detection

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

Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the Digital Database for Screening Mammography. Mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Laws' texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve ( A(z)) of 0.989. Results suggest that MCs' ST texture analysis can contribute to computer-aided diagnosis of breast cancer.

Breast cancer detection and diagnosis has been a difficult problem to be solved in the medical field. Recently, the field has seen the use of information technology and it's found that the diagnosis process has gained a lot of improvements. While most of the raw digital mammographic images cannot be read by radiologist through naked eye, the images processed by the proposed technique draws fine micro-calcifications. After the pre-processing, later stages i.e., feature extraction and classification have achieved significant performance results. In the proposed technique, input image is first enhanced through laplacian filtering, then wavelet, statistical and a feature extracted from Histogram is used for classification in to cancerous or non-cancerous image. On classification with Artificial Neural Network, Daubechies-8 with 50% low frequency components along with statistical features produced 92.8% true detection rate with 12.5% positive false rate.

In this article the authors evaluate a recently proposed variable dose (VD)-digital breast tomosynthesis (DBT) acquisition technique in terms of the detection accuracy for breast masses and microcalcification (MC) clusters. With this technique, approximately half of the total dose is used for one center projection and the remaining dose is split among the other tomosynthesis projection views. This acquisition method would yield both a projection view and a reconstruction view. One of the aims of this study was to evaluate whether the center projection alone of the VD acquisition can provide equal or superior MC detection in comparison to the 3D images from uniform dose (UD)-DBT. Another aim was to compare the mass-detection capabilities of 3D reconstructions from VD-DBT and UD-DBT. In a localization receiver operating characteristic (LROC) observer study of MC detection, the authors compared the center projection of a VD acquisitioh scheme (at 2 mGy dose) with detector pixel size of 100 microm with the UD-DBT reconstruction (at 4 mGy dose) obtained with a voxel size of 100 microm. MCs with sizes of 150 and 180 microm were used in the study, with each cluster consisting of seven MCs distributed randomly within a small volume. Reconstructed images in UD-DBT were obtained from a projection set that had a total of 4 mGy dose. The current study shows that for MC detection, using the center projection alone of VD acquisition scheme performs worse with area under the LROC curve (AL) of 0.76 than when using the 3D reconstructed image using the UD acquisition scheme (AL=0.84). A 2D ANOVA found a statistically significant difference (p=0.038) at a significance level of 0.05. In the current study, although a reconstructed image was also available using the VD acquisition scheme, it was not used to assist the MC detection task which was done using the center projection alone. In the case of evaluation of detection accuracy of masses, the reconstruction with VD-DBT (AL=0.71) was compared to that obtained from the UD-DBT (AL=0.78). The authors found no statistically significant difference between the two (p-value=0.22), although all the observers performed better for UD-DBT.